Introduction to Time Series Modeling
Kitagawa, Genshiro
2010-01-01
In time series modeling, the behavior of a certain phenomenon is expressed in relation to the past values of itself and other covariates. Since many important phenomena in statistical analysis are actually time series and the identification of conditional distribution of the phenomenon is an essential part of the statistical modeling, it is very important and useful to learn fundamental methods of time series modeling. Illustrating how to build models for time series using basic methods, "Introduction to Time Series Modeling" covers numerous time series models and the various tools f
International Nuclear Information System (INIS)
Vajna, Szabolcs; Kertész, János; Tóth, Bálint
2013-01-01
Many human-related activities show power-law decaying interevent time distribution with exponents usually varying between 1 and 2. We study a simple task-queuing model, which produces bursty time series due to the non-trivial dynamics of the task list. The model is characterized by a priority distribution as an input parameter, which describes the choice procedure from the list. We give exact results on the asymptotic behaviour of the model and we show that the interevent time distribution is power-law decaying for any kind of input distributions that remain normalizable in the infinite list limit, with exponents tunable between 1 and 2. The model satisfies a scaling law between the exponents of interevent time distribution (β) and autocorrelation function (α): α + β = 2. This law is general for renewal processes with power-law decaying interevent time distribution. We conclude that slowly decaying autocorrelation function indicates long-range dependence only if the scaling law is violated. (paper)
Time series modeling, computation, and inference
Prado, Raquel
2010-01-01
The authors systematically develop a state-of-the-art analysis and modeling of time series. … this book is well organized and well written. The authors present various statistical models for engineers to solve problems in time series analysis. Readers no doubt will learn state-of-the-art techniques from this book.-Hsun-Hsien Chang, Computing Reviews, March 2012My favorite chapters were on dynamic linear models and vector AR and vector ARMA models.-William Seaver, Technometrics, August 2011… a very modern entry to the field of time-series modelling, with a rich reference list of the current lit
FOURIER SERIES MODELS THROUGH TRANSFORMATION
African Journals Online (AJOL)
DEPT
This study considers the application of Fourier series analysis (FSA) to seasonal time series data. The ultimate objective of the study is to construct an FSA model that can lead to reliable forecast. Specifically, the study evaluates data for the assumptions of time series analysis; applies the necessary transformation to the ...
Models for dependent time series
Tunnicliffe Wilson, Granville; Haywood, John
2015-01-01
Models for Dependent Time Series addresses the issues that arise and the methodology that can be applied when the dependence between time series is described and modeled. Whether you work in the economic, physical, or life sciences, the book shows you how to draw meaningful, applicable, and statistically valid conclusions from multivariate (or vector) time series data.The first four chapters discuss the two main pillars of the subject that have been developed over the last 60 years: vector autoregressive modeling and multivariate spectral analysis. These chapters provide the foundational mater
Stochastic models for time series
Doukhan, Paul
2018-01-01
This book presents essential tools for modelling non-linear time series. The first part of the book describes the main standard tools of probability and statistics that directly apply to the time series context to obtain a wide range of modelling possibilities. Functional estimation and bootstrap are discussed, and stationarity is reviewed. The second part describes a number of tools from Gaussian chaos and proposes a tour of linear time series models. It goes on to address nonlinearity from polynomial or chaotic models for which explicit expansions are available, then turns to Markov and non-Markov linear models and discusses Bernoulli shifts time series models. Finally, the volume focuses on the limit theory, starting with the ergodic theorem, which is seen as the first step for statistics of time series. It defines the distributional range to obtain generic tools for limit theory under long or short-range dependences (LRD/SRD) and explains examples of LRD behaviours. More general techniques (central limit ...
Fourier series models through transformation | Omekara | Global ...
African Journals Online (AJOL)
This study considers the application of Fourier series analysis (FSA) to seasonal time series data. The ultimate objective of the study is to construct an FSA model that can lead to reliable forecast. Specifically, the study evaluates data for the assumptions of time series analysis; applies the necessary transformation to the ...
Lag space estimation in time series modelling
DEFF Research Database (Denmark)
Goutte, Cyril
1997-01-01
The purpose of this article is to investigate some techniques for finding the relevant lag-space, i.e. input information, for time series modelling. This is an important aspect of time series modelling, as it conditions the design of the model through the regressor vector a.k.a. the input layer...
Computer system organization the B5700/B6700 series
Organick, Elliott I
1973-01-01
Computer System Organization: The B5700/B6700 Series focuses on the organization of the B5700/B6700 Series developed by Burroughs Corp. More specifically, it examines how computer systems can (or should) be organized to support, and hence make more efficient, the running of computer programs that evolve with characteristically similar information structures.Comprised of nine chapters, this book begins with a background on the development of the B5700/B6700 operating systems, paying particular attention to their hardware/software architecture. The discussion then turns to the block-structured p
Modelling conditional heteroscedasticity in nonstationary series
Cizek, P.; Cizek, P.; Härdle, W.K.; Weron, R.
2011-01-01
A vast amount of econometrical and statistical research deals with modeling financial time series and their volatility, which measures the dispersion of a series at a point in time (i.e., conditional variance). Although financial markets have been experiencing many shorter and longer periods of
Forecasting with nonlinear time series models
DEFF Research Database (Denmark)
Kock, Anders Bredahl; Teräsvirta, Timo
applied to economic fore- casting problems, is briefly highlighted. A number of large published studies comparing macroeconomic forecasts obtained using different time series models are discussed, and the paper also contains a small simulation study comparing recursive and direct forecasts in a partic......In this paper, nonlinear models are restricted to mean nonlinear parametric models. Several such models popular in time series econo- metrics are presented and some of their properties discussed. This in- cludes two models based on universal approximators: the Kolmogorov- Gabor polynomial model...
A Series of Synthetic Organic Experiments Demonstrating Physical Organic Principles.
Sayed, Yousry; And Others
1989-01-01
Describes several common synthetic organic transformations involving alkenes, alcohols, alkyl halides, and ketones. Includes concepts on kinetic versus thermodynamic control of reaction, rearrangement of a secondary carbocation to a tertiary cation, and the effect of the size of the base on orientation during elimination. (MVL)
MODELLING OF ORDINAL TIME SERIES BY PROPORTIONAL ODDS MODEL
Directory of Open Access Journals (Sweden)
Serpil AKTAŞ ALTUNAY
2013-06-01
Full Text Available Categorical time series data with random time dependent covariates often arise when the variable categories are assigned as categorical. There are several other models that have been proposed in the literature for the analysis of categorical time series. For example, Markov chain models, integer autoregressive processes, discrete ARMA models can be utilized for modeling of categorical time series. In general, the choice of model depends on the measurement of study variables: nominal, ordinal and interval. However, regression theory is successful approach for categorical time series which is based on generalized linear models and partial likelihood inference. One of the models for ordinal time series in regression theory is proportional odds model. In this study, proportional odds model approach to ordinal categorical time series is investigated based on a real air pollution data set and the results are discussed.
Building Chaotic Model From Incomplete Time Series
Siek, Michael; Solomatine, Dimitri
2010-05-01
This paper presents a number of novel techniques for building a predictive chaotic model from incomplete time series. A predictive chaotic model is built by reconstructing the time-delayed phase space from observed time series and the prediction is made by a global model or adaptive local models based on the dynamical neighbors found in the reconstructed phase space. In general, the building of any data-driven models depends on the completeness and quality of the data itself. However, the completeness of the data availability can not always be guaranteed since the measurement or data transmission is intermittently not working properly due to some reasons. We propose two main solutions dealing with incomplete time series: using imputing and non-imputing methods. For imputing methods, we utilized the interpolation methods (weighted sum of linear interpolations, Bayesian principle component analysis and cubic spline interpolation) and predictive models (neural network, kernel machine, chaotic model) for estimating the missing values. After imputing the missing values, the phase space reconstruction and chaotic model prediction are executed as a standard procedure. For non-imputing methods, we reconstructed the time-delayed phase space from observed time series with missing values. This reconstruction results in non-continuous trajectories. However, the local model prediction can still be made from the other dynamical neighbors reconstructed from non-missing values. We implemented and tested these methods to construct a chaotic model for predicting storm surges at Hoek van Holland as the entrance of Rotterdam Port. The hourly surge time series is available for duration of 1990-1996. For measuring the performance of the proposed methods, a synthetic time series with missing values generated by a particular random variable to the original (complete) time series is utilized. There exist two main performance measures used in this work: (1) error measures between the actual
From Taylor series to Taylor models
International Nuclear Information System (INIS)
Berz, Martin
1997-01-01
An overview of the background of Taylor series methods and the utilization of the differential algebraic structure is given, and various associated techniques are reviewed. The conventional Taylor methods are extended to allow for a rigorous treatment of bounds for the remainder of the expansion in a similarly universal way. Utilizing differential algebraic and functional analytic arguments on the set of Taylor models, arbitrary order integrators with rigorous remainder treatment are developed. The integrators can meet pre-specified accuracy requirements in a mathematically strict way, and are a stepping stone towards fully rigorous estimates of stability of repetitive systems
From Taylor series to Taylor models
International Nuclear Information System (INIS)
Berz, M.
1997-01-01
An overview of the background of Taylor series methods and the utilization of the differential algebraic structure is given, and various associated techniques are reviewed. The conventional Taylor methods are extended to allow for a rigorous treatment of bounds for the remainder of the expansion in a similarly universal way. Utilizing differential algebraic and functional analytic arguments on the set of Taylor models, arbitrary order integrators with rigorous remainder treatment are developed. The integrators can meet pre-specified accuracy requirements in a mathematically strict way, and are a stepping stone towards fully rigorous estimates of stability of repetitive systems. copyright 1997 American Institute of Physics
Time series modeling for syndromic surveillance
Directory of Open Access Journals (Sweden)
Mandl Kenneth D
2003-01-01
Full Text Available Abstract Background Emergency department (ED based syndromic surveillance systems identify abnormally high visit rates that may be an early signal of a bioterrorist attack. For example, an anthrax outbreak might first be detectable as an unusual increase in the number of patients reporting to the ED with respiratory symptoms. Reliably identifying these abnormal visit patterns requires a good understanding of the normal patterns of healthcare usage. Unfortunately, systematic methods for determining the expected number of (ED visits on a particular day have not yet been well established. We present here a generalized methodology for developing models of expected ED visit rates. Methods Using time-series methods, we developed robust models of ED utilization for the purpose of defining expected visit rates. The models were based on nearly a decade of historical data at a major metropolitan academic, tertiary care pediatric emergency department. The historical data were fit using trimmed-mean seasonal models, and additional models were fit with autoregressive integrated moving average (ARIMA residuals to account for recent trends in the data. The detection capabilities of the model were tested with simulated outbreaks. Results Models were built both for overall visits and for respiratory-related visits, classified according to the chief complaint recorded at the beginning of each visit. The mean absolute percentage error of the ARIMA models was 9.37% for overall visits and 27.54% for respiratory visits. A simple detection system based on the ARIMA model of overall visits was able to detect 7-day-long simulated outbreaks of 30 visits per day with 100% sensitivity and 97% specificity. Sensitivity decreased with outbreak size, dropping to 94% for outbreaks of 20 visits per day, and 57% for 10 visits per day, all while maintaining a 97% benchmark specificity. Conclusions Time series methods applied to historical ED utilization data are an important tool
Fisher information framework for time series modeling
Venkatesan, R. C.; Plastino, A.
2017-08-01
A robust prediction model invoking the Takens embedding theorem, whose working hypothesis is obtained via an inference procedure based on the minimum Fisher information principle, is presented. The coefficients of the ansatz, central to the working hypothesis satisfy a time independent Schrödinger-like equation in a vector setting. The inference of (i) the probability density function of the coefficients of the working hypothesis and (ii) the establishing of constraint driven pseudo-inverse condition for the modeling phase of the prediction scheme, is made, for the case of normal distributions, with the aid of the quantum mechanical virial theorem. The well-known reciprocity relations and the associated Legendre transform structure for the Fisher information measure (FIM, hereafter)-based model in a vector setting (with least square constraints) are self-consistently derived. These relations are demonstrated to yield an intriguing form of the FIM for the modeling phase, which defines the working hypothesis, solely in terms of the observed data. Cases for prediction employing time series' obtained from the: (i) the Mackey-Glass delay-differential equation, (ii) one ECG signal from the MIT-Beth Israel Deaconess Hospital (MIT-BIH) cardiac arrhythmia database, and (iii) one ECG signal from the Creighton University ventricular tachyarrhythmia database. The ECG samples were obtained from the Physionet online repository. These examples demonstrate the efficiency of the prediction model. Numerical examples for exemplary cases are provided.
Vector bilinear autoregressive time series model and its superiority ...
African Journals Online (AJOL)
In this research, a vector bilinear autoregressive time series model was proposed and used to model three revenue series (X1, X2, X3) . The “orders” of the three series were identified on the basis of the distribution of autocorrelation and partial autocorrelation functions and were used to construct the vector bilinear models.
Modeling Time Series Data for Supervised Learning
Baydogan, Mustafa Gokce
2012-01-01
Temporal data are increasingly prevalent and important in analytics. Time series (TS) data are chronological sequences of observations and an important class of temporal data. Fields such as medicine, finance, learning science and multimedia naturally generate TS data. Each series provide a high-dimensional data vector that challenges the learning…
Stochastic modelling of regional archaeomagnetic series
Hellio, G.; Gillet, N.; Bouligand, C.; Jault, D.
2014-11-01
We report a new method to infer continuous time-series of the declination, inclination and intensity of the magnetic field from archaeomagnetic data. Adopting a Bayesian perspective, we need to specify a priori knowledge about the time evolution of the magnetic field. It consists in a time correlation function that we choose to be compatible with present knowledge about the geomagnetic time spectra. The results are presented as distributions of possible values for the declination, inclination or intensity. We find that the methodology can be adapted to account for the age uncertainties of archaeological artefacts and we use Markov chain Monte Carlo to explore the possible dates of observations. We apply the method to intensity data sets from Mari, Syria and to intensity and directional data sets from Paris, France. Our reconstructions display more rapid variations than previous studies and we find that the possible values of geomagnetic field elements are not necessarily normally distributed. Another output of the model is better age estimates of archaeological artefacts.
TIME SERIES ANALYSIS USING A UNIQUE MODEL OF TRANSFORMATION
Directory of Open Access Journals (Sweden)
Goran Klepac
2007-12-01
Full Text Available REFII1 model is an authorial mathematical model for time series data mining. The main purpose of that model is to automate time series analysis, through a unique transformation model of time series. An advantage of this approach of time series analysis is the linkage of different methods for time series analysis, linking traditional data mining tools in time series, and constructing new algorithms for analyzing time series. It is worth mentioning that REFII model is not a closed system, which means that we have a finite set of methods. At first, this is a model for transformation of values of time series, which prepares data used by different sets of methods based on the same model of transformation in a domain of problem space. REFII model gives a new approach in time series analysis based on a unique model of transformation, which is a base for all kind of time series analysis. The advantage of REFII model is its possible application in many different areas such as finance, medicine, voice recognition, face recognition and text mining.
vector bilinear autoregressive time series model and its superiority ...
African Journals Online (AJOL)
In this research, a vector bilinear autoregressive time series model was proposed and used to model three revenue series(. )t ... showed that vector bilinear autoregressive (BIVAR) models provide better estimates than the long embraced linear models. ... order moving average (MA) polynomials on backward shift operator B ...
Time Series Forecasting Energy-efficient Organization of Wireless Sensor Networks
Directory of Open Access Journals (Sweden)
Dao-Wei Bi
2007-09-01
Full Text Available Due to their wide potential applications, wireless sensor networks have recentlyreceived tremendous attention. The strict energy constraints of sensor nodes result in thegreat challenges for energy efficiency. This paper investigates the energy efficiency problemand proposes an energy-efficient organization method with time series forecasting. Theorganization of wireless sensor networks is formulated for target tracking. Target model,multi-sensor model and energy model are defined accordingly. For the target trackingapplication, target localization is achieved by collaborative sensing with multi-sensor fusion.The historical localization results are utilized for adaptive target trajectory forecasting.Empirical mode decomposition is implemented to extract the inherent variation modes in thetime series of a target trajectory. Future target position is derived from autoregressivemoving average (ARMA models, which forecast the decomposition components,respectively. Moreover, the energy-efficient organization method is presented to enhance theenergy efficiency of wireless sensor networks. The sensor nodes implement sensing tasksaccording to the probability awakening in a distributed manner. When the sensor nodestransfer their observations to achieve data fusion, the routing scheme is obtained by antcolony optimization. Thus, both the operation and communication energy consumption canbe minimized. Experimental results verify that the combination of the ARMA model andempirical mode decomposition can estimate the target position efficiently and energy savingis achieved by the proposed organization method in wireless sensor networks.
Indian Academy of Sciences (India)
Madhu
molecular era. The second episode is familiar to historians of 20th century biology (Sapp 1987). Recent studies have enriched it, as well as thrown light on the first use of ciliates as models. 2. ... low level of consciousness in these organisms. A fascinating part of ... phenomena are due to self-replicating particles present in.
Forecasting the Reference Evapotranspiration Using Time Series Model
Directory of Open Access Journals (Sweden)
H. Zare Abyaneh
2016-10-01
Full Text Available Introduction: Reference evapotranspiration is one of the most important factors in irrigation timing and field management. Moreover, reference evapotranspiration forecasting can play a vital role in future developments. Therefore in this study, the seasonal autoregressive integrated moving average (ARIMA model was used to forecast the reference evapotranspiration time series in the Esfahan, Semnan, Shiraz, Kerman, and Yazd synoptic stations. Materials and Methods: In the present study in all stations (characteristics of the synoptic stations are given in Table 1, the meteorological data, including mean, maximum and minimum air temperature, relative humidity, dry-and wet-bulb temperature, dew-point temperature, wind speed, precipitation, air vapor pressure and sunshine hours were collected from the Islamic Republic of Iran Meteorological Organization (IRIMO for the 41 years from 1965 to 2005. The FAO Penman-Monteith equation was used to calculate the monthly reference evapotranspiration in the five synoptic stations and the evapotranspiration time series were formed. The unit root test was used to identify whether the time series was stationary, then using the Box-Jenkins method, seasonal ARIMA models were applied to the sample data. Table 1. The geographical location and climate conditions of the synoptic stations Station\tGeographical location\tAltitude (m\tMean air temperature (°C\tMean precipitation (mm\tClimate, according to the De Martonne index classification Longitude (E\tLatitude (N Annual\tMin. and Max. Esfahan\t51° 40'\t32° 37'\t1550.4\t16.36\t9.4-23.3\t122\tArid Semnan\t53° 33'\t35° 35'\t1130.8\t18.0\t12.4-23.8\t140\tArid Shiraz\t52° 36'\t29° 32'\t1484\t18.0\t10.2-25.9\t324\tSemi-arid Kerman\t56° 58'\t30° 15'\t1753.8\t15.6\t6.7-24.6\t142\tArid Yazd\t54° 17'\t31° 54'\t1237.2\t19.2\t11.8-26.0\t61\tArid Results and Discussion: The monthly meteorological data were used as input for the Ref-ET software and monthly reference
Hidden Markov Models for Time Series An Introduction Using R
Zucchini, Walter
2009-01-01
Illustrates the flexibility of HMMs as general-purpose models for time series data. This work presents an overview of HMMs for analyzing time series data, from continuous-valued, circular, and multivariate series to binary data, bounded and unbounded counts and categorical observations.
Fourier Series, the DFT and Shape Modelling
DEFF Research Database (Denmark)
Skoglund, Karl
2004-01-01
This report provides an introduction to Fourier series, the discrete Fourier transform, complex geometry and Fourier descriptors for shape analysis. The content is aimed at undergraduate and graduate students who wish to learn about Fourier analysis in general, as well as its application to shape...
Modelling VLSI circuits using Taylor series
Kocina, Filip; Nečasová, Gabriela; Veigend, Petr; Chaloupka, Jan; Šátek, Václav; Kunovský, Jiří
2017-07-01
The paper introduces the capacitor substitution for CMOS logic gates, i.e. NANDs, NORs and inverters. It reveals the necessity of a very accurate and fast method for solving this problem. Therefore the Modern Taylor Series Method (MTSM) is used which provides an automatic choice of a higher order during the computation and a larger integration step size while keeping desired accuracy.
Multiple Time Series Ising Model for Financial Market Simulations
International Nuclear Information System (INIS)
Takaishi, Tetsuya
2015-01-01
In this paper we propose an Ising model which simulates multiple financial time series. Our model introduces the interaction which couples to spins of other systems. Simulations from our model show that time series exhibit the volatility clustering that is often observed in the real financial markets. Furthermore we also find non-zero cross correlations between the volatilities from our model. Thus our model can simulate stock markets where volatilities of stocks are mutually correlated
forecasting with nonlinear time series model: a monte-carlo
African Journals Online (AJOL)
PUBLICATIONS1
erated recursively up to any step greater than one. For nonlinear time series model, point forecast for step one can be done easily like in the linear case but forecast for a step greater than or equal to ..... London. Franses, P. H. (1998). Time series models for business and Economic forecasting, Cam- bridge University press.
Estimation of pure autoregressive vector models for revenue series ...
African Journals Online (AJOL)
This paper aims at applying multivariate approach to Box and Jenkins univariate time series modeling to three vector series. General Autoregressive Vector Models with time varying coefficients are estimated. The first vector is a response vector, while others are predictor vectors. By matrix expansion each vector, whether ...
Time Series Modeling for Structural Response Prediction
1988-11-14
results for 2nd mode. 69 5. 3DOF simulated data. 71 6. Experimental data. 72 7. Simulated data. 75 8. MPEM estimates for MDOF data with closely spaced...vector Ssteering matrix of residual time series 2DOF Two-degree-of-freedom 2LS Two-stage Least Squares Method 3DOF Three-degree-of-freedom x SUMMARY A...70 Table 5: 3DOF Simulated Data (fd= 1 ,10 ,25 ; C=.01,.0l,.0l; Amp=1,l,l; 256 pts, f,=2000 Hz) Algorithm grv noise higher mode grv, 4th mode, bias 40
Time series modelling of overflow structures
DEFF Research Database (Denmark)
Carstensen, J.; Harremoës, P.
1997-01-01
The dynamics of a storage pipe is examined using a grey-box model based on on-line measured data. The grey-box modelling approach uses a combination of physically-based and empirical terms in the model formulation. The model provides an on-line state estimate of the overflows, pumping capacities...... to the overflow structures. The capacity of a pump draining the storage pipe has been estimated for two rain events, revealing that the pump was malfunctioning during the first rain event. The grey-box modelling approach is applicable for automated on-line surveillance and control. (C) 1997 IAWQ. Published...
Time series sightability modeling of animal populations.
Directory of Open Access Journals (Sweden)
Althea A ArchMiller
Full Text Available Logistic regression models-or "sightability models"-fit to detection/non-detection data from marked individuals are often used to adjust for visibility bias in later detection-only surveys, with population abundance estimated using a modified Horvitz-Thompson (mHT estimator. More recently, a model-based alternative for analyzing combined detection/non-detection and detection-only data was developed. This approach seemed promising, since it resulted in similar estimates as the mHT when applied to data from moose (Alces alces surveys in Minnesota. More importantly, it provided a framework for developing flexible models for analyzing multiyear detection-only survey data in combination with detection/non-detection data. During initial attempts to extend the model-based approach to multiple years of detection-only data, we found that estimates of detection probabilities and population abundance were sensitive to the amount of detection-only data included in the combined (detection/non-detection and detection-only analysis. Subsequently, we developed a robust hierarchical modeling approach where sightability model parameters are informed only by the detection/non-detection data, and we used this approach to fit a fixed-effects model (FE model with year-specific parameters and a temporally-smoothed model (TS model that shares information across years via random effects and a temporal spline. The abundance estimates from the TS model were more precise, with decreased interannual variability relative to the FE model and mHT abundance estimates, illustrating the potential benefits from model-based approaches that allow information to be shared across years.
SEASONAL AUTOREGRESSIVE INTEGRATED MOVING AVERAGE MODEL FOR PRECIPITATION TIME SERIES
Yan Wang; Meng Gao; Xinghua Chang; Xiyong Hou
2012-01-01
Predicting the trend of precipitation is a difficult task in meteorology and environmental sciences. Statistical approaches from time series analysis provide an alternative way for precipitation prediction. The ARIMA model incorporating seasonal characteristics, which is referred to as seasonal ARIMA model was presented. The time series data is the monthly precipitation data in Yantai, China and the period is from 1961 to 2011. The model was denoted as SARIMA (1, 0, 1) (0, 1, 1)12 in this stu...
Time series sightability modeling of animal populations
ArchMiller, Althea A.; Dorazio, Robert; St. Clair, Katherine; Fieberg, John R.
2018-01-01
Logistic regression models—or “sightability models”—fit to detection/non-detection data from marked individuals are often used to adjust for visibility bias in later detection-only surveys, with population abundance estimated using a modified Horvitz-Thompson (mHT) estimator. More recently, a model-based alternative for analyzing combined detection/non-detection and detection-only data was developed. This approach seemed promising, since it resulted in similar estimates as the mHT when applied to data from moose (Alces alces) surveys in Minnesota. More importantly, it provided a framework for developing flexible models for analyzing multiyear detection-only survey data in combination with detection/non-detection data. During initial attempts to extend the model-based approach to multiple years of detection-only data, we found that estimates of detection probabilities and population abundance were sensitive to the amount of detection-only data included in the combined (detection/non-detection and detection-only) analysis. Subsequently, we developed a robust hierarchical modeling approach where sightability model parameters are informed only by the detection/non-detection data, and we used this approach to fit a fixed-effects model (FE model) with year-specific parameters and a temporally-smoothed model (TS model) that shares information across years via random effects and a temporal spline. The abundance estimates from the TS model were more precise, with decreased interannual variability relative to the FE model and mHT abundance estimates, illustrating the potential benefits from model-based approaches that allow information to be shared across years.
DEFF Research Database (Denmark)
Madsen, Henrik; Rasmussen, Peter F.; Rosbjerg, Dan
1997-01-01
Two different models for analyzing extreme hydrologic events, based on, respectively, partial duration series (PDS) and annual maximum series (AMS), are compared. The PDS model assumes a generalized Pareto distribution for modeling threshold exceedances corresponding to a generalized extreme value...... distribution for annual maxima. The performance of the two models in terms of the uncertainty of the T-year event estimator is evaluated in the cases of estimation with, respectively, the maximum likelihood (ML) method, the method of moments (MOM), and the method of probability weighted moments (PWM...... of the considered methods reveals that in general, one should use the PDS model with MOM estimation for negative shape parameters, the PDS model with exponentially distributed exceedances if the shape parameter is close to zero, the AMS model with MOM estimation for moderately positive shape parameters, and the PDS...
Time domain series system definition and gear set reliability modeling
International Nuclear Information System (INIS)
Xie, Liyang; Wu, Ningxiang; Qian, Wenxue
2016-01-01
Time-dependent multi-configuration is a typical feature for mechanical systems such as gear trains and chain drives. As a series system, a gear train is distinct from a traditional series system, such as a chain, in load transmission path, system-component relationship, system functioning manner, as well as time-dependent system configuration. Firstly, the present paper defines time-domain series system to which the traditional series system reliability model is not adequate. Then, system specific reliability modeling technique is proposed for gear sets, including component (tooth) and subsystem (tooth-pair) load history description, material priori/posterior strength expression, time-dependent and system specific load-strength interference analysis, as well as statistically dependent failure events treatment. Consequently, several system reliability models are developed for gear sets with different tooth numbers in the scenario of tooth root material ultimate tensile strength failure. The application of the models is discussed in the last part, and the differences between the system specific reliability model and the traditional series system reliability model are illustrated by virtue of several numerical examples. - Highlights: • A new type of series system, i.e. time-domain multi-configuration series system is defined, that is of great significance to reliability modeling. • Multi-level statistical analysis based reliability modeling method is presented for gear transmission system. • Several system specific reliability models are established for gear set reliability estimation. • The differences between the traditional series system reliability model and the new model are illustrated.
Long Memory Models to Generate Synthetic Hydrological Series
Directory of Open Access Journals (Sweden)
Guilherme Armando de Almeida Pereira
2014-01-01
Full Text Available In Brazil, much of the energy production comes from hydroelectric plants whose planning is not trivial due to the strong dependence on rainfall regimes. This planning is accomplished through optimization models that use inputs such as synthetic hydrologic series generated from the statistical model PAR(p (periodic autoregressive. Recently, Brazil began the search for alternative models able to capture the effects that the traditional model PAR(p does not incorporate, such as long memory effects. Long memory in a time series can be defined as a significant dependence between lags separated by a long period of time. Thus, this research develops a study of the effects of long dependence in the series of streamflow natural energy in the South subsystem, in order to estimate a long memory model capable of generating synthetic hydrologic series.
Environmental parameters series. 3. Concentration factors of radionuclides in freshwater organisms
International Nuclear Information System (INIS)
1994-03-01
This report outlines recent research activities of Radioactive Waste Management Center. Aiming to estimate the radiation dose of man exposed to radioactive materials in an environment, construction of a calculation model on the transfer of radionuclide in the environment was attempted. This issue, Environmental parameter series No.3 includes six reports on the factors related to environmental concentration for radionuclides. The title of the reports are as follows; Factors modifying the concentration factor (CF), Evaluation of accumulation of radionuclides in brackish water organisms, Dose assessment, CF derived from Japanese limnological data, Data table of CF and Metabolic parameters in relation to bioaccumulation of elements by organisms. In addition to collect and arrange the existing data, CF was calculated based on the concentration of stable elements in various lakes and rivers in Japan. (M.N.)
[Non-organic visual loss. A series of 5 cases].
Santos-Bueso, E; Sáenz-Francés, F; García-Sáenz, S; Martínez-de-la-Casa, J M; García-Feijoo, J
2015-01-01
Non-organic visual loss is the presence of ocular symptoms without an organic base that justifies it, and can occur in up to 5% of the children attending Ophthalmology Outpatients. A suspicion and the management of this situation are essential for a proper diagnosis, not only to avoid unnecessary referrals to other specialties, but also to avoid health spending, in addition to reducing parental distress by the possible presence of eye disease in their children. Copyright © 2013 Asociación Española de Pediatría. Published by Elsevier Espana. All rights reserved.
Tempered fractional time series model for turbulence in geophysical flows
Meerschaert, Mark M.; Sabzikar, Farzad; Phanikumar, Mantha S.; Zeleke, Aklilu
2014-09-01
We propose a new time series model for velocity data in turbulent flows. The new model employs tempered fractional calculus to extend the classical 5/3 spectral model of Kolmogorov. Application to wind speed and water velocity in a large lake are presented, to demonstrate the practical utility of the model.
Tempered fractional time series model for turbulence in geophysical flows
International Nuclear Information System (INIS)
Meerschaert, Mark M; Sabzikar, Farzad; Phanikumar, Mantha S; Zeleke, Aklilu
2014-01-01
We propose a new time series model for velocity data in turbulent flows. The new model employs tempered fractional calculus to extend the classical 5/3 spectral model of Kolmogorov. Application to wind speed and water velocity in a large lake are presented, to demonstrate the practical utility of the model. (paper)
forecasting with nonlinear time series model: a monte-carlo ...
African Journals Online (AJOL)
PUBLICATIONS1
with nonlinear time series model by comparing the RMSE with the traditional bootstrap and. Monte-Carlo method of forecasting. We use the logistic smooth transition autoregressive. (LSTAR) model as a case study. We first consider a linear model called the AR. (p) model of order p which satisfies the follow- ing linear ...
DEFF Research Database (Denmark)
Madsen, Henrik; Pearson, Charles P.; Rosbjerg, Dan
1997-01-01
Two regional estimation schemes, based on, respectively, partial duration series (PDS) and annual maximum series (AMS), are compared. The PDS model assumes a generalized Pareto (GP) distribution for modeling threshold exceedances corresponding to a generalized extreme value (GEV) distribution......-way grouping based on annual average rainfall is sufficient to attain homogeneity for PDS, whereas a further partitioning is necessary for AMS. In determination of the regional parent distribution using L-moment ratio diagrams, PDS data, in contrast to AMS data, provide an unambiguous interpretation......, supporting a GP distribution....
Organolithium and organomagnesium compounds of the naphthalene series in organic synthesis
International Nuclear Information System (INIS)
Pozharskii, Alexander F; Ryabtsova, Oksana V
2006-01-01
The review summarises procedures for the synthesis of organolithium and organomagnesium compounds of the naphthalene series, including binaphthyls, the properties of these compounds and their use in organic synthesis.
With string model to time series forecasting
Pinčák, Richard; Bartoš, Erik
2015-10-01
Overwhelming majority of econometric models applied on a long term basis in the financial forex market do not work sufficiently well. The reason is that transaction costs and arbitrage opportunity are not included, as this does not simulate the real financial markets. Analyses are not conducted on the non equidistant date but rather on the aggregate date, which is also not a real financial case. In this paper, we would like to show a new way how to analyze and, moreover, forecast financial market. We utilize the projections of the real exchange rate dynamics onto the string-like topology in the OANDA market. The latter approach allows us to build the stable prediction models in trading in the financial forex market. The real application of the multi-string structures is provided to demonstrate our ideas for the solution of the problem of the robust portfolio selection. The comparison with the trend following strategies was performed, the stability of the algorithm on the transaction costs for long trade periods was confirmed.
Koopman Operator Framework for Time Series Modeling and Analysis
Surana, Amit
2018-01-01
We propose an interdisciplinary framework for time series classification, forecasting, and anomaly detection by combining concepts from Koopman operator theory, machine learning, and linear systems and control theory. At the core of this framework is nonlinear dynamic generative modeling of time series using the Koopman operator which is an infinite-dimensional but linear operator. Rather than working with the underlying nonlinear model, we propose two simpler linear representations or model forms based on Koopman spectral properties. We show that these model forms are invariants of the generative model and can be readily identified directly from data using techniques for computing Koopman spectral properties without requiring the explicit knowledge of the generative model. We also introduce different notions of distances on the space of such model forms which is essential for model comparison/clustering. We employ the space of Koopman model forms equipped with distance in conjunction with classical machine learning techniques to develop a framework for automatic feature generation for time series classification. The forecasting/anomaly detection framework is based on using Koopman model forms along with classical linear systems and control approaches. We demonstrate the proposed framework for human activity classification, and for time series forecasting/anomaly detection in power grid application.
Stochastic modeling of hourly rainfall times series in Campania (Italy)
Giorgio, M.; Greco, R.
2009-04-01
Occurrence of flowslides and floods in small catchments is uneasy to predict, since it is affected by a number of variables, such as mechanical and hydraulic soil properties, slope morphology, vegetation coverage, rainfall spatial and temporal variability. Consequently, landslide risk assessment procedures and early warning systems still rely on simple empirical models based on correlation between recorded rainfall data and observed landslides and/or river discharges. Effectiveness of such systems could be improved by reliable quantitative rainfall prediction, which can allow gaining larger lead-times. Analysis of on-site recorded rainfall height time series represents the most effective approach for a reliable prediction of local temporal evolution of rainfall. Hydrological time series analysis is a widely studied field in hydrology, often carried out by means of autoregressive models, such as AR, ARMA, ARX, ARMAX (e.g. Salas [1992]). Such models gave the best results when applied to the analysis of autocorrelated hydrological time series, like river flow or level time series. Conversely, they are not able to model the behaviour of intermittent time series, like point rainfall height series usually are, especially when recorded with short sampling time intervals. More useful for this issue are the so-called DRIP (Disaggregated Rectangular Intensity Pulse) and NSRP (Neymann-Scott Rectangular Pulse) model [Heneker et al., 2001; Cowpertwait et al., 2002], usually adopted to generate synthetic point rainfall series. In this paper, the DRIP model approach is adopted, in which the sequence of rain storms and dry intervals constituting the structure of rainfall time series is modeled as an alternating renewal process. Final aim of the study is to provide a useful tool to implement an early warning system for hydrogeological risk management. Model calibration has been carried out with hourly rainfall hieght data provided by the rain gauges of Campania Region civil
Models for Pooled Time-Series Cross-Section Data
Directory of Open Access Journals (Sweden)
Lawrence E Raffalovich
2015-07-01
Full Text Available Several models are available for the analysis of pooled time-series cross-section (TSCS data, defined as “repeated observations on fixed units” (Beck and Katz 1995. In this paper, we run the following models: (1 a completely pooled model, (2 fixed effects models, and (3 multi-level/hierarchical linear models. To illustrate these models, we use a Generalized Least Squares (GLS estimator with cross-section weights and panel-corrected standard errors (with EViews 8 on the cross-national homicide trends data of forty countries from 1950 to 2005, which we source from published research (Messner et al. 2011. We describe and discuss the similarities and differences between the models, and what information each can contribute to help answer substantive research questions. We conclude with a discussion of how the models we present may help to mitigate validity threats inherent in pooled time-series cross-section data analysis.
Small Signal Audiosusceptibility Model for Series Resonant Converter
G., Subhash Joshi T.; John, Vinod
2018-01-01
Models that accurately predict the output voltage ripple magnitude are essential for applications with stringent performance target for it. Impact of dc input ripple on the output ripple for a Series Resonant Converter (SRC) using discrete domain exact discretization modelling method is analysed in this paper. A novel discrete state space model along with a small signal model for SRC considering 3 state variables is presented. The audiosusceptibility (AS) transfer function which relates the i...
RADON CONCENTRATION TIME SERIES MODELING AND APPLICATION DISCUSSION.
Stránský, V; Thinová, L
2017-11-01
In the year 2010 a continual radon measurement was established at Mladeč Caves in the Czech Republic using a continual radon monitor RADIM3A. In order to model radon time series in the years 2010-15, the Box-Jenkins Methodology, often used in econometrics, was applied. Because of the behavior of radon concentrations (RCs), a seasonal integrated, autoregressive moving averages model with exogenous variables (SARIMAX) has been chosen to model the measured time series. This model uses the time series seasonality, previously acquired values and delayed atmospheric parameters, to forecast RC. The developed model for RC time series is called regARIMA(5,1,3). Model residuals could be retrospectively compared with seismic evidence of local or global earthquakes, which occurred during the RCs measurement. This technique enables us to asses if continuously measured RC could serve an earthquake precursor. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.
High-temperature series expansions for random Potts models
Directory of Open Access Journals (Sweden)
M.Hellmund
2005-01-01
Full Text Available We discuss recently generated high-temperature series expansions for the free energy and the susceptibility of random-bond q-state Potts models on hypercubic lattices. Using the star-graph expansion technique, quenched disorder averages can be calculated exactly for arbitrary uncorrelated coupling distributions while keeping the disorder strength p as well as the dimension d as symbolic parameters. We present analyses of the new series for the susceptibility of the Ising (q=2 and 4-state Potts model in three dimensions up to the order 19 and 18, respectively, and compare our findings with results from field-theoretical renormalization group studies and Monte Carlo simulations.
Quality Quandaries- Time Series Model Selection and Parsimony
DEFF Research Database (Denmark)
Bisgaard, Søren; Kulahci, Murat
2009-01-01
Some of the issues involved in selecting adequate models for time series data are discussed using an example concerning the number of users of an Internet server. The process of selecting an appropriate model is subjective and requires experience and judgment. The authors believe an important...... consideration in model selection should be parameter parsimony. They favor the use of parsimonious mixed ARMA models, noting that research has shown that a model building strategy that considers only autoregressive representations will lead to non-parsimonious models and to loss of forecasting accuracy....
Parameterizing unconditional skewness in models for financial time series
DEFF Research Database (Denmark)
He, Changli; Silvennoinen, Annastiina; Teräsvirta, Timo
In this paper we consider the third-moment structure of a class of time series models. It is often argued that the marginal distribution of financial time series such as returns is skewed. Therefore it is of importance to know what properties a model should possess if it is to accommodate...... unconditional skewness. We consider modelling the unconditional mean and variance using models that respond nonlinearly or asymmetrically to shocks. We investigate the implications of these models on the third-moment structure of the marginal distribution as well as conditions under which the unconditional...... distribution exhibits skewness and nonzero third-order autocovariance structure. In this respect, an asymmetric or nonlinear specification of the conditional mean is found to be of greater importance than the properties of the conditional variance. Several examples are discussed and, whenever possible...
Model and Variable Selection Procedures for Semiparametric Time Series Regression
Directory of Open Access Journals (Sweden)
Risa Kato
2009-01-01
Full Text Available Semiparametric regression models are very useful for time series analysis. They facilitate the detection of features resulting from external interventions. The complexity of semiparametric models poses new challenges for issues of nonparametric and parametric inference and model selection that frequently arise from time series data analysis. In this paper, we propose penalized least squares estimators which can simultaneously select significant variables and estimate unknown parameters. An innovative class of variable selection procedure is proposed to select significant variables and basis functions in a semiparametric model. The asymptotic normality of the resulting estimators is established. Information criteria for model selection are also proposed. We illustrate the effectiveness of the proposed procedures with numerical simulations.
Forecasting with nonlinear time series model: A Monte-Carlo ...
African Journals Online (AJOL)
In this paper, we propose a new method of forecasting with nonlinear time series model using Monte-Carlo Bootstrap method. This new method gives better result in terms of forecast root mean squared error (RMSE) when compared with the traditional Bootstrap method and Monte-Carlo method of forecasting using a ...
Sparse time series chain graphical models for reconstructing genetic networks
Abegaz, Fentaw; Wit, Ernst
We propose a sparse high-dimensional time series chain graphical model for reconstructing genetic networks from gene expression data parametrized by a precision matrix and autoregressive coefficient matrix. We consider the time steps as blocks or chains. The proposed approach explores patterns of
Modeling irregularly spaced residual series as a continuous stochastic process
Von Asmuth, J.R.; Bierkens, M.F.P.
2005-01-01
In this paper, the background and functioning of a simple but effective continuous time approach for modeling irregularly spaced residual series is presented. The basic equations were published earlier by von Asmuth et al. (2002), who used them as part of a continuous time transfer function noise
Multivariate time series modeling of selected childhood diseases in ...
African Journals Online (AJOL)
This paper is focused on modeling the five most prevalent childhood diseases in Akwa Ibom State using a multivariate approach to time series. An aggregate of 78,839 reported cases of malaria, upper respiratory tract infection (URTI), Pneumonia, anaemia and tetanus were extracted from five randomly selected hospitals in ...
Combined Forecasts from Linear and Nonlinear Time Series Models
N. Terui (Nobuhiko); H.K. van Dijk (Herman)
1999-01-01
textabstractCombined forecasts from a linear and a nonlinear model are investigated for time series with possibly nonlinear characteristics. The forecasts are combined by a constant coefficient regression method as well as a time varying method. The time varying method allows for a locally
Combined forecasts from linear and nonlinear time series models
N. Terui (Nobuhiko); H.K. van Dijk (Herman)
1999-01-01
textabstractCombined forecasts from a linear and a nonlinear model are investigated for time series with possibly nonlinear characteristics. The forecasts are combined by a constant coefficient regression method as well as a time varying method. The time varying method allows for a locally
The Time Is Right to Focus on Model Organism Metabolomes.
Edison, Arthur S; Hall, Robert D; Junot, Christophe; Karp, Peter D; Kurland, Irwin J; Mistrik, Robert; Reed, Laura K; Saito, Kazuki; Salek, Reza M; Steinbeck, Christoph; Sumner, Lloyd W; Viant, Mark R
2016-02-15
Model organisms are an essential component of biological and biomedical research that can be used to study specific biological processes. These organisms are in part selected for facile experimental study. However, just as importantly, intensive study of a small number of model organisms yields important synergies as discoveries in one area of science for a given organism shed light on biological processes in other areas, even for other organisms. Furthermore, the extensive knowledge bases compiled for each model organism enable systems-level understandings of these species, which enhance the overall biological and biomedical knowledge for all organisms, including humans. Building upon extensive genomics research, we argue that the time is now right to focus intensively on model organism metabolomes. We propose a grand challenge for metabolomics studies of model organisms: to identify and map all metabolites onto metabolic pathways, to develop quantitative metabolic models for model organisms, and to relate organism metabolic pathways within the context of evolutionary metabolomics, i.e., phylometabolomics. These efforts should focus on a series of established model organisms in microbial, animal and plant research.
The Time Is Right to Focus on Model Organism Metabolomes
Directory of Open Access Journals (Sweden)
Arthur S. Edison
2016-02-01
Full Text Available Model organisms are an essential component of biological and biomedical research that can be used to study specific biological processes. These organisms are in part selected for facile experimental study. However, just as importantly, intensive study of a small number of model organisms yields important synergies as discoveries in one area of science for a given organism shed light on biological processes in other areas, even for other organisms. Furthermore, the extensive knowledge bases compiled for each model organism enable systems-level understandings of these species, which enhance the overall biological and biomedical knowledge for all organisms, including humans. Building upon extensive genomics research, we argue that the time is now right to focus intensively on model organism metabolomes. We propose a grand challenge for metabolomics studies of model organisms: to identify and map all metabolites onto metabolic pathways, to develop quantitative metabolic models for model organisms, and to relate organism metabolic pathways within the context of evolutionary metabolomics, i.e., phylometabolomics. These efforts should focus on a series of established model organisms in microbial, animal and plant research.
Modeling of Organic Effects on Aerosols Growth
Caboussat, A.; Amundson, N. R.; He, J.; Seinfeld, J. H.
2006-05-01
Over the last two decades, a series of modules has been developed in the atmospheric modeling community to predict the phase transition, multistage growth phenomena, crystallization and evaporation of inorganic aerosols. In the same time, the water interactions of particles containing organic constituents have been recognized as an important factor for aerosol activation and cloud formation. However, the research on hygroscopicity of organic-containing aerosols, motivated by the organic effect on aerosol growth and activation, has gathered much less attention. We present here a new model (UHAERO), that is both efficient and rigorously computes phase separation and liquid-liquid equilibrium for organic particles, as well as the dynamics partitioning between gas and particulate phases, with emphasis on the role of water vapor in the gas-liquid partitioning. The model does not rely on any a priori specification of the phases present in certain atmospheric conditions. The determination of the thermodynamic equilibrium is based on the minimization of the Gibbs free energy. The mass transfer between the particle and the bulk gas phase is dynamically driven by the difference between bulk gas pressure and the gas pressure at the surface of a particle. The multicomponent phase equilibrium for a closed organic aerosol system at constant temperature and pressure and for specified feeds is the solution to the liquid-liquid equilibrium problem arising from the constrained minimization of the Gibbs free energy. A geometrical concept of phase simplex (phase separation) is introduced to characterize the thermodynamic equilibrium. The computation of the mass fluxes is achieved by coupling the thermodynamics of the organic aerosol particle and the determination of the mass fluxes. Numerical results show the efficiency of the model, which make it suitable for insertion in global three- dimensional air quality models. The Gibbs free energy is modeled by the UNIFAC model to illustrate
Modeling Philippine Stock Exchange Composite Index Using Time Series Analysis
Gayo, W. S.; Urrutia, J. D.; Temple, J. M. F.; Sandoval, J. R. D.; Sanglay, J. E. A.
2015-06-01
This study was conducted to develop a time series model of the Philippine Stock Exchange Composite Index and its volatility using the finite mixture of ARIMA model with conditional variance equations such as ARCH, GARCH, EG ARCH, TARCH and PARCH models. Also, the study aimed to find out the reason behind the behaviorof PSEi, that is, which of the economic variables - Consumer Price Index, crude oil price, foreign exchange rate, gold price, interest rate, money supply, price-earnings ratio, Producers’ Price Index and terms of trade - can be used in projecting future values of PSEi and this was examined using Granger Causality Test. The findings showed that the best time series model for Philippine Stock Exchange Composite index is ARIMA(1,1,5) - ARCH(1). Also, Consumer Price Index, crude oil price and foreign exchange rate are factors concluded to Granger cause Philippine Stock Exchange Composite Index.
Thermomechanical constitutive modeling of polyurethane-series shape memory polymer
Energy Technology Data Exchange (ETDEWEB)
Tobushi, H.; Ito, N.; Takata, K. [Aichi Inst. of Technol., Nagoya (Japan). Dept. of Mech. Eng.; Hayashi, S. [Nagoya Research and Development Center, Mitsubishi Heavy Industries, Ltd., Nagoya (Japan)
2000-07-01
In order to describe the thermomechanical properties in shape memory polymer of polyurethane series, a thermomechanical constitutive model was developed. In order to describe the variation in mechanical properties due to the glass transition, coefficients in the model were expressed by a single exponential function of temperature. The proposed theory expressed well the thermomechanical properties of the material, such as shape fixity and shape recovery. (orig.)
Fundamental State Space Time Series Models for JEPX Electricity Prices
Ofuji, Kenta; Kanemoto, Shigeru
Time series models are popular in attempts to model and forecast price dynamics in various markets. In this paper, we have formulated two state space models and tested them for its applicability to power price modeling and forecasting using JEPX (Japan Electric Power eXchange) data. The state space models generally have a high degree of flexibility with its time-dependent state transition matrix and system equation configurations. Based on empirical data analysis and past literatures, we used calculation assumptions to a) extract stochastic trend component to capture non-stationarity, and b) detect structural changes underlying in the market. The stepwise calculation algorithm followed that of Kalman Filter. We then evaluated the two models' forecasting capabilities, in comparison with ordinary AR (autoregressive) and ARCH (autoregressive conditional heteroskedasticity) models. By choosing proper explanatory variables, the latter state space model yielded as good a forecasting capability as that of the AR and the ARCH models for a short forecasting horizon.
GRAM Series of Atmospheric Models for Aeroentry and Aeroassist
Duvall, Aleta; Justus, C. G.; Keller, Vernon W.
2005-01-01
The eight destinations in the Solar System with sufficient atmosphere for either aeroentry or aeroassist, including aerocapture, are: Venus, Earth, Mars, Jupiter, Saturn; Uranus. and Neptune, and Saturn's moon Titan. Engineering-level atmospheric models for four of these (Earth, Mars, Titan, and Neptune) have been developed for use in NASA's systems analysis studies of aerocapture applications in potential future missions. Work has recently commenced on development of a similar atmospheric model for Venus. This series of MSFC-sponsored models is identified as the Global Reference Atmosphere Model (GRAM) series. An important capability of all of the models in the GRAM series is their ability to simulate quasi-random perturbations for Monte Carlo analyses in developing guidance, navigation and control algorithms, and for thermal systems design. Example applications for Earth aeroentry and Mars aerocapture systems analysis studies are presented and illustrated. Current and planned updates to the Earth and Mars atmospheric models, in support of NASA's new exploration vision, are also presented.
Model organisms and target discovery.
Muda, Marco; McKenna, Sean
2004-09-01
The wealth of information harvested from full genomic sequencing projects has not generated a parallel increase in the number of novel targets for therapeutic intervention. Several pharmaceutical companies have realized that novel drug targets can be identified and validated using simple model organisms. After decades of service in basic research laboratories, yeasts, worms, flies, fishes, and mice are now the cornerstones of modern drug discovery programs.: © 2004 Elsevier Ltd . All rights reserved.
A Comparative Study of Portmanteau Tests for Univariate Time Series Models
Directory of Open Access Journals (Sweden)
Sohail Chand
2006-07-01
Full Text Available Time series model diagnostic checking is the most important stage of time series model building. In this paper the comparison among several suggested diagnostic tests has been made using the simulation time series data.
Indian Academy of Sciences (India)
2013-01-22
Jan 22, 2013 ... (Fax, 33-144-323941; Email, morange@biologie.ens.fr). 1. Introduction. In 1968, Walther Stoeckenius summarized in Science the con- clusions of a meeting held the previous year at Frascati near. Rome on 'membrane modelling and membrane formation'. (Stoeckenius 1968). The diversity of the issues ...
Time Series Analysis, Modeling and Applications A Computational Intelligence Perspective
Chen, Shyi-Ming
2013-01-01
Temporal and spatiotemporal data form an inherent fabric of the society as we are faced with streams of data coming from numerous sensors, data feeds, recordings associated with numerous areas of application embracing physical and human-generated phenomena (environmental data, financial markets, Internet activities, etc.). A quest for a thorough analysis, interpretation, modeling and prediction of time series comes with an ongoing challenge for developing models that are both accurate and user-friendly (interpretable). The volume is aimed to exploit the conceptual and algorithmic framework of Computational Intelligence (CI) to form a cohesive and comprehensive environment for building models of time series. The contributions covered in the volume are fully reflective of the wealth of the CI technologies by bringing together ideas, algorithms, and numeric studies, which convincingly demonstrate their relevance, maturity and visible usefulness. It reflects upon the truly remarkable diversity of methodological a...
Degeneracy of time series models: The best model is not always the correct model
International Nuclear Information System (INIS)
Judd, Kevin; Nakamura, Tomomichi
2006-01-01
There are a number of good techniques for finding, in some sense, the best model of a deterministic system given a time series of observations. We examine a problem called model degeneracy, which has the consequence that even when a perfect model of a system exists, one does not find it using the best techniques currently available. The problem is illustrated using global polynomial models and the theory of Groebner bases
Identification of neutral biochemical network models from time series data
Directory of Open Access Journals (Sweden)
Maia Marco
2009-05-01
Full Text Available Abstract Background The major difficulty in modeling biological systems from multivariate time series is the identification of parameter sets that endow a model with dynamical behaviors sufficiently similar to the experimental data. Directly related to this parameter estimation issue is the task of identifying the structure and regulation of ill-characterized systems. Both tasks are simplified if the mathematical model is canonical, i.e., if it is constructed according to strict guidelines. Results In this report, we propose a method for the identification of admissible parameter sets of canonical S-systems from biological time series. The method is based on a Monte Carlo process that is combined with an improved version of our previous parameter optimization algorithm. The method maps the parameter space into the network space, which characterizes the connectivity among components, by creating an ensemble of decoupled S-system models that imitate the dynamical behavior of the time series with sufficient accuracy. The concept of sloppiness is revisited in the context of these S-system models with an exploration not only of different parameter sets that produce similar dynamical behaviors but also different network topologies that yield dynamical similarity. Conclusion The proposed parameter estimation methodology was applied to actual time series data from the glycolytic pathway of the bacterium Lactococcus lactis and led to ensembles of models with different network topologies. In parallel, the parameter optimization algorithm was applied to the same dynamical data upon imposing a pre-specified network topology derived from prior biological knowledge, and the results from both strategies were compared. The results suggest that the proposed method may serve as a powerful exploration tool for testing hypotheses and the design of new experiments.
Identification of neutral biochemical network models from time series data.
Vilela, Marco; Vinga, Susana; Maia, Marco A Grivet Mattoso; Voit, Eberhard O; Almeida, Jonas S
2009-05-05
The major difficulty in modeling biological systems from multivariate time series is the identification of parameter sets that endow a model with dynamical behaviors sufficiently similar to the experimental data. Directly related to this parameter estimation issue is the task of identifying the structure and regulation of ill-characterized systems. Both tasks are simplified if the mathematical model is canonical, i.e., if it is constructed according to strict guidelines. In this report, we propose a method for the identification of admissible parameter sets of canonical S-systems from biological time series. The method is based on a Monte Carlo process that is combined with an improved version of our previous parameter optimization algorithm. The method maps the parameter space into the network space, which characterizes the connectivity among components, by creating an ensemble of decoupled S-system models that imitate the dynamical behavior of the time series with sufficient accuracy. The concept of sloppiness is revisited in the context of these S-system models with an exploration not only of different parameter sets that produce similar dynamical behaviors but also different network topologies that yield dynamical similarity. The proposed parameter estimation methodology was applied to actual time series data from the glycolytic pathway of the bacterium Lactococcus lactis and led to ensembles of models with different network topologies. In parallel, the parameter optimization algorithm was applied to the same dynamical data upon imposing a pre-specified network topology derived from prior biological knowledge, and the results from both strategies were compared. The results suggest that the proposed method may serve as a powerful exploration tool for testing hypotheses and the design of new experiments.
Time series regression model for infectious disease and weather.
Imai, Chisato; Armstrong, Ben; Chalabi, Zaid; Mangtani, Punam; Hashizume, Masahiro
2015-10-01
Time series regression has been developed and long used to evaluate the short-term associations of air pollution and weather with mortality or morbidity of non-infectious diseases. The application of the regression approaches from this tradition to infectious diseases, however, is less well explored and raises some new issues. We discuss and present potential solutions for five issues often arising in such analyses: changes in immune population, strong autocorrelations, a wide range of plausible lag structures and association patterns, seasonality adjustments, and large overdispersion. The potential approaches are illustrated with datasets of cholera cases and rainfall from Bangladesh and influenza and temperature in Tokyo. Though this article focuses on the application of the traditional time series regression to infectious diseases and weather factors, we also briefly introduce alternative approaches, including mathematical modeling, wavelet analysis, and autoregressive integrated moving average (ARIMA) models. Modifications proposed to standard time series regression practice include using sums of past cases as proxies for the immune population, and using the logarithm of lagged disease counts to control autocorrelation due to true contagion, both of which are motivated from "susceptible-infectious-recovered" (SIR) models. The complexity of lag structures and association patterns can often be informed by biological mechanisms and explored by using distributed lag non-linear models. For overdispersed models, alternative distribution models such as quasi-Poisson and negative binomial should be considered. Time series regression can be used to investigate dependence of infectious diseases on weather, but may need modifying to allow for features specific to this context. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.
A Parsimonious Bootstrap Method to Model Natural Inflow Energy Series
Directory of Open Access Journals (Sweden)
Fernando Luiz Cyrino Oliveira
2014-01-01
Full Text Available The Brazilian energy generation and transmission system is quite peculiar in its dimension and characteristics. As such, it can be considered unique in the world. It is a high dimension hydrothermal system with huge participation of hydro plants. Such strong dependency on hydrological regimes implies uncertainties related to the energetic planning, requiring adequate modeling of the hydrological time series. This is carried out via stochastic simulations of monthly inflow series using the family of Periodic Autoregressive models, PAR(p, one for each period (month of the year. In this paper it is shown the problems in fitting these models by the current system, particularly the identification of the autoregressive order “p” and the corresponding parameter estimation. It is followed by a proposal of a new approach to set both the model order and the parameters estimation of the PAR(p models, using a nonparametric computational technique, known as Bootstrap. This technique allows the estimation of reliable confidence intervals for the model parameters. The obtained results using the Parsimonious Bootstrap Method of Moments (PBMOM produced not only more parsimonious model orders but also adherent stochastic scenarios and, in the long range, lead to a better use of water resources in the energy operation planning.
Deriving dynamic marketing effectiveness from econometric time series models
Horváth, C.; Franses, Ph.H.B.F.
2003-01-01
textabstractTo understand the relevance of marketing efforts, it has become standard practice to estimate the long-run and short-run effects of the marketing-mix, using, say, weekly scanner data. A common vehicle for this purpose is an econometric time series model. Issues that are addressed in the literature are unit roots, cointegration, structural breaks and impulse response functions. In this paper we summarize the most important concepts by reviewing all possible empirical cases that can...
Modelling of series of types of automated trenchless works tunneling
Gendarz, P.; Rzasinski, R.
2016-08-01
Microtunneling is the newest method for making underground installations. Show method is the result of experience and methods applied in other, previous methods of trenchless underground works. It is considered reasonable to elaborate a series of types of construction of tunneling machines, to develop this particular earthworks method. There are many design solutions of machines, but the current goal is to develop non - excavation robotized machine. Erosion machines with main dimensions of the tunnels which are: 1600, 2000, 2500, 3150 are design with use of the computer aided methods. Series of types of construction of tunneling machines creating process was preceded by analysis of current state. The verification of practical methodology of creating the systematic part series was based on the designed erosion machines series of types. There were developed: method of construction similarity of the erosion machines, algorithmic methods of quantitative construction attributes variant analyzes in the I-DEAS advanced graphical program, relational and program parameterization. There manufacturing process of the parts will be created, which allows to verify the technological process on the CNC machines. The models of designed will be modified and the construction will be consulted with erosion machine users and manufacturers like: Tauber Rohrbau GmbH & Co.KG from Minster, OHL ZS a.s. from Brna,. The companies’ acceptance will result in practical verification by JUMARPOL company.
Unsupervised Classification During Time-Series Model Building.
Gates, Kathleen M; Lane, Stephanie T; Varangis, E; Giovanello, K; Guiskewicz, K
2017-01-01
Researchers who collect multivariate time-series data across individuals must decide whether to model the dynamic processes at the individual level or at the group level. A recent innovation, group iterative multiple model estimation (GIMME), offers one solution to this dichotomy by identifying group-level time-series models in a data-driven manner while also reliably recovering individual-level patterns of dynamic effects. GIMME is unique in that it does not assume homogeneity in processes across individuals in terms of the patterns or weights of temporal effects. However, it can be difficult to make inferences from the nuances in varied individual-level patterns. The present article introduces an algorithm that arrives at subgroups of individuals that have similar dynamic models. Importantly, the researcher does not need to decide the number of subgroups. The final models contain reliable group-, subgroup-, and individual-level patterns that enable generalizable inferences, subgroups of individuals with shared model features, and individual-level patterns and estimates. We show that integrating community detection into the GIMME algorithm improves upon current standards in two important ways: (1) providing reliable classification and (2) increasing the reliability in the recovery of individual-level effects. We demonstrate this method on functional MRI from a sample of former American football players.
Single-Index Additive Vector Autoregressive Time Series Models
LI, YEHUA
2009-09-01
We study a new class of nonlinear autoregressive models for vector time series, where the current vector depends on single-indexes defined on the past lags and the effects of different lags have an additive form. A sufficient condition is provided for stationarity of such models. We also study estimation of the proposed model using P-splines, hypothesis testing, asymptotics, selection of the order of the autoregression and of the smoothing parameters and nonlinear forecasting. We perform simulation experiments to evaluate our model in various settings. We illustrate our methodology on a climate data set and show that our model provides more accurate yearly forecasts of the El Niño phenomenon, the unusual warming of water in the Pacific Ocean. © 2009 Board of the Foundation of the Scandinavian Journal of Statistics.
Modeling, design, and optimization of Mindwalker series elastic joint.
Wang, Shiqian; Meijneke, Cor; van der Kooij, Herman
2013-06-01
Weight and power autonomy are limiting the daily use of wearable exoskeleton. Lightweight, efficient and powerful actuation system are not easy to achieve. Choosing the right combinations of existing technologies, such as battery, gear and motor is not a trivial task. In this paper, we propose an optimization framework by setting up a power-based quasi-static model of the exoskeleton joint drivetrain. The goal is to find the most efficient and lightweight combinations. This framework can be generalized for other similar applications by extending or accommodating the model to their own needs. We also present the Mindwalker exoskeleton joint, for which a novel series elastic actuator, consisting of a ballscrew-driven linear actuator and a double spiral spring, was developed and tested. This linear actuator is capable of outputting 960 W power and the exoskeleton joint can output 100 Nm peak torque continuously. The double spiral spring can sense torque between 0.08Nm and 100 Nm and it exhibits linearity of 99.99%, with no backlash or hysteresis. The series elastic joint can track a chirp torque profile with amplitude of 100 Nm over 6 Hz (large torque bandwidth) and for small torque (2 Nm peak-to-peak), it has a bandwidth over 38 Hz. The integrated exoskeleton joint, including the ballscrew-driven linear actuator, the series spring, electronics and the metal housing which hosts these components, weighs 2.9 kg.
Modeling Periodic Impulsive Effects on Online TV Series Diffusion.
Fu, Peihua; Zhu, Anding; Fang, Qiwen; Wang, Xi
Online broadcasting substantially affects the production, distribution, and profit of TV series. In addition, online word-of-mouth significantly affects the diffusion of TV series. Because on-demand streaming rates are the most important factor that influences the earnings of online video suppliers, streaming statistics and forecasting trends are valuable. In this paper, we investigate the effects of periodic impulsive stimulation and pre-launch promotion on on-demand streaming dynamics. We consider imbalanced audience feverish distribution using an impulsive susceptible-infected-removed(SIR)-like model. In addition, we perform a correlation analysis of online buzz volume based on Baidu Index data. We propose a PI-SIR model to evolve audience dynamics and translate them into on-demand streaming fluctuations, which can be observed and comprehended by online video suppliers. Six South Korean TV series datasets are used to test the model. We develop a coarse-to-fine two-step fitting scheme to estimate the model parameters, first by fitting inter-period accumulation and then by fitting inner-period feverish distribution. We find that audience members display similar viewing habits. That is, they seek new episodes every update day but fade away. This outcome means that impulsive intensity plays a crucial role in on-demand streaming diffusion. In addition, the initial audience size and online buzz are significant factors. On-demand streaming fluctuation is highly correlated with online buzz fluctuation. To stimulate audience attention and interpersonal diffusion, it is worthwhile to invest in promotion near update days. Strong pre-launch promotion is also a good marketing tool to improve overall performance. It is not advisable for online video providers to promote several popular TV series on the same update day. Inter-period accumulation is a feasible forecasting tool to predict the future trend of the on-demand streaming amount. The buzz in public social communities
Modeling Periodic Impulsive Effects on Online TV Series Diffusion.
Directory of Open Access Journals (Sweden)
Peihua Fu
Full Text Available Online broadcasting substantially affects the production, distribution, and profit of TV series. In addition, online word-of-mouth significantly affects the diffusion of TV series. Because on-demand streaming rates are the most important factor that influences the earnings of online video suppliers, streaming statistics and forecasting trends are valuable. In this paper, we investigate the effects of periodic impulsive stimulation and pre-launch promotion on on-demand streaming dynamics. We consider imbalanced audience feverish distribution using an impulsive susceptible-infected-removed(SIR-like model. In addition, we perform a correlation analysis of online buzz volume based on Baidu Index data.We propose a PI-SIR model to evolve audience dynamics and translate them into on-demand streaming fluctuations, which can be observed and comprehended by online video suppliers. Six South Korean TV series datasets are used to test the model. We develop a coarse-to-fine two-step fitting scheme to estimate the model parameters, first by fitting inter-period accumulation and then by fitting inner-period feverish distribution.We find that audience members display similar viewing habits. That is, they seek new episodes every update day but fade away. This outcome means that impulsive intensity plays a crucial role in on-demand streaming diffusion. In addition, the initial audience size and online buzz are significant factors. On-demand streaming fluctuation is highly correlated with online buzz fluctuation.To stimulate audience attention and interpersonal diffusion, it is worthwhile to invest in promotion near update days. Strong pre-launch promotion is also a good marketing tool to improve overall performance. It is not advisable for online video providers to promote several popular TV series on the same update day. Inter-period accumulation is a feasible forecasting tool to predict the future trend of the on-demand streaming amount. The buzz in public
Optimization of recurrent neural networks for time series modeling
DEFF Research Database (Denmark)
Pedersen, Morten With
1997-01-01
The present thesis is about optimization of recurrent neural networks applied to time series modeling. In particular is considered fully recurrent networks working from only a single external input, one layer of nonlinear hidden units and a li near output unit applied to prediction of discrete time...... series. The overall objective s are to improve training by application of second-order methods and to improve generalization ability by architecture optimization accomplished by pruning. The major topics covered in the thesis are: 1. The problem of training recurrent networks is analyzed from a numerical...... of solution obtained as well as computation time required. 3. A theoretical definition of the generalization error for recurrent networks is provided. This definition justifies a commonly adopted approach for estimating generalization ability. 4. The viability of pruning recurrent networks by the Optimal...
Modeling financial time series with S-plus
Zivot, Eric
2003-01-01
The field of financial econometrics has exploded over the last decade This book represents an integration of theory, methods, and examples using the S-PLUS statistical modeling language and the S+FinMetrics module to facilitate the practice of financial econometrics This is the first book to show the power of S-PLUS for the analysis of time series data It is written for researchers and practitioners in the finance industry, academic researchers in economics and finance, and advanced MBA and graduate students in economics and finance Readers are assumed to have a basic knowledge of S-PLUS and a solid grounding in basic statistics and time series concepts Eric Zivot is an associate professor and Gary Waterman Distinguished Scholar in the Economics Department at the University of Washington, and is co-director of the nascent Professional Master's Program in Computational Finance He regularly teaches courses on econometric theory, financial econometrics and time series econometrics, and is the recipient of the He...
Clustering Multivariate Time Series Using Hidden Markov Models
Directory of Open Access Journals (Sweden)
Shima Ghassempour
2014-03-01
Full Text Available In this paper we describe an algorithm for clustering multivariate time series with variables taking both categorical and continuous values. Time series of this type are frequent in health care, where they represent the health trajectories of individuals. The problem is challenging because categorical variables make it difficult to define a meaningful distance between trajectories. We propose an approach based on Hidden Markov Models (HMMs, where we first map each trajectory into an HMM, then define a suitable distance between HMMs and finally proceed to cluster the HMMs with a method based on a distance matrix. We test our approach on a simulated, but realistic, data set of 1,255 trajectories of individuals of age 45 and over, on a synthetic validation set with known clustering structure, and on a smaller set of 268 trajectories extracted from the longitudinal Health and Retirement Survey. The proposed method can be implemented quite simply using standard packages in R and Matlab and may be a good candidate for solving the difficult problem of clustering multivariate time series with categorical variables using tools that do not require advanced statistic knowledge, and therefore are accessible to a wide range of researchers.
Assimilation of LAI time-series in crop production models
Kooistra, Lammert; Rijk, Bert; Nannes, Louis
2014-05-01
Agriculture is worldwide a large consumer of freshwater, nutrients and land. Spatial explicit agricultural management activities (e.g., fertilization, irrigation) could significantly improve efficiency in resource use. In previous studies and operational applications, remote sensing has shown to be a powerful method for spatio-temporal monitoring of actual crop status. As a next step, yield forecasting by assimilating remote sensing based plant variables in crop production models would improve agricultural decision support both at the farm and field level. In this study we investigated the potential of remote sensing based Leaf Area Index (LAI) time-series assimilated in the crop production model LINTUL to improve yield forecasting at field level. The effect of assimilation method and amount of assimilated observations was evaluated. The LINTUL-3 crop production model was calibrated and validated for a potato crop on two experimental fields in the south of the Netherlands. A range of data sources (e.g., in-situ soil moisture and weather sensors, destructive crop measurements) was used for calibration of the model for the experimental field in 2010. LAI from cropscan field radiometer measurements and actual LAI measured with the LAI-2000 instrument were used as input for the LAI time-series. The LAI time-series were assimilated in the LINTUL model and validated for a second experimental field on which potatoes were grown in 2011. Yield in 2011 was simulated with an R2 of 0.82 when compared with field measured yield. Furthermore, we analysed the potential of assimilation of LAI into the LINTUL-3 model through the 'updating' assimilation technique. The deviation between measured and simulated yield decreased from 9371 kg/ha to 8729 kg/ha when assimilating weekly LAI measurements in the LINTUL model over the season of 2011. LINTUL-3 furthermore shows the main growth reducing factors, which are useful for farm decision support. The combination of crop models and sensor
Empirical investigation on modeling solar radiation series with ARMA–GARCH models
International Nuclear Information System (INIS)
Sun, Huaiwei; Yan, Dong; Zhao, Na; Zhou, Jianzhong
2015-01-01
Highlights: • Apply 6 ARMA–GARCH(-M) models to model and forecast solar radiation. • The ARMA–GARCH(-M) models produce more accurate radiation forecasting than conventional methods. • Show that ARMA–GARCH-M models are more effective for forecasting solar radiation mean and volatility. • The ARMA–EGARCH-M is robust and the ARMA–sGARCH-M is very competitive. - Abstract: Simulation of radiation is one of the most important issues in solar utilization. Time series models are useful tools in the estimation and forecasting of solar radiation series and their changes. In this paper, the effectiveness of autoregressive moving average (ARMA) models with various generalized autoregressive conditional heteroskedasticity (GARCH) processes, namely ARMA–GARCH models are evaluated for their effectiveness in radiation series. Six different GARCH approaches, which contain three different ARMA–GARCH models and corresponded GARCH in mean (ARMA–GARCH-M) models, are applied in radiation data sets from two representative climate stations in China. Multiple evaluation metrics of modeling sufficiency are used for evaluating the performances of models. The results show that the ARMA–GARCH(-M) models are effective in radiation series estimation. Both in fitting and prediction of radiation series, the ARMA–GARCH(-M) models show better modeling sufficiency than traditional models, while ARMA–EGARCH-M models are robustness in two sites and the ARMA–sGARCH-M models appear very competitive. Comparisons of statistical diagnostics and model performance clearly show that the ARMA–GARCH-M models make the mean radiation equations become more sufficient. It is recommended the ARMA–GARCH(-M) models to be the preferred method to use in the modeling of solar radiation series
Towards model evaluation and identification using Self-Organizing Maps
Directory of Open Access Journals (Sweden)
M. Herbst
2008-04-01
Full Text Available The reduction of information contained in model time series through the use of aggregating statistical performance measures is very high compared to the amount of information that one would like to draw from it for model identification and calibration purposes. It has been readily shown that this loss imposes important limitations on model identification and -diagnostics and thus constitutes an element of the overall model uncertainty. In this contribution we present an approach using a Self-Organizing Map (SOM to circumvent the identifiability problem induced by the low discriminatory power of aggregating performance measures. Instead, a Self-Organizing Map is used to differentiate the spectrum of model realizations, obtained from Monte-Carlo simulations with a distributed conceptual watershed model, based on the recognition of different patterns in time series. Further, the SOM is used instead of a classical optimization algorithm to identify those model realizations among the Monte-Carlo simulation results that most closely approximate the pattern of the measured discharge time series. The results are analyzed and compared with the manually calibrated model as well as with the results of the Shuffled Complex Evolution algorithm (SCE-UA. In our study the latter slightly outperformed the SOM results. The SOM method, however, yields a set of equivalent model parameterizations and therefore also allows for confining the parameter space to a region that closely represents a measured data set. This particular feature renders the SOM potentially useful for future model identification applications.
Self-organising mixture autoregressive model for non-stationary time series modelling.
Ni, He; Yin, Hujun
2008-12-01
Modelling non-stationary time series has been a difficult task for both parametric and nonparametric methods. One promising solution is to combine the flexibility of nonparametric models with the simplicity of parametric models. In this paper, the self-organising mixture autoregressive (SOMAR) network is adopted as a such mixture model. It breaks time series into underlying segments and at the same time fits local linear regressive models to the clusters of segments. In such a way, a global non-stationary time series is represented by a dynamic set of local linear regressive models. Neural gas is used for a more flexible structure of the mixture model. Furthermore, a new similarity measure has been introduced in the self-organising network to better quantify the similarity of time series segments. The network can be used naturally in modelling and forecasting non-stationary time series. Experiments on artificial, benchmark time series (e.g. Mackey-Glass) and real-world data (e.g. numbers of sunspots and Forex rates) are presented and the results show that the proposed SOMAR network is effective and superior to other similar approaches.
Organization Development: Strategies and Models.
Beckhard, Richard
This book, written for managers, specialists, and students of management, is based largely on the author's experience in helping organization leaders with planned-change efforts, and on related experience of colleagues in the field. Chapter 1 presents the background and causes for the increased concern with organization development and planned…
Faults on gamma projector Model 660 series for industrial radiography
International Nuclear Information System (INIS)
Sukhri Ahmad; Arshad Yassin; Saidi Rajab; Shaharudin Sayuti; Abd Nassir Ibrahim; Abd Razak Hamzah
2005-01-01
The main objective of this paper is to present MINTs experience pertaining to gamma projector maintenance activity. In Malaysia there are more than 100 gamma projectors that need to be maintained annually. Most of these projectors are of Tech-Ops Model 660 series portable gamma radiography systems, primarily for industrial radiography. The portability feature of the system provides both a safe means of transporting the radioactive source and operating flexibility, particularly useful in areas where access is limited. In Malaysia, Malaysian Institute for Nuclear Technology Research (MINT) has been approved as the National Gamma Projector Maintenance Centre by the Atomic Energy Licensing Board (AELB). This approval entitles MINT to undertake projector maintenance activity for all projectors throughout the country. Within 10 years of operation, MINT has dealt with thousands of projectors. (Author)
Reactions of 3d-series metallocenes with organic cadmium compounds
International Nuclear Information System (INIS)
Razuvaev, G.A.; Mar'in, V.P.; Vyshinskaya, L.I.; Grinval'd, I.I.; Spiridonova, N.N.
1987-01-01
Interaction of organic cadmium compounds and 3d-series metallocenes, Cp 2 M (M=V, Cr, Mn, Ni, Co) has been studied. It is shown that direction of these reactions is determined by metallocene nature. Reactions of oxidizing addition leading to σ-complexes formation are characteristic for vanadium and chromium metallocenes. When reacting cobaltocene with R 2 Cd, R group introduction to cyclopentadienyl ring and elimination of cobalt diene complexes take place. Manganocene and nickelocene interaction goes through the stage of complex formation with transition metal - cadmium bond
International Nuclear Information System (INIS)
Zachara, J.M.; Wobber, F.J.
1984-11-01
Model compounds are finding increasing use in environmental research. These individual compounds are selected as surrogates of important contaminants present in energy/defense wastes and their leachates and are used separately or as mixtures in research to define the anticipated or ''model'' environmental behavior of key waste components and to probe important physicochemical mechanisms involved in transport and fate. A seminar was held in Germantown, Maryland, April 24-25, 1984 to discuss the nature of model organic compounds being used for subsurface transport research. The seminar included participants experienced in the fields of environmental chemistry, microbiology, geohydrology, biology, and analytic chemistry. The objectives of the seminar were two-fold: (1) to review the rationale for the selection of organic compounds adopted by research groups working on the subsurface transport of organics, and (2) to evaluate the use of individual compounds to bracket the behavior of compound classes and compound constructs to approximate the behavior of complex organic mixtures
Water quality management using statistical analysis and time-series prediction model
Parmar, Kulwinder Singh; Bhardwaj, Rashmi
2014-12-01
This paper deals with water quality management using statistical analysis and time-series prediction model. The monthly variation of water quality standards has been used to compare statistical mean, median, mode, standard deviation, kurtosis, skewness, coefficient of variation at Yamuna River. Model validated using R-squared, root mean square error, mean absolute percentage error, maximum absolute percentage error, mean absolute error, maximum absolute error, normalized Bayesian information criterion, Ljung-Box analysis, predicted value and confidence limits. Using auto regressive integrated moving average model, future water quality parameters values have been estimated. It is observed that predictive model is useful at 95 % confidence limits and curve is platykurtic for potential of hydrogen (pH), free ammonia, total Kjeldahl nitrogen, dissolved oxygen, water temperature (WT); leptokurtic for chemical oxygen demand, biochemical oxygen demand. Also, it is observed that predicted series is close to the original series which provides a perfect fit. All parameters except pH and WT cross the prescribed limits of the World Health Organization /United States Environmental Protection Agency, and thus water is not fit for drinking, agriculture and industrial use.
Prediction of traffic-related nitrogen oxides concentrations using Structural Time-Series models
Lawson, Anneka Ruth; Ghosh, Bidisha; Broderick, Brian
2011-09-01
Ambient air quality monitoring, modeling and compliance to the standards set by European Union (EU) directives and World Health Organization (WHO) guidelines are required to ensure the protection of human and environmental health. Congested urban areas are most susceptible to traffic-related air pollution which is the most problematic source of air pollution in Ireland. Long-term continuous real-time monitoring of ambient air quality at such urban centers is essential but often not realistic due to financial and operational constraints. Hence, the development of a resource-conservative ambient air quality monitoring technique is essential to ensure compliance with the threshold values set by the standards. As an intelligent and advanced statistical methodology, a Structural Time Series (STS) based approach has been introduced in this paper to develop a parsimonious and computationally simple air quality model. In STS methodology, the different components of a time-series dataset such as the trend, seasonal, cyclical and calendar variations can be modeled separately. To test the effectiveness of the proposed modeling strategy, average hourly concentrations of nitrogen dioxide and nitrogen oxides from a congested urban arterial in Dublin city center were modeled using STS methodology. The prediction error estimates from the developed air quality model indicate that the STS model can be a useful tool in predicting nitrogen dioxide and nitrogen oxides concentrations in urban areas and will be particularly useful in situations where the information on external variables such as meteorology or traffic volume is not available.
Model Organisms Fact Sheet: Using Model Organisms to Study Health and Disease
... research organisms to explore the basic biology and chemistry of life. Scientists decide which organism to study ... and much is already known about their genetic makeup . For these and other reasons, studying model organisms ...
Modelling organic particles in the atmosphere
International Nuclear Information System (INIS)
Couvidat, Florian
2012-01-01
Organic aerosol formation in the atmosphere is investigated via the development of a new model named H 2 O (Hydrophilic/Hydrophobic Organics). First, a parameterization is developed to take into account secondary organic aerosol formation from isoprene oxidation. It takes into account the effect of nitrogen oxides on organic aerosol formation and the hydrophilic properties of the aerosols. This parameterization is then implemented in H 2 O along with some other developments and the results of the model are compared to organic carbon measurements over Europe. Model performance is greatly improved by taking into account emissions of primary semi-volatile compounds, which can form secondary organic aerosols after oxidation or can condense when temperature decreases. If those emissions are not taken into account, a significant underestimation of organic aerosol concentrations occurs in winter. The formation of organic aerosols over an urban area was also studied by simulating organic aerosols concentration over the Paris area during the summer campaign of Megapoli (July 2009). H 2 O gives satisfactory results over the Paris area, although a peak of organic aerosol concentrations from traffic, which does not appear in the measurements, appears in the model simulation during rush hours. It could be due to an underestimation of the volatility of organic aerosols. It is also possible that primary and secondary organic compounds do not mix well together and that primary semi volatile compounds do not condense on an organic aerosol that is mostly secondary and highly oxidized. Finally, the impact of aqueous-phase chemistry was studied. The mechanism for the formation of secondary organic aerosol includes in-cloud oxidation of glyoxal, methylglyoxal, methacrolein and methylvinylketone, formation of methyltetrols in the aqueous phase of particles and cloud droplets, and the in-cloud aging of organic aerosols. The impact of wet deposition is also studied to better estimate the
Elmore, Donald E.; Guayasamin, Ryann C.; Kieffer, Madeleine E.
2010-01-01
As computational modeling plays an increasingly central role in biochemical research, it is important to provide students with exposure to common modeling methods in their undergraduate curriculum. This article describes a series of computer labs designed to introduce undergraduate students to energy minimization, molecular dynamics simulations,…
Liu, Jinxuan
2012-12-04
A novel class of metal organic frameworks (MOFs) has been synthesized from Cu-acetate and dicarboxylic acids using liquid phase epitaxy. The SURMOF-2 isoreticular series exhibits P4 symmetry, for the longest linker a channel-size of 3 3 nm2 is obtained, one of the largest values reported for any MOF so far. High quality, ab-initio electronic structure calculations confirm the stability of a regular packing of (Cu++) 2-carboxylate paddle-wheel planes with P4 symmetry and reveal, that the SURMOF-2 structures are in fact metastable, with a fairly large activation barrier for the transition to the bulk MOF-2 structures exhibiting a lower, twofold (P2 or C2) symmetry. The theoretical calculations also allow identifying the mechanism for the low-temperature epitaxial growth process and to explain, why a synthesis of this highly interesting, new class of high-symmetry, metastable MOFs is not possible using the conventional solvothermal process.
International Nuclear Information System (INIS)
Cullings, H. M.; Kawamura, H.; Chen, J.
2012-01-01
The computational phantoms used in dosimetry system DS86 and re-used in DS02 were derived from models and methods developed at Oak Ridge National Laboratories (ORNL) in the US, but referred to Japanese anthropometric data for the Japanese population of 1945, from studies conducted at the Japanese National Inst. of Radiological Sciences and other sources. The phantoms developed for DS86 were limited to three hermaphroditic models: infant, child and adult. After comparing data from Japanese and Western populations, phantoms were adapted from the pre-existing ORNL series, adjusting some organs in the adult phantom to reflect differences between Japanese and Western data, but not in the infant and child phantoms. To develop a new and larger series of more age- and sex-specific models, it appears necessary to rely on the original Japanese data and values derived from them, which can directly provide population-average body dimensions for various ages. Those data were re-analysed in conjunction with other Asian data for an Asian Reference Man model, providing a rather complete table of organ weights that could be used to scale organs for growth during childhood and adolescence. Although the resulting organ volumes might have some inaccuracies in relation to true population-average values, this is a minor concern because in the DS02 context organ size per se is less important than the correct body size and correct placement of the organ in the body. (authors)
2010-03-19
... DEPARTMENT OF COMMERCE National Oceanic and Atmospheric Administration NOAA Is Hosting a Series of Informational Webinars for Individuals and Organizations To Learn About the Proposed NOAA Climate Service AGENCY... webinars for individuals and organizations to learn about the proposed NOAA Climate Service and to provide...
Sriyudthsak, Kansuporn; Shiraishi, Fumihide; Hirai, Masami Yokota
2016-01-01
The high-throughput acquisition of metabolome data is greatly anticipated for the complete understanding of cellular metabolism in living organisms. A variety of analytical technologies have been developed to acquire large-scale metabolic profiles under different biological or environmental conditions. Time series data are useful for predicting the most likely metabolic pathways because they provide important information regarding the accumulation of metabolites, which implies causal relationships in the metabolic reaction network. Considerable effort has been undertaken to utilize these data for constructing a mathematical model merging system properties and quantitatively characterizing a whole metabolic system in toto. However, there are technical difficulties between benchmarking the provision and utilization of data. Although, hundreds of metabolites can be measured, which provide information on the metabolic reaction system, simultaneous measurement of thousands of metabolites is still challenging. In addition, it is nontrivial to logically predict the dynamic behaviors of unmeasurable metabolite concentrations without sufficient information on the metabolic reaction network. Yet, consolidating the advantages of advancements in both metabolomics and mathematical modeling remain to be accomplished. This review outlines the conceptual basis of and recent advances in technologies in both the research fields. It also highlights the potential for constructing a large-scale mathematical model by estimating model parameters from time series metabolome data in order to comprehensively understand metabolism at the systems level.
Cardiac Electromechanical Models: From Cell to Organ
Directory of Open Access Journals (Sweden)
Natalia A Trayanova
2011-08-01
Full Text Available The heart is a multiphysics and multiscale system that has driven the development of the most sophisticated mathematical models at the frontiers of computation physiology and medicine. This review focuses on electromechanical (EM models of the heart from the molecular level of myofilaments to anatomical models of the organ. Because of the coupling in terms of function and emergent behaviors at each level of biological hierarchy, separation of behaviors at a given scale is difficult. Here, a separation is drawn at the cell level so that the first half addresses subcellular/single cell models and the second half addresses organ models. At the subcelluar level, myofilament models represent actin-myosin interaction and Ca-based activation. Myofilament models and their refinements represent an overview of the development in the field. The discussion of specific models emphasizes the roles of cooperative mechanisms and sarcomere length dependence of contraction force, considered the cellular basis of the Frank-Starling law. A model of electrophysiology and Ca handling can be coupled to a myofilament model to produce an EM cell model, and representative examples are summarized to provide an overview of the progression of field. The second half of the review covers organ-level models that require solution of the electrical component as a reaction-diffusion system and the mechanical component, in which active tension generated by the myocytes produces deformation of the organ as described by the equations of continuum mechanics. As outlined in the review, different organ-level models have chosen to use different ionic and myofilament models depending on the specific application; this choice has been largely dictated by compromises between model complexity and computational tractability. The review also addresses application areas of EM models such as cardiac resynchronization therapy and the role of mechano-electric coupling in arrhythmias and
Project-matrix models of marketing organization
Directory of Open Access Journals (Sweden)
Gutić Dragutin
2009-01-01
Full Text Available Unlike theory and practice of corporation organization, in marketing organization numerous forms and contents at its disposal are not reached until this day. It can be well estimated that marketing organization today in most of our companies and in almost all its parts, noticeably gets behind corporation organization. Marketing managers have always been occupied by basic, narrow marketing activities as: sales growth, market analysis, market growth and market share, marketing research, introduction of new products, modification of products, promotion, distribution etc. They rarely found it necessary to focus a bit more to different aspects of marketing management, for example: marketing planning and marketing control, marketing organization and leading. This paper deals with aspects of project - matrix marketing organization management. Two-dimensional and more-dimensional models are presented. Among two-dimensional, these models are analyzed: Market management/products management model; Products management/management of product lifecycle phases on market model; Customers management/marketing functions management model; Demand management/marketing functions management model; Market positions management/marketing functions management model. .
The Zebrafish Model Organism Database (ZFIN)
U.S. Department of Health & Human Services — ZFIN serves as the zebrafish model organism database. It aims to: a) be the community database resource for the laboratory use of zebrafish, b) develop and support...
A Sandwich-Type Standard Error Estimator of SEM Models with Multivariate Time Series
Zhang, Guangjian; Chow, Sy-Miin; Ong, Anthony D.
2011-01-01
Structural equation models are increasingly used as a modeling tool for multivariate time series data in the social and behavioral sciences. Standard error estimators of SEM models, originally developed for independent data, require modifications to accommodate the fact that time series data are inherently dependent. In this article, we extend a…
time series modeling of daily abandoned calls in a call centre
African Journals Online (AJOL)
DJFLEX
Models for evaluating and predicting the short periodic time series in daily abandoned calls in a call center are developed. Abandonment of calls due to impatient is an identified problem among most call centers. The two competing models were derived using Fourier series and the Box and Jenkins modeling approaches.
Time series modeling of daily abandoned calls in a call centre ...
African Journals Online (AJOL)
Models for evaluating and predicting the short periodic time series in daily abandoned calls in a call center are developed. Abandonment of calls due to impatient is an identified problem among most call centers. The two competing models were derived using Fourier series and the Box and Jenkins modeling approaches.
Modeling Virtual Organization Architecture with the Virtual Organization Breeding Methodology
Paszkiewicz, Zbigniew; Picard, Willy
While Enterprise Architecture Modeling (EAM) methodologies become more and more popular, an EAM methodology tailored to the needs of virtual organizations (VO) is still to be developed. Among the most popular EAM methodologies, TOGAF has been chosen as the basis for a new EAM methodology taking into account characteristics of VOs presented in this paper. In this new methodology, referred as Virtual Organization Breeding Methodology (VOBM), concepts developed within the ECOLEAD project, e.g. the concept of Virtual Breeding Environment (VBE) or the VO creation schema, serve as fundamental elements for development of VOBM. VOBM is a generic methodology that should be adapted to a given VBE. VOBM defines the structure of VBE and VO architectures in a service-oriented environment, as well as an architecture development method for virtual organizations (ADM4VO). Finally, a preliminary set of tools and methods for VOBM is given in this paper.
Complex Systems and Self-organization Modelling
Bertelle, Cyrille; Kadri-Dahmani, Hakima
2009-01-01
The concern of this book is the use of emergent computing and self-organization modelling within various applications of complex systems. The authors focus their attention both on the innovative concepts and implementations in order to model self-organizations, but also on the relevant applicative domains in which they can be used efficiently. This book is the outcome of a workshop meeting within ESM 2006 (Eurosis), held in Toulouse, France in October 2006.
Time series modeling of soil moisture dynamics on a steep mountainous hillside
Kim, Sanghyun
2016-05-01
The response of soil moisture to rainfall events along hillslope transects is an important hydrologic process and a critical component of interactions between soil vegetation and the atmosphere. In this context, the research described in this article addresses the spatial distribution of soil moisture as a function of topography. In order to characterize the temporal variation in soil moisture on a steep mountainous hillside, a transfer function, including a model for noise, was introduced. Soil moisture time series with similar rainfall amounts, but different wetness gradients were measured in the spring and fall. Water flux near the soil moisture sensors was modeled and mathematical expressions were developed to provide a basis for input-output modeling of rainfall and soil moisture using hydrological processes such as infiltration, exfiltration and downslope lateral flow. The characteristics of soil moisture response can be expressed in terms of model structure. A seasonal comparison of models reveals differences in soil moisture response to rainfall, possibly associated with eco-hydrological process and evapotranspiration. Modeling results along the hillslope indicate that the spatial structure of the soil moisture response patterns mainly appears in deeper layers. Similarities between topographic attributes and stochastic model structures are spatially organized. The impact of temporal and spatial discretization scales on parameter expression is addressed in the context of modeling results that link rainfall events and soil moisture.
A prediction method based on wavelet transform and multiple models fusion for chaotic time series
International Nuclear Information System (INIS)
Zhongda, Tian; Shujiang, Li; Yanhong, Wang; Yi, Sha
2017-01-01
In order to improve the prediction accuracy of chaotic time series, a prediction method based on wavelet transform and multiple models fusion is proposed. The chaotic time series is decomposed and reconstructed by wavelet transform, and approximate components and detail components are obtained. According to different characteristics of each component, least squares support vector machine (LSSVM) is used as predictive model for approximation components. At the same time, an improved free search algorithm is utilized for predictive model parameters optimization. Auto regressive integrated moving average model (ARIMA) is used as predictive model for detail components. The multiple prediction model predictive values are fusion by Gauss–Markov algorithm, the error variance of predicted results after fusion is less than the single model, the prediction accuracy is improved. The simulation results are compared through two typical chaotic time series include Lorenz time series and Mackey–Glass time series. The simulation results show that the prediction method in this paper has a better prediction.
a model for nonlinear innovation in time series
African Journals Online (AJOL)
DJFLEX
heteroscedastic errors are common in financial and econometric time series. The conditional variance may be specified as nonlinear autoregressive conditional heteroscedasticity ...... applied econometrics, 8, 31 – 49. Rao, C. R., 1973. Linear statistical inference and its applications, 2nd edition. New york: John Wiley.
Evolution of Black-Box Models Based on Volterra Series
Directory of Open Access Journals (Sweden)
Daniel D. Silveira
2015-01-01
Full Text Available This paper presents a historical review of the many behavioral models actually used to model radio frequency power amplifiers and a new classification of these behavioral models. It also discusses the evolution of these models, from a single polynomial to multirate Volterra models, presenting equations and estimation methods. New trends in RF power amplifier behavioral modeling are suggested.
Tolstov, Georgi P
1962-01-01
Richard A. Silverman's series of translations of outstanding Russian textbooks and monographs is well-known to people in the fields of mathematics, physics, and engineering. The present book is another excellent text from this series, a valuable addition to the English-language literature on Fourier series.This edition is organized into nine well-defined chapters: Trigonometric Fourier Series, Orthogonal Systems, Convergence of Trigonometric Fourier Series, Trigonometric Series with Decreasing Coefficients, Operations on Fourier Series, Summation of Trigonometric Fourier Series, Double Fourie
González-Díaz, Humberto; Bonet, Isis; Terán, Carmen; De Clercq, Erik; Bello, Rafael; García, Maria M; Santana, Lourdes; Uriarte, Eugenio
2007-05-01
Developing a model for predicting anticancer activity of any classes of organic compounds based on molecular structure is very important goal for medicinal chemist. Different molecular descriptors can be used to solve this problem. Stochastic molecular descriptors so-called the MARCH-INSIDE approach, shown to be very successful in drug design. Nevertheless, the structural diversity of compounds is so vast that we may need non-linear models such as artificial neural networks (ANN) instead of linear ones. SmartMLP-ANN analysis used to model the anticancer activity of organic compounds has shown high average accuracy of 93.79% (train performance) and predictability of 90.88% (validation performance) for the 8:3-MLP topology with different training and predicting series. This ANN model favourably compares with respect to a previous linear discriminant analysis (LDA) model [H. González-Díaz et al., J. Mol. Model 9 (2003) 395] that showed only 80.49% of accuracy and 79.34% of predictability. The present SmartMLP approach employed shorter training times of only 10h while previous models give accuracies of 70-89% only after 25-46 h of training. In order to illustrate the practical use of the model in bioorganic medicinal chemistry, we report the in silico prediction, and in vitro evaluation of six new synthetic tegafur analogues having IC(50) values in a broad range between 37.1 and 138 microgmL(-1) for leukemia (L1210/0) and human T-lymphocyte (Molt4/C8, CEM/0) cells. Theoretical predictions coincide very well with experimental results.
Directory of Open Access Journals (Sweden)
Evan L. Williams
2014-12-01
Full Text Available A strategy that is often used for designing low band gap polymers involves the incorporation of electron-rich (donor and electron-deficient (acceptor conjugated segments within the polymer backbone. In this paper we investigate such a series of Diketopyrrolopyrrole (DPP-based co-polymers. The co-polymers consisted of a DPP unit attached to a phenylene, naphthalene, or anthracene unit. Additionally, polymers utilizing either the thiophene-flanked DPP or the furan-flanked DPP units paired with the naphthalene comonomer were compared. As these polymers have been used as donor materials and subsequent hole transporting materials in organic solar cells, we are specifically interested in characterizing the optical absorption of the hole polaron of these DPP based copolymers. We employ chemical doping, electrochemical doping, and photoinduced absorption (PIA studies to probe the hole polaron absorption spectra. While some donor-acceptor polymers have shown an appreciable capacity to generate free charge carriers upon photoexcitation, no polaron signal was observed in the PIA spectrum of the polymers in this study. The relations between molecular structure and optical properties are discussed.
Learning restricted Boolean network model by time-series data.
Ouyang, Hongjia; Fang, Jie; Shen, Liangzhong; Dougherty, Edward R; Liu, Wenbin
2014-01-01
Restricted Boolean networks are simplified Boolean networks that are required for either negative or positive regulations between genes. Higa et al. (BMC Proc 5:S5, 2011) proposed a three-rule algorithm to infer a restricted Boolean network from time-series data. However, the algorithm suffers from a major drawback, namely, it is very sensitive to noise. In this paper, we systematically analyze the regulatory relationships between genes based on the state switch of the target gene and propose an algorithm with which restricted Boolean networks may be inferred from time-series data. We compare the proposed algorithm with the three-rule algorithm and the best-fit algorithm based on both synthetic networks and a well-studied budding yeast cell cycle network. The performance of the algorithms is evaluated by three distance metrics: the normalized-edge Hamming distance [Formula: see text], the normalized Hamming distance of state transition [Formula: see text], and the steady-state distribution distance μ (ssd). Results show that the proposed algorithm outperforms the others according to both [Formula: see text] and [Formula: see text], whereas its performance according to μ (ssd) is intermediate between best-fit and the three-rule algorithms. Thus, our new algorithm is more appropriate for inferring interactions between genes from time-series data.
Estimating the basic reproduction rate of HFMD using the time series SIR model in Guangdong, China.
Directory of Open Access Journals (Sweden)
Zhicheng Du
Full Text Available Hand, foot, and mouth disease (HFMD has caused a substantial burden of disease in China, especially in Guangdong Province. Based on notifiable cases, we use the time series Susceptible-Infected-Recovered model to estimate the basic reproduction rate (R0 and the herd immunity threshold, understanding the transmission and persistence of HFMD more completely for efficient intervention in this province. The standardized difference between the reported and fitted time series of HFMD was 0.009 (<0.2. The median basic reproduction rate of total, enterovirus 71, and coxsackievirus 16 cases in Guangdong were 4.621 (IQR: 3.907-5.823, 3.023 (IQR: 2.289-4.292 and 7.767 (IQR: 6.903-10.353, respectively. The heatmap of R0 showed semiannual peaks of activity, including a major peak in spring and early summer (about the 12th week followed by a smaller peak in autumn (about the 36th week. The county-level model showed that Longchuan (R0 = 33, Gaozhou (R0 = 24, Huazhou (R0 = 23 and Qingxin (R0 = 19 counties have higher basic reproduction rate than other counties in the province. The epidemic of HFMD in Guangdong Province is still grim, and strategies like the World Health Organization's expanded program on immunization need to be implemented. An elimination of HFMD in Guangdong might need a Herd Immunity Threshold of 78%.
Tracking the Transformation and Preservation of Organic Biomarkers in a Varved Sediment-Core Series
Tolu, J.; Bigler, C.; Bindler, R.
2014-12-01
An important premise for reconstructing environmental changes using sediment records is to understand which environmental information reaches the lake bottom and how diagenetic processes may affect the proxies, such as terrestrial and aquatic organic biomarkers. We can tackle this question using a unique series of varved sediment cores collected from the lake Nylandssjön (northern Sweden). In addition to limnological and sediment trap sampling since 2001, we have a collection of freeze cores taken in late winter and stored since 1979, which allows us to track individual varve years (e.g., 1978) over time (~30 years). A previous study using this collection showed that 23 % of C and 35 % of N were lost during the first 25 years with a C:N ratio increase of ≈21, suggesting important implications for diagenetic effects on organic biomarkers. To assess the preservation/transformation of organic biomarkers, we developed a new Pyrolysis-Gas Chromatography/Mass Spectrometry method that allows the rapid determination of biomarkers from the common OM classes (e.g., plant waxes, microbial lipids, lignins) using sub-mg sample sizes and thus applicable to high-resolution sampling of the varved sediment (Tolu et al., under review). Our results show that the different biomarkers exhibit a broad spectrum of reactivities over ~30 years -% change determined by ([Peak area at t] - [Peak area at t=0])/ [peak area at t=0] x 100-. For example: 67-80 % of the algal chlorophyll-derived product 'phytene' is lost depending which single varve year is followed over time (e.g., 1979). Only 12-32 % of "pristene", the degraded form of algal chlorophyll, is lost. The guaiacyl and syringyl lignin units are affected by a smaller loss, i.e. 5-15 %, and the S/G ratio, indicative of angiosperm/gymnosperm plant input remains stable, which is contrary to previous work on non-varved lake sediments. Considering all biomarkers, the degradation/production plateaued after ~15 years, which indicates that
The conceptual model of organization social responsibility
LUO, Lan; WEI, Jingfu
2014-01-01
With the developing of the research of CSR, people more and more deeply noticethat the corporate should take responsibility. Whether other organizations besides corporatesshould not take responsibilities beyond their field? This paper puts forward theconcept of organization social responsibility on the basis of the concept of corporate socialresponsibility and other theories. And the conceptual models are built based on theconception, introducing the OSR from three angles: the types of organi...
Mapping model behaviour using Self-Organizing Maps
Directory of Open Access Journals (Sweden)
M. Herbst
2009-03-01
Full Text Available Hydrological model evaluation and identification essentially involves extracting and processing information from model time series. However, the type of information extracted by statistical measures has only very limited meaning because it does not relate to the hydrological context of the data. To overcome this inadequacy we exploit the diagnostic evaluation concept of Signature Indices, in which model performance is measured using theoretically relevant characteristics of system behaviour. In our study, a Self-Organizing Map (SOM is used to process the Signatures extracted from Monte-Carlo simulations generated by the distributed conceptual watershed model NASIM. The SOM creates a hydrologically interpretable mapping of overall model behaviour, which immediately reveals deficits and trade-offs in the ability of the model to represent the different functional behaviours of the watershed. Further, it facilitates interpretation of the hydrological functions of the model parameters and provides preliminary information regarding their sensitivities. Most notably, we use this mapping to identify the set of model realizations (among the Monte-Carlo data that most closely approximate the observed discharge time series in terms of the hydrologically relevant characteristics, and to confine the parameter space accordingly. Our results suggest that Signature Index based SOMs could potentially serve as tools for decision makers inasmuch as model realizations with specific Signature properties can be selected according to the purpose of the model application. Moreover, given that the approach helps to represent and analyze multi-dimensional distributions, it could be used to form the basis of an optimization framework that uses SOMs to characterize the model performance response surface. As such it provides a powerful and useful way to conduct model identification and model uncertainty analyses.
Fitting ARMA Time Series by Structural Equation Models.
van Buuren, Stef
1997-01-01
This paper outlines how the stationary ARMA (p,q) model (G. Box and G. Jenkins, 1976) can be specified as a structural equation model. Maximum likelihood estimates for the parameters in the ARMA model can be obtained by software for fitting structural equation models. The method is applied to three problem types. (SLD)
Modeling Large Time Series for Efficient Approximate Query Processing
DEFF Research Database (Denmark)
Perera, Kasun S; Hahmann, Martin; Lehner, Wolfgang
2015-01-01
-wise aggregation to derive the models. These models are initially created from the original data and are kept in the database along with it. Subsequent queries are answered using the stored models rather than scanning and processing the original datasets. In order to support model query processing, we maintain...
Time Series Modelling of Syphilis Incidence in China from 2005 to 2012
Zhang, Xingyu; Zhang, Tao; Pei, Jiao; Liu, Yuanyuan; Li, Xiaosong; Medrano-Gracia, Pau
2016-01-01
Background The infection rate of syphilis in China has increased dramatically in recent decades, becoming a serious public health concern. Early prediction of syphilis is therefore of great importance for heath planning and management. Methods In this paper, we analyzed surveillance time series data for primary, secondary, tertiary, congenital and latent syphilis in mainland China from 2005 to 2012. Seasonality and long-term trend were explored with decomposition methods. Autoregressive integrated moving average (ARIMA) was used to fit a univariate time series model of syphilis incidence. A separate multi-variable time series for each syphilis type was also tested using an autoregressive integrated moving average model with exogenous variables (ARIMAX). Results The syphilis incidence rates have increased three-fold from 2005 to 2012. All syphilis time series showed strong seasonality and increasing long-term trend. Both ARIMA and ARIMAX models fitted and estimated syphilis incidence well. All univariate time series showed highest goodness-of-fit results with the ARIMA(0,0,1)×(0,1,1) model. Conclusion Time series analysis was an effective tool for modelling the historical and future incidence of syphilis in China. The ARIMAX model showed superior performance than the ARIMA model for the modelling of syphilis incidence. Time series correlations existed between the models for primary, secondary, tertiary, congenital and latent syphilis. PMID:26901682
Extracting the relevant delays in time series modelling
DEFF Research Database (Denmark)
Goutte, Cyril
1997-01-01
selection, and more precisely stepwise forward selection. The method is compared to other forward selection schemes, as well as to a nonparametric tests aimed at estimating the embedding dimension of time series. The final application extends these results to the efficient estimation of FIR filters on some......In this contribution, we suggest a convenient way to use generalisation error to extract the relevant delays from a time-varying process, i.e. the delays that lead to the best prediction performance. We design a generalisation-based algorithm that takes its inspiration from traditional variable...
Clustering gene expression time series data using an infinite Gaussian process mixture model.
McDowell, Ian C; Manandhar, Dinesh; Vockley, Christopher M; Schmid, Amy K; Reddy, Timothy E; Engelhardt, Barbara E
2018-01-01
Transcriptome-wide time series expression profiling is used to characterize the cellular response to environmental perturbations. The first step to analyzing transcriptional response data is often to cluster genes with similar responses. Here, we present a nonparametric model-based method, Dirichlet process Gaussian process mixture model (DPGP), which jointly models data clusters with a Dirichlet process and temporal dependencies with Gaussian processes. We demonstrate the accuracy of DPGP in comparison to state-of-the-art approaches using hundreds of simulated data sets. To further test our method, we apply DPGP to published microarray data from a microbial model organism exposed to stress and to novel RNA-seq data from a human cell line exposed to the glucocorticoid dexamethasone. We validate our clusters by examining local transcription factor binding and histone modifications. Our results demonstrate that jointly modeling cluster number and temporal dependencies can reveal shared regulatory mechanisms. DPGP software is freely available online at https://github.com/PrincetonUniversity/DP_GP_cluster.
Clustering gene expression time series data using an infinite Gaussian process mixture model.
Directory of Open Access Journals (Sweden)
Ian C McDowell
2018-01-01
Full Text Available Transcriptome-wide time series expression profiling is used to characterize the cellular response to environmental perturbations. The first step to analyzing transcriptional response data is often to cluster genes with similar responses. Here, we present a nonparametric model-based method, Dirichlet process Gaussian process mixture model (DPGP, which jointly models data clusters with a Dirichlet process and temporal dependencies with Gaussian processes. We demonstrate the accuracy of DPGP in comparison to state-of-the-art approaches using hundreds of simulated data sets. To further test our method, we apply DPGP to published microarray data from a microbial model organism exposed to stress and to novel RNA-seq data from a human cell line exposed to the glucocorticoid dexamethasone. We validate our clusters by examining local transcription factor binding and histone modifications. Our results demonstrate that jointly modeling cluster number and temporal dependencies can reveal shared regulatory mechanisms. DPGP software is freely available online at https://github.com/PrincetonUniversity/DP_GP_cluster.
Modeling refractive metasurfaces in series as a single metasurface
Toor, Fatima; Guneratne, Ananda C.
2016-03-01
Metasurfaces are boundaries between two media that are engineered to induce an abrupt phase shift in propagating light over a distance comparable to the wavelength of the light. Metasurface applications exploit this rapid phase shift to allow for precise control of wavefronts. The phase gradient is used to compute the angle at which light is refracted using the generalized Snell's Law. [1] In practice, refractive metasurfaces are designed using a relatively small number of phaseshifting elements such that the phase gradient is discrete rather than continuous. Designing such a metasurface requires finding phase-shifting elements that cover a full range of phases (a phase range) from 0 to 360 degrees. We demonstrate an analytical technique to calculate the refraction angle due to multiple metasurfaces arranged in series without needing to account for the effect of each individual metasurface. The phase gradients of refractive metasurfaces in series may be summed to obtain the phase gradient of a single equivalent refractive metasurface. This result is relevant to any application that requires a system with multiple metasurfaces, such as biomedical imaging [2], wavefront correctors [3], and beam shaping [4].
On the Practice of Bayesian Inference in Basic Economic Time Series Models using Gibbs Sampling
M.D. de Pooter (Michiel); R. Segers (René); H.K. van Dijk (Herman)
2006-01-01
textabstractSeveral lessons learned from a Bayesian analysis of basic economic time series models by means of the Gibbs sampling algorithm are presented. Models include the Cochrane-Orcutt model for serial correlation, the Koyck distributed lag model, the Unit Root model, the Instrumental Variables
A generalized exponential time series regression model for electricity prices
DEFF Research Database (Denmark)
Haldrup, Niels; Knapik, Oskar; Proietti, Tomasso
on the estimated model, the best linear predictor is constructed. Our modeling approach provides good fit within sample and outperforms competing benchmark predictors in terms of forecasting accuracy. We also find that building separate models for each hour of the day and averaging the forecasts is a better...
COMPUTER MODEL FOR ORGANIC FERTILIZER EVALUATION
Lončarić, Zdenko; Vukobratović, Marija; Ragaly, Peter; Filep, Tibor; Popović, Brigita; Karalić, Krunoslav; Vukobratović, Želimir
2009-01-01
Evaluation of manures, composts and growing media quality should include enough properties to enable an optimal use from productivity and environmental points of view. The aim of this paper is to describe basic structure of organic fertilizer (and growing media) evaluation model to present the model example by comparison of different manures as well as example of using plant growth experiment for calculating impact of pH and EC of growing media on lettuce plant growth. The basic structure of ...
Resveratrol and Lifespan in Model Organisms.
Pallauf, Kathrin; Rimbach, Gerald; Rupp, Petra Maria; Chin, Dawn; Wolf, Insa M A
2016-01-01
Resveratrol may possess life-prolonging and health-benefitting properties, some of which may resemble the effect of caloric restriction (CR). CR appears to prolong the lifespan of model organisms in some studies and may benefit human health. However, for humans, restricting food intake for an extended period of time seems impracticable and substances imitating the beneficial effects of CR without having to reduce food intake could improve health in an aging and overweight population. We have reviewed the literature studying the influence of resveratrol on the lifespan of model organisms including yeast, flies, worms, and rodents. We summarize the in vivo findings, describe modulations of molecular targets and gene expression observed in vivo and in vitro, and discuss how these changes may contribute to lifespan extension. Data from clinical studies are summarized to provide an insight about the potential of resveratrol supplementation in humans. Resveratrol supplementation has been shown to prolong lifespan in approximately 60% of the studies conducted in model organisms. However, current literature is contradictory, indicating that the lifespan effects of resveratrol vary strongly depending on the model organism. While worms and killifish seemed very responsive to resveratrol, resveratrol failed to affect lifespan in the majority of the studies conducted in flies and mice. Furthermore, factors such as dose, gender, genetic background and diet composition may contribute to the high variance in the observed effects. It remains inconclusive whether resveratrol is indeed a CR mimetic and possesses life-prolonging properties. The limited bioavailability of resveratrol may further impede its potential effects.
Integrated modelling of two xenobiotic organic compounds
DEFF Research Database (Denmark)
Lindblom, Erik Ulfson; Gernaey, K.V.; Henze, Mogens
2006-01-01
This paper presents a dynamic mathematical model that describes the fate and transport of two selected xenobiotic organic compounds (XOCs) in a simplified representation. of an integrated urban wastewater system. A simulation study, where the xenobiotics bisphenol A and pyrene are used as reference...
A STRATEGIC MANAGEMENT MODEL FOR SERVICE ORGANIZATIONS
Andreea ZAMFIR
2013-01-01
This paper provides a knowledge-based strategic management of services model, with a view to emphasise an approach to gaining competitive advantage through knowledge, people and networking. The long-term evolution of the service organization is associated with the way in which the strategic management is practised.
78 FR 76248 - Special Conditions: Airbus, Model A350-900 Series Airplane; Side Stick Controller
2013-12-17
..., Model A350-900 Series Airplane; Side Stick Controller AGENCY: Federal Aviation Administration (FAA), DOT... associated with side stick controllers which require limited pilot force because they are operated by only... A350-900 series airplane is equipped with two side stick controllers instead of the conventional...
Modeling BAS Dysregulation in Bipolar Disorder : Illustrating the Potential of Time Series Analysis
Hamaker, Ellen L.; Grasman, Raoul P P P; Kamphuis, Jan Henk
2016-01-01
Time series analysis is a technique that can be used to analyze the data from a single subject and has great potential to investigate clinically relevant processes like affect regulation. This article uses time series models to investigate the assumed dysregulation of affect that is associated with
Mixed Portmanteau Test for Diagnostic Checking of Time Series Models
Directory of Open Access Journals (Sweden)
Sohail Chand
2014-01-01
Full Text Available Model criticism is an important stage of model building and thus goodness of fit tests provides a set of tools for diagnostic checking of the fitted model. Several tests are suggested in literature for diagnostic checking. These tests use autocorrelation or partial autocorrelation in the residuals to criticize the adequacy of fitted model. The main idea underlying these portmanteau tests is to identify if there is any dependence structure which is yet unexplained by the fitted model. In this paper, we suggest mixed portmanteau tests based on autocorrelation and partial autocorrelation functions of the residuals. We derived the asymptotic distribution of the mixture test and studied its size and power using Monte Carlo simulations.
Emergent organization in a model market
Yadav, Avinash Chand; Manchanda, Kaustubh; Ramaswamy, Ramakrishna
2017-09-01
We study the collective behaviour of interacting agents in a simple model of market economics that was originally introduced by Nørrelykke and Bak. A general theoretical framework for interacting traders on an arbitrary network is presented, with the interaction consisting of buying (namely consumption) and selling (namely production) of commodities. Extremal dynamics is introduced by having the agent with least profit in the market readjust prices, causing the market to self-organize. In addition to examining this model market on regular lattices in two-dimensions, we also study the cases of random complex networks both with and without community structures. Fluctuations in an activity signal exhibit properties that are characteristic of avalanches observed in models of self-organized criticality, and these can be described by power-law distributions when the system is in the critical state.
Biophysical Modeling of Respiratory Organ Motion
Werner, René
Methods to estimate respiratory organ motion can be divided into two groups: biophysical modeling and image registration. In image registration, motion fields are directly extracted from 4D ({D}+{t}) image sequences, often without concerning knowledge about anatomy and physiology in detail. In contrast, biophysical approaches aim at identification of anatomical and physiological aspects of breathing dynamics that are to be modeled. In the context of radiation therapy, biophysical modeling of respiratory organ motion commonly refers to the framework of continuum mechanics and elasticity theory, respectively. Underlying ideas and corresponding boundary value problems of those approaches are described in this chapter, along with a brief comparison to image registration-based motion field estimation.
A Feature Fusion Based Forecasting Model for Financial Time Series
Guo, Zhiqiang; Wang, Huaiqing; Liu, Quan; Yang, Jie
2014-01-01
Predicting the stock market has become an increasingly interesting research area for both researchers and investors, and many prediction models have been proposed. In these models, feature selection techniques are used to pre-process the raw data and remove noise. In this paper, a prediction model is constructed to forecast stock market behavior with the aid of independent component analysis, canonical correlation analysis, and a support vector machine. First, two types of features are extracted from the historical closing prices and 39 technical variables obtained by independent component analysis. Second, a canonical correlation analysis method is utilized to combine the two types of features and extract intrinsic features to improve the performance of the prediction model. Finally, a support vector machine is applied to forecast the next day's closing price. The proposed model is applied to the Shanghai stock market index and the Dow Jones index, and experimental results show that the proposed model performs better in the area of prediction than other two similar models. PMID:24971455
SEM Based CARMA Time Series Modeling for Arbitrary N.
Oud, Johan H L; Voelkle, Manuel C; Driver, Charles C
2018-01-01
This article explains in detail the state space specification and estimation of first and higher-order autoregressive moving-average models in continuous time (CARMA) in an extended structural equation modeling (SEM) context for N = 1 as well as N > 1. To illustrate the approach, simulations will be presented in which a single panel model (T = 41 time points) is estimated for a sample of N = 1,000 individuals as well as for samples of N = 100 and N = 50 individuals, followed by estimating 100 separate models for each of the one-hundred N = 1 cases in the N = 100 sample. Furthermore, we will demonstrate how to test the difference between the full panel model and each N = 1 model by means of a subject-group-reproducibility test. Finally, the proposed analyses will be applied in an empirical example, in which the relationships between mood at work and mood at home are studied in a sample of N = 55 women. All analyses are carried out by ctsem, an R-package for continuous time modeling, interfacing to OpenMx.
Prahutama, Alan; Suparti; Wahyu Utami, Tiani
2018-03-01
Regression analysis is an analysis to model the relationship between response variables and predictor variables. The parametric approach to the regression model is very strict with the assumption, but nonparametric regression model isn’t need assumption of model. Time series data is the data of a variable that is observed based on a certain time, so if the time series data wanted to be modeled by regression, then we should determined the response and predictor variables first. Determination of the response variable in time series is variable in t-th (yt), while the predictor variable is a significant lag. In nonparametric regression modeling, one developing approach is to use the Fourier series approach. One of the advantages of nonparametric regression approach using Fourier series is able to overcome data having trigonometric distribution. In modeling using Fourier series needs parameter of K. To determine the number of K can be used Generalized Cross Validation method. In inflation modeling for the transportation sector, communication and financial services using Fourier series yields an optimal K of 120 parameters with R-square 99%. Whereas if it was modeled by multiple linear regression yield R-square 90%.
Applying Time Series Analysis Model to Temperature Data in Greenhouses
Directory of Open Access Journals (Sweden)
Abdelhafid Hasni
2011-03-01
Full Text Available The objective of the research is to find an appropriate Seasonal Auto-Regressive Integrated Moving Average (SARIMA Model for fitting the inside air temperature (Tin of a naturally ventilated greenhouse under Mediterranean conditions by considering the minimum of Akaike Information Criterion (AIC. The results of fitting were as follows: the best SARIMA Model for fitting air temperature of greenhouse is SARIMA (1,0,0 (1,0,224.
MODELLING INTERNATIONAL OILSEED PRICES: AN APPLICATION OF THE STRUCTURAL TIME SERIES MODEL
Directory of Open Access Journals (Sweden)
Jaweriah Hazrana
2017-04-01
Full Text Available The fundamentals characterizing agricultural commodity prices have often been debated in research and policy circles. Building on limitations in the existing literature, the present study conducts an integrated test and empirically analyses the international price of palm and soybean oil from 1960(1 to 2016(8. For this purpose the univariate Structural Time Series Model based on the state space framework is applied. This approach allows flexibility to model complex stochastic movements, seasonality, cyclical patterns and incorporate intervention analysis. Estimation is based on the Maximum Likelihood method via the Kalman Filter. The results establish that both series exhibit a stochastic long term trend punctuated by multiple breaks. The findings also uncover the presence of cyclicality which results in price swings of varying duration and amplitude. The model works well as a description of oilseed prices and improves awareness of their separate structural components. These are fundamental to design country and commodity specific policy strategies and respond to volatile market conditions. The results underscore that contrary to previous price spikes most of the drivers of the mid 2000s price spikes are structural and on the demand side. These new drivers in oilseed markets suggest the possibility of fundamental change in price behaviour with longer-lasting effects
TIME SERIES MODELS OF THREE SETS OF RXTE OBSERVATIONS OF 4U 1543–47
International Nuclear Information System (INIS)
Koen, C.
2013-01-01
The X-ray nova 4U 1543–47 was in a different physical state (low/hard, high/soft, and very high) during the acquisition of each of the three time series analyzed in this paper. Standard time series models of the autoregressive moving average (ARMA) family are fitted to these series. The low/hard data can be adequately modeled by a simple low-order model with fixed coefficients, once the slowly varying mean count rate has been accounted for. The high/soft series requires a higher order model, or an ARMA model with variable coefficients. The very high state is characterized by a succession of 'dips', with roughly equal depths. These seem to appear independently of one another. The underlying stochastic series can again be modeled by an ARMA form, or roughly as the sum of an ARMA series and white noise. The structuring of each model in terms of short-lived aperiodic and 'quasi-periodic' components is discussed.
Mathematical Model of Thyristor Inverter Including a Series-parallel Resonant Circuit
Directory of Open Access Journals (Sweden)
Miroslaw Luft
2008-01-01
Full Text Available The article presents a mathematical model of thyristor inverter including a series-parallel resonant circuit with theaid of state variable method. Maple procedures are used to compute current and voltage waveforms in the inverter.
Mathematical model of thyristor inverter including a series-parallel resonant circuit
Luft, M.; Szychta, E.
2008-01-01
The article presents a mathematical model of thyristor inverter including a series-parallel resonant circuit with the aid of state variable method. Maple procedures are used to compute current and voltage waveforms in the inverter.
Mathematical Model of Thyristor Inverter Including a Series-parallel Resonant Circuit
Miroslaw Luft; Elzbieta Szychta
2008-01-01
The article presents a mathematical model of thyristor inverter including a series-parallel resonant circuit with theaid of state variable method. Maple procedures are used to compute current and voltage waveforms in the inverter.
Model of a synthetic wind speed time series generator
DEFF Research Database (Denmark)
Negra, N.B.; Holmstrøm, O.; Bak-Jensen, B.
2008-01-01
is described and some statistical issues (seasonal characteristics, autocorrelation functions, average values and distribution functions) are used for verification. The output of the model has been designed as input for sequential Monte Carlo simulation; however, it is expected that it can be used for other...... of the main elements to consider for this purpose is the model of the wind speed that is usually required as input. Wind speed measurements may represent a solution for this problem, but, for techniques such as sequential Monte Carlo simulation, they have to be long enough in order to describe a wide range...
Modeling Financial Time Series Based on a Market Microstructure Model with Leverage Effect
Directory of Open Access Journals (Sweden)
Yanhui Xi
2016-01-01
Full Text Available The basic market microstructure model specifies that the price/return innovation and the volatility innovation are independent Gaussian white noise processes. However, the financial leverage effect has been found to be statistically significant in many financial time series. In this paper, a novel market microstructure model with leverage effects is proposed. The model specification assumed a negative correlation in the errors between the price/return innovation and the volatility innovation. With the new representations, a theoretical explanation of leverage effect is provided. Simulated data and daily stock market indices (Shanghai composite index, Shenzhen component index, and Standard and Poor’s 500 Composite index via Bayesian Markov Chain Monte Carlo (MCMC method are used to estimate the leverage market microstructure model. The results verify the effectiveness of the model and its estimation approach proposed in the paper and also indicate that the stock markets have strong leverage effects. Compared with the classical leverage stochastic volatility (SV model in terms of DIC (Deviance Information Criterion, the leverage market microstructure model fits the data better.
Bayesian Modelling of fMRI Time Series
DEFF Research Database (Denmark)
Højen-Sørensen, Pedro; Hansen, Lars Kai; Rasmussen, Carl Edward
2000-01-01
We present a Hidden Markov Model (HMM) for inferring the hidden psychological state (or neural activity) during single trial fMRI activation experiments with blocked task paradigms. Inference is based on Bayesian methodology, using a combination of analytical and a variety of Markov Chain Monte...
Linear models for multivariate, time series, and spatial data
Christensen, Ronald
1991-01-01
This is a companion volume to Plane Answers to Complex Questions: The Theory 0/ Linear Models. It consists of six additional chapters written in the same spirit as the last six chapters of the earlier book. Brief introductions are given to topics related to linear model theory. No attempt is made to give a comprehensive treatment of the topics. Such an effort would be futile. Each chapter is on a topic so broad that an in depth discussion would require a book-Iength treatment. People need to impose structure on the world in order to understand it. There is a limit to the number of unrelated facts that anyone can remem ber. If ideas can be put within a broad, sophisticatedly simple structure, not only are they easier to remember but often new insights become avail able. In fact, sophisticatedly simple models of the world may be the only ones that work. I have often heard Arnold Zellner say that, to the best of his knowledge, this is true in econometrics. The process of modeling is fundamental to understand...
Spatially adaptive mixture modeling for analysis of FMRI time series.
Vincent, Thomas; Risser, Laurent; Ciuciu, Philippe
2010-04-01
Within-subject analysis in fMRI essentially addresses two problems, the detection of brain regions eliciting evoked activity and the estimation of the underlying dynamics. In Makni et aL, 2005 and Makni et aL, 2008, a detection-estimation framework has been proposed to tackle these problems jointly, since they are connected to one another. In the Bayesian formalism, detection is achieved by modeling activating and nonactivating voxels through independent mixture models (IMM) within each region while hemodynamic response estimation is performed at a regional scale in a nonparametric way. Instead of IMMs, in this paper we take advantage of spatial mixture models (SMM) for their nonlinear spatial regularizing properties. The proposed method is unsupervised and spatially adaptive in the sense that the amount of spatial correlation is automatically tuned from the data and this setting automatically varies across brain regions. In addition, the level of regularization is specific to each experimental condition since both the signal-to-noise ratio and the activation pattern may vary across stimulus types in a given brain region. These aspects require the precise estimation of multiple partition functions of underlying Ising fields. This is addressed efficiently using first path sampling for a small subset of fields and then using a recently developed fast extrapolation technique for the large remaining set. Simulation results emphasize that detection relying on supervised SMM outperforms its IMM counterpart and that unsupervised spatial mixture models achieve similar results without any hand-tuning of the correlation parameter. On real datasets, the gain is illustrated in a localizer fMRI experiment: brain activations appear more spatially resolved using SMM in comparison with classical general linear model (GLM)-based approaches, while estimating a specific parcel-based HRF shape. Our approach therefore validates the treatment of unsmoothed fMRI data without fixed GLM
Bayesian Modelling of fMRI Time Series
DEFF Research Database (Denmark)
Højen-Sørensen, Pedro; Hansen, Lars Kai; Rasmussen, Carl Edward
2000-01-01
We present a Hidden Markov Model (HMM) for inferring the hidden psychological state (or neural activity) during single trial fMRI activation experiments with blocked task paradigms. Inference is based on Bayesian methodology, using a combination of analytical and a variety of Markov Chain Monte...... Carlo (MCMC) sampling techniques. The advantage of this method is that detection of short time learning effects between repeated trials is possible since inference is based only on single trial experiments....
Sensitivity analysis of machine-learning models of hydrologic time series
O'Reilly, A. M.
2017-12-01
Sensitivity analysis traditionally has been applied to assessing model response to perturbations in model parameters, where the parameters are those model input variables adjusted during calibration. Unlike physics-based models where parameters represent real phenomena, the equivalent of parameters for machine-learning models are simply mathematical "knobs" that are automatically adjusted during training/testing/verification procedures. Thus the challenge of extracting knowledge of hydrologic system functionality from machine-learning models lies in their very nature, leading to the label "black box." Sensitivity analysis of the forcing-response behavior of machine-learning models, however, can provide understanding of how the physical phenomena represented by model inputs affect the physical phenomena represented by model outputs.As part of a previous study, hybrid spectral-decomposition artificial neural network (ANN) models were developed to simulate the observed behavior of hydrologic response contained in multidecadal datasets of lake water level, groundwater level, and spring flow. Model inputs used moving window averages (MWA) to represent various frequencies and frequency-band components of time series of rainfall and groundwater use. Using these forcing time series, the MWA-ANN models were trained to predict time series of lake water level, groundwater level, and spring flow at 51 sites in central Florida, USA. A time series of sensitivities for each MWA-ANN model was produced by perturbing forcing time-series and computing the change in response time-series per unit change in perturbation. Variations in forcing-response sensitivities are evident between types (lake, groundwater level, or spring), spatially (among sites of the same type), and temporally. Two generally common characteristics among sites are more uniform sensitivities to rainfall over time and notable increases in sensitivities to groundwater usage during significant drought periods.
Lattao, Charisma; Cao, Xiaoyan; Mao, Jingdong; Schmidt-Rohr, Klaus; Pignatello, Joseph J
2014-05-06
Chars from wildfires and soil amendments (biochars) are strong adsorbents that can impact the fate of organic compounds in soil, yet the effects of solute and adsorbent properties on sorption are poorly understood. We studied sorption of benzene, naphthalene, and 1,4-dinitrobenzene from water to a series of wood chars made anaerobically at different heat treatment temperatures (HTT) from 300 to 700 °C, and to graphite as a nonporous, unfunctionalized reference adsorbent. Peak suppression in the NMR spectrum by sorption of the paramagnetic relaxation probe TEMPO indicated that only a small fraction of char C atoms lie near sorption sites. Sorption intensity for all solutes maximized with the 500 °C char, but failed to trend regularly with N2 or CO2 surface area, micropore volume, mesopore volume, H/C ratio, O/C ratio, aromatic fused ring size, or HTT. A model relating sorption intensity to a weighted sum of microporosity and mesoporosity was more successful. Sorption isotherm linearity declined progressively with carbonization of the char. Application of a thermodynamic model incorporating solvent-water and char-graphite partition coefficients permitted for the first time quantification of steric (size exclusion in pores) and π-π electron donor-acceptor (EDA) free energy contributions, relative to benzene. Steric hindrance for naphthalene increases exponentially from 9 to 16 kJ/mol (∼ 1.6-2.9 log units of sorption coefficient) with the fraction of porosity in small micropores. π-π EDA interactions of dinitrobenzene contribute -17 to -19 kJ/mol (3-3.4 log units of sorption coefficient) to sorption on graphite, but less on chars. π-π EDA interaction of naphthalene on graphite is small (-2 to 2 kJ/mol). The results show that sorption is a complex function of char properties and solute molecular structure, and not very predictable on the basis of readily determined char properties.
Research on power grid loss prediction model based on Granger causality property of time series
Energy Technology Data Exchange (ETDEWEB)
Wang, J. [North China Electric Power Univ., Beijing (China); State Grid Corp., Beijing (China); Yan, W.P.; Yuan, J. [North China Electric Power Univ., Beijing (China); Xu, H.M.; Wang, X.L. [State Grid Information and Telecommunications Corp., Beijing (China)
2009-03-11
This paper described a method of predicting power transmission line losses using the Granger causality property of time series. The stable property of the time series was investigated using unit root tests. The Granger causality relationship between line losses and other variables was then determined. Granger-caused time series were then used to create the following 3 prediction models: (1) a model based on line loss binomials that used electricity sales to predict variables, (2) a model that considered both power sales and grid capacity, and (3) a model based on autoregressive distributed lag (ARDL) approaches that incorporated both power sales and the square of power sales as variables. A case study of data from China's electric power grid between 1980 and 2008 was used to evaluate model performance. Results of the study showed that the model error rates ranged between 2.7 and 3.9 percent. 6 refs., 3 tabs., 1 fig.
Nonlinearity, Breaks, and Long-Range Dependence in Time-Series Models
DEFF Research Database (Denmark)
Hillebrand, Eric Tobias; Medeiros, Marcelo C.
We study the simultaneous occurrence of long memory and nonlinear effects, such as parameter changes and threshold effects, in ARMA time series models and apply our modeling framework to daily realized volatility. Asymptotic theory for parameter estimation is developed and two model building...
DEFF Research Database (Denmark)
Kamel, S.; Jurado, F.; Chen, Zhe
2015-01-01
This paper presents an implicit modeling of Static Synchronous Series Compensator (SSSC) in Newton–Raphson load flow method. The algorithm of load flow is based on the revised current injection formulation. The developed model of SSSC is depended on the current injection approach. In this model...
A Four-Stage Hybrid Model for Hydrological Time Series Forecasting
Di, Chongli; Yang, Xiaohua; Wang, Xiaochao
2014-01-01
Hydrological time series forecasting remains a difficult task due to its complicated nonlinear, non-stationary and multi-scale characteristics. To solve this difficulty and improve the prediction accuracy, a novel four-stage hybrid model is proposed for hydrological time series forecasting based on the principle of ‘denoising, decomposition and ensemble’. The proposed model has four stages, i.e., denoising, decomposition, components prediction and ensemble. In the denoising stage, the empirical mode decomposition (EMD) method is utilized to reduce the noises in the hydrological time series. Then, an improved method of EMD, the ensemble empirical mode decomposition (EEMD), is applied to decompose the denoised series into a number of intrinsic mode function (IMF) components and one residual component. Next, the radial basis function neural network (RBFNN) is adopted to predict the trend of all of the components obtained in the decomposition stage. In the final ensemble prediction stage, the forecasting results of all of the IMF and residual components obtained in the third stage are combined to generate the final prediction results, using a linear neural network (LNN) model. For illustration and verification, six hydrological cases with different characteristics are used to test the effectiveness of the proposed model. The proposed hybrid model performs better than conventional single models, the hybrid models without denoising or decomposition and the hybrid models based on other methods, such as the wavelet analysis (WA)-based hybrid models. In addition, the denoising and decomposition strategies decrease the complexity of the series and reduce the difficulties of the forecasting. With its effective denoising and accurate decomposition ability, high prediction precision and wide applicability, the new model is very promising for complex time series forecasting. This new forecast model is an extension of nonlinear prediction models. PMID:25111782
On Fire regime modelling using satellite TM time series
Oddi, F.; . Ghermandi, L.; Lanorte, A.; Lasaponara, R.
2009-04-01
Wildfires can cause an environment deterioration modifying vegetation dynamics because they have the capacity of changing vegetation diversity and physiognomy. In semiarid regions, like the northwestern Patagonia, fire disturbance is also important because it could impact on the potential productivity of the ecosystem. There is reduction plant biomass and with that reducing the animal carrying capacity and/or the forest site quality with negative economics implications. Therefore knowledge of the fires regime in a region is of great importance to understand and predict the responses of vegetation and its possible effect on the regional economy. Studies of this type at a landscape level can be addressed using GIS tools. Satellite imagery allows detect burned areas and through a temporary analysis can be determined to fire regime and detecting changes at landscape scale. The study area of work is located on the east of the city of Bariloche including the San Ramon Ranch (22,000 ha) and its environs in the ecotone formed by the sub Antarctic forest and the patagonian steppe. We worked with multiespectral Landsat TM images and Landsat ETM + 30m spatial resolution obtained at different times. For the spatial analysis we used the software Erdas Imagine 9.0 and ArcView 3.3. A discrimination of vegetation types has made and was determined areas affected by fires in different years. We determined the level of change on vegetation induced by fire. In the future the use of high spatial resolution images combined with higher spectral resolution will allows distinguish burned areas with greater precision on study area. Also the use of digital terrain models derived from satellite imagery associated with climatic variables will allows model the relationship between them and the dynamics of vegetation.
Hybrid model for forecasting time series with trend, seasonal and salendar variation patterns
Suhartono; Rahayu, S. P.; Prastyo, D. D.; Wijayanti, D. G. P.; Juliyanto
2017-09-01
Most of the monthly time series data in economics and business in Indonesia and other Moslem countries not only contain trend and seasonal, but also affected by two types of calendar variation effects, i.e. the effect of the number of working days or trading and holiday effects. The purpose of this research is to develop a hybrid model or a combination of several forecasting models to predict time series that contain trend, seasonal and calendar variation patterns. This hybrid model is a combination of classical models (namely time series regression and ARIMA model) and/or modern methods (artificial intelligence method, i.e. Artificial Neural Networks). A simulation study was used to show that the proposed procedure for building the hybrid model could work well for forecasting time series with trend, seasonal and calendar variation patterns. Furthermore, the proposed hybrid model is applied for forecasting real data, i.e. monthly data about inflow and outflow of currency at Bank Indonesia. The results show that the hybrid model tend to provide more accurate forecasts than individual forecasting models. Moreover, this result is also in line with the third results of the M3 competition, i.e. the hybrid model on average provides a more accurate forecast than the individual model.
A probabilistic method for constructing wave time-series at inshore locations using model scenarios
Long, Joseph W.; Plant, Nathaniel G.; Dalyander, P. Soupy; Thompson, David M.
2014-01-01
Continuous time-series of wave characteristics (height, period, and direction) are constructed using a base set of model scenarios and simple probabilistic methods. This approach utilizes an archive of computationally intensive, highly spatially resolved numerical wave model output to develop time-series of historical or future wave conditions without performing additional, continuous numerical simulations. The archive of model output contains wave simulations from a set of model scenarios derived from an offshore wave climatology. Time-series of wave height, period, direction, and associated uncertainties are constructed at locations included in the numerical model domain. The confidence limits are derived using statistical variability of oceanographic parameters contained in the wave model scenarios. The method was applied to a region in the northern Gulf of Mexico and assessed using wave observations at 12 m and 30 m water depths. Prediction skill for significant wave height is 0.58 and 0.67 at the 12 m and 30 m locations, respectively, with similar performance for wave period and direction. The skill of this simplified, probabilistic time-series construction method is comparable to existing large-scale, high-fidelity operational wave models but provides higher spatial resolution output at low computational expense. The constructed time-series can be developed to support a variety of applications including climate studies and other situations where a comprehensive survey of wave impacts on the coastal area is of interest.
Energy Technology Data Exchange (ETDEWEB)
NONE
2000-03-01
The FA3100 series industrial personal computer was added with the highest order model 7010 mounted with the Pentium(reg sing) III processor(550 MHz) which is of the highest speed in Japan. In association with the increase in information processing amount, applications requiring higher CPU processing speed are increasing also in the field of FA (Factory Automation). The model can respond to this demand. The number of expansion slot required for industrial use is eleven in total, providing affluent expandability. The three-series disk bay allowing devices to be mounted only by removing the front panel contains disks that can be selected according to their applications, such as the hard disk, duplicated hard disk, silicon disk, CD-ROM, and photo-magnetic (MO) disk. (translated by NEDO)
Travel Cost Inference from Sparse, Spatio-Temporally Correlated Time Series Using Markov Models
DEFF Research Database (Denmark)
Yang, Bin; Guo, Chenjuan; Jensen, Christian S.
2013-01-01
of such time series offers insight into the underlying system and enables prediction of system behavior. While the techniques presented in the paper apply more generally, we consider the case of transportation systems and aim to predict travel cost from GPS tracking data from probe vehicles. Specifically, each...... road segment has an associated travel-cost time series, which is derived from GPS data. We use spatio-temporal hidden Markov models (STHMM) to model correlations among different traffic time series. We provide algorithms that are able to learn the parameters of an STHMM while contending...... with the sparsity, spatio-temporal correlation, and heterogeneity of the time series. Using the resulting STHMM, near future travel costs in the transportation network, e.g., travel time or greenhouse gas emissions, can be inferred, enabling a variety of routing services, e.g., eco-routing. Empirical studies...
Travel cost inference from sparse, spatio-temporally correlated time series using markov models
DEFF Research Database (Denmark)
Yang, B.; Guo, C.; Jensen, C.S.
2013-01-01
of such time series offers insight into the underlying system and enables prediction of system behavior. While the techniques presented in the paper apply more generally, we consider the case of transportation systems and aim to predict travel cost from GPS tracking data from probe vehicles. Specifically, each...... road segment has an associated travel-cost time series, which is derived from GPS data. We use spatio-temporal hidden Markov models (STHMM) to model correlations among different traffic time series. We provide algorithms that are able to learn the parameters of an STHMM while contending...... with the sparsity, spatio-temporal correlation, and heterogeneity of the time series. Using the resulting STHMM, near future travel costs in the transportation network, e.g., travel time or greenhouse gas emissions, can be inferred, enabling a variety of routing services, e.g., eco-routing. Empirical studies...
The Exponential Model for the Spectrum of a Time Series: Extensions and Applications
DEFF Research Database (Denmark)
Proietti, Tommaso; Luati, Alessandra
The exponential model for the spectrum of a time series and its fractional extensions are based on the Fourier series expansion of the logarithm of the spectral density. The coefficients of the expansion form the cepstrum of the time series. After deriving the cepstrum of important classes of time...... to the log-spectrum. We then propose two extensions. The first deals with replacing the logarithmic link with a more general Box-Cox link, which encompasses also the identity and the inverse links: this enables nesting alternative spectral estimation methods (autoregressive, exponential, etc.) under the same...
Characteristics of the LeRC/Hughes J-series 30-cm engineering model thruster
Collett, C. R.; Poeschel, R. L.; Kami, S.
1981-01-01
As a consequence of endurance and structural tests performed on 900-series engineering model thrusters (EMT), several modifications in design were found to be necessary for achieving performance goals. The modified thruster is known as the J-series EMT. The most important of the design modifications affect the accelerator grid, gimbal mount, cathode polepiece, and wiring harness. The paper discusses the design modifications incorporated, the condition(s) they corrected, and the characteristics of the modified thruster.
Directory of Open Access Journals (Sweden)
Matthias Eifler
2017-12-01
Full Text Available Standard compliant parameter calculation in surface topography analysis takes the manufacturing process into account. Thus, the measurement technician can be supported with automated suggestions for preprocessing, filtering and evaluation of the measurement data based on the character of the surface topography. Artificial neuronal networks (ANN are one approach for the recognition or classification of technical surfaces. However the required set of training data for ANN is often not available, especially when data acquisition is time consuming or expensive—as e.g., measuring surface topography. Thus, generation of artificial (simulated data becomes of interest. An approach from time series analysis is chosen and examined regarding its suitability for the description of technical surfaces: the ARMAsel model, an approach for time series modelling which is capable of choosing the statistical model with the smallest prediction error and the best number of coefficients for a certain surface. With a reliable model which features the relevant stochastic properties of a surface, a generation of training data for classifiers of artificial neural networks is possible. Based on the determined ARMA-coefficients from the ARMAsel-approach, with only few measured datasets many different artificial surfaces can be generated which can be used for training classifiers of an artificial neural network. In doing so, an improved calculation of the model input data for the generation of artificial surfaces is possible as the training data generation is based on actual measurement data. The trained artificial neural network is tested with actual measurement data of surfaces that were manufactured with varying manufacturing methods and a recognition rate of the according manufacturing principle between 60% and 78% can be determined. This means that based on only few measured datasets, stochastic surface information of various manufacturing principles can be extracted
Fluctuation complexity of agent-based financial time series model by stochastic Potts system
Hong, Weijia; Wang, Jun
2015-03-01
Financial market is a complex evolved dynamic system with high volatilities and noises, and the modeling and analyzing of financial time series are regarded as the rather challenging tasks in financial research. In this work, by applying the Potts dynamic system, a random agent-based financial time series model is developed in an attempt to uncover the empirical laws in finance, where the Potts model is introduced to imitate the trading interactions among the investing agents. Based on the computer simulation in conjunction with the statistical analysis and the nonlinear analysis, we present numerical research to investigate the fluctuation behaviors of the proposed time series model. Furthermore, in order to get a robust conclusion, we consider the daily returns of Shanghai Composite Index and Shenzhen Component Index, and the comparison analysis of return behaviors between the simulation data and the actual data is exhibited.
Modelling Changes in the Unconditional Variance of Long Stock Return Series
DEFF Research Database (Denmark)
Amado, Cristina; Teräsvirta, Timo
show that the long-memory property in volatility may be explained by ignored changes in the unconditional variance of the long series. Finally, based on a formal statistical test we find evidence of the superiority of volatility forecast accuracy of the new model over the GJR-GARCH model at all......In this paper we develop a testing and modelling procedure for describing the long-term volatility movements over very long return series. For the purpose, we assume that volatility is multiplicatively decomposed into a conditional and an unconditional component as in Amado and Teräsvirta (2011...
Modelling changes in the unconditional variance of long stock return series
DEFF Research Database (Denmark)
Amado, Cristina; Teräsvirta, Timo
2014-01-01
that the apparent long memory property in volatility may be interpreted as changes in the unconditional variance of the long series. Finally, based on a formal statistical test we find evidence of the superiority of volatility forecasting accuracy of the new model over the GJR-GARCH model at all horizons for eight......In this paper we develop a testing and modelling procedure for describing the long-term volatility movements over very long daily return series. For this purpose we assume that volatility is multiplicatively decomposed into a conditional and an unconditional component as in Amado and Teräsvirta...
International Nuclear Information System (INIS)
Dong, Xiangyuan; Guo, Shuqing
2008-01-01
In this paper, a novel image reconstruction method for electrical capacitance tomography (ECT) based on the combined series and parallel model is presented. A regularization technique is used to obtain a stabilized solution of the inverse problem. Also, the adaptive coefficient of the combined model is deduced by numerical optimization. Simulation results indicate that it can produce higher quality images when compared to the algorithm based on the parallel or series models for the cases tested in this paper. It provides a new algorithm for ECT application
Biomedical time series clustering based on non-negative sparse coding and probabilistic topic model.
Wang, Jin; Liu, Ping; F H She, Mary; Nahavandi, Saeid; Kouzani, Abbas
2013-09-01
Biomedical time series clustering that groups a set of unlabelled temporal signals according to their underlying similarity is very useful for biomedical records management and analysis such as biosignals archiving and diagnosis. In this paper, a new framework for clustering of long-term biomedical time series such as electrocardiography (ECG) and electroencephalography (EEG) signals is proposed. Specifically, local segments extracted from the time series are projected as a combination of a small number of basis elements in a trained dictionary by non-negative sparse coding. A Bag-of-Words (BoW) representation is then constructed by summing up all the sparse coefficients of local segments in a time series. Based on the BoW representation, a probabilistic topic model that was originally developed for text document analysis is extended to discover the underlying similarity of a collection of time series. The underlying similarity of biomedical time series is well captured attributing to the statistic nature of the probabilistic topic model. Experiments on three datasets constructed from publicly available EEG and ECG signals demonstrates that the proposed approach achieves better accuracy than existing state-of-the-art methods, and is insensitive to model parameters such as length of local segments and dictionary size. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.
Virtuous organization: A structural equation modeling approach
Directory of Open Access Journals (Sweden)
Majid Zamahani
2013-02-01
Full Text Available For years, the idea of virtue was unfavorable among researchers and virtues were traditionally considered as culture-specific, relativistic and they were supposed to be associated with social conservatism, religious or moral dogmatism, and scientific irrelevance. Virtue and virtuousness have been recently considered seriously among organizational researchers. The proposed study of this paper examines the relationships between leadership, organizational culture, human resource, structure and processes, care for community and virtuous organization. Structural equation modeling is employed to investigate the effects of each variable on other components. The data used in this study consists of questionnaire responses from employees in Payam e Noor University in Yazd province. A total of 250 questionnaires were sent out and a total of 211 valid responses were received. Our results have revealed that all the five variables have positive and significant impacts on virtuous organization. Among the five variables, organizational culture has the most direct impact (0.80 and human resource has the most total impact (0.844 on virtuous organization.
Mesoscopic kinetic Monte Carlo modeling of organic photovoltaic device characteristics
Kimber, Robin G. E.; Wright, Edward N.; O'Kane, Simon E. J.; Walker, Alison B.; Blakesley, James C.
2012-12-01
Measured mobility and current-voltage characteristics of single layer and photovoltaic (PV) devices composed of poly{9,9-dioctylfluorene-co-bis[N,N'-(4-butylphenyl)]bis(N,N'-phenyl-1,4-phenylene)diamine} (PFB) and poly(9,9-dioctylfluorene-co-benzothiadiazole) (F8BT) have been reproduced by a mesoscopic model employing the kinetic Monte Carlo (KMC) approach. Our aim is to show how to avoid the uncertainties common in electrical transport models arising from the need to fit a large number of parameters when little information is available, for example, a single current-voltage curve. Here, simulation parameters are derived from a series of measurements using a self-consistent “building-blocks” approach, starting from data on the simplest systems. We found that site energies show disorder and that correlations in the site energies and a distribution of deep traps must be included in order to reproduce measured charge mobility-field curves at low charge densities in bulk PFB and F8BT. The parameter set from the mobility-field curves reproduces the unipolar current in single layers of PFB and F8BT and allows us to deduce charge injection barriers. Finally, by combining these disorder descriptions and injection barriers with an optical model, the external quantum efficiency and current densities of blend and bilayer organic PV devices can be successfully reproduced across a voltage range encompassing reverse and forward bias, with the recombination rate the only parameter to be fitted, found to be 1×107 s-1. These findings demonstrate an approach that removes some of the arbitrariness present in transport models of organic devices, which validates the KMC as an accurate description of organic optoelectronic systems, and provides information on the microscopic origins of the device behavior.
The partial duration series method in regional index-flood modeling
DEFF Research Database (Denmark)
Madsen, Henrik; Rosbjerg, Dan
1997-01-01
A regional index-flood method based on the partial duration series model is introduced. The model comprises the assumptions of a Poisson-distributed number of threshold exceedances and generalized Pareto (GP) distributed peak magnitudes. The regional T-year event estimator is based on a regional...
A new model for reliability optimization of series-parallel systems with non-homogeneous components
International Nuclear Information System (INIS)
Feizabadi, Mohammad; Jahromi, Abdolhamid Eshraghniaye
2017-01-01
In discussions related to reliability optimization using redundancy allocation, one of the structures that has attracted the attention of many researchers, is series-parallel structure. In models previously presented for reliability optimization of series-parallel systems, there is a restricting assumption based on which all components of a subsystem must be homogeneous. This constraint limits system designers in selecting components and prevents achieving higher levels of reliability. In this paper, a new model is proposed for reliability optimization of series-parallel systems, which makes possible the use of non-homogeneous components in each subsystem. As a result of this flexibility, the process of supplying system components will be easier. To solve the proposed model, since the redundancy allocation problem (RAP) belongs to the NP-hard class of optimization problems, a genetic algorithm (GA) is developed. The computational results of the designed GA are indicative of high performance of the proposed model in increasing system reliability and decreasing costs. - Highlights: • In this paper, a new model is proposed for reliability optimization of series-parallel systems. • In the previous models, there is a restricting assumption based on which all components of a subsystem must be homogeneous. • The presented model provides a possibility for the subsystems’ components to be non- homogeneous in the required conditions. • The computational results demonstrate the high performance of the proposed model in improving reliability and reducing costs.
Applying ARIMA model for annual volume time series of the Magdalena River
Directory of Open Access Journals (Sweden)
Gloria Amaris
2017-04-01
Conclusions: The simulated results obtained with the ARIMA model compared to the observed data showed a fairly good adjustment of the minimum and maximum magnitudes. This allows concluding that it is a good tool for estimating minimum and maximum volumes, even though this model is not capable of simulating the exact behaviour of an annual volume time series.
Development of Simulink-Based SiC MOSFET Modeling Platform for Series Connected Devices
DEFF Research Database (Denmark)
Tsolaridis, Georgios; Ilves, Kalle; Reigosa, Paula Diaz
2016-01-01
A new MATLAB/Simulink-based modeling platform has been developed for SiC MOSFET power modules. The modeling platform describes the electrical behavior f a single 1.2 kV/ 350 A SiC MOSFET power module, as well as the series connection of two of them. A fast parameter initialization is followed...
Algorithms for global total least squares modelling of finite multivariable time series
Roorda, Berend
1995-01-01
In this paper we present several algorithms related to the global total least squares (GTLS) modelling of multivariable time series observed over a finite time interval. A GTLS model is a linear, time-invariant finite-dimensional system with a behaviour that has minimal Frobenius distance to a given
LSOT: A Lightweight Self-Organized Trust Model in VANETs
Directory of Open Access Journals (Sweden)
Zhiquan Liu
2016-01-01
Full Text Available With the advances in automobile industry and wireless communication technology, Vehicular Ad hoc Networks (VANETs have attracted the attention of a large number of researchers. Trust management plays an important role in VANETs. However, it is still at the preliminary stage and the existing trust models cannot entirely conform to the characteristics of VANETs. This work proposes a novel Lightweight Self-Organized Trust (LSOT model which contains trust certificate-based and recommendation-based trust evaluations. Both the supernodes and trusted third parties are not needed in our model. In addition, we comprehensively consider three factor weights to ease the collusion attack in trust certificate-based trust evaluation, and we utilize the testing interaction method to build and maintain the trust network and propose a maximum local trust (MLT algorithm to identify trustworthy recommenders in recommendation-based trust evaluation. Furthermore, a fully distributed VANET scenario is deployed based on the famous Advogato dataset and a series of simulations and analysis are conducted. The results illustrate that our LSOT model significantly outperforms the excellent experience-based trust (EBT and Lightweight Cross-domain Trust (LCT models in terms of evaluation performance and robustness against the collusion attack.
Hong, Soonil; Kang, Hongkyu; Kim, Geunjin; Lee, Seongyu; Kim, Seok; Lee, Jong-Hoon; Lee, Jinho; Yi, Minjin; Kim, Junghwan; Back, Hyungcheol; Kim, Jae-Ryoung; Lee, Kwanghee
2016-01-05
The fabrication of organic photovoltaic modules via printing techniques has been the greatest challenge for their commercial manufacture. Current module architecture, which is based on a monolithic geometry consisting of serially interconnecting stripe-patterned subcells with finite widths, requires highly sophisticated patterning processes that significantly increase the complexity of printing production lines and cause serious reductions in module efficiency due to so-called aperture loss in series connection regions. Herein we demonstrate an innovative module structure that can simultaneously reduce both patterning processes and aperture loss. By using a charge recombination feature that occurs at contacts between electron- and hole-transport layers, we devise a series connection method that facilitates module fabrication without patterning the charge transport layers. With the successive deposition of component layers using slot-die and doctor-blade printing techniques, we achieve a high module efficiency reaching 7.5% with area of 4.15 cm(2).
Dynamic modeling and simulation of a two-stage series-parallel vibration isolation system
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Rong Guo
2016-07-01
Full Text Available A two-stage series-parallel vibration isolation system is already widely used in various industrial fields. However, when the researchers analyze the vibration characteristics of a mechanical system, the system is usually regarded as a single-stage one composed of two substructures. The dynamic modeling of a two-stage series-parallel vibration isolation system using frequency response function–based substructuring method has not been studied. Therefore, this article presents the source-path-receiver model and the substructure property identification model of such a system. These two models make up the transfer path model of the system. And the model is programmed by MATLAB. To verify the proposed transfer path model, a finite element model simulating a vehicle system, which is a typical two-stage series-parallel vibration isolation system, is developed. The substructure frequency response functions and system level frequency response functions can be obtained by MSC Patran/Nastran and LMS Virtual.lab based on the finite element model. Next, the system level frequency response functions are substituted into the transfer path model to predict the substructural frequency response functions and the system response of the coupled structure can then be further calculated. By comparing the predicted results and exact value, the model proves to be correct. Finally, the random noise is introduced into several relevant system level frequency response functions for error sensitivity analysis. The system level frequency response functions that are most sensitive to the random error are found. Since a two-stage series-parallel system has not been well studied, the proposed transfer path model improves the dynamic theory of the multi-stage vibration isolation system. Moreover, the validation process of the model here actually provides an example for acoustic and vibration transfer path analysis based on the proposed model. And it is worth noting that the
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T. V. O. Fabson
2011-11-01
Full Text Available Bullwhip (or whiplash effect is an observed phenomenon in forecast driven distribution channeland careful management of these effects is of great importance to managers of supply chain.Bullwhip effect refers to situations where orders to the suppliers tend to have larger variance thansales to the buyer (demand distortion and the distortion increases as we move up the supply chain.Due to the fact that demand of customer for product is unstable, business managers must forecast inorder to properly position inventory and other resources. Forecasts are statistically based and in mostcases, are not very accurate. The existence of forecast errors made it necessary for organizations tooften carry an inventory buffer called “safety stock”. Moving up the supply chain from the end userscustomers to raw materials supplier there is a lot of variation in demand that can be observed, whichcall for greater need for safety stock.This study compares the efficacy of simulation and Time Series model in quantifying the bullwhipeffects in supply chain management.
New insights into soil temperature time series modeling: linear or nonlinear?
Bonakdari, Hossein; Moeeni, Hamid; Ebtehaj, Isa; Zeynoddin, Mohammad; Mahoammadian, Abdolmajid; Gharabaghi, Bahram
2018-03-01
Soil temperature (ST) is an important dynamic parameter, whose prediction is a major research topic in various fields including agriculture because ST has a critical role in hydrological processes at the soil surface. In this study, a new linear methodology is proposed based on stochastic methods for modeling daily soil temperature (DST). With this approach, the ST series components are determined to carry out modeling and spectral analysis. The results of this process are compared with two linear methods based on seasonal standardization and seasonal differencing in terms of four DST series. The series used in this study were measured at two stations, Champaign and Springfield, at depths of 10 and 20 cm. The results indicate that in all ST series reviewed, the periodic term is the most robust among all components. According to a comparison of the three methods applied to analyze the various series components, it appears that spectral analysis combined with stochastic methods outperformed the seasonal standardization and seasonal differencing methods. In addition to comparing the proposed methodology with linear methods, the ST modeling results were compared with the two nonlinear methods in two forms: considering hydrological variables (HV) as input variables and DST modeling as a time series. In a previous study at the mentioned sites, Kim and Singh Theor Appl Climatol 118:465-479, (2014) applied the popular Multilayer Perceptron (MLP) neural network and Adaptive Neuro-Fuzzy Inference System (ANFIS) nonlinear methods and considered HV as input variables. The comparison results signify that the relative error projected in estimating DST by the proposed methodology was about 6%, while this value with MLP and ANFIS was over 15%. Moreover, MLP and ANFIS models were employed for DST time series modeling. Due to these models' relatively inferior performance to the proposed methodology, two hybrid models were implemented: the weights and membership function of MLP and
Geer, F.C. van; Zuur, A.F.
1997-01-01
This paper advocates an approach to extend single-output Box-Jenkins transfer/noise models for several groundwater head series to a multiple-output transfer/noise model. The approach links several groundwater head series and enables a spatial interpolation in terms of time series analysis. Our
Effective low-order models for atmospheric dynamics and time series analysis.
Gluhovsky, Alexander; Grady, Kevin
2016-02-01
The paper focuses on two interrelated problems: developing physically sound low-order models (LOMs) for atmospheric dynamics and employing them as novel time-series models to overcome deficiencies in current atmospheric time series analysis. The first problem is warranted since arbitrary truncations in the Galerkin method (commonly used to derive LOMs) may result in LOMs that violate fundamental conservation properties of the original equations, causing unphysical behaviors such as unbounded solutions. In contrast, the LOMs we offer (G-models) are energy conserving, and some retain the Hamiltonian structure of the original equations. This work examines LOMs from recent publications to show that all of them that are physically sound can be converted to G-models, while those that cannot lack energy conservation. Further, motivated by recent progress in statistical properties of dynamical systems, we explore G-models for a new role of atmospheric time series models as their data generating mechanisms are well in line with atmospheric dynamics. Currently used time series models, however, do not specifically utilize the physics of the governing equations and involve strong statistical assumptions rarely met in real data.
A Seasonal Time-Series Model Based on Gene Expression Programming for Predicting Financial Distress
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Ching-Hsue Cheng
2018-01-01
Full Text Available The issue of financial distress prediction plays an important and challenging research topic in the financial field. Currently, there have been many methods for predicting firm bankruptcy and financial crisis, including the artificial intelligence and the traditional statistical methods, and the past studies have shown that the prediction result of the artificial intelligence method is better than the traditional statistical method. Financial statements are quarterly reports; hence, the financial crisis of companies is seasonal time-series data, and the attribute data affecting the financial distress of companies is nonlinear and nonstationary time-series data with fluctuations. Therefore, this study employed the nonlinear attribute selection method to build a nonlinear financial distress prediction model: that is, this paper proposed a novel seasonal time-series gene expression programming model for predicting the financial distress of companies. The proposed model has several advantages including the following: (i the proposed model is different from the previous models lacking the concept of time series; (ii the proposed integrated attribute selection method can find the core attributes and reduce high dimensional data; and (iii the proposed model can generate the rules and mathematical formulas of financial distress for providing references to the investors and decision makers. The result shows that the proposed method is better than the listing classifiers under three criteria; hence, the proposed model has competitive advantages in predicting the financial distress of companies.
Modeling global persistent organic chemicals in clouds
Mao, Xiaoxuan; Gao, Hong; Huang, Tao; Zhang, Lisheng; Ma, Jianmin
2014-10-01
A cloud model was implemented in a global atmospheric transport model to simulate cloud liquid water content and quantify the influence of clouds on gas/aqueous phase partitioning of persistent organic chemicals (POCs). Partitioning fractions of gas/aqueous and particle phases in clouds for three POCs α-hexachlorocyclohexane (α-HCH), polychlorinated biphenyl-28 (PCB-28), and PCB-138 in a cloudy atmosphere were estimated. Results show that the partition fraction of these selected chemicals depend on cloud liquid water content (LWC) and air temperature. We calculated global distribution of water droplet/ice particle-air partitioning coefficients of the three chemicals in clouds. The partition fractions at selected model grids in the Northern Hemisphere show that α-HCH, a hydrophilic chemical, is sorbed strongly onto cloud water droplets. The computed partition fractions at four selected model grids show that α-HCH tends to be sorbed onto clouds over land (source region) from summer to early fall, and over ocean from late spring to early fall. 20-60% of α-HCH is able to be sorbed to cloud waters over mid-latitude oceans during summer days. PCB-138, one of hydrophobic POCs, on the other hand, tends to be sorbed to particles in the atmosphere subject to air temperature. We also show that, on seasonal or annual average, 10-20% of averaged PCB-28 over the Northern Hemisphere could be sorbed onto clouds, leading to reduction of its gas-phase concentration in the atmosphere.
Nonlinear time series modeling and forecasting the seismic data of the Hindu Kush region
Khan, Muhammad Yousaf; Mittnik, Stefan
2017-11-01
In this study, we extended the application of linear and nonlinear time models in the field of earthquake seismology and examined the out-of-sample forecast accuracy of linear Autoregressive (AR), Autoregressive Conditional Duration (ACD), Self-Exciting Threshold Autoregressive (SETAR), Threshold Autoregressive (TAR), Logistic Smooth Transition Autoregressive (LSTAR), Additive Autoregressive (AAR), and Artificial Neural Network (ANN) models for seismic data of the Hindu Kush region. We also extended the previous studies by using Vector Autoregressive (VAR) and Threshold Vector Autoregressive (TVAR) models and compared their forecasting accuracy with linear AR model. Unlike previous studies that typically consider the threshold model specifications by using internal threshold variable, we specified these models with external transition variables and compared their out-of-sample forecasting performance with the linear benchmark AR model. The modeling results show that time series models used in the present study are capable of capturing the dynamic structure present in the seismic data. The point forecast results indicate that the AR model generally outperforms the nonlinear models. However, in some cases, threshold models with external threshold variables specification produce more accurate forecasts, indicating that specification of threshold time series models is of crucial importance. For raw seismic data, the ACD model does not show an improved out-of-sample forecasting performance over the linear AR model. The results indicate that the AR model is the best forecasting device to model and forecast the raw seismic data of the Hindu Kush region.
Nonlinear time series modeling and forecasting the seismic data of the Hindu Kush region
Khan, Muhammad Yousaf; Mittnik, Stefan
2018-01-01
In this study, we extended the application of linear and nonlinear time models in the field of earthquake seismology and examined the out-of-sample forecast accuracy of linear Autoregressive (AR), Autoregressive Conditional Duration (ACD), Self-Exciting Threshold Autoregressive (SETAR), Threshold Autoregressive (TAR), Logistic Smooth Transition Autoregressive (LSTAR), Additive Autoregressive (AAR), and Artificial Neural Network (ANN) models for seismic data of the Hindu Kush region. We also extended the previous studies by using Vector Autoregressive (VAR) and Threshold Vector Autoregressive (TVAR) models and compared their forecasting accuracy with linear AR model. Unlike previous studies that typically consider the threshold model specifications by using internal threshold variable, we specified these models with external transition variables and compared their out-of-sample forecasting performance with the linear benchmark AR model. The modeling results show that time series models used in the present study are capable of capturing the dynamic structure present in the seismic data. The point forecast results indicate that the AR model generally outperforms the nonlinear models. However, in some cases, threshold models with external threshold variables specification produce more accurate forecasts, indicating that specification of threshold time series models is of crucial importance. For raw seismic data, the ACD model does not show an improved out-of-sample forecasting performance over the linear AR model. The results indicate that the AR model is the best forecasting device to model and forecast the raw seismic data of the Hindu Kush region.
Modeling annual Coffee production in Ghana using ARIMA time series Model
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E. Harris
2013-07-01
Full Text Available In the international commodity trade, coffee, which represents the world’s most valuable tropical agricultural commodity, comes next to oil. Indeed, it is estimated that about 40 million people in the major producing countries in Africa derive their livelihood from coffee, with Africa accounting for about 12 per cent of global production. The paper applied Autoregressive Integrated Moving Average (ARIMA time series model to study the behavior of Ghana’s annual coffee production as well as make five years forecasts. Annual coffee production data from 1990 to 2010 was obtained from Ghana cocoa board and analyzed using ARIMA. The results showed that in general, the trend of Ghana’s total coffee production follows an upward and downward movement. The best model arrived at on the basis of various diagnostics, selection and an evaluation criterion was ARIMA (0,3,1. Finally, the forecast figures base on Box- Jenkins method showed that Ghana’s annual coffee production will decrease continuously in the next five (5 years, all things being equal
Bayesian dynamic modeling of time series of dengue disease case counts.
Martínez-Bello, Daniel Adyro; López-Quílez, Antonio; Torres-Prieto, Alexander
2017-07-01
The aim of this study is to model the association between weekly time series of dengue case counts and meteorological variables, in a high-incidence city of Colombia, applying Bayesian hierarchical dynamic generalized linear models over the period January 2008 to August 2015. Additionally, we evaluate the model's short-term performance for predicting dengue cases. The methodology shows dynamic Poisson log link models including constant or time-varying coefficients for the meteorological variables. Calendar effects were modeled using constant or first- or second-order random walk time-varying coefficients. The meteorological variables were modeled using constant coefficients and first-order random walk time-varying coefficients. We applied Markov Chain Monte Carlo simulations for parameter estimation, and deviance information criterion statistic (DIC) for model selection. We assessed the short-term predictive performance of the selected final model, at several time points within the study period using the mean absolute percentage error. The results showed the best model including first-order random walk time-varying coefficients for calendar trend and first-order random walk time-varying coefficients for the meteorological variables. Besides the computational challenges, interpreting the results implies a complete analysis of the time series of dengue with respect to the parameter estimates of the meteorological effects. We found small values of the mean absolute percentage errors at one or two weeks out-of-sample predictions for most prediction points, associated with low volatility periods in the dengue counts. We discuss the advantages and limitations of the dynamic Poisson models for studying the association between time series of dengue disease and meteorological variables. The key conclusion of the study is that dynamic Poisson models account for the dynamic nature of the variables involved in the modeling of time series of dengue disease, producing useful
Bayesian dynamic modeling of time series of dengue disease case counts.
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Daniel Adyro Martínez-Bello
2017-07-01
Full Text Available The aim of this study is to model the association between weekly time series of dengue case counts and meteorological variables, in a high-incidence city of Colombia, applying Bayesian hierarchical dynamic generalized linear models over the period January 2008 to August 2015. Additionally, we evaluate the model's short-term performance for predicting dengue cases. The methodology shows dynamic Poisson log link models including constant or time-varying coefficients for the meteorological variables. Calendar effects were modeled using constant or first- or second-order random walk time-varying coefficients. The meteorological variables were modeled using constant coefficients and first-order random walk time-varying coefficients. We applied Markov Chain Monte Carlo simulations for parameter estimation, and deviance information criterion statistic (DIC for model selection. We assessed the short-term predictive performance of the selected final model, at several time points within the study period using the mean absolute percentage error. The results showed the best model including first-order random walk time-varying coefficients for calendar trend and first-order random walk time-varying coefficients for the meteorological variables. Besides the computational challenges, interpreting the results implies a complete analysis of the time series of dengue with respect to the parameter estimates of the meteorological effects. We found small values of the mean absolute percentage errors at one or two weeks out-of-sample predictions for most prediction points, associated with low volatility periods in the dengue counts. We discuss the advantages and limitations of the dynamic Poisson models for studying the association between time series of dengue disease and meteorological variables. The key conclusion of the study is that dynamic Poisson models account for the dynamic nature of the variables involved in the modeling of time series of dengue disease
Modeling and Forecasting of Water Demand in Isfahan Using Underlying Trend Concept and Time Series
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H. Sadeghi
2016-02-01
Full Text Available Introduction: Accurate water demand modeling for the city is very important for forecasting and policies adoption related to water resources management. Thus, for future requirements of water estimation, forecasting and modeling, it is important to utilize models with little errors. Water has a special place among the basic human needs, because it not hampers human life. The importance of the issue of water management in the extraction and consumption, it is necessary as a basic need. Municipal water applications is include a variety of water demand for domestic, public, industrial and commercial. Predicting the impact of urban water demand in better planning of water resources in arid and semiarid regions are faced with water restrictions. Materials and Methods: One of the most important factors affecting the changing technological advances in production and demand functions, we must pay special attention to the layout pattern. Technology development is concerned not only technically, but also other aspects such as personal, non-economic factors (population, geographical and social factors can be analyzed. Model examined in this study, a regression model is composed of a series of structural components over time allows changed invisible accidentally. Explanatory variables technology (both crystalline and amorphous in a model according to which the material is said to be better, but because of the lack of measured variables over time can not be entered in the template. Model examined in this study, a regression model is composed of a series of structural component invisible accidentally changed over time allows. In this study, structural time series (STSM and ARMA time series models have been used to model and estimate the water demand in Isfahan. Moreover, in order to find the efficient procedure, both models have been compared to each other. The desired data in this research include water consumption in Isfahan, water price and the monthly pay
Zhang, Jian; Yang, Xiao-hua; Chen, Xiao-juan
2015-01-01
Due to nonlinear and multiscale characteristics of temperature time series, a new model called wavelet network model based on multiple criteria decision making (WNMCDM) has been proposed, which combines the advantage of wavelet analysis, multiple criteria decision making, and artificial neural network. One case for forecasting extreme monthly maximum temperature of Miyun Reservoir has been conducted to examine the performance of WNMCDM model. Compared with nearest neighbor bootstrapping regr...
Forecasting electricity spot-prices using linear univariate time-series models
International Nuclear Information System (INIS)
Cuaresma, Jesus Crespo; Hlouskova, Jaroslava; Kossmeier, Stephan; Obersteiner, Michael
2004-01-01
This paper studies the forecasting abilities of a battery of univariate models on hourly electricity spot prices, using data from the Leipzig Power Exchange. The specifications studied include autoregressive models, autoregressive-moving average models and unobserved component models. The results show that specifications, where each hour of the day is modelled separately present uniformly better forecasting properties than specifications for the whole time-series, and that the inclusion of simple probabilistic processes for the arrival of extreme price events can lead to improvements in the forecasting abilities of univariate models for electricity spot prices. (Author)
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Rui Xue
2015-01-01
Full Text Available Although bus passenger demand prediction has attracted increased attention during recent years, limited research has been conducted in the context of short-term passenger demand forecasting. This paper proposes an interactive multiple model (IMM filter algorithm-based model to predict short-term passenger demand. After aggregated in 15 min interval, passenger demand data collected from a busy bus route over four months were used to generate time series. Considering that passenger demand exhibits various characteristics in different time scales, three time series were developed, named weekly, daily, and 15 min time series. After the correlation, periodicity, and stationarity analyses, time series models were constructed. Particularly, the heteroscedasticity of time series was explored to achieve better prediction performance. Finally, IMM filter algorithm was applied to combine individual forecasting models with dynamically predicted passenger demand for next interval. Different error indices were adopted for the analyses of individual and hybrid models. The performance comparison indicates that hybrid model forecasts are superior to individual ones in accuracy. Findings of this study are of theoretical and practical significance in bus scheduling.
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Guy J. Abel
2013-12-01
Full Text Available Background: Population forecasts are widely used for public policy purposes. Methods to quantify the uncertainty in forecasts tend to ignore model uncertainty and to be based on a single model. Objective: In this paper, we use Bayesian time series models to obtain future population estimates with associated measures of uncertainty. The models are compared based on Bayesian posterior model probabilities, which are then used to provide model-averaged forecasts. Methods: The focus is on a simple projection model with the historical data representing population change in England and Wales from 1841 to 2007. Bayesian forecasts to the year 2032 are obtained based on a range of models, including autoregression models, stochastic volatility models and random variance shift models. The computational steps to fit each of these models using the OpenBUGS software via R are illustrated. Results: We show that the Bayesian approach is adept in capturing multiple sources of uncertainty in population projections, including model uncertainty. The inclusion of non-constant variance improves the fit of the models and provides more realistic predictive uncertainty levels. The forecasting methodology is assessed through fitting the models to various truncated data series.
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Yolanda Navarro-Abal
2012-12-01
Full Text Available Television fiction series sometimes generate an unreal vision of life, especially among young people, becoming a mirror in which they can see themselves reflected. The series become models of values, attitudes, skills and behaviours that tend to be imitated by some viewers. The aim of this study was to analyze the conflict management behavioural styles presented by the main characters of television fiction series. Thus, we evaluated the association between these styles and the age and sex of the main characters, as well as the nationality and genre of the fiction series. 16 fiction series were assessed by selecting two characters of both sexes from each series. We adapted the Rahim Organizational Conflict Inventory-II for observing and recording the data. The results show that there is no direct association between the conflict management behavioural styles presented in the drama series and the sex of the main characters. However, associations were found between these styles and the age of the characters and the genre of the fiction series.
Reactions of the nitrate radical with a series og reduced organic sulfur-compounds in air
DEFF Research Database (Denmark)
JENSEN, NR; HJORTH, J; LOHSE, C
1992-01-01
A 480 L evacuable reaction chamber, equipped with FT-IR spectroscopy on-line and ion chromatography off-line, has been used to study the gas phase reaction between the nitrate radical, NO3, and the reduced organic sulphur compounds CH3CH2SH, (CH3CH2)2S, (CH3CH2)2S2, and CH3CH2SCH3 in air...... reaction products and intermediates observed in the infrared spectra, mechanisms are proposed for the reactions between the NO3 radical and the four reduced organic sulphur compounds. The results of this study, together with those from previous experiments performed in this laboratory on CH3SCH3, CH3SH......) reduced organic sulphur compounds was found to be H-atom abstraction, probably after the formation of an initial adduct. For the reaction between NO3 and R-S-S-R type compounds, evidence for an addition-decomposition reaction, as the initial steps, was obtained. R-S-, R-S(O)., and R-S(O)2. appear...
A Series Solution of the Cauchy Problem for Turing Reaction-diffusion Model
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L. Päivärinta
2011-12-01
Full Text Available In this paper, the series pattern solution of the Cauchy problem for Turing reaction-diffusion model is obtained by using the homotopy analysis method (HAM. Turing reaction-diffusion model is nonlinear reaction-diffusion system which usually has power-law nonlinearities or may be rewritten in the form of power-law nonlinearities. Using the HAM, it is possible to find the exact solution or an approximate solution of the problem. This technique provides a series of functions which converges rapidly to the exact solution of the problem. The efficiency of the approach will be shown by applying the procedure on two problems. Furthermore, the so-called homotopy-Pade technique (HPT is applied to enlarge the convergence region and rate of solution series given by the HAM.
Markov Chain Modelling for Short-Term NDVI Time Series Forecasting
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Stepčenko Artūrs
2016-12-01
Full Text Available In this paper, the NDVI time series forecasting model has been developed based on the use of discrete time, continuous state Markov chain of suitable order. The normalised difference vegetation index (NDVI is an indicator that describes the amount of chlorophyll (the green mass and shows the relative density and health of vegetation; therefore, it is an important variable for vegetation forecasting. A Markov chain is a stochastic process that consists of a state space. This stochastic process undergoes transitions from one state to another in the state space with some probabilities. A Markov chain forecast model is flexible in accommodating various forecast assumptions and structures. The present paper discusses the considerations and techniques in building a Markov chain forecast model at each step. Continuous state Markov chain model is analytically described. Finally, the application of the proposed Markov chain model is illustrated with reference to a set of NDVI time series data.
The Benefit of Multi-Mission Altimetry Series for the Calibration of Hydraulic Models
Domeneghetti, Alessio; Tarpanelli, Angelica; Tourian, Mohammad J.; Brocca, Luca; Moramarco, Tommaso; Castellarin, Attilio; Sneeuw, Nico
2016-04-01
The growing availability of satellite altimetric time series during last decades has fostered their use in many hydrological and hydraulic applications. However, the use of remotely sensed water level series still remains hampered by the limited temporal resolution that characterizes each sensor (i.e. revisit time varying from 10 to 35 days), as well as by the accuracy of different instrumentation adopted for monitoring inland water. As a consequence, each sensor is characterized by distinctive potentials and limitations that constrain its use for hydrological applications. In this study we refer to a stretch of about 140 km of the Po River (the longest Italian river) in order to investigate the performance of different altimetry series for the calibration of a quasi-2d model built with detailed topographic information. The usefulness of remotely sensed water surface elevation is tested using data collected by different altimetry missions (i.e., ERS-2, ENVISAT, TOPEX/Poseidon, JASON-2 and SARAL/Altika) by investigating the effect of (i) record length (i.e. number of satellite measurements provided by a given sensor at a specific satellite track) and (ii) data uncertainty (i.e. altimetry measurements errors). Since the relatively poor time resolution of satellites constrains the operational use of altimetric time series, in this study we also investigate the use of multi-mission altimetry series obtained by merging datasets sensed by different sensors over the study area. Benefits of the highest temporal frequency of multi-mission series are tested by calibrating the quasi-2d model referring in turn to original satellite series and multi-mission datasets. Jason-2 and ENVISAT outperform other sensors, ensuring the reliability on the calibration process for shorter time series. The multi-mission dataset appears particularly reliable and suitable for the calibration of hydraulic model. If short time periods are considered, the performance of the multi-mission dataset
Wohlmann, Anita; Steinberg, Ruth
2016-12-01
While the separation of body and mind (and the entailing metaphor of the body as a machine) has been a cornerstone of Western medicine for a long time, reactions to organ transplantation among others challenge this clear-cut dichotomy. The limits of the machine-body have been negotiated in science fiction, most canonically in Mary Shelley's Frankenstein (1818). Since then, Frankenstein's monster itself has become a motif that permeates both medical and fictional discourses. Neal Shusterman's contemporary dystology for young adults, Unwind, draws on traditional concepts of the machine-body and the Frankenstein myth. This article follows one of the young protagonists in the series, who is entirely constructed from donated tissue, and analyses how Shusterman explores the complicated relationship between body and mind and between self and other as the teenager matures into an adult. It will be shown that, by framing the story of a transplanted individual along the lines of a coming-of-age narrative, Shusterman inter-relates the acceptance of a donor organ with the transitional space of adolescence and positions the quest for embodied selfhood at the centre of both developments. By highlighting the interconnections between medical discourse and a literary tradition, the potential contribution of the series to the treatment and understanding of post-transplant patients will be addressed. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.
Modeling and Computation of Thermodynamic Equilibrium for Mixtures of Inorganic and Organic Species
Caboussat, A.; Amundson, N. R.; He, J.; Martynenko, A. V.; Seinfeld, J. H.
2007-05-01
A series of modules has been developed in the atmospheric modeling community to predict the phase transition, crystallization and evaporation of inorganic aerosols. Modules for the computation of the thermodynamics of pure organic-containing aerosols have been developed more recently; however, the modeling of aerosols containing mixtures of inorganic and organic compounds has gathered less attention. We present here a model (UHAERO), that is flexible, efficient and rigorously computes the thermodynamic equilibrium of atmospheric particles containing inorganic and organic compounds. It is applied first to mixtures of inorganic electrolytes and dicarboxylic acids, and then to thermodynamic equilibria including crystallization and liquid-liquid phase separation. The model does not rely on any a priori specification of the phases present in certain atmospheric conditions. The multicomponent phase equilibrium for a closed organic aerosol system at constant temperature and pressure and for specified feeds is the solution to the equilibrium problem arising from the constrained minimization of the Gibbs free energy. For mixtures of inorganic electrolytes and dissociated organics, organic salts appear at equilibrium in the aqueous phase. In the general case, liquid-liquid phase separations happen and electrolytes dissociate in both aqueous and organic liquid phases. The Gibbs free energy is modeled by the UNIFAC model for the organic compounds, the PSC model for the inorganic constituents and a Pitzer model for interactions. The difficulty comes from the accurate estimation of interactions in the modeling of the activity coefficients. An accurate and efficient method for the computation of the minimum of energy is used to compute phase diagrams for mixtures of inorganic and organic species. Numerical results show the efficiency of the model for mixtures of inorganic electrolytes and organic acids, which make it suitable for insertion in global three-dimensional air quality
Transfer function modeling of the monthly accumulated rainfall series over the Iberian Peninsula
Energy Technology Data Exchange (ETDEWEB)
Mateos, Vidal L.; Garcia, Jose A.; Serrano, Antonio; De la Cruz Gallego, Maria [Departamento de Fisica, Universidad de Extremadura, Badajoz (Spain)
2002-10-01
In order to improve the results given by Autoregressive Moving-Average (ARMA) modeling for the monthly accumulated rainfall series taken at 19 observatories of the Iberian Peninsula, a Discrete Linear Transfer Function Noise (DLTFN) model was applied taking the local pressure series (LP), North Atlantic sea level pressure series (SLP) and North Atlantic sea surface temperature (SST) as input variables, and the rainfall series as the output series. In all cases, the performance of the DLTFN models, measured by the explained variance of the rainfall series, is better than the performance given by the ARMA modeling. The best performance is given by the models that take the local pressure as the input variable, followed by the sea level pressure models and the sea surface temperature models. Geographically speaking, the models fitted to those observatories located in the west of the Iberian Peninsula work better than those on the north and east of the Peninsula. Also, it was found that there is a region located between 0 N and 20 N, which shows the highest cross-correlation between SST and the peninsula rainfalls. This region moves to the west and northwest off the Peninsula when the SLP series are used. [Spanish] Con el objeto de mejorar los resultados porporcionados por los modelos Autorregresivo Media Movil (ARMA) ajustados a las precipitaciones mensuales acumuladas registradas en 19 observatorios de la Peninsula Iberica se han usado modelos de funcion de transferencia (DLTFN) en los que se han empleado como variable independiente la presion local (LP), la presion a nivel del mar (SLP) o la temperatura de agua del mar (SST) en el Atlantico Norte. En todos los casos analizados, los resultados obtenidos con los modelos DLTFN, medidos mediante la varianza explicada por el modelo, han sido mejores que los resultados proporcionados por los modelos ARMA. Los mejores resultados han sido dados por aquellos modelos que usan la presion local como variable de entrada, seguidos
Using the mean approach in pooling cross-section and time series data for regression modelling
International Nuclear Information System (INIS)
Nuamah, N.N.N.N.
1989-12-01
The mean approach is one of the methods for pooling cross section and time series data for mathematical-statistical modelling. Though a simple approach, its results are sometimes paradoxical in nature. However, researchers still continue using it for its simplicity. Here, the paper investigates the nature and source of such unwanted phenomena. (author). 7 refs
Particle Markov Chain Monte Carlo Techniques of Unobserved Component Time Series Models Using Ox
DEFF Research Database (Denmark)
Nonejad, Nima
This paper details Particle Markov chain Monte Carlo techniques for analysis of unobserved component time series models using several economic data sets. PMCMC combines the particle filter with the Metropolis-Hastings algorithm. Overall PMCMC provides a very compelling, computationally fast...
76 FR 36390 - Airworthiness Directives; The Boeing Company Model 747SP Series Airplanes
2011-06-22
... power control modules (PCM). This proposed AD was prompted by a report of a rudder hard-over event on a... rudder PCM manifold, which could result in a hard-over of the rudder surface leading to an increase in... of a Model 747-400 series airplane of a lower rudder hard-over event caused by a lower rudder PCM...
75 FR 38945 - Airworthiness Directives; The Boeing Company Model 777-200 and -300 Series Airplanes
2010-07-07
... certain Model 777-200 and -300 series airplanes. This proposed AD would require installing new operational software in the cabin management system, and loading new software into the mass memory card. This proposed..., 2006. The service bulletin describes procedures for installing new operational software in the cabin...
The River Basin Model: Computer Output. Water Pollution Control Research Series.
Envirometrics, Inc., Washington, DC.
This research report is part of the Water Pollution Control Research Series which describes the results and progress in the control and abatement of pollution in our nation's waters. The River Basin Model described is a computer-assisted decision-making tool in which a number of computer programs simulate major processes related to water use that…
Modeling the impact of forecast-based regime switches on macroeconomic time series
K. Bel (Koen); R. Paap (Richard)
2013-01-01
textabstractForecasts of key macroeconomic variables may lead to policy changes of governments, central banks and other economic agents. Policy changes in turn lead to structural changes in macroeconomic time series models. To describe this phenomenon we introduce a logistic smooth transition
Barry T. Wilson; Joseph F. Knight; Ronald E. McRoberts
2018-01-01
Imagery from the Landsat Program has been used frequently as a source of auxiliary data for modeling land cover, as well as a variety of attributes associated with tree cover. With ready access to all scenes in the archive since 2008 due to the USGS Landsat Data Policy, new approaches to deriving such auxiliary data from dense Landsat time series are required. Several...
A robust interrupted time series model for analyzing complex health care intervention data
Cruz, Maricela
2017-08-29
Current health policy calls for greater use of evidence-based care delivery services to improve patient quality and safety outcomes. Care delivery is complex, with interacting and interdependent components that challenge traditional statistical analytic techniques, in particular, when modeling a time series of outcomes data that might be
Comparison of time series models for predicting campylobacteriosis risk in New Zealand.
Al-Sakkaf, A; Jones, G
2014-05-01
Predicting campylobacteriosis cases is a matter of considerable concern in New Zealand, after the number of the notified cases was the highest among the developed countries in 2006. Thus, there is a need to develop a model or a tool to predict accurately the number of campylobacteriosis cases as the Microbial Risk Assessment Model used to predict the number of campylobacteriosis cases failed to predict accurately the number of actual cases. We explore the appropriateness of classical time series modelling approaches for predicting campylobacteriosis. Finding the most appropriate time series model for New Zealand data has additional practical considerations given a possible structural change, that is, a specific and sudden change in response to the implemented interventions. A univariate methodological approach was used to predict monthly disease cases using New Zealand surveillance data of campylobacteriosis incidence from 1998 to 2009. The data from the years 1998 to 2008 were used to model the time series with the year 2009 held out of the data set for model validation. The best two models were then fitted to the full 1998-2009 data and used to predict for each month of 2010. The Holt-Winters (multiplicative) and ARIMA (additive) intervention models were considered the best models for predicting campylobacteriosis in New Zealand. It was noticed that the prediction by an additive ARIMA with intervention was slightly better than the prediction by a Holt-Winter multiplicative method for the annual total in year 2010, the former predicting only 23 cases less than the actual reported cases. It is confirmed that classical time series techniques such as ARIMA with intervention and Holt-Winters can provide a good prediction performance for campylobacteriosis risk in New Zealand. The results reported by this study are useful to the New Zealand Health and Safety Authority's efforts in addressing the problem of the campylobacteriosis epidemic. © 2013 Blackwell Verlag GmbH.
COMPUTER MODEL FOR ORGANIC FERTILIZER EVALUATION
Directory of Open Access Journals (Sweden)
Zdenko Lončarić
2009-12-01
Full Text Available Evaluation of manures, composts and growing media quality should include enough properties to enable an optimal use from productivity and environmental points of view. The aim of this paper is to describe basic structure of organic fertilizer (and growing media evaluation model to present the model example by comparison of different manures as well as example of using plant growth experiment for calculating impact of pH and EC of growing media on lettuce plant growth. The basic structure of the model includes selection of quality indicators, interpretations of indicators value, and integration of interpreted values into new indexes. The first step includes data input and selection of available data as a basic or additional indicators depending on possible use as fertilizer or growing media. The second part of the model uses inputs for calculation of derived quality indicators. The third step integrates values into three new indexes: fertilizer, growing media, and environmental index. All three indexes are calculated on the basis of three different groups of indicators: basic value indicators, additional value indicators and limiting factors. The possible range of indexes values is 0-10, where range 0-3 means low, 3-7 medium and 7-10 high quality. Comparing fresh and composted manures, higher fertilizer and environmental indexes were determined for composted manures, and the highest fertilizer index was determined for composted pig manure (9.6 whereas the lowest for fresh cattle manure (3.2. Composted manures had high environmental index (6.0-10 for conventional agriculture, but some had no value (environmental index = 0 for organic agriculture because of too high zinc, copper or cadmium concentrations. Growing media indexes were determined according to their impact on lettuce growth. Growing media with different pH and EC resulted in very significant impacts on height, dry matter mass and leaf area of lettuce seedlings. The highest lettuce
pplication of Time-series Modeling to Predict Infiltration of Different Soil Textures
Directory of Open Access Journals (Sweden)
S. Vazirpour
2016-10-01
Full Text Available Introduction: Infiltration is one of the most important parameters affecting irrigation. For this reason, measuring and estimating this parameter is very important, particularly when designing and managing irrigation systems. Infiltration affects water flow and solute transport in the soil surface and subsurface. Due to temporal and spatial variability, Many measurements are needed to explain the average soil infiltration characteristics under field conditions. Stochastic characteristics of the different natural phenomena led to the application of random variables and time series in predicting the performance of these phenomena. Time-series analysis is a simple and efficient method for prediction, which is widely used in various sciences. However, a few researches have investigated the time-series modeling to predict soil infiltration characteristics. In this study, capability of time series in estimating infiltration rate for different soil textures was evaluated. Materials and methods: For this purpose, the 60 and 120 minutes data of double ring infiltrometer test in Lali plain, Khuzestan, Iran, with its proposed time intervals (0, 1, 3, 5, 10, 15, 20, 30, 45, 60, 80, 100, 120, 150, 180, 210, 240 minutes were used to predict cumulative infiltration until the end of the experiment time for heavy (clay, medium (loam and light (sand soil textures. Moreover, used parameters of Kostiakov-Lewis equation recommended by NRCS, 24 hours cumulative infiltration curves were applied in time-series modeling for six different soil textures (clay, clay loam, silty, silty loam, sandy loam and sand. Different time-series models including Autoregressive (AR, Moving Average (MA, Autoregressive Moving Average (ARMA, autoregressive integrated moving average (ARIMA, ARMA model with eXogenous variables (ARMAX and AR model with eXogenous variables (ARX were evaluated in predicting cumulative infiltration. Autocorrelation and partial autocorrelation charts for each
Organic production in a dynamic CGE model
DEFF Research Database (Denmark)
Jacobsen, Lars Bo
2004-01-01
Concerns about the impact of modern agriculture on the environment have in recent years led to an interest in supporting the development of organic farming. In addition to environmental benefits, the aim is to encourage the provision of other “multifunctional” properties of organic farming...... agricultural sector and each secondary food industry has been split into two separate industries: one producing organic products, the other producing conventional products. The substitution nests in private consumption have also been altered to emphasise the pair wise substitution between organic...... and conventional products. One of the most important regulations regarding organic production concerns the conversion period, that is the period where the farmer starts to use organic production methods until the farmland and the production are considered organic. Currently organic production methods have...
Nonlinear Fluctuation Behavior of Financial Time Series Model by Statistical Physics System
Directory of Open Access Journals (Sweden)
Wuyang Cheng
2014-01-01
Full Text Available We develop a random financial time series model of stock market by one of statistical physics systems, the stochastic contact interacting system. Contact process is a continuous time Markov process; one interpretation of this model is as a model for the spread of an infection, where the epidemic spreading mimics the interplay of local infections and recovery of individuals. From this financial model, we study the statistical behaviors of return time series, and the corresponding behaviors of returns for Shanghai Stock Exchange Composite Index (SSECI and Hang Seng Index (HSI are also comparatively studied. Further, we investigate the Zipf distribution and multifractal phenomenon of returns and price changes. Zipf analysis and MF-DFA analysis are applied to investigate the natures of fluctuations for the stock market.
A spatial time series framework for modeling daily precipitationat regional scales
Energy Technology Data Exchange (ETDEWEB)
Kyriakidis, Phaedon C.; Miller, Norman L.; Kim, Jinwon
2001-11-14
In this paper, a framework for stochastic spatiotemporal modeling of daily precipitation in a hindcast mode is presented. Observed precipitation levels in space and time are modeled as a joint realization of a collection of space-indexed time series, one for each spatial location. Time series model parameters are spatially varying, thus capturing space-time interactions. Stochastic simulation, i.e., the procedure of generating alternative precipitation realizations (synthetic fields) over the space-time domain of interest (Deutsch and Journel, 1998), is employed for ensemble prediction. The simulated daily precipitation fields reproduce a data-based histogram and spatiotemporal covariance model, and identify the measured precipitation values at the rain gauges (conditional simulation). Such synthetic precipitation fields can be used in a Monte Carlo framework for risk analysis studies in hydrologic impact assessment investigations.
On determining the prediction limits of mathematical models for time series
International Nuclear Information System (INIS)
Peluso, E.; Gelfusa, M.; Lungaroni, M.; Talebzadeh, S.; Gaudio, P.; Murari, A.; Contributors, JET
2016-01-01
Prediction is one of the main objectives of scientific analysis and it refers to both modelling and forecasting. The determination of the limits of predictability is an important issue of both theoretical and practical relevance. In the case of modelling time series, reached a certain level in performance in either modelling or prediction, it is often important to assess 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, an information theoretic approach is proposed to address this issue and quantify the quality of the models and/or predictions. The excellent properties of the proposed indicator have been proved with the help of a systematic series of numerical tests and a concrete example of extreme relevance for nuclear fusion.
The partial duration series method in regional index-flood modeling
DEFF Research Database (Denmark)
Madsen, Henrik; Rosbjerg, Dan
1997-01-01
A regional index-flood method based on the partial duration series model is introduced. The model comprises the assumptions of a Poisson-distributed number of threshold exceedances and generalized Pareto (GP) distributed peak magnitudes. The regional T-year event estimator is based on a regional ...... preferable to at-site estimation in moderately heterogeneous and homogeneous regions for large sample sizes. Modest intersite dependence has only a small effect on the performance of the regional index-flood estimator.......A regional index-flood method based on the partial duration series model is introduced. The model comprises the assumptions of a Poisson-distributed number of threshold exceedances and generalized Pareto (GP) distributed peak magnitudes. The regional T-year event estimator is based on a regional...
Stochastic Modeling of Rainfall Series in Kelantan Using an Advanced Weather Generator
Directory of Open Access Journals (Sweden)
A. H. Syafrina
2018-02-01
Full Text Available Weather generator is a numerical tool that uses existing meteorological records to generate series of synthetic weather data. The AWE-GEN (Advanced Weather Generator model has been successful in producing a broad range of temporal scale weather variables, ranging from the high-frequency hourly values to the low-frequency inter-annual variability. In Malaysia, AWE-GEN has produced reliable projections of extreme rainfall events for some parts of Peninsular Malaysia. This study focuses on the use of AWE-GEN model to assess rainfall distribution in Kelantan. Kelantan is situated on the north east of the Peninsular, a region which is highly susceptible to flood. Embedded within the AWE-GEN model is the Neyman Scott process which employs parameters to represent physical rainfall characteristics. The use of correct probability distributions to represent the parameters is imperative to allow reliable results to be produced. This study compares the performance of two probability distributions, Weibull and Gamma to represent rainfall intensity and the better distribution found was used subsequently to simulate hourly scaled rainfall series. Thirty years of hourly scaled meteorological data from two stations in Kelantan were used in model construction. Results indicate that both probability distributions are capable of replicating the rainfall series at both stations very well, however numerical evaluations suggested that Gamma performs better. Despite Gamma not being a heavy tailed distribution, it is able to replicate the key characteristics of rainfall series and particularly extreme values. The overall simulation results showed that the AWE-GEN model is capable of generating tropical rainfall series which could be beneficial in flood preparedness studies in areas vulnerable to flood.
2013-12-19
... series airplanes have fly-by-wire controls, fully software-configurable avionics, and fiber-optic... Series Airplanes; Rechargeable Lithium Ion Batteries and Battery Systems AGENCY: Federal Aviation... Boeing Model 777- 200, -300, and -300ER series airplanes. These airplanes as modified by the ARINC...
Automated Bayesian model development for frequency detection in biological time series
Directory of Open Access Journals (Sweden)
Oldroyd Giles ED
2011-06-01
Full Text Available Abstract Background A first step in building a mathematical model of a biological system is often the analysis of the temporal behaviour of key quantities. Mathematical relationships between the time and frequency domain, such as Fourier Transforms and wavelets, are commonly used to extract information about the underlying signal from a given time series. This one-to-one mapping from time points to frequencies inherently assumes that both domains contain the complete knowledge of the system. However, for truncated, noisy time series with background trends this unique mapping breaks down and the question reduces to an inference problem of identifying the most probable frequencies. Results In this paper we build on the method of Bayesian Spectrum Analysis and demonstrate its advantages over conventional methods by applying it to a number of test cases, including two types of biological time series. Firstly, oscillations of calcium in plant root cells in response to microbial symbionts are non-stationary and noisy, posing challenges to data analysis. Secondly, circadian rhythms in gene expression measured over only two cycles highlights the problem of time series with limited length. The results show that the Bayesian frequency detection approach can provide useful results in specific areas where Fourier analysis can be uninformative or misleading. We demonstrate further benefits of the Bayesian approach for time series analysis, such as direct comparison of different hypotheses, inherent estimation of noise levels and parameter precision, and a flexible framework for modelling the data without pre-processing. Conclusions Modelling in systems biology often builds on the study of time-dependent phenomena. Fourier Transforms are a convenient tool for analysing the frequency domain of time series. However, there are well-known limitations of this method, such as the introduction of spurious frequencies when handling short and noisy time series, and
PSO-MISMO modeling strategy for multistep-ahead time series prediction.
Bao, Yukun; Xiong, Tao; Hu, Zhongyi
2014-05-01
Multistep-ahead time series prediction is one of the most challenging research topics in the field of time series modeling and prediction, and is continually under research. Recently, the multiple-input several multiple-outputs (MISMO) modeling strategy has been proposed as a promising alternative for multistep-ahead time series prediction, exhibiting advantages compared with the two currently dominating strategies, the iterated and the direct strategies. Built on the established MISMO strategy, this paper proposes a particle swarm optimization (PSO)-based MISMO modeling strategy, which is capable of determining the number of sub-models in a self-adaptive mode, with varying prediction horizons. Rather than deriving crisp divides with equal-size s prediction horizons from the established MISMO, the proposed PSO-MISMO strategy, implemented with neural networks, employs a heuristic to create flexible divides with varying sizes of prediction horizons and to generate corresponding sub-models, providing considerable flexibility in model construction, which has been validated with simulated and real datasets.
A regional and nonstationary model for partial duration series of extreme rainfall
DEFF Research Database (Denmark)
Gregersen, Ida Bülow; Madsen, Henrik; Rosbjerg, Dan
2017-01-01
of extreme rainfall. The framework is built on a partial duration series approach with a nonstationary, regional threshold value. The model is based on generalized linear regression solved by generalized estimation equations. It allows a spatial correlation between the stations in the network and accounts...... furthermore for variable observation periods at each station and in each year. Marginal regional and temporal regression models solved by generalized least squares are used to validate and discuss the results of the full spatiotemporal model. The model is applied on data from a large Danish rain gauge network...
Estimates by bootstrap interval for time series forecasts obtained by theta model
Directory of Open Access Journals (Sweden)
Daniel Steffen
2017-03-01
Full Text Available In this work, are developed an experimental computer program in Matlab language version 7.1 from the univariate method for time series forecasting called Theta, and implementation of resampling technique known as computer intensive "bootstrap" to estimate the prediction for the point forecast obtained by this method by confidence interval. To solve this problem built up an algorithm that uses Monte Carlo simulation to obtain the interval estimation for forecasts. The Theta model presented in this work was very efficient in M3 Makridakis competition, where tested 3003 series. It is based on the concept of modifying the local curvature of the time series obtained by a coefficient theta (Θ. In it's simplest approach the time series is decomposed into two lines theta representing terms of long term and short term. The prediction is made by combining the forecast obtained by fitting lines obtained with the theta decomposition. The results of Mape's error obtained for the estimates confirm the favorable results to the method of M3 competition being a good alternative for time series forecast.
Generation of future high-resolution rainfall time series with a disaggregation model
Müller, Hannes; Haberlandt, Uwe
2017-04-01
High-resolution rainfall data are needed in many fields of hydrology and water resources management. For analyzes of future rainfall condition climate scenarios exist with hourly values of rainfall. However, the direct usage of these data is associated with uncertainties which can be indicated by comparisons of observations and C20 control runs. An alternative is the derivation of changes of rainfall behavior over the time from climate simulations. Conclusions about future rainfall conditions can be drawn by adding these changes to observed time series. A multiplicative cascade model is used in this investigation for the disaggregation of daily rainfall amounts to hourly values. Model parameters can be estimated by REMO rainfall time series (UBA-, BfG- and ENS-realization), based on ECHAM5. Parameter estimation is carried out for C20 period as well as near term and long term future (2021-2050 and 2071-2100). Change factors for both future periods are derived by parameter comparisons and added to the parameters estimated from observed time series. This enables the generation of hourly rainfall time series from observed daily values with respect to future changes. The investigation is carried out for rain gauges in Lower Saxony. Generated Time series are analyzed regarding statistical characteristics, e.g. extreme values, event-based (wet spell duration and amounts, dry spell duration, …) and continuum characteristics (average intensity, fraction of dry intervals,…). The generation of the time series is validated by comparing the changes in the statistical characteristics from the REMO data and from the disaggregated data.
Unified Series-Shunt Compensator for PQ Analysis using Dynamic Phasor Modeling and EMT Simulation
Hannan, M. A.; Mohamed, Azah; Hussain, Aini
2010-06-01
Modeling of unified series-shunt compensator (USSC) and its PQ analysis of a simple test system is simulated based on dynamic phasor model and EMT program. Its aim is to investigate the overall efficiency of USSC for power quality (PQ) analysis and results will be compared with EMTP like simulation. The dynamic phasor model is implemented in Matlab/Simulink toolbox where as the EMT model simulation of the USSC uses the PSCAD/EMTDC software. Credible solutions to the PQ problems on the distribution network have been analyzed using dynamic phasor model and EMT model simulation techniques. Simulation results of the USSC dynamic phasor model including the system makes a perfect agreement with the detailed time-domain EMTP like PSCAD/EMTDC simulation. It is found that the dynamic behavior of USSC phasor model have very good potential application in analyzing overall PQ issues, faster in speed and higher accuracy as compared with PSCAD/EMTDC simulation.
He, Yuning
2015-01-01
Safety of unmanned aerial systems (UAS) is paramount, but the large number of dynamically changing controller parameters makes it hard to determine if the system is currently stable, and the time before loss of control if not. We propose a hierarchical statistical model using Treed Gaussian Processes to predict (i) whether a flight will be stable (success) or become unstable (failure), (ii) the time-to-failure if unstable, and (iii) time series outputs for flight variables. We first classify the current flight input into success or failure types, and then use separate models for each class to predict the time-to-failure and time series outputs. As different inputs may cause failures at different times, we have to model variable length output curves. We use a basis representation for curves and learn the mappings from input to basis coefficients. We demonstrate the effectiveness of our prediction methods on a NASA neuro-adaptive flight control system.
Intuitionistic Fuzzy Time Series Forecasting Model Based on Intuitionistic Fuzzy Reasoning
Directory of Open Access Journals (Sweden)
Ya’nan Wang
2016-01-01
Full Text Available Fuzzy sets theory cannot describe the data comprehensively, which has greatly limited the objectivity of fuzzy time series in uncertain data forecasting. In this regard, an intuitionistic fuzzy time series forecasting model is built. In the new model, a fuzzy clustering algorithm is used to divide the universe of discourse into unequal intervals, and a more objective technique for ascertaining the membership function and nonmembership function of the intuitionistic fuzzy set is proposed. On these bases, forecast rules based on intuitionistic fuzzy approximate reasoning are established. At last, contrast experiments on the enrollments of the University of Alabama and the Taiwan Stock Exchange Capitalization Weighted Stock Index are carried out. The results show that the new model has a clear advantage of improving the forecast accuracy.
[Prediction of epidemic tendency of schistosomiasis with time-series model in Hubei Province].
Chen, Yan-Yan; Cai, Shun-Xiang; Xiao, Ying; Jiang, Yong; Shan, Xiao-Wei; Zhang, Juan; Liu, Jian-Bing
2014-12-01
To study the endemic trend of schistosomiasis japonica in Hubei Province, so as to provide the theoretical basis for surveillance and forecasting of schistosomiasis. The time-series auto regression integrated moving average (ARIMA) model was applied to fit the infection rate of residents of Hubei Province from 1987 to 2013, and to predict the short-term trend of infection rate. The actual values of infection rate of residents were all in the 95% confidence internals of value predicted by the ARIMA model. The prediction showed that the infection rate of residents of Hubei Province would continue to decrease slowly. The time-series ARIMA model has good prediction accuracy, and could be used for the short-term forecasting of schistosomiasis.
The Gaussian Graphical Model in Cross-Sectional and Time-Series Data.
Epskamp, Sacha; Waldorp, Lourens J; Mõttus, René; Borsboom, Denny
2018-04-16
We discuss the Gaussian graphical model (GGM; an undirected network of partial correlation coefficients) and detail its utility as an exploratory data analysis tool. The GGM shows which variables predict one-another, allows for sparse modeling of covariance structures, and may highlight potential causal relationships between observed variables. We describe the utility in three kinds of psychological data sets: data sets in which consecutive cases are assumed independent (e.g., cross-sectional data), temporally ordered data sets (e.g., n = 1 time series), and a mixture of the 2 (e.g., n > 1 time series). In time-series analysis, the GGM can be used to model the residual structure of a vector-autoregression analysis (VAR), also termed graphical VAR. Two network models can then be obtained: a temporal network and a contemporaneous network. When analyzing data from multiple subjects, a GGM can also be formed on the covariance structure of stationary means-the between-subjects network. We discuss the interpretation of these models and propose estimation methods to obtain these networks, which we implement in the R packages graphicalVAR and mlVAR. The methods are showcased in two empirical examples, and simulation studies on these methods are included in the supplementary materials.
Timmerman, Mariek E.; Kiers, Henk A.L.
A class of four simultaneous component models for the exploratory analysis of multivariate time series collected from more than one subject simultaneously is discussed. In each of the models, the multivariate time series of each subject is decomposed into a few series of component scores and a
Goel, Honey; Sinha, V R; Thareja, Suresh; Aggarwal, Saurabh; Kumar, Manoj
2011-08-30
The quinolones belong to a family of synthetic potent broad-spectrum antibiotics and particularly active against gram-negative organisms, especially Pseudomonas aeruginosa. A 3D-QSPkR approach has been used to obtain the quantitative structure pharmacokinetic relationship for a series of quinolone drugs using SOMFA. The series consisting of 28 molecules have been investigated for their pharmacokinetic performance using biological half life (t(1/2)). A statistically validated robust model for a diverse group of quinolone drugs having flexibility in structure and pharmacokinetic profile (t(1/2)) obtained using SOMFA having good cross-validated correlation coefficient r(cv)(2) (0.6847), non cross-validated correlation coefficient r(2) values (0.7310) and high F-test value (33.9663). Analysis of 3D-QSPkR models through electrostatic and shape grids provide useful information about the shape and electrostatic potential contributions on t(1/2). The analysis of SOMFA results provide an insight for the generation of novel molecular architecture of quinolones with optimal half life and improved biological profile. Copyright © 2011 Elsevier B.V. All rights reserved.
International Nuclear Information System (INIS)
Jafri, Y.Z.; Kamal, L.
2007-01-01
Various statistical techniques was used on five-year data from 1998-2002 of average humidity, rainfall, maximum and minimum temperatures, respectively. The relationships to regression analysis time series (RATS) were developed for determining the overall trend of these climate parameters on the basis of which forecast models can be corrected and modified. We computed the coefficient of determination as a measure of goodness of fit, to our polynomial regression analysis time series (PRATS). The correlation to multiple linear regression (MLR) and multiple linear regression analysis time series (MLRATS) were also developed for deciphering the interdependence of weather parameters. Spearman's rand correlation and Goldfeld-Quandt test were used to check the uniformity or non-uniformity of variances in our fit to polynomial regression (PR). The Breusch-Pagan test was applied to MLR and MLRATS, respectively which yielded homoscedasticity. We also employed Bartlett's test for homogeneity of variances on a five-year data of rainfall and humidity, respectively which showed that the variances in rainfall data were not homogenous while in case of humidity, were homogenous. Our results on regression and regression analysis time series show the best fit to prediction modeling on climatic data of Quetta, Pakistan. (author)
Loredo, Thomas; Budavari, Tamas; Scargle, Jeffrey D.
2018-01-01
This presentation provides an overview of open-source software packages addressing two challenging classes of astrostatistics problems. (1) CUDAHM is a C++ framework for hierarchical Bayesian modeling of cosmic populations, leveraging graphics processing units (GPUs) to enable applying this computationally challenging paradigm to large datasets. CUDAHM is motivated by measurement error problems in astronomy, where density estimation and linear and nonlinear regression must be addressed for populations of thousands to millions of objects whose features are measured with possibly complex uncertainties, potentially including selection effects. An example calculation demonstrates accurate GPU-accelerated luminosity function estimation for simulated populations of $10^6$ objects in about two hours using a single NVIDIA Tesla K40c GPU. (2) Time Series Explorer (TSE) is a collection of software in Python and MATLAB for exploratory analysis and statistical modeling of astronomical time series. It comprises a library of stand-alone functions and classes, as well as an application environment for interactive exploration of times series data. The presentation will summarize key capabilities of this emerging project, including new algorithms for analysis of irregularly-sampled time series.
Organization model and formalized description of nuclear enterprise information system
International Nuclear Information System (INIS)
Yuan Feng; Song Yafeng; Li Xudong
2012-01-01
Organization model is one of the most important models of Nuclear Enterprise Information System (NEIS). Scientific and reasonable organization model is the prerequisite that NEIS has robustness and extendibility, and is also the foundation of the integration of heterogeneous system. Firstly, the paper describes the conceptual model of the NEIS on ontology chart, which provides a consistent semantic framework of organization. Then it discusses the relations between the concepts in detail. Finally, it gives the formalized description of the organization model of NEIS based on six-tuple array. (authors)
A scalable database model for multiparametric time series: a volcano observatory case study
Montalto, Placido; Aliotta, Marco; Cassisi, Carmelo; Prestifilippo, Michele; Cannata, Andrea
2014-05-01
The variables collected by a sensor network constitute a heterogeneous data source that needs to be properly organized in order to be used in research and geophysical monitoring. With the time series term we refer to a set of observations of a given phenomenon acquired sequentially in time. When the time intervals are equally spaced one speaks of period or sampling frequency. Our work describes in detail a possible methodology for storage and management of time series using a specific data structure. We designed a framework, hereinafter called TSDSystem (Time Series Database System), in order to acquire time series from different data sources and standardize them within a relational database. The operation of standardization provides the ability to perform operations, such as query and visualization, of many measures synchronizing them using a common time scale. The proposed architecture follows a multiple layer paradigm (Loaders layer, Database layer and Business Logic layer). Each layer is specialized in performing particular operations for the reorganization and archiving of data from different sources such as ASCII, Excel, ODBC (Open DataBase Connectivity), file accessible from the Internet (web pages, XML). In particular, the loader layer performs a security check of the working status of each running software through an heartbeat system, in order to automate the discovery of acquisition issues and other warning conditions. Although our system has to manage huge amounts of data, performance is guaranteed by using a smart partitioning table strategy, that keeps balanced the percentage of data stored in each database table. TSDSystem also contains modules for the visualization of acquired data, that provide the possibility to query different time series on a specified time range, or follow the realtime signal acquisition, according to a data access policy from the users.
Time series modeling for analysis and control advanced autopilot and monitoring systems
Ohtsu, Kohei; Kitagawa, Genshiro
2015-01-01
This book presents multivariate time series methods for the analysis and optimal control of feedback systems. Although ships’ autopilot systems are considered through the entire book, the methods set forth in this book can be applied to many other complicated, large, or noisy feedback control systems for which it is difficult to derive a model of the entire system based on theory in that subject area. The basic models used in this method are the multivariate autoregressive model with exogenous variables (ARX) model and the radial bases function net-type coefficients ARX model. The noise contribution analysis can then be performed through the estimated autoregressive (AR) model and various types of autopilot systems can be designed through the state–space representation of the models. The marine autopilot systems addressed in this book include optimal controllers for course-keeping motion, rolling reduction controllers with rudder motion, engine governor controllers, noise adaptive autopilots, route-tracki...
Directory of Open Access Journals (Sweden)
Parneet Paul
2013-02-01
Full Text Available The computer modelling and simulation of wastewater treatment plant and their specific technologies, such as membrane bioreactors (MBRs, are becoming increasingly useful to consultant engineers when designing, upgrading, retrofitting, operating and controlling these plant. This research uses traditional phenomenological mechanistic models based on MBR filtration and biochemical processes to measure the effectiveness of alternative and novel time series models based upon input–output system identification methods. Both model types are calibrated and validated using similar plant layouts and data sets derived for this purpose. Results prove that although both approaches have their advantages, they also have specific disadvantages as well. In conclusion, the MBR plant designer and/or operator who wishes to use good quality, calibrated models to gain a better understanding of their process, should carefully consider which model type is selected based upon on what their initial modelling objectives are. Each situation usually proves unique.
Transgenesis in non-model organisms: the case of Parhyale.
Kontarakis, Zacharias; Pavlopoulos, Anastasios
2014-01-01
One of the most striking manifestations of Hox gene activity is the morphological and functional diversity of arthropod body plans, segments, and associated appendages. Among arthropod models, the amphipod crustacean Parhyale hawaiensis satisfies a number of appealing biological and technical requirements to study the Hox control of tissue and organ morphogenesis. Parhyale embryos undergo direct development from fertilized eggs into miniature adults within 10 days and are amenable to all sorts of embryological and functional genetic manipulations. Furthermore, each embryo develops a series of specialized appendages along the anterior-posterior body axis, offering exceptional material to probe the genetic basis of appendage patterning, growth, and differentiation. Here, we describe the methodologies and techniques required for transgenesis-based gain-of-function studies of Hox genes in Parhyale embryos. First, we introduce a protocol for efficient microinjection of early-stage Parhyale embryos. Second, we describe the application of fast and reliable assays to test the activity of the Minos DNA transposon in embryos. Third, we present the use of Minos-based transgenesis vectors to generate stable and transient transgenic Parhyale. Finally, we describe the development and application of a conditional heat-inducible misexpression system to study the role of the Hox gene Ultrabithorax in Parhyale appendage specialization. Beyond providing a useful resource for Parhyalists, this chapter also aims to provide a road map for researchers working on other emerging model organisms. Acknowledging the time and effort that need to be invested in developing transgenic approaches in new species, it is all worth it considering the wide scope of experimentation that opens up once transgenesis is established.
Watanabe, Hayafumi; Sano, Yukie; Takayasu, Hideki; Takayasu, Misako
2016-11-01
To elucidate the nontrivial empirical statistical properties of fluctuations of a typical nonsteady time series representing the appearance of words in blogs, we investigated approximately 3 ×109 Japanese blog articles over a period of six years and analyze some corresponding mathematical models. First, we introduce a solvable nonsteady extension of the random diffusion model, which can be deduced by modeling the behavior of heterogeneous random bloggers. Next, we deduce theoretical expressions for both the temporal and ensemble fluctuation scalings of this model, and demonstrate that these expressions can reproduce all empirical scalings over eight orders of magnitude. Furthermore, we show that the model can reproduce other statistical properties of time series representing the appearance of words in blogs, such as functional forms of the probability density and correlations in the total number of blogs. As an application, we quantify the abnormality of special nationwide events by measuring the fluctuation scalings of 1771 basic adjectives.
A time series model: First-order integer-valued autoregressive (INAR(1))
Simarmata, D. M.; Novkaniza, F.; Widyaningsih, Y.
2017-07-01
Nonnegative integer-valued time series arises in many applications. A time series model: first-order Integer-valued AutoRegressive (INAR(1)) is constructed by binomial thinning operator to model nonnegative integer-valued time series. INAR (1) depends on one period from the process before. The parameter of the model can be estimated by Conditional Least Squares (CLS). Specification of INAR(1) is following the specification of (AR(1)). Forecasting in INAR(1) uses median or Bayesian forecasting methodology. Median forecasting methodology obtains integer s, which is cumulative density function (CDF) until s, is more than or equal to 0.5. Bayesian forecasting methodology forecasts h-step-ahead of generating the parameter of the model and parameter of innovation term using Adaptive Rejection Metropolis Sampling within Gibbs sampling (ARMS), then finding the least integer s, where CDF until s is more than or equal to u . u is a value taken from the Uniform(0,1) distribution. INAR(1) is applied on pneumonia case in Penjaringan, Jakarta Utara, January 2008 until April 2016 monthly.
Extracting Knowledge From Time Series An Introduction to Nonlinear Empirical Modeling
Bezruchko, Boris P
2010-01-01
This book addresses the fundamental question of how to construct mathematical models for the evolution of dynamical systems from experimentally-obtained time series. It places emphasis on chaotic signals and nonlinear modeling and discusses different approaches to the forecast of future system evolution. In particular, it teaches readers how to construct difference and differential model equations depending on the amount of a priori information that is available on the system in addition to the experimental data sets. This book will benefit graduate students and researchers from all natural sciences who seek a self-contained and thorough introduction to this subject.
Stifter, Cynthia A.; Rovine, Michael
2015-01-01
The focus of the present longitudinal study, to examine mother-infant interaction during the administration of immunizations at 2 and 6?months of age, used hidden Markov modelling, a time series approach that produces latent states to describe how mothers and infants work together to bring the infant to a soothed state. Results revealed a…
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Asaranti Kar
2015-01-01
Full Text Available Introduction: Neural tube defects (NTD are a group of serious birth defects occurring due to defective closure of neural tube during embryonic development. It comprises of anencephaly, encephalocele and spina bifida. We conducted this prospective fetal autopsy series to study the rate and distribution of NTD, analyze the reproductive factors and risk factors, note any associated anomalies and evaluate the organ weights and their deviation from normal. Materials and Methods: This was a prospective study done over a period of 6 years from August, 2007 to July, 2013. All cases of NTDs delivered as abortion, still born and live born were included. The reproductive and risk factors like age, parity, multiple births, previous miscarriage, obesity, diabetes mellitus, socioeconomic status and use of folic acid during pregnancy were collected.Autopsy was performed according to Virchow′s technique. Detail external and internal examination were carried out to detect any associated anomalies. Gross and microscopic examination of organs were done. Results: Out of 210 cases of fetal and perinatal autopsy done, 72 (34.28% had NTD constituting 49 cases of anencephaly, 16 spina bifida and 7 cases of encephalocele. The mothers in these cases predominantly were within 25-29 years (P = 0.02 and primy (P = 0.01. Female sex was more commonly affected than males (M:F = 25:47, P = 0.0005 There was no history of folate use in majority of cases. Organ weight deviations were >2 standard deviation low in most of the cases. Most common associated anomalies were adrenal hypoplasia and thymic hyperplasia. Conclusion: The authors have made an attempt to study NTD cases in respect to maternal reproductive and risk factors and their association with NTD along with the organ weight deviation and associated anomalies. This so far in our knowledge is an innovative study which was not found in literature even after extensive search.
Wilson, Barry T.; Knight, Joseph F.; McRoberts, Ronald E.
2018-03-01
Imagery from the Landsat Program has been used frequently as a source of auxiliary data for modeling land cover, as well as a variety of attributes associated with tree cover. With ready access to all scenes in the archive since 2008 due to the USGS Landsat Data Policy, new approaches to deriving such auxiliary data from dense Landsat time series are required. Several methods have previously been developed for use with finer temporal resolution imagery (e.g. AVHRR and MODIS), including image compositing and harmonic regression using Fourier series. The manuscript presents a study, using Minnesota, USA during the years 2009-2013 as the study area and timeframe. The study examined the relative predictive power of land cover models, in particular those related to tree cover, using predictor variables based solely on composite imagery versus those using estimated harmonic regression coefficients. The study used two common non-parametric modeling approaches (i.e. k-nearest neighbors and random forests) for fitting classification and regression models of multiple attributes measured on USFS Forest Inventory and Analysis plots using all available Landsat imagery for the study area and timeframe. The estimated Fourier coefficients developed by harmonic regression of tasseled cap transformation time series data were shown to be correlated with land cover, including tree cover. Regression models using estimated Fourier coefficients as predictor variables showed a two- to threefold increase in explained variance for a small set of continuous response variables, relative to comparable models using monthly image composites. Similarly, the overall accuracies of classification models using the estimated Fourier coefficients were approximately 10-20 percentage points higher than the models using the image composites, with corresponding individual class accuracies between six and 45 percentage points higher.
International Nuclear Information System (INIS)
Janker, Karl Albert
2015-01-01
This thesis describes a model which generates renewable power generation time series as input data for energy system models. The focus is on photovoltaic systems and wind turbines. The basis is a high resolution global raster data set of weather data for many years. This data is validated, corrected and preprocessed. The composition of the hourly generation data is done via simulation of the respective technology. The generated time series are aggregated for different regions and are validated against historical production time series.
Time-series regression models to study the short-term effects of environmental factors on health
Tobías, Aureli; Saez, Marc
2004-01-01
Time series regression models are especially suitable in epidemiology for evaluating short-term effects of time-varying exposures on health. The problem is that potential for confounding in time series regression is very high. Thus, it is important that trend and seasonality are properly accounted for. Our paper reviews the statistical models commonly used in time-series regression methods, specially allowing for serial correlation, make them potentially useful for selected epidemiological pu...
Applications of soft computing in time series forecasting simulation and modeling techniques
Singh, Pritpal
2016-01-01
This book reports on an in-depth study of fuzzy time series (FTS) modeling. It reviews and summarizes previous research work in FTS modeling and also provides a brief introduction to other soft-computing techniques, such as artificial neural networks (ANNs), rough sets (RS) and evolutionary computing (EC), focusing on how these techniques can be integrated into different phases of the FTS modeling approach. In particular, the book describes novel methods resulting from the hybridization of FTS modeling approaches with neural networks and particle swarm optimization. It also demonstrates how a new ANN-based model can be successfully applied in the context of predicting Indian summer monsoon rainfall. Thanks to its easy-to-read style and the clear explanations of the models, the book can be used as a concise yet comprehensive reference guide to fuzzy time series modeling, and will be valuable not only for graduate students, but also for researchers and professionals working for academic, business and governmen...
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Xin Liang
2018-01-01
Full Text Available In this paper, an anomalous advection-dispersion model involving a new general Liouville–Caputo fractional-order derivative is addressed for the first time. The series solutions of the general fractional advection-dispersion equations are obtained with the aid of the Laplace transform. The results are given to demonstrate the efficiency of the proposed formulations to describe the anomalous advection dispersion processes.
Analisis Tingkat Motivasi Siswa Dalam Pembelajaran IPA Model Advance Organizer Berbasis Proyek
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Tasiwan -
2014-04-01
Full Text Available atur kemajuan (advance organizer berbasis proyek. Sampel penelitian dipilih secara acak. Pada kelas eksperimen diterapkan model pembelajaran advance organizer berbasis proyek sedangkan pada kelas kontrol diterapkan pembelajaran langsung (direct instruction tanpa advance organizer. Sebelum pembelajaran di kelas, siswa eksperimen dikelompokkan menjadi 8 kelompok yang terdiri atas 4 – 5 siswa. Setiap kelompok ditugaskan untuk merealisasikan proyek bel listrik, rangkaian arus seri – paralel, dan tuas. Produk proyek digunakan dalam pembelajaran dikelas sebagai advance organizer. Data diperoleh melalui observasi partisipatif, penilaian produk, peta konsep, laporan eksperimen, dan angket. Instrumen motivasi menggunakan skala motivasi ARCS. Hasil penelitian menunjukkan bahwa kelas eksperimen memiliki tingkat motivasi lebih baik dalam aspek perhatian, relevansi, kepercayaan diri, dan kepuasan pembelajaran dengan rata – rata tingkat motivasi sebesar 77,20, sedangkan tanpa advance organizer berbasis proyek sebesar 71,10. Disarankan siswa diberikan kemandirian penuh dalam proyek. This study was conducted to analyze the level of student motivation in learning science through models of advance organizer based project . Samples were selected at random . In the experimental class advance organizer applied learning model based on a class project while learning control direct instruction without advance organizer . Prior learning in the classroom , students are grouped into 8 experimental groups consisting of 4-5 students . Each group was assigned a project to realize an electric bell , the circuit current series - parallel , and lever . Products used in a learning class project as advance organizer . The data obtained through participant observation , assessment product , concept maps , experimental reports , and questionnaires . Motivation instrument using ARCS motivation scale . Results showed that the experimental class had better motivation level
Yang, Zhong-Zhi; Lin, Xiao-Ting; Zhao, Dong-Xia
2016-06-01
A new ABEEMσπ/MM method for fast calculation of molecular total energy is established by combining ABEEMσπ model with force field representation, where ABEEMσπ is the atom-bond electronegativity equalization model at the σπ level. The calibrated parameters are suitable and transferable. This paper demonstrates that the total molecular energies for series of alcohols, aldehydes, carboxylic acids and peptides calculated by ABEEMσπ/MM method are in fair agreement with those obtained from calculations of ab initio MP2/6-311++G(d, p) method with mean absolute deviation (MAD) being 1.45 kcal/mol and their linear correlation coefficients being 1.0000. Thus it opens good prospects for wide applications to chemical and biological systems.
International Nuclear Information System (INIS)
Yang, Hyun-Ho; Han, Chang-Hoon; Lee, Jeong Oen; Yoon, Jun-Bo
2014-01-01
As a powerful method to reduce actuation voltage in an electrostatic micro-actuator, we propose and investigate an electrostatic micro-actuator with a pre-charged series capacitor. In contrast to a conventional electrostatic actuator, the injected pre-charges into the series capacitor can freely modulate the pull-in voltage of the proposed actuator even after the completion of fabrication. The static characteristics of the proposed actuator were investigated by first developing analytical models based on a parallel-plate capacitor model. We then successfully designed and demonstrated a micro-switch with a pre-charged series capacitor. The pull-in voltage of the fabricated micro-switch was reduced from 65.4 to 0.6 V when pre-charged with 46.3 V. The on-resistance of the fabricated micro-switch was almost the same as the initial one, even when the device was pre-charged, which was demonstrated for the first time. All results from the analytical models, finite element method simulations, and measurements were in good agreement with deviations of less than 10%. This work can be favorably adapted to electrostatic micro-switches which need a low actuation voltage without noticeable degradation of performance. (paper)
N. Basturk (Nalan); C. Cakmakli (Cem); S.P. Ceyhan (Pinar); H.K. van Dijk (Herman)
2012-01-01
textabstractChanging time series properties of US inflation and economic activity are analyzed within a class of extended Phillips Curve (PC) models. First, the misspecification effects of mechanical removal of low frequency movements of these series on posterior inference of a basic PC model are
Basturk, N.; Cakmakli, C.; Ceyhan, P.; van Dijk, H.K.
2013-01-01
Changing time series properties of US inflation and economic activity are analyzed within a class of extended Phillips Curve (PC) models. First, the misspecification effects of mechanical removal of low frequency movements of these series on posterior inference of a basic PC model are analyzed using
2013-12-17
.... As with the previous fly-by-wire airplanes, the FAA has no regulatory or safety reason to inhibit the...-0905; Notice No. 25-13-28-SC] Special Conditions: Airbus, Model A350-900 Series Airplane; Flight... Airbus Model A350- 900 series airplanes. These airplanes will have a novel or unusual design feature(s...
Klein, A.A.B.; Melard, G.; Zahaf, T.
2000-01-01
The Fisher information matrix is of fundamental importance for the analysis of parameter estimation of time series models. In this paper the exact information matrix of a multivariate Gaussian time series model expressed in state space form is derived. A computationally efficient procedure is used
a Landsat Time-Series Stacks Model for Detection of Cropland Change
Chen, J.; Chen, J.; Zhang, J.
2017-09-01
Global, timely, accurate and cost-effective cropland monitoring with a fine spatial resolution will dramatically improve our understanding of the effects of agriculture on greenhouse gases emissions, food safety, and human health. Time-series remote sensing imagery have been shown particularly potential to describe land cover dynamics. The traditional change detection techniques are often not capable of detecting land cover changes within time series that are severely influenced by seasonal difference, which are more likely to generate pseuso changes. Here,we introduced and tested LTSM ( Landsat time-series stacks model), an improved Continuous Change Detection and Classification (CCDC) proposed previously approach to extract spectral trajectories of land surface change using a dense Landsat time-series stacks (LTS). The method is expected to eliminate pseudo changes caused by phenology driven by seasonal patterns. The main idea of the method is that using all available Landsat 8 images within a year, LTSM consisting of two term harmonic function are estimated iteratively for each pixel in each spectral band .LTSM can defines change area by differencing the predicted and observed Landsat images. The LTSM approach was compared with change vector analysis (CVA) method. The results indicated that the LTSM method correctly detected the "true change" without overestimating the "false" one, while CVA pointed out "true change" pixels with a large number of "false changes". The detection of change areas achieved an overall accuracy of 92.37 %, with a kappa coefficient of 0.676.
Big Data impacts on stochastic Forecast Models: Evidence from FX time series
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Sebastian Dietz
2013-12-01
Full Text Available With the rise of the Big Data paradigm new tasks for prediction models appeared. In addition to the volume problem of such data sets nonlinearity becomes important, as the more detailed data sets contain also more comprehensive information, e.g. about non regular seasonal or cyclical movements as well as jumps in time series. This essay compares two nonlinear methods for predicting a high frequency time series, the USD/Euro exchange rate. The first method investigated is Autoregressive Neural Network Processes (ARNN, a neural network based nonlinear extension of classical autoregressive process models from time series analysis (see Dietz 2011. Its advantage is its simple but scalable time series process model architecture, which is able to include all kinds of nonlinearities based on the universal approximation theorem of Hornik, Stinchcombe and White 1989 and the extensions of Hornik 1993. However, restrictions related to the numeric estimation procedures limit the flexibility of the model. The alternative is a Support Vector Machine Model (SVM, Vapnik 1995. The two methods compared have different approaches of error minimization (Empirical error minimization at the ARNN vs. structural error minimization at the SVM. Our new finding is, that time series data classified as “Big Data” need new methods for prediction. Estimation and prediction was performed using the statistical programming language R. Besides prediction results we will also discuss the impact of Big Data on data preparation and model validation steps. Normal 0 21 false false false DE X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Normale Tabelle"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin:0cm; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:10.0pt; font-family:"Times New Roman","serif";}
McGrail, B. Peter; Brown, Daryl R.; Thallapally, Praveen K.
2016-08-02
Methods for releasing associated guest materials from a metal organic framework are provided. Methods for associating guest materials with a metal organic framework are also provided. Methods are provided for selectively associating or dissociating guest materials with a metal organic framework. Systems for associating or dissociating guest materials within a series of metal organic frameworks are provided. Thermal energy transfer assemblies are provided. Methods for transferring thermal energy are also provided.
A Time-Series Water Level Forecasting Model Based on Imputation and Variable Selection Method
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Jun-He Yang
2017-01-01
Full Text Available Reservoirs are important for households and impact the national economy. This paper proposed a time-series forecasting model based on estimating a missing value followed by variable selection to forecast the reservoir’s water level. This study collected data from the Taiwan Shimen Reservoir as well as daily atmospheric data from 2008 to 2015. The two datasets are concatenated into an integrated dataset based on ordering of the data as a research dataset. The proposed time-series forecasting model summarily has three foci. First, this study uses five imputation methods to directly delete the missing value. Second, we identified the key variable via factor analysis and then deleted the unimportant variables sequentially via the variable selection method. Finally, the proposed model uses a Random Forest to build the forecasting model of the reservoir’s water level. This was done to compare with the listing method under the forecasting error. These experimental results indicate that the Random Forest forecasting model when applied to variable selection with full variables has better forecasting performance than the listing model. In addition, this experiment shows that the proposed variable selection can help determine five forecast methods used here to improve the forecasting capability.
Zhang, Yong; Zhong, Miner; Geng, Nana; Jiang, Yunjian
2017-01-01
The market demand for electric vehicles (EVs) has increased in recent years. Suitable models are necessary to understand and forecast EV sales. This study presents a singular spectrum analysis (SSA) as a univariate time-series model and vector autoregressive model (VAR) as a multivariate model. Empirical results suggest that SSA satisfactorily indicates the evolving trend and provides reasonable results. The VAR model, which comprised exogenous parameters related to the market on a monthly basis, can significantly improve the prediction accuracy. The EV sales in China, which are categorized into battery and plug-in EVs, are predicted in both short term (up to December 2017) and long term (up to 2020), as statistical proofs of the growth of the Chinese EV industry. PMID:28459872
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Mansoor Ahmed Siddiqui
2017-06-01
Full Text Available This research work is aimed at optimizing the availability of a framework comprising of two units linked together in series configuration utilizing Markov Model and Monte Carlo (MC Simulation techniques. In this article, effort has been made to develop a maintenance model that incorporates three distinct states for each unit, while taking into account their different levels of deterioration. Calculations are carried out using the proposed model for two distinct cases of corrective repair, namely perfect and imperfect repairs, with as well as without opportunistic maintenance. Initially, results are accomplished using an analytical technique i.e., Markov Model. Validation of the results achieved is later carried out with the help of MC Simulation. In addition, MC Simulation based codes also work well for the frameworks that follow non-exponential failure and repair rates, and thus overcome the limitations offered by the Markov Model.
Zhang, Yong; Zhong, Miner; Geng, Nana; Jiang, Yunjian
2017-01-01
The market demand for electric vehicles (EVs) has increased in recent years. Suitable models are necessary to understand and forecast EV sales. This study presents a singular spectrum analysis (SSA) as a univariate time-series model and vector autoregressive model (VAR) as a multivariate model. Empirical results suggest that SSA satisfactorily indicates the evolving trend and provides reasonable results. The VAR model, which comprised exogenous parameters related to the market on a monthly basis, can significantly improve the prediction accuracy. The EV sales in China, which are categorized into battery and plug-in EVs, are predicted in both short term (up to December 2017) and long term (up to 2020), as statistical proofs of the growth of the Chinese EV industry.
MODELING OF MANAGEMENT PROCESSES IN AN ORGANIZATION
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Stefan Iovan
2016-05-01
Full Text Available When driving any major change within an organization, strategy and execution are intrinsic to a project’s success. Nevertheless, closing the gap between strategy and execution remains a challenge for many organizations [1]. Companies tend to focus more on execution than strategy for quick results, instead of taking the time needed to understand the parts that make up the whole, so the right execution plan can be put in place to deliver the best outcomes. A large part of this understands that business operations don’t fit neatly within the traditional organizational hierarchy. Business processes are often messy, collaborative efforts that cross teams, departments and systems, making them difficult to manage within a hierarchical structure [2]. Business process management (BPM fills this gap by redefining an organization according to its end-to-end processes, so opportunities for improvement can be identified and processes streamlined for growth, revenue and transformation. This white paper provides guidelines on what to consider when using business process applications to solve your BPM initiatives, and the unique capabilities software systems provides that can help ensure both your project’s success and the success of your organization as a whole. majority of medium and small businesses, big companies and even some guvermental organizations [2].
Time Series Model of Wind Speed for Multi Wind Turbines based on Mixed Copula
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Nie Dan
2016-01-01
Full Text Available Because wind power is intermittent, random and so on, large scale grid will directly affect the safe and stable operation of power grid. In order to make a quantitative study on the characteristics of the wind speed of wind turbine, the wind speed time series model of the multi wind turbine generator is constructed by using the mixed Copula-ARMA function in this paper, and a numerical example is also given. The research results show that the model can effectively predict the wind speed, ensure the efficient operation of the wind turbine, and provide theoretical basis for the stability of wind power grid connected operation.
Olowe, Kayode O; Kumarasamy, Muthukrishnavellaisamy
2017-10-01
Discharge of organic waste results in high nutrient pollution of the water bodies which is a major menace to the environment. A high quantity of nutrients such as ammonia causes a reduction in the dissolved oxygen level and induces algal growth in the water bodies. Water quality models have been the tools to evaluate the rate at which streams can disperse the pollutants they receive. Many water quality models are flawed either because of their inadequacy to completely simulate the advection component of the pollutant transport, or because of the limited application of the models, due to inaccurate estimation of model parameters. The hybrid cell in series (HCIS) developed by Ghosh et al. (2004) has been able to overcome such difficulties associated with the mixing cell-based models. Thus, the current study focuses on developing an analytical solution for the pollutant transport of the ammonia concentration through the plug flow, the first and second well-mixed cells of the HCIS model. The HCIS model coupled with the first order kinetic equation for ammonia nutrient was developed to simulate the ammonia pollutant concentration in the water column. The ammonia concentration at various points along the river system was assessed by considering the effects of the transformation of ammonia to nitrite, the uptake of ammonia by the algae, the respiration rate of the algae and the input of benthic source to the ammonia concentration in the water column. The proposed model was tested using synthetic data, and the HCIS-NH 3 model simulations for spatial and temporal variation of ammonia pollutant transport were analysed. The simulated results of the HCIS-NH 3 model agreed with the Fickian-based advection-dispersion equation (ADE) for simulating ammonia concentration solved using an explicit finite difference scheme. The HCIS-NH 3 model also showed a good agreement with the observed data from the Umgeni River, except during rainy periods.
Self-Organizing Map Models of Language Acquisition
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Ping eLi
2013-11-01
Full Text Available Connectionist models have had a profound impact on theories of language. While most early models were inspired by the classic PDP architecture, recent models of language have explored various other types of models, including self-organizing models for language acquisition. In this paper we aim at providing a review of the latter type of models, and highlight a number of simulation experiments that we have conducted based on these models. We show that self-organizing connectionist models can provide significant insights into long-standing debates in both monolingual and bilingual language development.
Bernaola-Galván, Pedro A.; Gómez-Extremera, Manuel; Romance, A. Ramón; Carpena, Pedro
2017-09-01
The correlation properties of the magnitude of a time series are associated with nonlinear and multifractal properties and have been applied in a great variety of fields. Here we have obtained the analytical expression of the autocorrelation of the magnitude series (C|x |) of a linear Gaussian noise as a function of its autocorrelation (Cx). For both, models and natural signals, the deviation of C|x | from its expectation in linear Gaussian noises can be used as an index of nonlinearity that can be applied to relatively short records and does not require the presence of scaling in the time series under study. In a model of artificial Gaussian multifractal signal we use this approach to analyze the relation between nonlinearity and multifractallity and show that the former implies the latter but the reverse is not true. We also apply this approach to analyze experimental data: heart-beat records during rest and moderate exercise. For each individual subject, we observe higher nonlinearities during rest. This behavior is also achieved on average for the analyzed set of 10 semiprofessional soccer players. This result agrees with the fact that other measures of complexity are dramatically reduced during exercise and can shed light on its relationship with the withdrawal of parasympathetic tone and/or the activation of sympathetic activity during physical activity.
Bernaola-Galván, Pedro A; Gómez-Extremera, Manuel; Romance, A Ramón; Carpena, Pedro
2017-09-01
The correlation properties of the magnitude of a time series are associated with nonlinear and multifractal properties and have been applied in a great variety of fields. Here we have obtained the analytical expression of the autocorrelation of the magnitude series (C_{|x|}) of a linear Gaussian noise as a function of its autocorrelation (C_{x}). For both, models and natural signals, the deviation of C_{|x|} from its expectation in linear Gaussian noises can be used as an index of nonlinearity that can be applied to relatively short records and does not require the presence of scaling in the time series under study. In a model of artificial Gaussian multifractal signal we use this approach to analyze the relation between nonlinearity and multifractallity and show that the former implies the latter but the reverse is not true. We also apply this approach to analyze experimental data: heart-beat records during rest and moderate exercise. For each individual subject, we observe higher nonlinearities during rest. This behavior is also achieved on average for the analyzed set of 10 semiprofessional soccer players. This result agrees with the fact that other measures of complexity are dramatically reduced during exercise and can shed light on its relationship with the withdrawal of parasympathetic tone and/or the activation of sympathetic activity during physical activity.
A new Markov-chain-related statistical approach for modelling synthetic wind power time series
International Nuclear Information System (INIS)
Pesch, T; Hake, J F; Schröders, S; Allelein, H J
2015-01-01
The integration of rising shares of volatile wind power in the generation mix is a major challenge for the future energy system. To address the uncertainties involved in wind power generation, models analysing and simulating the stochastic nature of this energy source are becoming increasingly important. One statistical approach that has been frequently used in the literature is the Markov chain approach. Recently, the method was identified as being of limited use for generating wind time series with time steps shorter than 15–40 min as it is not capable of reproducing the autocorrelation characteristics accurately. This paper presents a new Markov-chain-related statistical approach that is capable of solving this problem by introducing a variable second lag. Furthermore, additional features are presented that allow for the further adjustment of the generated synthetic time series. The influences of the model parameter settings are examined by meaningful parameter variations. The suitability of the approach is demonstrated by an application analysis with the example of the wind feed-in in Germany. It shows that—in contrast to conventional Markov chain approaches—the generated synthetic time series do not systematically underestimate the required storage capacity to balance wind power fluctuation. (paper)
Faht, Guilherme; da Silva, Marcos Rivail; Pinheiro, Adilson; Kaufmann, Vander; de Aguida, Leandro Mazzuco
2012-08-01
The quality of results of an environmental monitoring plan is limited to the weakest component, which could be the analytical approach or sampling method. Considering both the possibilities and the fragility that sampling methods offer, this environmental monitoring study focused on the uncertainties caused by the time component. Four time series of nutrient concentration at two sampling points (PB1 and PB2) in the Ribeirão Garcia basin in Blumenau, Brazil, which were significantly correlated to the spatial component, were considered with a 2-hour resolution to develop efficient sampling models. These models were based on the time at which there was the highest tendency toward adverse environmental effects. Fourier spectral analysis was used to evaluated the time series and resulted in two sampling models: (1) the SMCP (sampling model for critical period) that operated with 100% efficiency for registering the highest concentration of nutrients and was valid for 83% of the studied parameters; and (2) the SMGCP (sampling model for global critical period) that operated with 83 and 50% efficiency for PB1 and PB2, respectively.
Structural Time Series Model for El Niño Prediction
Petrova, Desislava; Koopman, Siem Jan; Ballester, Joan; Rodo, Xavier
2015-04-01
ENSO is a dominant feature of climate variability on inter-annual time scales destabilizing weather patterns throughout the globe, and having far-reaching socio-economic consequences. It does not only lead to extensive rainfall and flooding in some regions of the world, and anomalous droughts in others, thus ruining local agriculture, but also substantially affects the marine ecosystems and the sustained exploitation of marine resources in particular coastal zones, especially the Pacific South American coast. As a result, forecasting of ENSO and especially of the warm phase of the oscillation (El Niño/EN) has long been a subject of intense research and improvement. Thus, the present study explores a novel method for the prediction of the Niño 3.4 index. In the state-of-the-art the advantageous statistical modeling approach of Structural Time Series Analysis has not been applied. Therefore, we have developed such a model using a State Space approach for the unobserved components of the time series. Its distinguishing feature is that observations consist of various components - level, seasonality, cycle, disturbance, and regression variables incorporated as explanatory covariates. These components are aimed at capturing the various modes of variability of the N3.4 time series. They are modeled separately, then combined in a single model for analysis and forecasting. Customary statistical ENSO prediction models essentially use SST, SLP and wind stress in the equatorial Pacific. We introduce new regression variables - subsurface ocean temperature in the western equatorial Pacific, motivated by recent (Ramesh and Murtugudde, 2012) and classical research (Jin, 1997), (Wyrtki, 1985), showing that subsurface processes and heat accumulation there are fundamental for initiation of an El Niño event; and a southern Pacific temperature-difference tracer, the Rossbell dipole, leading EN by about nine months (Ballester, 2011).
Directory of Open Access Journals (Sweden)
Magdy A. El-Tawil
2012-07-01
Full Text Available The homotopy analysis method (HAM is used to find approximate analytical solutions of continuous population models for single and interacting species. The homotopy analysis method contains the auxiliary parameter $hbar,$ which provides us with a simple way to adjust and control the convergence region of series solution. the solutions are compared with the numerical results obtained using NDSolve, an ordinary differential equation solver found in the Mathematica package and a good agreement is found. Also the solutions are compared with the available analytic results obtained by other methods and more accurate and convergent series solution found. The convergence region is also computed which shows the validity of the HAM solution. This method is reliable and manageable.
Sample correlations of infinite variance time series models: an empirical and theoretical study
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Jason Cohen
1998-01-01
Full Text Available When the elements of a stationary ergodic time series have finite variance the sample correlation function converges (with probability 1 to the theoretical correlation function. What happens in the case where the variance is infinite? In certain cases, the sample correlation function converges in probability to a constant, but not always. If within a class of heavy tailed time series the sample correlation functions do not converge to a constant, then more care must be taken in making inferences and in model selection on the basis of sample autocorrelations. We experimented with simulating various heavy tailed stationary sequences in an attempt to understand what causes the sample correlation function to converge or not to converge to a constant. In two new cases, namely the sum of two independent moving averages and a random permutation scheme, we are able to provide theoretical explanations for a random limit of the sample autocorrelation function as the sample grows.
Directory of Open Access Journals (Sweden)
Morteza Hatami
2017-10-01
Full Text Available Epidemiological studies conducted in the past two decades indicate that air pollution causes increase in cardiovascular, breathing and chronic bronchitis disorders and even causes cardiovascular mortality. Therefore, the aim of this study was to investigate the relationship between meteorological parameters, air pollution and cardiovascular mortality in the city of Mashhad in 2014 by a time series model. Data on mortality from cardiovascular disease, meteorological parameters and air pollution in 2014 were gathered from Paradises organization, meteorology organization and pollutant monitoring center, respectively. Then the relationship between these parameters was analyzed using correlation coefficient, generalized linear regression, time series models and comparison of means. The results of the study showed that the highest rate of cardiovascular mortality related to Sulfur dioxide, nitrogen dioxide and then PM2.5. So that each unit increase in SO2, NO2 and PM2.5 pollutants adds to the rate of cardiovascular mortality by 22.5, 2.9 and 0.69, respectively. Pressure, wind speed and rainfall have a significant association with mortality. So that each unit decrease in pressure and wind speed, increases the rate of cardiovascular mortality by 2.79 and 15.77, respectively. It was also found that in the case of one-unit increase in rainfall, the possibility of mortality from the mentioned disease goes up by 3.8 units. It was also found that one-year increase of the age increases the mortality caused by these diseases up to 0.57 percent. Furthermore, the highest rate of cardiovascular mortality related to cold periods of the year. Therefore, considering the growing trend of air pollution and its health effects on human health, performing actions and effective solutions is important in the field of controlling and reducing air pollution in Iranian metropolis including Mashhad.
Cheng, C. M.; Peng, Z. K.; Zhang, W. M.; Meng, G.
2017-03-01
Nonlinear problems have drawn great interest and extensive attention from engineers, physicists and mathematicians and many other scientists because most real systems are inherently nonlinear in nature. To model and analyze nonlinear systems, many mathematical theories and methods have been developed, including Volterra series. In this paper, the basic definition of the Volterra series is recapitulated, together with some frequency domain concepts which are derived from the Volterra series, including the general frequency response function (GFRF), the nonlinear output frequency response function (NOFRF), output frequency response function (OFRF) and associated frequency response function (AFRF). The relationship between the Volterra series and other nonlinear system models and nonlinear problem solving methods are discussed, including the Taylor series, Wiener series, NARMAX model, Hammerstein model, Wiener model, Wiener-Hammerstein model, harmonic balance method, perturbation method and Adomian decomposition. The challenging problems and their state of arts in the series convergence study and the kernel identification study are comprehensively introduced. In addition, a detailed review is then given on the applications of Volterra series in mechanical engineering, aeroelasticity problem, control engineering, electronic and electrical engineering.
The initiative on Model Organism Proteomes (iMOP) Session
DEFF Research Database (Denmark)
Schrimpf, Sabine P; Mering, Christian von; Bendixen, Emøke
2012-01-01
iMOP – the Initiative on Model Organism Proteomes – was accepted as a new HUPO initiative at the Ninth HUPO meeting in Sydney in 2010. A goal of iMOP is to integrate research groups working on a great diversity of species into a model organism community. At the Tenth HUPO meeting in Geneva...
Competency modeling targeted on promotion of organizations towards VO involvement
Ermilova, E.; Afsarmanesh, H.
2008-01-01
During the last decades, a number of models is introduced in research, addressing different perspectives of the organizations’ competencies in collaborative networks. This paper introduces the "4C-model", developed to address competencies of organizations, involved in Virtual organizations Breeding
Analysis of hohlraum energetics of the SG series and the NIF experiments with energy balance model
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Guoli Ren
2017-01-01
Full Text Available The basic energy balance model is applied to analyze the hohlraum energetics data from the Shenguang (SG series laser facilities and the National Ignition Facility (NIF experiments published in the past few years. The analysis shows that the overall hohlraum energetics data are in agreement with the energy balance model within 20% deviation. The 20% deviation might be caused by the diversity in hohlraum parameters, such as material, laser pulse, gas filling density, etc. In addition, the NIF's ignition target designs and our ignition target designs given by simulations are also in accordance with the energy balance model. This work confirms the value of the energy balance model for ignition target design and experimental data assessment, and demonstrates that the NIF energy is enough to achieve ignition if a 1D spherical radiation drive could be created, meanwhile both the laser plasma instabilities and hydrodynamic instabilities could be suppressed.
Modeling the Explicit Chemistry of Anthropogenic and Biogenic Organic Aerosols
Energy Technology Data Exchange (ETDEWEB)
Madronich, Sasha [Univ. Corporation for Atmospheric Research, Boulder, CO (United States)
2015-12-09
The atmospheric burden of Secondary Organic Aerosols (SOA) remains one of the most important yet uncertain aspects of the radiative forcing of climate. This grant focused on improving our quantitative understanding of SOA formation and evolution, by developing, applying, and improving a highly detailed model of atmospheric organic chemistry, the Generation of Explicit Chemistry and Kinetics of Organics in the Atmosphere (GECKO-A) model. Eleven (11) publications have resulted from this grant.
Indian Academy of Sciences (India)
ensis fruit. 4. SERIES ARTICLES. Evolution of the Atmosphere and Oceans: Evidence from Geological Records. Evolution of the Early Atmosphere. P V Sukumaran. 11 Electrostatics in Chemistry. Electrostatic Models for Weak Molecular ...
Eymen, Abdurrahman; Köylü, Ümran
2018-02-01
Local climate change is determined by analysis of long-term recorded meteorological data. In the statistical analysis of the meteorological data, the Mann-Kendall rank test, which is one of the non-parametrical tests, has been used; on the other hand, for determining the power of the trend, Theil-Sen method has been used on the data obtained from 16 meteorological stations. The stations cover the provinces of Kayseri, Sivas, Yozgat, and Nevşehir in the Central Anatolia region of Turkey. Changes in land-use affect local climate. Dams are structures that cause major changes on the land. Yamula Dam is located 25 km northwest of Kayseri. The dam has huge water body which is approximately 85 km2. The mentioned tests have been used for detecting the presence of any positive or negative trend in meteorological data. The meteorological data in relation to the seasonal average, maximum, and minimum values of the relative humidity and seasonal average wind speed have been organized as time series and the tests have been conducted accordingly. As a result of these tests, the following have been identified: increase was observed in minimum relative humidity values in the spring, summer, and autumn seasons. As for the seasonal average wind speed, decrease was detected for nine stations in all seasons, whereas increase was observed in four stations. After the trend analysis, pre-dam mean relative humidity time series were modeled with Autoregressive Integrated Moving Averages (ARIMA) model which is statistical modeling tool. Post-dam relative humidity values were predicted by ARIMA models.
Visibility Modeling and Forecasting for Abu Dhabi using Time Series Analysis Method
Eibedingil, I. G.; Abula, B.; Afshari, A.; Temimi, M.
2015-12-01
Land-Atmosphere interactions-their strength, directionality and evolution-are one of the main sources of uncertainty in contemporary climate modeling. A particularly crucial role in sustaining and modulating land-atmosphere interaction is the one of aerosols and dusts. Aerosols are tiny particles suspended in the air ranging from a few nanometers to a few hundred micrometers in diameter. Furthermore, the amount of dust and fog in the atmosphere is an important measure of visibility, which is another dimension of land-atmosphere interactions. Visibility affects all form of traffic, aviation, land and sailing. Being able to predict the change of visibility in the air in advance enables relevant authorities to take necessary actions before the disaster falls. Time Series Analysis (TAS) method is an emerging technique for modeling and forecasting the behavior of land-atmosphere interactions, including visibility. This research assess the dynamics and evolution of visibility around Abu Dhabi International Airport (+24.4320 latitude, +54.6510 longitude, and 27m elevation) using mean daily visibility and mean daily wind speed. TAS has been first used to model and forecast the visibility, and then the Transfer Function Model has been applied, considering the wind speed as an exogenous variable. By considering the Akaike Information Criterion (AIC) and Mean Absolute Percentage Error (MAPE) as a statistical criteria, two forecasting models namely univarite time series model and transfer function model, were developed to forecast the visibility around Abu Dhabi International Airport for three weeks. Transfer function model improved the MAPE of the forecast significantly.
International Nuclear Information System (INIS)
Lefieux, V.
2007-10-01
Reseau de Transport d'Electricite (RTE), in charge of operating the French electric transportation grid, needs an accurate forecast of the power consumption in order to operate it correctly. The forecasts used everyday result from a model combining a nonlinear parametric regression and a SARIMA model. In order to obtain an adaptive forecasting model, nonparametric forecasting methods have already been tested without real success. In particular, it is known that a nonparametric predictor behaves badly with a great number of explanatory variables, what is commonly called the curse of dimensionality. Recently, semi parametric methods which improve the pure nonparametric approach have been proposed to estimate a regression function. Based on the concept of 'dimension reduction', one those methods (called MAVE : Moving Average -conditional- Variance Estimate) can apply to time series. We study empirically its effectiveness to predict the future values of an autoregressive time series. We then adapt this method, from a practical point of view, to forecast power consumption. We propose a partially linear semi parametric model, based on the MAVE method, which allows to take into account simultaneously the autoregressive aspect of the problem and the exogenous variables. The proposed estimation procedure is practically efficient. (author)
Energy Technology Data Exchange (ETDEWEB)
von Lilienfeld, O. Anatole [Department of Chemistry, Institute of Physical Chemistry and National Center for Computational Design and Discovery of Novel Materials, University of Basel, Basel Switzerland; Argonne Leadership Computing Facility, Argonne National Laboratory, 9700 S. Cass Avenue Lemont Illinois 60439; Ramakrishnan, Raghunathan [Department of Chemistry, Institute of Physical Chemistry and National Center for Computational Design and Discovery of Novel Materials, University of Basel, Basel Switzerland; Rupp, Matthias [Department of Chemistry, Institute of Physical Chemistry and National Center for Computational Design and Discovery of Novel Materials, University of Basel, Basel Switzerland; Knoll, Aaron [Mathematics and Computer Science Division, Argonne National Laboratory, Argonne Illinois 60439; Texas Advanced Computing Center, University of Texas, Austin Texas
2015-04-20
We introduce a fingerprint representation of molecules based on a Fourier series of atomic radial distribution functions. This fingerprint is unique (except for chirality), continuous, and differentiable with respect to atomic coordinates and nuclear charges. It is invariant with respect to translation, rotation, and nuclear permutation, and requires no preconceived knowledge about chemical bonding, topology, or electronic orbitals. As such, it meets many important criteria for a good molecular representation, suggesting its usefulness for machine learning models of molecular properties trained across chemical compound space. To assess the performance of this new descriptor, we have trained machine learning models of molecular enthalpies of atomization for training sets with up to 10 k organic molecules, drawn at random from a published set of 134 k organic molecules with an average atomization enthalpy of over 1770 kcal/mol. We validate the descriptor on all remaining molecules of the 134 k set. For a training set of 10 k molecules, the fingerprint descriptor achieves a mean absolute error of 8.0 kcal/mol. This is slightly worse than the performance attained using the Coulomb matrix, another popular alternative, reaching 6.2 kcal/mol for the same training and test sets. (c) 2015 Wiley Periodicals, Inc.
using stereochemistry models in teaching organic compounds
African Journals Online (AJOL)
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(Stereochemistry Model); the treatment had significant effect: students taught using. Stereochemistry Models ... ISSN 2227-5835. 93. Apart from the heavy conceptual demand on the memory capacity required of the ..... colors and sizes compared with the sketches on the chart that appear to be mock forms of the compounds.
Saccharomyces cerevisiae as a model organism: a comparative study.
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Hiren Karathia
Full Text Available BACKGROUND: Model organisms are used for research because they provide a framework on which to develop and optimize methods that facilitate and standardize analysis. Such organisms should be representative of the living beings for which they are to serve as proxy. However, in practice, a model organism is often selected ad hoc, and without considering its representativeness, because a systematic and rational method to include this consideration in the selection process is still lacking. METHODOLOGY/PRINCIPAL FINDINGS: In this work we propose such a method and apply it in a pilot study of strengths and limitations of Saccharomyces cerevisiae as a model organism. The method relies on the functional classification of proteins into different biological pathways and processes and on full proteome comparisons between the putative model organism and other organisms for which we would like to extrapolate results. Here we compare S. cerevisiae to 704 other organisms from various phyla. For each organism, our results identify the pathways and processes for which S. cerevisiae is predicted to be a good model to extrapolate from. We find that animals in general and Homo sapiens in particular are some of the non-fungal organisms for which S. cerevisiae is likely to be a good model in which to study a significant fraction of common biological processes. We validate our approach by correctly predicting which organisms are phenotypically more distant from S. cerevisiae with respect to several different biological processes. CONCLUSIONS/SIGNIFICANCE: The method we propose could be used to choose appropriate substitute model organisms for the study of biological processes in other species that are harder to study. For example, one could identify appropriate models to study either pathologies in humans or specific biological processes in species with a long development time, such as plants.
Fractality of profit landscapes and validation of time series models for stock prices
Yi, Il Gu; Oh, Gabjin; Kim, Beom Jun
2013-08-01
We apply a simple trading strategy for various time series of real and artificial stock prices to understand the origin of fractality observed in the resulting profit landscapes. The strategy contains only two parameters p and q, and the sell (buy) decision is made when the log return is larger (smaller) than p (-q). We discretize the unit square (p,q) ∈ [0,1] × [0,1] into the N × N square grid and the profit Π(p,q) is calculated at the center of each cell. We confirm the previous finding that local maxima in profit landscapes are scattered in a fractal-like fashion: the number M of local maxima follows the power-law form M ˜ Na, but the scaling exponent a is found to differ for different time series. From comparisons of real and artificial stock prices, we find that the fat-tailed return distribution is closely related to the exponent a ≈ 1.6 observed for real stock markets. We suggest that the fractality of profit landscape characterized by a ≈ 1.6 can be a useful measure to validate time series model for stock prices.
FY 2016 Status Report on the Modeling of the M8 Calibration Series using MAMMOTH
Energy Technology Data Exchange (ETDEWEB)
Baker, Benjamin Allen [Idaho National Lab. (INL), Idaho Falls, ID (United States); Ortensi, Javier [Idaho National Lab. (INL), Idaho Falls, ID (United States); DeHart, Mark David [Idaho National Lab. (INL), Idaho Falls, ID (United States)
2016-09-01
This report provides a summary of the progress made towards validating the multi-physics reactor analysis application MAMMOTH using data from measurements performed at the Transient Reactor Test facility, TREAT. The work completed consists of a series of comparisons of TREAT element types (standard and control rod assemblies) in small geometries as well as slotted mini-cores to reference Monte Carlo simulations to ascertain the accuracy of cross section preparation techniques. After the successful completion of these smaller problems, a full core model of the half slotted core used in the M8 Calibration series was assembled. Full core MAMMOTH simulations were compared to Serpent reference calculations to assess the cross section preparation process for this larger configuration. As part of the validation process the M8 Calibration series included a steady state wire irradiation experiment and coupling factors for the experiment region. The shape of the power distribution obtained from the MAMMOTH simulation shows excellent agreement with the experiment. Larger differences were encountered in the calculation of the coupling factors, but there is also great uncertainty on how the experimental values were obtained. Future work will focus on resolving some of these differences.
MODEL OF LEARNING ORGANIZATION IN BROADCASTING ORGANIZATION OF ISLAMIC REPUBLIC OF IRAN
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Reza Najafbagy
2010-11-01
Full Text Available This article tries to present a model of learning organization for Iran Broadcasting Organization which is under the management of the spiritual leader of Iran. The study is based on characteristics of Peter Senge’s original learning organization namely, personal stery, mental models, shared vision, team learning and systems thinking. The methodology was a survey research employed questionnaire among sample employees and managers of the Organization.Findings showed that the Organization is fairly far from an ffective learning organization.Moreover, it seems that employees’ performance in team learning and changes in mental models are more satisfactory than managers. Regarding other characteristics of learning organizations, there are similarities in learning attempts by employees and managers. The rganization lacks organizational vision, and consequently there is no shared vision in the Organization. It also is in need of organizational culture. As a kind of state-owned organization, there s no need of financial support which affect the need for learning organization. It also does not face the threat of sustainabilitybecause there is no competitive organization.Findings also show that IBO need a fundamental change in its rganizational learning process. In this context, the general idea is to unfreeze the mindset of leadership of IBO and creating a visionand organizational culture based on learning and staff development. Then gradually through incremental effective change and continual organizational learning process in dividual, team and organization levels engage in development and reinforcement of skills of personal mastery, mental models, shared vision, team learning and systems thinking, should lead IBO to learning organization.
Dynamics modeling for sugar cane sucrose estimation using time series satellite imagery
Zhao, Yu; Justina, Diego Della; Kazama, Yoriko; Rocha, Jansle Vieira; Graziano, Paulo Sergio; Lamparelli, Rubens Augusto Camargo
2016-10-01
Sugarcane, as one of the most mainstay crop in Brazil, plays an essential role in ethanol production. To monitor sugarcane crop growth and predict sugarcane sucrose content, remote sensing technology plays an essential role while accurate and timely crop growth information is significant, in particularly for large scale farming. We focused on the issues of sugarcane sucrose content estimation using time-series satellite image. Firstly, we calculated the spectral features and vegetation indices to make them be correspondence to the sucrose accumulation biological mechanism. Secondly, we improved the statistical regression model considering more other factors. The evaluation was performed and we got precision of 90% which is about 20% higher than the conventional method. The validation results showed that prediction accuracy using our sugarcane growth modeling and improved mix model is satisfied.
Non-stationary time series modeling on caterpillars pest of palm oil for early warning system
Setiyowati, Susi; Nugraha, Rida F.; Mukhaiyar, Utriweni
2015-12-01
The oil palm production has an important role for the plantation and economic sector in Indonesia. One of the important problems in the cultivation of oil palm plantation is pests which causes damage to the quality of fruits. The caterpillar pest which feed palm tree's leaves will cause decline in quality of palm oil production. Early warning system is needed to minimize losses due to this pest. Here, we applied non-stationary time series modeling, especially the family of autoregressive models to predict the number of pests based on its historical data. We realized that there is some uniqueness of these pests data, i.e. the spike value that occur almost periodically. Through some simulations and case study, we obtain that the selection of constant factor has a significance influence to the model so that it can shoot the spikes value precisely.
Kane, Michael J; Price, Natalie; Scotch, Matthew; Rabinowitz, Peter
2014-08-13
Time series models can play an important role in disease prediction. Incidence data can be used to predict the future occurrence of disease events. Developments in modeling approaches provide an opportunity to compare different time series models for predictive power. We applied ARIMA and Random Forest time series models to incidence data of outbreaks of highly pathogenic avian influenza (H5N1) in Egypt, available through the online EMPRES-I system. We found that the Random Forest model outperformed the ARIMA model in predictive ability. Furthermore, we found that the Random Forest model is effective for predicting outbreaks of H5N1 in Egypt. Random Forest time series modeling provides enhanced predictive ability over existing time series models for the prediction of infectious disease outbreaks. This result, along with those showing the concordance between bird and human outbreaks (Rabinowitz et al. 2012), provides a new approach to predicting these dangerous outbreaks in bird populations based on existing, freely available data. Our analysis uncovers the time-series structure of outbreak severity for highly pathogenic avain influenza (H5N1) in Egypt.
A closed-loop power controller model of series-resonant-inverter-fitted induction heating system
Directory of Open Access Journals (Sweden)
Pal Palash
2016-12-01
Full Text Available This paper presents a mathematical model of a power controller for a high-frequency induction heating system based on a modified half-bridge series resonant inverter. The output real power is precise over the heating coil, and this real power is processed as a feedback signal that contends a closed-loop topology with a proportional-integral-derivative controller. This technique enables both control of the closed-loop power and determination of the stability of the high-frequency inverter. Unlike the topologies of existing power controllers, the proposed topology enables direct control of the real power of the high-frequency inverter.
2010-09-30
Hyperfast Modeling of Nonlinear Ocean Waves A. R. Osborne Dipartimento di Fisica Generale, Università di Torino Via Pietro Giuria 1, 10125...PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES) Universit?i Torino,Dipartimento di Fisica Generale,Via Pietro Giuria 1,10125 Torino, Italy, 8. PERFORMING
Multivariate time series modeling of short-term system scale irrigation demand
Perera, Kushan C.; Western, Andrew W.; George, Biju; Nawarathna, Bandara
2015-12-01
Travel time limits the ability of irrigation system operators to react to short-term irrigation demand fluctuations that result from variations in weather, including very hot periods and rainfall events, as well as the various other pressures and opportunities that farmers face. Short-term system-wide irrigation demand forecasts can assist in system operation. Here we developed a multivariate time series (ARMAX) model to forecast irrigation demands with respect to aggregated service points flows (IDCGi, ASP) and off take regulator flows (IDCGi, OTR) based across 5 command areas, which included area covered under four irrigation channels and the study area. These command area specific ARMAX models forecast 1-5 days ahead daily IDCGi, ASP and IDCGi, OTR using the real time flow data recorded at the service points and the uppermost regulators and observed meteorological data collected from automatic weather stations. The model efficiency and the predictive performance were quantified using the root mean squared error (RMSE), Nash-Sutcliffe model efficiency coefficient (NSE), anomaly correlation coefficient (ACC) and mean square skill score (MSSS). During the evaluation period, NSE for IDCGi, ASP and IDCGi, OTR across 5 command areas were ranged 0.98-0.78. These models were capable of generating skillful forecasts (MSSS ⩾ 0.5 and ACC ⩾ 0.6) of IDCGi, ASP and IDCGi, OTR for all 5 lead days and IDCGi, ASP and IDCGi, OTR forecasts were better than using the long term monthly mean irrigation demand. Overall these predictive performance from the ARMAX time series models were higher than almost all the previous studies we are aware. Further, IDCGi, ASP and IDCGi, OTR forecasts have improved the operators' ability to react for near future irrigation demand fluctuations as the developed ARMAX time series models were self-adaptive to reflect the short-term changes in the irrigation demand with respect to various pressures and opportunities that farmers' face, such as
Modeling commodity salam contract between two parties for discrete and continuous time series
Hisham, Azie Farhani Badrol; Jaffar, Maheran Mohd
2017-08-01
In order for Islamic finance to remain competitive as the conventional, there needs a new development of Islamic compliance product such as Islamic derivative that can be used to manage the risk. However, under syariah principles and regulations, all financial instruments must not be conflicting with five syariah elements which are riba (interest paid), rishwah (corruption), gharar (uncertainty or unnecessary risk), maysir (speculation or gambling) and jahl (taking advantage of the counterparty's ignorance). This study has proposed a traditional Islamic contract namely salam that can be built as an Islamic derivative product. Although a lot of studies has been done on discussing and proposing the implementation of salam contract as the Islamic product however they are more into qualitative and law issues. Since there is lack of quantitative study of salam contract being developed, this study introduces mathematical models that can value the appropriate salam price for a commodity salam contract between two parties. In modeling the commodity salam contract, this study has modified the existing conventional derivative model and come out with some adjustments to comply with syariah rules and regulations. The cost of carry model has been chosen as the foundation to develop the commodity salam model between two parties for discrete and continuous time series. However, the conventional time value of money results from the concept of interest that is prohibited in Islam. Therefore, this study has adopted the idea of Islamic time value of money which is known as the positive time preference, in modeling the commodity salam contract between two parties for discrete and continuous time series.
Directory of Open Access Journals (Sweden)
Lukas Falat
2014-01-01
Full Text Available In this paper, authors apply feed-forward artificial neural network (ANN of RBF type into the process of modelling and forecasting the future value of USD/CAD time series. Authors test the customized version of the RBF and add the evolutionary approach into it. They also combine the standard algorithm for adapting weights in neural network with an unsupervised clustering algorithm called K-means. Finally, authors suggest the new hybrid model as a combination of a standard ANN and a moving average for error modeling that is used to enhance the outputs of the network using the error part of the original RBF. Using high-frequency data, they examine the ability to forecast exchange rate values for the horizon of one day. To determine the forecasting efficiency, authors perform the comparative out-of-sample analysis of the suggested hybrid model with statistical models and the standard neural network.
DEFF Research Database (Denmark)
Finlay, Chris; Olsen, Nils; Tøffner-Clausen, Lars
Ten months of data from ESA's Swarm mission, together with recent ground observatory monthly means, are used to update the CHAOS series of geomagnetic field models with a focus on time-changes of the core field. As for previous CHAOS field models quiet-time, night-side, data selection criteria......th order spline representation with knot points spaced at 0.5 year intervals. The resulting field model is able to consistently fit data from six independent low Earth orbit satellites: Oersted, CHAMP, SAC-C and the three Swarm satellites. As an example, we present comparisons of the excellent model...... fit obtained to both the Swarm data and the CHAMP data. The new model also provides a good description of observatory secular variation, capturing rapid field evolution events during the past decade. Maps of the core surface field and its secular variation can already be extracted in the Swarm-era. We...
Poole, Sandra; Vis, Marc; Knight, Rodney; Seibert, Jan
2017-01-01
Ecologically relevant streamflow characteristics (SFCs) of ungauged catchments are often estimated from simulated runoff of hydrologic models that were originally calibrated on gauged catchments. However, SFC estimates of the gauged donor catchments and subsequently the ungauged catchments can be substantially uncertain when models are calibrated using traditional approaches based on optimization of statistical performance metrics (e.g., Nash–Sutcliffe model efficiency). An improved calibration strategy for gauged catchments is therefore crucial to help reduce the uncertainties of estimated SFCs for ungauged catchments. The aim of this study was to improve SFC estimates from modeled runoff time series in gauged catchments by explicitly including one or several SFCs in the calibration process. Different types of objective functions were defined consisting of the Nash–Sutcliffe model efficiency, single SFCs, or combinations thereof. We calibrated a bucket-type runoff model (HBV – Hydrologiska Byråns Vattenavdelning – model) for 25 catchments in the Tennessee River basin and evaluated the proposed calibration approach on 13 ecologically relevant SFCs representing major flow regime components and different flow conditions. While the model generally tended to underestimate the tested SFCs related to mean and high-flow conditions, SFCs related to low flow were generally overestimated. The highest estimation accuracies were achieved by a SFC-specific model calibration. Estimates of SFCs not included in the calibration process were of similar quality when comparing a multi-SFC calibration approach to a traditional model efficiency calibration. For practical applications, this implies that SFCs should preferably be estimated from targeted runoff model calibration, and modeled estimates need to be carefully interpreted.
Pool, Sandra; Vis, Marc J. P.; Knight, Rodney R.; Seibert, Jan
2017-11-01
Ecologically relevant streamflow characteristics (SFCs) of ungauged catchments are often estimated from simulated runoff of hydrologic models that were originally calibrated on gauged catchments. However, SFC estimates of the gauged donor catchments and subsequently the ungauged catchments can be substantially uncertain when models are calibrated using traditional approaches based on optimization of statistical performance metrics (e.g., Nash-Sutcliffe model efficiency). An improved calibration strategy for gauged catchments is therefore crucial to help reduce the uncertainties of estimated SFCs for ungauged catchments. The aim of this study was to improve SFC estimates from modeled runoff time series in gauged catchments by explicitly including one or several SFCs in the calibration process. Different types of objective functions were defined consisting of the Nash-Sutcliffe model efficiency, single SFCs, or combinations thereof. We calibrated a bucket-type runoff model (HBV - Hydrologiska Byråns Vattenavdelning - model) for 25 catchments in the Tennessee River basin and evaluated the proposed calibration approach on 13 ecologically relevant SFCs representing major flow regime components and different flow conditions. While the model generally tended to underestimate the tested SFCs related to mean and high-flow conditions, SFCs related to low flow were generally overestimated. The highest estimation accuracies were achieved by a SFC-specific model calibration. Estimates of SFCs not included in the calibration process were of similar quality when comparing a multi-SFC calibration approach to a traditional model efficiency calibration. For practical applications, this implies that SFCs should preferably be estimated from targeted runoff model calibration, and modeled estimates need to be carefully interpreted.
CONTRIBUTIONS TO THE DEVELOPMENT OF A MODEL OF ECO TECHNOLOGIC ORGANIZATION
Directory of Open Access Journals (Sweden)
Dan DOBROTĂ
2012-05-01
Full Text Available The paper present a series of contributions to the development of a model of eco technologic organization. Managers of various organizations generally recognized the need for change, as a way to cope with competitive pressures, but many do not understand how the change should be implemented. The key to success is to integrate employees, their roles and responsibilities within the organization in a structure of processes. A process-based approach and starting with the declaration of vision and mission, analyzing critical success factors and identifying the basic processes, it is the most effective way of employment of staff in the process of change In these conditions paper addresses notions of implementation of the change in the industrial organizations: organizational change process, consequences of ignoring the change, internal and external factors of change, actions needing change
Directory of Open Access Journals (Sweden)
S. M. Barbosa
2006-01-01
Full Text Available This work addresses the autoregressive modelling of sea level time series from TOPEX/Poseidon satellite altimetry mission. Datasets from remote sensing applications are typically very large and correlated both in time and space. Multivariate analysis methods are useful tools to summarise and extract information from such large space-time datasets. Multivariate autoregressive analysis is a generalisation of Principal Oscillation Pattern (POP analysis, widely used in the geosciences for the extraction of dynamical modes by eigen-decomposition of a first order autoregressive model fitted to the multivariate dataset of observations. The extension of the POP methodology to autoregressions of higher order, although increasing the difficulties in estimation, allows one to model a larger class of complex systems. Here, sea level variability in the North Atlantic is modelled by a third order multivariate autoregressive model estimated by stepwise least squares. Eigen-decomposition of the fitted model yields physically-interpretable seasonal modes. The leading autoregressive mode is an annual oscillation and exhibits a very homogeneous spatial structure in terms of amplitude reflecting the large scale coherent behaviour of the annual pattern in the Northern hemisphere. The phase structure reflects the seesaw pattern between the western and eastern regions in the tropical North Atlantic associated with the trade winds regime. The second mode is close to a semi-annual oscillation. Multivariate autoregressive models provide a useful framework for the description of time-varying fields while enclosing a predictive potential.
Modeling the influence of organic acids on soil weathering
Lawrence, Corey R.; Harden, Jennifer W.; Maher, Kate
2014-01-01
Biological inputs and organic matter cycling have long been regarded as important factors in the physical and chemical development of soils. In particular, the extent to which low molecular weight organic acids, such as oxalate, influence geochemical reactions has been widely studied. Although the effects of organic acids are diverse, there is strong evidence that organic acids accelerate the dissolution of some minerals. However, the influence of organic acids at the field-scale and over the timescales of soil development has not been evaluated in detail. In this study, a reactive-transport model of soil chemical weathering and pedogenic development was used to quantify the extent to which organic acid cycling controls mineral dissolution rates and long-term patterns of chemical weathering. Specifically, oxalic acid was added to simulations of soil development to investigate a well-studied chronosequence of soils near Santa Cruz, CA. The model formulation includes organic acid input, transport, decomposition, organic-metal aqueous complexation and mineral surface complexation in various combinations. Results suggest that although organic acid reactions accelerate mineral dissolution rates near the soil surface, the net response is an overall decrease in chemical weathering. Model results demonstrate the importance of organic acid input concentrations, fluid flow, decomposition and secondary mineral precipitation rates on the evolution of mineral weathering fronts. In particular, model soil profile evolution is sensitive to kaolinite precipitation and oxalate decomposition rates. The soil profile-scale modeling presented here provides insights into the influence of organic carbon cycling on soil weathering and pedogenesis and supports the need for further field-scale measurements of the flux and speciation of reactive organic compounds.
Daphnia as an Emerging Epigenetic Model Organism
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Kami D. M. Harris
2012-01-01
Full Text Available Daphnia offer a variety of benefits for the study of epigenetics. Daphnia’s parthenogenetic life cycle allows the study of epigenetic effects in the absence of confounding genetic differences. Sex determination and sexual reproduction are epigenetically determined as are several other well-studied alternate phenotypes that arise in response to environmental stressors. Additionally, there is a large body of ecological literature available, recently complemented by the genome sequence of one species and transgenic technology. DNA methylation has been shown to be altered in response to toxicants and heavy metals, although investigation of other epigenetic mechanisms is only beginning. More thorough studies on DNA methylation as well as investigation of histone modifications and RNAi in sex determination and predator-induced defenses using this ecologically and evolutionarily important organism will contribute to our understanding of epigenetics.
Porta, Alberto; Bari, Vlasta; Ranuzzi, Giovanni; De Maria, Beatrice; Baselli, Giuseppe
2017-09-01
We propose a multiscale complexity (MSC) method assessing irregularity in assigned frequency bands and being appropriate for analyzing the short time series. It is grounded on the identification of the coefficients of an autoregressive model, on the computation of the mean position of the poles generating the components of the power spectral density in an assigned frequency band, and on the assessment of its distance from the unit circle in the complex plane. The MSC method was tested on simulations and applied to the short heart period (HP) variability series recorded during graded head-up tilt in 17 subjects (age from 21 to 54 years, median = 28 years, 7 females) and during paced breathing protocols in 19 subjects (age from 27 to 35 years, median = 31 years, 11 females) to assess the contribution of time scales typical of the cardiac autonomic control, namely in low frequency (LF, from 0.04 to 0.15 Hz) and high frequency (HF, from 0.15 to 0.5 Hz) bands to the complexity of the cardiac regulation. The proposed MSC technique was compared to a traditional model-free multiscale method grounded on information theory, i.e., multiscale entropy (MSE). The approach suggests that the reduction of HP variability complexity observed during graded head-up tilt is due to a regularization of the HP fluctuations in LF band via a possible intervention of sympathetic control and the decrement of HP variability complexity observed during slow breathing is the result of the regularization of the HP variations in both LF and HF bands, thus implying the action of physiological mechanisms working at time scales even different from that of respiration. MSE did not distinguish experimental conditions at time scales larger than 1. Over a short time series MSC allows a more insightful association between cardiac control complexity and physiological mechanisms modulating cardiac rhythm compared to a more traditional tool such as MSE.
Evaluation of a Global Vegetation Model using time series of satellite vegetation indices
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F. Maignan
2011-12-01
Full Text Available Atmospheric CO_{2} drives most of the greenhouse effect increase. One major uncertainty on the future rate of increase of CO_{2} in the atmosphere is the impact of the anticipated climate change on the vegetation. Dynamic Global Vegetation Models (DGVM are used to address this question. ORCHIDEE is such a DGVM that has proven useful for climate change studies. However, there is no objective and methodological way to accurately assess each new available version on the global scale. In this paper, we submit a methodological evaluation of ORCHIDEE by correlating satellite-derived Vegetation Index time series against those of the modeled Fraction of absorbed Photosynthetically Active Radiation (FPAR. A perfect correlation between the two is not expected, however an improvement of the model should lead to an increase of the overall performance.
We detail two case studies in which model improvements are demonstrated, using our methodology. In the first one, a new phenology version in ORCHIDEE is shown to bring a significant impact on the simulated annual cycles, in particular for C3 Grasses and C3 Crops. In the second case study, we compare the simulations when using two different weather fields to drive ORCHIDEE. The ERA-Interim forcing leads to a better description of the FPAR interannual anomalies than the simulation forced by a mixed CRU-NCEP dataset. This work shows that long time series of satellite observations, despite their uncertainties, can identify weaknesses in global vegetation models, a necessary first step to improving them.
Bodmer, James E; English, Anthony; Brady, Megan; Blackwell, Ken; Haxhinasto, Kari; Fotedar, Sunaina; Borgman, Kurt; Bai, Er-Wei; Moy, Alan B
2005-09-01
Transendothelial impedance across an endothelial monolayer grown on a microelectrode has previously been modeled as a repeating pattern of disks in which the electrical circuit consists of a resistor and capacitor in series. Although this numerical model breaks down barrier function into measurements of cell-cell adhesion, cell-matrix adhesion, and membrane capacitance, such solution parameters can be inaccurate without understanding model stability and error. In this study, we have evaluated modeling stability and error by using a chi(2) evaluation and Levenberg-Marquardt nonlinear least-squares (LM-NLS) method of the real and/or imaginary data in which the experimental measurement is compared with the calculated measurement derived by the model. Modeling stability and error were dependent on current frequency and the type of experimental data modeled. Solution parameters of cell-matrix adhesion were most susceptible to modeling instability. Furthermore, the LM-NLS method displayed frequency-dependent instability of the solution parameters, regardless of whether the real or imaginary data were analyzed. However, the LM-NLS method identified stable and reproducible solution parameters between all types of experimental data when a defined frequency spectrum of the entire data set was selected on the basis of a criterion of minimizing error. The frequency bandwidth that produced stable solution parameters varied greatly among different data types. Thus a numerical model based on characterizing transendothelial impedance as a resistor and capacitor in series and as a repeating pattern of disks is not sufficient to characterize the entire frequency spectrum of experimental transendothelial impedance.
Stochastic modeling for time series InSAR: with emphasis on atmospheric effects
Cao, Yunmeng; Li, Zhiwei; Wei, Jianchao; Hu, Jun; Duan, Meng; Feng, Guangcai
2018-02-01
Despite the many applications of time series interferometric synthetic aperture radar (TS-InSAR) techniques in geophysical problems, error analysis and assessment have been largely overlooked. Tropospheric propagation error is still the dominant error source of InSAR observations. However, the spatiotemporal variation of atmospheric effects is seldom considered in the present standard TS-InSAR techniques, such as persistent scatterer interferometry and small baseline subset interferometry. The failure to consider the stochastic properties of atmospheric effects not only affects the accuracy of the estimators, but also makes it difficult to assess the uncertainty of the final geophysical results. To address this issue, this paper proposes a network-based variance-covariance estimation method to model the spatiotemporal variation of tropospheric signals, and to estimate the temporal variance-covariance matrix of TS-InSAR observations. The constructed stochastic model is then incorporated into the TS-InSAR estimators both for parameters (e.g., deformation velocity, topography residual) estimation and uncertainty assessment. It is an incremental and positive improvement to the traditional weighted least squares methods to solve the multitemporal InSAR time series. The performance of the proposed method is validated by using both simulated and real datasets.
A Virtual Machine Migration Strategy Based on Time Series Workload Prediction Using Cloud Model
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Yanbing Liu
2014-01-01
Full Text Available Aimed at resolving the issues of the imbalance of resources and workloads at data centers and the overhead together with the high cost of virtual machine (VM migrations, this paper proposes a new VM migration strategy which is based on the cloud model time series workload prediction algorithm. By setting the upper and lower workload bounds for host machines, forecasting the tendency of their subsequent workloads by creating a workload time series using the cloud model, and stipulating a general VM migration criterion workload-aware migration (WAM, the proposed strategy selects a source host machine, a destination host machine, and a VM on the source host machine carrying out the task of the VM migration. Experimental results and analyses show, through comparison with other peer research works, that the proposed method can effectively avoid VM migrations caused by momentary peak workload values, significantly lower the number of VM migrations, and dynamically reach and maintain a resource and workload balance for virtual machines promoting an improved utilization of resources in the entire data center.
Cooling load calculation by the radiant time series method - effect of solar radiation models
Energy Technology Data Exchange (ETDEWEB)
Costa, Alexandre M.S. [Universidade Estadual de Maringa (UEM), PR (Brazil)], E-mail: amscosta@uem.br
2010-07-01
In this work was analyzed numerically the effect of three different models for solar radiation on the cooling load calculated by the radiant time series' method. The solar radiation models implemented were clear sky, isotropic sky and anisotropic sky. The radiant time series' method (RTS) was proposed by ASHRAE (2001) for replacing the classical methods of cooling load calculation, such as TETD/TA. The method is based on computing the effect of space thermal energy storage on the instantaneous cooling load. The computing is carried out by splitting the heat gain components in convective and radiant parts. Following the radiant part is transformed using time series, which coefficients are a function of the construction type and heat gain (solar or non-solar). The transformed result is added to the convective part, giving the instantaneous cooling load. The method was applied for investigate the influence for an example room. The location used was - 23 degree S and 51 degree W and the day was 21 of January, a typical summer day in the southern hemisphere. The room was composed of two vertical walls with windows exposed to outdoors with azimuth angles equals to west and east directions. The output of the different models of solar radiation for the two walls in terms of direct and diffuse components as well heat gains were investigated. It was verified that the clear sky exhibited the less conservative (higher values) for the direct component of solar radiation, with the opposite trend for the diffuse component. For the heat gain, the clear sky gives the higher values, three times higher for the peek hours than the other models. Both isotropic and anisotropic models predicted similar magnitude for the heat gain. The same behavior was also verified for the cooling load. The effect of room thermal inertia was decreasing the cooling load during the peak hours. On the other hand the higher thermal inertia values are the greater for the non peak hours. The effect
Guarnaccia, Claudio; Quartieri, Joseph; Tepedino, Carmine
2017-06-01
One of the most hazardous physical polluting agents, considering their effects on human health, is acoustical noise. Airports are a strong source of acoustical noise, due to the airplanes turbines, to the aero-dynamical noise of transits, to the acceleration or the breaking during the take-off and landing phases of aircrafts, to the road traffic around the airport, etc.. The monitoring and the prediction of the acoustical level emitted by airports can be very useful to assess the impact on human health and activities. In the airports noise scenario, thanks to flights scheduling, the predominant sources may have a periodic behaviour. Thus, a Time Series Analysis approach can be adopted, considering that a general trend and a seasonal behaviour can be highlighted and used to build a predictive model. In this paper, two different approaches are adopted, thus two predictive models are constructed and tested. The first model is based on deterministic decomposition and is built composing the trend, that is the long term behaviour, the seasonality, that is the periodic component, and the random variations. The second model is based on seasonal autoregressive moving average, and it belongs to the stochastic class of models. The two different models are fitted on an acoustical level dataset collected close to the Nice (France) international airport. Results will be encouraging and will show good prediction performances of both the adopted strategies. A residual analysis is performed, in order to quantify the forecasting error features.
Scaling symmetry, renormalization, and time series modeling: the case of financial assets dynamics.
Zamparo, Marco; Baldovin, Fulvio; Caraglio, Michele; Stella, Attilio L
2013-12-01
We present and discuss a stochastic model of financial assets dynamics based on the idea of an inverse renormalization group strategy. With this strategy we construct the multivariate distributions of elementary returns based on the scaling with time of the probability density of their aggregates. In its simplest version the model is the product of an endogenous autoregressive component and a random rescaling factor designed to embody also exogenous influences. Mathematical properties like increments' stationarity and ergodicity can be proven. Thanks to the relatively low number of parameters, model calibration can be conveniently based on a method of moments, as exemplified in the case of historical data of the S&P500 index. The calibrated model accounts very well for many stylized facts, like volatility clustering, power-law decay of the volatility autocorrelation function, and multiscaling with time of the aggregated return distribution. In agreement with empirical evidence in finance, the dynamics is not invariant under time reversal, and, with suitable generalizations, skewness of the return distribution and leverage effects can be included. The analytical tractability of the model opens interesting perspectives for applications, for instance, in terms of obtaining closed formulas for derivative pricing. Further important features are the possibility of making contact, in certain limits, with autoregressive models widely used in finance and the possibility of partially resolving the long- and short-memory components of the volatility, with consistent results when applied to historical series.
A Long-Term Prediction Model of Beijing Haze Episodes Using Time Series Analysis
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Xiaoping Yang
2016-01-01
Full Text Available The rapid industrial development has led to the intermittent outbreak of pm2.5 or haze in developing countries, which has brought about great environmental issues, especially in big cities such as Beijing and New Delhi. We investigated the factors and mechanisms of haze change and present a long-term prediction model of Beijing haze episodes using time series analysis. We construct a dynamic structural measurement model of daily haze increment and reduce the model to a vector autoregressive model. Typical case studies on 886 continuous days indicate that our model performs very well on next day’s Air Quality Index (AQI prediction, and in severely polluted cases (AQI ≥ 300 the accuracy rate of AQI prediction even reaches up to 87.8%. The experiment of one-week prediction shows that our model has excellent sensitivity when a sudden haze burst or dissipation happens, which results in good long-term stability on the accuracy of the next 3–7 days’ AQI prediction.
Personality organization, five-factor model, and mental health.
Laverdière, Olivier; Gamache, Dominick; Diguer, Louis; Hébert, Etienne; Larochelle, Sébastien; Descôteaux, Jean
2007-10-01
Otto Kernberg has developed a model of personality and psychological functioning centered on the concept of personality organization. The purpose of this study is to empirically examine the relationships between this model, the five-factor model, and mental health. The Personality Organization Diagnostic Form (Diguer et al., The Personality Organization Diagnostic Form-II (PODF-II), 2001), the NEO Five-Factor Inventory (Costa and McCrae, Revised NEO Personality Inventory (NEO-PI-R) and NEO Five-Factor Inventory (NEO-FFI) Professional Manual. 1992a), and the Health-Sickness Rating Scale (Luborsky, Arch Gen Psychiatry. 1962;7:407-417) were used to assess these constructs. Results show that personality organization and personality factors are distinct but interrelated constructs and that both contribute in similar proportion to mental health. Results also suggest that the integration of personality organization and factors can provide clinicians and researchers with an enriched understanding of psychological functioning.
DEFF Research Database (Denmark)
Madsen, Henrik; Rosbjerg, Dan
1997-01-01
A regional estimation procedure that combines the index-flood concept with an empirical Bayes method for inferring regional information is introduced. The model is based on the partial duration series approach with generalized Pareto (GP) distributed exceedances. The prior information of the model...... parameters is inferred from regional data using generalized least squares (GLS) regression. Two different Bayesian T-year event estimators are introduced: a linear estimator that requires only some moments of the prior distributions to be specified and a parametric estimator that is based on specified...... families of prior distributions. The regional method is applied to flood records from 48 New Zealand catchments. In the case of a strongly heterogeneous intersite correlation structure, the GLS procedure provides a more efficient estimate of the regional GP shape parameter as compared to the usually...
An Application of the Coherent Noise Model for the Prediction of Aftershock Magnitude Time Series
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Stavros-Richard G. Christopoulos
2017-01-01
Full Text Available Recently, the study of the coherent noise model has led to a simple (binary prediction algorithm for the forthcoming earthquake magnitude in aftershock sequences. This algorithm is based on the concept of natural time and exploits the complexity exhibited by the coherent noise model. Here, using the relocated catalogue from Southern California Seismic Network for 1981 to June 2011, we evaluate the application of this algorithm for the aftershocks of strong earthquakes of magnitude M≥6. The study is also extended by using the Global Centroid Moment Tensor Project catalogue to the case of the six strongest earthquakes in the Earth during the last almost forty years. The predictor time series exhibits the ubiquitous 1/f noise behavior.
Time Series Neural Network Model for Part-of-Speech Tagging Indonesian Language
Tanadi, Theo
2018-03-01
Part-of-speech tagging (POS tagging) is an important part in natural language processing. Many methods have been used to do this task, including neural network. This paper models a neural network that attempts to do POS tagging. A time series neural network is modelled to solve the problems that a basic neural network faces when attempting to do POS tagging. In order to enable the neural network to have text data input, the text data will get clustered first using Brown Clustering, resulting a binary dictionary that the neural network can use. To further the accuracy of the neural network, other features such as the POS tag, suffix, and affix of previous words would also be fed to the neural network.
Designing a Composite Service Organization (Through Mathematical Modeling
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Prof. Dr. A. Z. Memon
2006-01-01
Full Text Available Suppose we have a class of similar service organizations each of which is characterized by the same numerically measurable input/output characteristics. Even if the amount of any input does not differ in them, one or more organizations can be expected to outperform the others in one or more production aspects. Our interest lies in comparing the output efficiency levels of all service organizations. For it we use mathematical modeling, mainly linear programming to design a composite organization with new input measures which relative to a specific organization should have a higher level of efficiency with regard to all output measures. The other purpose of this paper is to evaluate the output characteristics of this proposed service organization. The paper also touches some other highly important planning features of this organization.
Stochastic models in the DORIS position time series: estimates for IDS contribution to ITRF2014
Klos, Anna; Bogusz, Janusz; Moreaux, Guilhem
2017-11-01
This paper focuses on the investigation of the deterministic and stochastic parts of the Doppler Orbitography and Radiopositioning Integrated by Satellite (DORIS) weekly time series aligned to the newest release of ITRF2014. A set of 90 stations was divided into three groups depending on when the data were collected at an individual station. To reliably describe the DORIS time series, we employed a mathematical model that included the long-term nonlinear signal, linear trend, seasonal oscillations and a stochastic part, all being estimated with maximum likelihood estimation. We proved that the values of the parameters delivered for DORIS data are strictly correlated with the time span of the observations. The quality of the most recent data has significantly improved. Not only did the seasonal amplitudes decrease over the years, but also, and most importantly, the noise level and its type changed significantly. Among several tested models, the power-law process may be chosen as the preferred one for most of the DORIS data. Moreover, the preferred noise model has changed through the years from an autoregressive process to pure power-law noise with few stations characterised by a positive spectral index. For the latest observations, the medians of the velocity errors were equal to 0.3, 0.3 and 0.4 mm/year, respectively, for the North, East and Up components. In the best cases, a velocity uncertainty of DORIS sites of 0.1 mm/year is achievable when the appropriate coloured noise model is taken into consideration.
Inference of quantitative models of bacterial promoters from time-series reporter gene data.
Stefan, Diana; Pinel, Corinne; Pinhal, Stéphane; Cinquemani, Eugenio; Geiselmann, Johannes; de Jong, Hidde
2015-01-01
The inference of regulatory interactions and quantitative models of gene regulation from time-series transcriptomics data has been extensively studied and applied to a range of problems in drug discovery, cancer research, and biotechnology. The application of existing methods is commonly based on implicit assumptions on the biological processes under study. First, the measurements of mRNA abundance obtained in transcriptomics experiments are taken to be representative of protein concentrations. Second, the observed changes in gene expression are assumed to be solely due to transcription factors and other specific regulators, while changes in the activity of the gene expression machinery and other global physiological effects are neglected. While convenient in practice, these assumptions are often not valid and bias the reverse engineering process. Here we systematically investigate, using a combination of models and experiments, the importance of this bias and possible corrections. We measure in real time and in vivo the activity of genes involved in the FliA-FlgM module of the E. coli motility network. From these data, we estimate protein concentrations and global physiological effects by means of kinetic models of gene expression. Our results indicate that correcting for the bias of commonly-made assumptions improves the quality of the models inferred from the data. Moreover, we show by simulation that these improvements are expected to be even stronger for systems in which protein concentrations have longer half-lives and the activity of the gene expression machinery varies more strongly across conditions than in the FliA-FlgM module. The approach proposed in this study is broadly applicable when using time-series transcriptome data to learn about the structure and dynamics of regulatory networks. In the case of the FliA-FlgM module, our results demonstrate the importance of global physiological effects and the active regulation of FliA and FlgM half-lives for
(Tropical) soil organic matter modelling: problems and prospects
Keulen, van H.
2001-01-01
Soil organic matter plays an important role in many physical, chemical and biological processes. However, the quantitative relations between the mineral and organic components of the soil and the relations with the vegetation are poorly understood. In such situations, the use of models is an
Self-organizing map models of language acquisition
Li, Ping; Zhao, Xiaowei
2013-01-01
Connectionist models have had a profound impact on theories of language. While most early models were inspired by the classic parallel distributed processing architecture, recent models of language have explored various other types of models, including self-organizing models for language acquisition. In this paper, we aim at providing a review of the latter type of models, and highlight a number of simulation experiments that we have conducted based on these models. We show that self-organizing connectionist models can provide significant insights into long-standing debates in both monolingual and bilingual language development. We suggest future directions in which these models can be extended, to better connect with behavioral and neural data, and to make clear predictions in testing relevant psycholinguistic theories. PMID:24312061
Social organization in the Minority Game model
Slanina, František
2000-10-01
We study the role of imitation within the Minority Game model of market. The players can exchange information locally, which leads to formation of groups which act as if they were single players. Coherent spatial areas of rich and poor agents result. We found that the global effectivity is optimized at certain value of the imitation probability, which decreases with increasing memory length. The social tensions are suppressed for large imitation probability, but generally the requirements of high global effectivity and low social tensions are in conflict.
Modelling the self-organization and collapse of complex networks
Indian Academy of Sciences (India)
Modelling the self-organization and collapse of complex networks. Sanjay Jain Department of Physics and Astrophysics, University of Delhi Jawaharlal Nehru Centre for Advanced Scientific Research, Bangalore Santa Fe Institute, Santa Fe, New Mexico.
How valuable are model organisms for transposable element studies?
Kidwell, M G; Evgen'ev, M B
1999-01-01
Model organisms have proved to be highly informative for many types of genetic studies involving 'conventional' genes. The results have often been successfully generalized to other closely related organisms and also, perhaps surprisingly frequently, to more distantly related organisms. Because of the wealth of previous knowledge and their availability and convenience, model organisms were often the species of choice for many of the earlier studies of transposable elements. The question arises whether the results of genetic studies of transposable elements in model organisms can be extrapolated in the same ways as those of conventional genes? A number of observations suggest that special care needs to be taken in generalizing the results from model organisms to other species. A hallmark of many transposable elements is their ability to amplify rapidly in species genomes. Rapid spread of a newly invaded element throughout a species range has also been demonstrated. The types and genomic copy numbers of transposable elements have been shown to differ greatly between some closely related species. Horizontal transfer of transposable elements appears to be more frequent than for nonmobile genes. Furthermore, the population structure of some model organisms has been subject to drastic recent changes that may have some bearing on their transposable element genomic complements. In order to initiate discussion of this question, several case studies of transposable elements in well-studied Drosophila species are presented.
The string prediction models as invariants of time series in the forex market
Pincak, R.
2013-12-01
In this paper we apply a new approach of string theory to the real financial market. The models are constructed with an idea of prediction models based on the string invariants (PMBSI). The performance of PMBSI is compared to support vector machines (SVM) and artificial neural networks (ANN) on an artificial and a financial time series. A brief overview of the results and analysis is given. The first model is based on the correlation function as invariant and the second one is an application based on the deviations from the closed string/pattern form (PMBCS). We found the difference between these two approaches. The first model cannot predict the behavior of the forex market with good efficiency in comparison with the second one which is, in addition, able to make relevant profit per year. The presented string models could be useful for portfolio creation and financial risk management in the banking sector as well as for a nonlinear statistical approach to data optimization.
Historical Streamflow Series Analysis Applied to Furnas HPP Reservoir Watershed Using the SWAT Model
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Viviane de Souza Dias
2018-04-01
Full Text Available Over the last few years, the operation of the Furnas Hydropower Plant (HPP reservoir, located in the Grande River Basin, has been threatened due to a significant reduction in inflow. In the region, hydrological modelling tools are being used and tested to support decision making and water sustainability. In this study, the streamflow was modelled in the area of direct influence of the Furnas HPP reservoir, and the Soil and Water Assessment Tool (SWAT model performance was verified for studies in the region. Analyses of sensitivity and uncertainty were undertaken using the Sequential Uncertainty Fitting algorithm (SUFI-2 with a Calibration Uncertainty Program (SWAT-CUP. The hydrological modelling, at a monthly scale, presented good results in the calibration (NS 0.86, with a slight reduction of the coefficient in the validation period (NS 0.64. The results suggested that this tool could be applied in future hydrological studies in the region of study. With the consideration that special attention should be given to the historical series used in the calibration and validation of the models. It is important to note that this region has high demands for water resources, primarily for agricultural use. Water demands must also be taken into account in future hydrological simulations. The validation of this methodology led to important contributions to the management of water resources in regions with tropical climates, whose climatological and geological reality resembles the one studied here.
Tracer kinetic model-driven registration for dynamic contrast-enhanced MRI time-series data.
Buonaccorsi, Giovanni A; O'Connor, James P B; Caunce, Angela; Roberts, Caleb; Cheung, Sue; Watson, Yvonne; Davies, Karen; Hope, Lynn; Jackson, Alan; Jayson, Gordon C; Parker, Geoffrey J M
2007-11-01
Dynamic contrast-enhanced MRI (DCE-MRI) time series data are subject to unavoidable physiological motion during acquisition (e.g., due to breathing) and this motion causes significant errors when fitting tracer kinetic models to the data, particularly with voxel-by-voxel fitting approaches. Motion correction is problematic, as contrast enhancement introduces new features into postcontrast images and conventional registration similarity measures cannot fully account for the increased image information content. A methodology is presented for tracer kinetic model-driven registration that addresses these problems by explicitly including a model of contrast enhancement in the registration process. The iterative registration procedure is focused on a tumor volume of interest (VOI), employing a three-dimensional (3D) translational transformation that follows only tumor motion. The implementation accurately removes motion corruption in a DCE-MRI software phantom and it is able to reduce model fitting errors and improve localization in 3D parameter maps in patient data sets that were selected for significant motion problems. Sufficient improvement was observed in the modeling results to salvage clinical trial DCE-MRI data sets that would otherwise have to be rejected due to motion corruption. Copyright 2007 Wiley-Liss, Inc.
Optimizing the De-Noise Neural Network Model for GPS Time-Series Monitoring of Structures
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Mosbeh R. Kaloop
2015-09-01
Full Text Available The Global Positioning System (GPS is recently used widely in structures and other applications. Notwithstanding, the GPS accuracy still suffers from the errors afflicting the measurements, particularly the short-period displacement of structural components. Previously, the multi filter method is utilized to remove the displacement errors. This paper aims at using a novel application for the neural network prediction models to improve the GPS monitoring time series data. Four prediction models for the learning algorithms are applied and used with neural network solutions: back-propagation, Cascade-forward back-propagation, adaptive filter and extended Kalman filter, to estimate which model can be recommended. The noise simulation and bridge’s short-period GPS of the monitoring displacement component of one Hz sampling frequency are used to validate the four models and the previous method. The results show that the Adaptive neural networks filter is suggested for de-noising the observations, specifically for the GPS displacement components of structures. Also, this model is expected to have significant influence on the design of structures in the low frequency responses and measurements’ contents.
An advection-based model to increase the temporal resolution of PIV time series.
Scarano, Fulvio; Moore, Peter
A numerical implementation of the advection equation is proposed to increase the temporal resolution of PIV time series. The method is based on the principle that velocity fluctuations are transported passively, similar to Taylor's hypothesis of frozen turbulence . In the present work, the advection model is extended to unsteady three-dimensional flows. The main objective of the method is that of lowering the requirement on the PIV repetition rate from the Eulerian frequency toward the Lagrangian one. The local trajectory of the fluid parcel is obtained by forward projection of the instantaneous velocity at the preceding time instant and backward projection from the subsequent time step. The trajectories are approximated by the instantaneous streamlines, which yields accurate results when the amplitude of velocity fluctuations is small with respect to the convective motion. The verification is performed with two experiments conducted at temporal resolutions significantly higher than that dictated by Nyquist criterion. The flow past the trailing edge of a NACA0012 airfoil closely approximates frozen turbulence , where the largest ratio between the Lagrangian and Eulerian temporal scales is expected. An order of magnitude reduction of the needed acquisition frequency is demonstrated by the velocity spectra of super-sampled series. The application to three-dimensional data is made with time-resolved tomographic PIV measurements of a transitional jet. Here, the 3D advection equation is implemented to estimate the fluid trajectories. The reduction in the minimum sampling rate by the use of super-sampling in this case is less, due to the fact that vortices occurring in the jet shear layer are not well approximated by sole advection at large time separation. Both cases reveal that the current requirements for time-resolved PIV experiments can be revised when information is poured from space to time . An additional favorable effect is observed by the analysis in the
Labour Quality Model for Organic Farming Food Chains
Gassner, B.; Freyer, B.; Leitner, H.
2008-01-01
The debate on labour quality in science is controversial as well as in the organic agriculture community. Therefore, we reviewed literature on different labour quality models and definitions, and had key informant interviews on labour quality issues with stakeholders in a regional oriented organic agriculture bread food chain. We developed a labour quality model with nine quality categories and discussed linkages to labour satisfaction, ethical values and IFOAM principles.
Business Model Innovation in Incumbent Organizations: : Challenges and Success Routes
Salama, Ahmad; Parvez, Khawar
2015-01-01
In this thesis major challenges of creating business models at incumbents within mature industries are identified along with a mitigation plan. Pressure is upon incumbent organizations in order to keep up with the latest rapid technological advancements, the launching of startups that almost cover every field of business and the continuous change in customers’ tastes and needs. That along with various factors either forced organizations to continually reevaluate their current business models ...
Reverse Osmosis Processing of Organic Model Compounds and Fermentation Broths
2006-04-01
key species found in the fermentation broth: ethanol, butanol, acetic acid, oxalic acid, lactic acid, and butyric acid. Correlations of the rejection...AFRL-ML-TY-TP-2007-4545 POSTPRINT REVERSE OSMOSIS PROCESSING OF ORGANIC MODEL COMPOUNDS AND FERMENTATION BROTHS Robert Diltz...TELEPHONE NUMBER (Include area code) Bioresource Technology 98 (2007) 686–695Reverse osmosis processing of organic model compounds and fermentation broths
Stepniak, Katarzyna; Klos, Anna; Bock, Olivier; Bogusz, Janusz
2016-04-01
GNSS Zenith Total Delay (ZTD) data is useful for numerical weather forecasting and climate analysis. Considering the fact that tropospheric delays over the mountainous areas are the most difficult to be modelled, we explored the influence of different troposphere models in Precise Point Positioning (PPP) mode. We used GPS data from 2008 to 2014 at 28 permanent EUPOS (European Position Determination System) stations, including 9 EPN (EUREF Permanent Network) ones, located in the Sudeten and Carpathians. The GPS data was processed in PPP mode using Bernese 5.2 GNSS software with the final IGS (International GNSS Service) orbits and clocks. Different processing variants were tested implying the newest mapping functions (Global Mapping Function - GMF, and Vienna Mapping Function - VMF1) as well as different time resolutions and constraints on estimated parameters (ZTD and gradients). Median trends and amplitudes of annual/semi-annual oscillations for ZTD series were determined with Weighted Least Squares Estimation (WLSE) obtaining 0.1±0.5 mm/year and 44.7 / 7.2 ± 5 mm, respectively. Power Spectral Densities (PSDs) were estimated using Lomb-Scargle method for each of individual variants. PSDs showed, except oscillations of year and half a year, many other significant peaks in ZTD time series at higher frequencies, about 60, 30, 24, 20, 15, 12, 10, 8, 7, 6, 5, 4 and 3 cpy. The proper subtraction of the periodicities is crucial, because they will make stochastic part appear to be artificially autocorrelated. In order to recognized the periodicities in the ZTD signal, we analyzed the ZTD differences between GPS-derived delays and ERA-Interim reanalysis. The results of analysis showed the significant change from station to station and between variants. According to these results the authors will indicate an optimal processing strategy concerning troposphere modelling.
NEW MODEL FOR QUANTIFICATION OF ICT DEPENDABLE ORGANIZATIONS RESILIENCE
Directory of Open Access Journals (Sweden)
Zora Arsovski
2011-03-01
Full Text Available Business environment today demands high reliable organizations in every segment to be competitive on the global market. Beside that, ICT sector is becoming irreplaceable in many fields of business, from the communication to the complex systems for process control and production. To fulfill those requirements and to develop further, many organizations worldwide are implementing business paradigm called - organizations resilience. Although resilience is well known term in many science fields, it is not well studied due to its complex nature. This paper is dealing with developing the new model for assessment and quantification of ICT dependable organizations resilience.
Knowledge Loss: A Defensive Model In Nuclear Research Organization Memory
International Nuclear Information System (INIS)
Mohamad Safuan Bin Sulaiman; Muhd Noor Muhd Yunus
2013-01-01
Knowledge is an essential part of research based organization. It should be properly managed to ensure that any pitfalls of knowledge retention due to knowledge loss of both tacit and explicit is mitigated. Audit of the knowledge entities exist in the organization is important to identify the size of critical knowledge. It is very much related to how much know-what, know-how and know-why experts exist in the organization. This study conceptually proposed a defensive model for Nuclear Malaysia's organization memory and application of Knowledge Loss Risk Assessment (KLRA) as an important tool for critical knowledge identification. (author)
International Nuclear Information System (INIS)
Burr, T.; Doak, J.; Howell, J.A.; Martinez, D.; Strittmatter, R.
1996-03-01
This report describes work performed during FY 95 for the Knowledge Fusion Project, which by the Department of Energy, Office of Nonproliferation and National Security. The project team selected satellite sensor data as the one main example to which its analysis algorithms would be applied. The specific sensor-fusion problem has many generic features that make it a worthwhile problem to attempt to solve in a general way. The generic problem is to recognize events of interest from multiple time series in a possibly noisy background. By implementing a suite of time series modeling and forecasting methods and using well-chosen alarm criteria, we reduce the number of false alarms. We then further reduce the number of false alarms by analyzing all suspicious sections of data, as judged by the alarm criteria, with pattern recognition methods. This report describes the implementation and application of this two-step process for separating events from unusual background. As a fortunate by-product of this activity, it is possible to gain a better understanding of the natural background
Garnier, Cédric; Mounier, Stéphane; Benaïm, Jean Yves
2004-10-01
Natural organic matter (NOM) behaviour towards proton is an important parameter to understand NOM fate in the environment. Moreover, it is necessary to determine NOM acid-base properties before investigating trace metals complexation by natural organic matter. This work focuses on the possibility to determine these acid-base properties by accurate and simple titrations, even at low organic matter concentrations. So, the experiments were conducted on concentrated and diluted solutions of extracted humic and fulvic acid from Laurentian River, on concentrated and diluted model solutions of well-known simple molecules (acetic and phenolic acids), and on natural samples from the Seine river (France) which are not pre-concentrated. Titration experiments were modelled by a 6 acidic-sites discrete model, except for the model solutions. The modelling software used, called PROSECE (Programme d'Optimisation et de SpEciation Chimique dans l'Environnement), has been developed in our laboratory, is based on the mass balance equilibrium resolution. The results obtained on extracted organic matter and model solutions point out a threshold value for a confident determination of the studied organic matter acid-base properties. They also show an aberrant decreasing carboxylic/phenolic ratio with increasing sample dilution. This shift is neither due to any conformational effect, since it is also observed on model solutions, nor to ionic strength variations which is controlled during all experiments. On the other hand, it could be the result of an electrode troubleshooting occurring at basic pH values, which effect is amplified at low total concentration of acidic sites. So, in our conditions, the limit for a correct modelling of NOM acid-base properties is defined as 0.04 meq of total analysed acidic sites concentration. As for the analysed natural samples, due to their high acidic sites content, it is possible to model their behaviour despite the low organic carbon concentration.
Model series in industrial robot systems. Sangyoyo robot system no kishu keiretsu
Energy Technology Data Exchange (ETDEWEB)
Hosono, T.; Ueno, T.; Izawa, T. (Meidensha Corp., Tokyo (Japan))
1993-06-11
Higher speed and rigidity are required in robots of which applications are expanding to other than material processing use. This paper describes systematization of model series according to applications, and development of industrial manipulators. As a result of expansion in workpieces requiring deburring. Such new application as cutting aluminum casting gates, and expanded application to handling works, requirements have emerged on improvement in robot functions and performances, and development of large capacity models that can handle loads of 100 kg or more. Therefore, five models have been developed that can meet torque requirements. It was learned that a robot that can carry a weight of up to 150 kg can handle almost all the applications. The improvement items included the expansion of available models, and function and performance improvements in the mechanisms and controls. General industrial manipulators have also been developed applying the master slave manipulator techniques used for remotely controlled maintenance works in nuclear energy related facilities. These manipulators are made so compact that they can be installed in small spaces in a factory, and facilitate maintenance works as a result of adopting an electric drive system. 4 refs., 5 figs., 7 tabs.
Bayesian models of thermal and pluviometric time series in the Fucino plateau
Directory of Open Access Journals (Sweden)
Adriana Trabucco
2011-09-01
Full Text Available This work was developed within the Project Metodologie e sistemi integrati per la qualificazione di produzioni orticole del Fucino (Methodologies and integrated systems for the classification of horticultural products in the Fucino plateau, sponsored by the Italian Ministry of Education, University and Research, Strategic Projects, Law 448/97. Agro-system managing, especially if necessary to achieve high quality in speciality crops, requires knowledge of main features and intrinsic variability of climate. Statistical models may properly summarize the structure existing behind the observed variability, furthermore they may support the agronomic manager by providing the probability that meteorological events happen in a time window of interest. More than 30 years of daily values collected in four sites located on the Fucino plateau, Abruzzo region, Italy, were studied by fitting Bayesian generalized linear models to air temperature maximum /minimum and rainfall time series. Bayesian predictive distributions of climate variables supporting decision-making processes were calculated at different timescales, 5-days for temperatures and 10-days for rainfall, both to reduce computational efforts and to simplify statistical model assumptions. Technicians and field operators, even with limited statistical training, may exploit the model output by inspecting graphs and climatic profiles of the cultivated areas during decision-making processes. Realizations taken from predictive distributions may also be used as input for agro-ecological models (e.g. models of crop growth, water balance. Fitted models may be exploited to monitor climatic changes and to revise climatic profiles of interest areas, periodically updating the probability distributions of target climatic variables. For the sake of brevity, the description of results is limited to just one of the four sites, and results for all other sites are available as supplementary information.
Drosophila melanogaster as a model organism to study nanotoxicity.
Ong, Cynthia; Yung, Lin-Yue Lanry; Cai, Yu; Bay, Boon-Huat; Baeg, Gyeong-Hun
2015-05-01
Drosophila melanogaster has been used as an in vivo model organism for the study of genetics and development since 100 years ago. Recently, the fruit fly Drosophila was also developed as an in vivo model organism for toxicology studies, in particular, the field of nanotoxicity. The incorporation of nanomaterials into consumer and biomedical products is a cause for concern as nanomaterials are often associated with toxicity in many in vitro studies. In vivo animal studies of the toxicity of nanomaterials with rodents and other mammals are, however, limited due to high operational cost and ethical objections. Hence, Drosophila, a genetically tractable organism with distinct developmental stages and short life cycle, serves as an ideal organism to study nanomaterial-mediated toxicity. This review discusses the basic biology of Drosophila, the toxicity of nanomaterials, as well as how the Drosophila model can be used to study the toxicity of various types of nanomaterials.
Investigating ecological speciation in non-model organisms
DEFF Research Database (Denmark)
Foote, Andrew David
2012-01-01
on killer whale evolutionary ecology in search of any difficulty in demonstrating causal links between variation in phenotype, ecology, and reproductive isolation in this non-model organism. Results: At present, we do not have enough evidence to conclude that adaptive phenotype traits linked to ecological...... speciation in non-model organisms that lead to this bias? What alternative approaches might redress the balance? Organism: Genetically differentiated types of the killer whale (Orcinus orca) exhibiting differences in prey preference, habitat use, morphology, and behaviour. Methods: Review of the literature...... variation underlie reproductive isolation between sympatric killer whale types. Perhaps ecological speciation has occurred, but it is hard to prove. We will probably face this outcome whenever we wish to address non-model organisms – species in which it is not easy to apply experimental approaches...
Modelling the fate of oxidisable organic contaminants in groundwater
DEFF Research Database (Denmark)
Barry, D.A.; Prommer, H.; Miller, C.T.
2002-01-01
modelling framework is illustrated by pertinent examples, showing the degradation of dissolved organics by microbial activity limited by the availability of nutrients or electron acceptors (i.e., changing redox states), as well as concomitant secondary reactions. Two field-scale modelling examples...... are discussed, the Vejen landfill (Denmark) and an example where metal contamination is remediated by redox changes wrought by injection of a dissolved organic compound. A summary is provided of current and likely future challenges to modelling of oxidisable organics in the subsurface. (C) 2002 Elsevier Science......Subsurface contamination by organic chemicals is a pervasive environmental problem, susceptible to remediation by natural or enhanced attenuation approaches or more highly engineered methods such as pump-and-treat, amongst others. Such remediation approaches, along with risk assessment...
Qian, Jiajie; Jennings, Brandon; Cwiertny, David M; Martinez, Andres
2017-11-15
We fabricated a suite of polymeric electrospun nanofiber mats (ENMs) and investigated their performance as next-generation passive sampler media for environmental monitoring of organic compounds. Electrospinning of common polymers [e.g., polyacrylonitrile (PAN), polymethyl methacrylate (PMMA), and polystyrene (PS), among others] yielded ENMs with reproducible control of nanofiber diameters (from 50 to 340 nm). The ENM performance was investigated initially with model hydrophilic (aniline and nitrobenzene) and hydrophobic (selected PCB congeners and dioxin) compounds, generally revealing fast chemical uptake into all of these ENMs, which was well described by a one compartment, first-order kinetic model. Typical times to reach 90% equilibrium (t 90% ) were ≤7 days under mixing conditions for all the ENMs and equilibrium timescales suggest that ENMs may be used in the field as an equilibrium-passive sampler, at least for our model compounds. Equilibrium partitioning coefficients (K ENM-W , L kg -1 ) averaged 2 and 4.7 log units for the hydrophilic and hydrophobic analytes, respectively. PAN, PMMA and PS were prioritized for additional studies because they exhibited not only the greatest capacity for simultaneous uptake of the entire model suite (log K ENM-W ∼1.5-6.2), but also fast uptake. For these optimized ENMs, the rates of uptake into PAN and PMMA were limited by aqueous phase diffusion to the nanofiber surface, and the rate-determining step for PS was analyte specific. Sorption isotherms also revealed that the environmental application of these optimized ENMs would occur within the linear uptake regime. We examined the ENM performance for the measurement of pore water concentrations from spiked soil and freshwater sediments. Soil and sediment studies not only yielded reproducible pore water concentrations and comparable values to other passive sampler materials, but also provided practical insights into ENM stability and fouling in such systems
Identification of two-phase flow regimes by time-series modeling
International Nuclear Information System (INIS)
King, C.H.; Ouyang, M.S.; Pei, B.S.
1987-01-01
The identification of two-phase flow patterns in pipes or ducts is important to the design and operation of thermal-hydraulic systems, especially in the nuclear reactor cores of boiling water reactors or in the steam generators of pressurized water reactors. Basically, two-phase flow shows some fluctuating characteristics even at steady-state conditions. These fluctuating characteristics can be analyzed by statistical methods for obtaining flow signatures. There have been a number of experimental studies conducted that are concerned with the statistical properties of void fraction or pressure pulsation in two-phase flow. In this study, the authors propose a new technique of identifying the patterns of air-water two-phase flow in a vertical pipe. This technique is based on analyzing the statistic characteristics of the pressure signals of the test loop by time-series modeling
Yang, Q.; Wang, Y.; Zhang, J.; Delgado, J.
2017-05-01
Accurate and reliable groundwater level forecasting models can help ensure the sustainable use of a watershed's aquifers for urban and rural water supply. In this paper, three time series analysis methods, Holt-Winters (HW), integrated time series (ITS), and seasonal autoregressive integrated moving average (SARIMA), are explored to simulate the groundwater level in a coastal aquifer, China. The monthly groundwater table depth data collected in a long time series from 2000 to 2011 are simulated and compared with those three time series models. The error criteria are estimated using coefficient of determination ( R 2), Nash-Sutcliffe model efficiency coefficient ( E), and root-mean-squared error. The results indicate that three models are all accurate in reproducing the historical time series of groundwater levels. The comparisons of three models show that HW model is more accurate in predicting the groundwater levels than SARIMA and ITS models. It is recommended that additional studies explore this proposed method, which can be used in turn to facilitate the development and implementation of more effective and sustainable groundwater management strategies.
Lin, Chih-Hsien Michelle; Lyubchich, Vyacheslav; Glibert, Patricia M
2018-03-01
The harmful dinoflagellate, Karlodnium veneficum, has been implicated in fish-kill and other toxic, harmful algal bloom (HAB) events in waters worldwide. Blooms of K. veneficum are known to be related to coastal nutrient enrichment but the relationship is complex because this HAB taxon relies not only on dissolved nutrients but also particulate prey, both of which have also changed over time. Here, applying cross-correlations of climate-related physical factors, nutrients and prey, with abundance of K. veneficum over a 10-year (2002-2011) period, a synthesis of the interactive effects of multiple factors on this species was developed for Chesapeake Bay, where blooms of the HAB have been increasing. Significant upward trends in the time series of K. veneficum were observed in the mesohaline stations of the Bay, but not in oligohaline tributary stations. For the mesohaline regions, riverine sources of nutrients with seasonal lags, together with particulate prey with zero lag, explained 15%-46% of the variation in the K. veneficum time series. For the oligohaline regions, nutrients and particulate prey generally showed significant decreasing trends with time, likely a reflection of nutrient reduction efforts. A conceptual model of mid-Bay blooms is presented, in which K. veneficum, derived from the oceanic end member of the Bay, may experience enhanced growth if it encounters prey originating from the tributaries with different patterns of nutrient loading and which are enriched in nitrogen. For all correlation models developed herein, prey abundance was a primary factor in predicting K. veneficum abundance. Copyright © 2018 Elsevier B.V. All rights reserved.
Directory of Open Access Journals (Sweden)
Suhartono Suhartono
2005-01-01
Full Text Available Many business and economic time series are non-stationary time series that contain trend and seasonal variations. Seasonality is a periodic and recurrent pattern caused by factors such as weather, holidays, or repeating promotions. A stochastic trend is often accompanied with the seasonal variations and can have a significant impact on various forecasting methods. In this paper, we will investigate and compare some forecasting methods for modeling time series with both trend and seasonal patterns. These methods are Winter's, Decomposition, Time Series Regression, ARIMA and Neural Networks models. In this empirical research, we study on the effectiveness of the forecasting performance, particularly to answer whether a complex method always give a better forecast than a simpler method. We use a real data, that is airline passenger data. The result shows that the more complex model does not always yield a better result than a simpler one. Additionally, we also find the possibility to do further research especially the use of hybrid model by combining some forecasting method to get better forecast, for example combination between decomposition (as data preprocessing and neural network model.
Organism-level models: When mechanisms and statistics fail us
Phillips, M. H.; Meyer, J.; Smith, W. P.; Rockhill, J. K.
2014-03-01
Purpose: To describe the unique characteristics of models that represent the entire course of radiation therapy at the organism level and to highlight the uses to which such models can be put. Methods: At the level of an organism, traditional model-building runs into severe difficulties. We do not have sufficient knowledge to devise a complete biochemistry-based model. Statistical model-building fails due to the vast number of variables and the inability to control many of them in any meaningful way. Finally, building surrogate models, such as animal-based models, can result in excluding some of the most critical variables. Bayesian probabilistic models (Bayesian networks) provide a useful alternative that have the advantages of being mathematically rigorous, incorporating the knowledge that we do have, and being practical. Results: Bayesian networks representing radiation therapy pathways for prostate cancer and head & neck cancer were used to highlight the important aspects of such models and some techniques of model-building. A more specific model representing the treatment of occult lymph nodes in head & neck cancer were provided as an example of how such a model can inform clinical decisions. A model of the possible role of PET imaging in brain cancer was used to illustrate the means by which clinical trials can be modelled in order to come up with a trial design that will have meaningful outcomes. Conclusions: Probabilistic models are currently the most useful approach to representing the entire therapy outcome process.
A Framework for Formal Modeling and Analysis of Organizations
Jonker, C.M.; Sharpanskykh, O.; Treur, J.; P., Yolum
2007-01-01
A new, formal, role-based, framework for modeling and analyzing both real world and artificial organizations is introduced. It exploits static and dynamic properties of the organizational model and includes the (frequently ignored) environment. The transition is described from a generic framework of
PENGEMBANGAN FOIL NACA SERI 2412 SEBAGAI SISTEM PENYELAMAN MODEL KAPAL SELAM
Directory of Open Access Journals (Sweden)
Ali Munazid
2015-06-01
Full Text Available Bentuk foil menghasilkan gaya angkat (lift force ketika foil dilewati oleh aliran fluida karena adanya pengaruh interaksi antara aliran fluida dengan permukaan foil yang mengakibatkan tekanan permukaan atas lebih kecil dari permukaan bawah. Bagaimana mengaplikasikan teori foil pada hydroplane kapal selam sebagai system penyelaman, dengan membalik foil maka lift force tersebut menjadi gaya ke bawah, dengan demikian memungkinkan kapal selam dapat menyelam, melayang dan bermanouver di bawah air, seperti halnya gerak pesawat terbang yang terbang dan melayang dengan menggunakan sayap. Dilakukan penelitian dan pengamatan terhadap kemampuan penyelaman (diving plan dari foil NACA seri 2412 pada model kapal selam, dengan mencari nilai Cl (coefisien lift di Laboratorium, serta mendesain bentuk badan kapal selam dan analisa gaya-gaya yang bekerja pada model kapal selam, jumlah gaya-gaya yang bekerja keatas lebih rendah dari gaya-gaya ke bawah maka kapal selam mampu menyelam. Penerapan Hydroplane sebagai diving plane dapat diterapkan, kemampuan penyelaman dipengaruhi oleh sudut flip Hydroplane dan kecepatan model, semakin besar kecepatan dan sudut flip maka semakin besar kedalaman penyelaman yang dapat dilakukan.
Fast and Scalable Gaussian Process Modeling with Applications to Astronomical Time Series
Foreman-Mackey, Daniel; Agol, Eric; Ambikasaran, Sivaram; Angus, Ruth
2017-12-01
The growing field of large-scale time domain astronomy requires methods for probabilistic data analysis that are computationally tractable, even with large data sets. Gaussian processes (GPs) are a popular class of models used for this purpose, but since the computational cost scales, in general, as the cube of the number of data points, their application has been limited to small data sets. In this paper, we present a novel method for GPs modeling in one dimension where the computational requirements scale linearly with the size of the data set. We demonstrate the method by applying it to simulated and real astronomical time series data sets. These demonstrations are examples of probabilistic inference of stellar rotation periods, asteroseismic oscillation spectra, and transiting planet parameters. The method exploits structure in the problem when the covariance function is expressed as a mixture of complex exponentials, without requiring evenly spaced observations or uniform noise. This form of covariance arises naturally when the process is a mixture of stochastically driven damped harmonic oscillators—providing a physical motivation for and interpretation of this choice—but we also demonstrate that it can be a useful effective model in some other cases. We present a mathematical description of the method and compare it to existing scalable GP methods. The method is fast and interpretable, with a range of potential applications within astronomical data analysis and beyond. We provide well-tested and documented open-source implementations of this method in C++, Python, and Julia.
Liu, Y.; Weisberg, R. H.
2017-12-01
The Lagrangian separation distance between the endpoints of simulated and observed drifter trajectories is often used to assess the performance of numerical particle trajectory models. However, the separation distance fails to indicate relative model performance in weak and strong current regions, such as a continental shelf and its adjacent deep ocean. A skill score is proposed based on the cumulative Lagrangian separation distances normalized by the associated cumulative trajectory lengths. The new metrics correctly indicates the relative performance of the Global HYCOM in simulating the strong currents of the Gulf of Mexico Loop Current and the weaker currents of the West Florida Shelf in the eastern Gulf of Mexico. In contrast, the Lagrangian separation distance alone gives a misleading result. Also, the observed drifter position series can be used to reinitialize the trajectory model and evaluate its performance along the observed trajectory, not just at the drifter end position. The proposed dimensionless skill score is particularly useful when the number of drifter trajectories is limited and neither a conventional Eulerian-based velocity nor a Lagrangian-based probability density function may be estimated.
Petridis, Loukas; Ambaye, Haile; Jagadamma, Sindhu; Kilbey, S Michael; Lokitz, Bradley S; Lauter, Valeria; Mayes, Melanie A
2014-01-01
The complexity of the mineral-organic carbon interface may influence the extent of stabilization of organic carbon compounds in soils, which is important for global climate futures. The nanoscale structure of a model interface was examined here by depositing films of organic carbon compounds of contrasting chemical character, hydrophilic glucose and amphiphilic stearic acid, onto a soil mineral analogue (Al2O3). Neutron reflectometry, a technique which provides depth-sensitive insight into the organization of the thin films, indicates that glucose molecules reside in a layer between Al2O3 and stearic acid, a result that was verified by water contact angle measurements. Molecular dynamics simulations reveal the thermodynamic driving force behind glucose partitioning on the mineral interface: The entropic penalty of confining the less mobile glucose on the mineral surface is lower than for stearic acid. The fundamental information obtained here helps rationalize how complex arrangements of organic carbon on soil mineral surfaces may arise.
Real-time GPS Satellite Clock Error Prediction Based On No-stationary Time Series Model
Wang, Q.; Xu, G.; Wang, F.
2009-04-01
Analysis Centers of the IGS provide precise satellite ephemeris for GPS data post-processing. The accuracy of orbit products is better than 5cm, and that of the satellite clock errors (SCE) approaches 0.1ns (igscb.jpl.nasa.gov), which can meet with the requirements of precise point positioning (PPP). Due to the 13 day-latency of the IGS final products, only the broadcast ephemeris and IGS ultra rapid products (predicted) are applicable for real time PPP (RT-PPP). Therefore, development of an approach to estimate high precise GPS SCE in real time is of particular importance for RT-PPP. Many studies have been carried out for forecasting the corrections using models, such as Linear Model (LM), Quadratic Polynomial Model (QPM), Quadratic Polynomial Model with Cyclic corrected Terms (QPM+CT), Grey Model (GM) and Kalman Filter Model (KFM), etc. However, the precisions of these models are generally in nanosecond level. The purpose of this study is to develop a method using which SCE forecasting for RT-PPP can be reached with a precision of sub-nanosecond. Analysis of the last 8 years IGS SCE data shown that predicted precision depend on the stability of the individual satellite clock. The clocks of the most recent GPS satellites (BLOCK IIR and BLOCK IIR-M) are more stable than that of the former GPS satellites (BLOCK IIA). For the stable satellite clock, the next 6 hours SCE can be easily predict with LM. The residuals of unstable satellite clocks are periodic ones with noise components. Dominant periods of residuals are found by using Fourier Transform and Spectrum Analysis. For the rest part of the residuals, an auto-regression model is used to determine their systematic trends. Summarized from this study, a no-stationary time series model can be proposed to predict GPS SCE in real time. This prediction model includes: linear term, cyclic corrected terms and auto-regression term, which are used to represent SCE trend, cyclic parts and rest of the errors, respectively
Xanthusbase: adapting wikipedia principles to a model organism database.
Arshinoff, Bradley I; Suen, Garret; Just, Eric M; Merchant, Sohel M; Kibbe, Warren A; Chisholm, Rex L; Welch, Roy D
2007-01-01
xanthusBase (http://www.xanthusbase.org) is the official model organism database (MOD) for the social bacterium Myxococcus xanthus. In many respects, M.xanthus represents the pioneer model organism (MO) for studying the genetic, biochemical, and mechanistic basis of prokaryotic multicellularity, a topic that has garnered considerable attention due to the significance of biofilms in both basic and applied microbiology research. To facilitate its utility, the design of xanthusBase incorporates open-source software, leveraging the cumulative experience made available through the Generic Model Organism Database (GMOD) project, MediaWiki (http://www.mediawiki.org), and dictyBase (http://www.dictybase.org), to create a MOD that is both highly useful and easily navigable. In addition, we have incorporated a unique Wikipedia-style curation model which exploits the internet's inherent interactivity, thus enabling M.xanthus and other myxobacterial researchers to contribute directly toward the ongoing genome annotation.
Investigating ecological speciation in non-model organisms
DEFF Research Database (Denmark)
Foote, Andrew David
2012-01-01
Background: Studies of ecological speciation tend to focus on a few model biological systems. In contrast, few studies on non-model organisms have been able to infer ecological speciation as the underlying mechanism of evolutionary divergence. Questions: What are the pitfalls in studying ecological...... on killer whale evolutionary ecology in search of any difficulty in demonstrating causal links between variation in phenotype, ecology, and reproductive isolation in this non-model organism. Results: At present, we do not have enough evidence to conclude that adaptive phenotype traits linked to ecological...... variation underlie reproductive isolation between sympatric killer whale types. Perhaps ecological speciation has occurred, but it is hard to prove. We will probably face this outcome whenever we wish to address non-model organisms – species in which it is not easy to apply experimental approaches...
A self-organized criticality model for plasma transport
International Nuclear Information System (INIS)
Carreras, B.A.; Newman, D.; Lynch, V.E.
1996-01-01
Many models of natural phenomena manifest the basic hypothesis of self-organized criticality (SOC). The SOC concept brings together the self-similarity on space and time scales that is common to many of these phenomena. The application of the SOC modelling concept to the plasma dynamics near marginal stability opens new possibilities of understanding issues such as Bohm scaling, profile consistency, broad band fluctuation spectra with universal characteristics and fast time scales. A model realization of self-organized criticality for plasma transport in a magnetic confinement device is presented. The model is based on subcritical resistive pressure-gradient-driven turbulence. Three-dimensional nonlinear calculations based on this model show the existence of transport under subcritical conditions. This model that includes fluctuation dynamics leads to results very similar to the running sandpile paradigm
An Ising model for metal-organic frameworks
Höft, Nicolas; Horbach, Jürgen; Martín-Mayor, Victor; Seoane, Beatriz
2017-08-01
We present a three-dimensional Ising model where lines of equal spins are frozen such that they form an ordered framework structure. The frame spins impose an external field on the rest of the spins (active spins). We demonstrate that this "porous Ising model" can be seen as a minimal model for condensation transitions of gas molecules in metal-organic frameworks. Using Monte Carlo simulation techniques, we compare the phase behavior of a porous Ising model with that of a particle-based model for the condensation of methane (CH4) in the isoreticular metal-organic framework IRMOF-16. For both models, we find a line of first-order phase transitions that end in a critical point. We show that the critical behavior in both cases belongs to the 3D Ising universality class, in contrast to other phase transitions in confinement such as capillary condensation.
Regional Persistent Organic Pollutants' Environmental Impact Assessment and Control Model
Directory of Open Access Journals (Sweden)
Jurgis Staniskis
2008-10-01
Full Text Available The sources of formation, environmental distribution and fate of persistent organic pollutants (POPs are increasingly seen as topics to be addressed and solved at the global scale. Therefore, there are already two international agreements concerning persistent organic pollutants: the Protocol of 1998 to the 1979 Convention on the Long-Range Transboundary Air Pollution on Persistent Organic Pollutants (Aarhus Protocol; and the Stockholm Convention on Persistent Organic Pollutants. For the assessment of environmental pollution of POPs, for the risk assessment, for the evaluation of new pollutants as potential candidates to be included in the POPs list of the Stokholmo or/and Aarhus Protocol, a set of different models are developed or under development. Multimedia models help describe and understand environmental processes leading to global contamination through POPs and actual risk to the environment and human health. However, there is a lack of the tools based on a systematic and integrated approach to POPs management difficulties in the region.
Leonelli, Sabina; Ankeny, Rachel A.; Nelson, Nicole C.; Ramsden, Edmund
2014-01-01
Argument We examine the criteria used to validate the use of nonhuman organisms in North-American alcohol addiction research from the 1950s to the present day. We argue that this field, where the similarities between behaviors in humans and non-humans are particularly difficult to assess, has addressed questions of model validity by transforming the situatedness of non-human organisms into an experimental tool. We demonstrate that model validity does not hinge on the standardization of one type of organism in isolation, as often the case with genetic model organisms. Rather, organisms are viewed as necessarily situated: they cannot be understood as a model for human behavior in isolation from their environmental conditions. Hence the environment itself is standardized as part of the modeling process; and model validity is assessed with reference to the environmental conditions under which organisms are studied. PMID:25233743
Ankeny, Rachel A; Leonelli, Sabina; Nelson, Nicole C; Ramsden, Edmund
2014-09-01
We examine the criteria used to validate the use of nonhuman organisms in North-American alcohol addiction research from the 1950s to the present day. We argue that this field, where the similarities between behaviors in humans and non-humans are particularly difficult to assess, has addressed questions of model validity by transforming the situatedness of non-human organisms into an experimental tool. We demonstrate that model validity does not hinge on the standardization of one type of organism in isolation, as often the case with genetic model organisms. Rather, organisms are viewed as necessarily situated: they cannot be understood as a model for human behavior in isolation from their environmental conditions. Hence the environment itself is standardized as part of the modeling process; and model validity is assessed with reference to the environmental conditions under which organisms are studied.
MODELLING CONSUMERS' DEMAND FOR ORGANIC FOOD PRODUCTS: THE SWEDISH EXPERIENCE
Directory of Open Access Journals (Sweden)
Manuchehr Irandoust
2016-07-01
Full Text Available This paper attempts to examine a few factors characterizing consumer preferences and behavior towards organic food products in the south of Sweden using a proportional odds model which captures the natural ordering of dependent variables and any inherent nonlinearities. The findings show that consumer's choice for organic food depends on perceived benefits of organic food (environment, health, and quality and consumer's perception and attitudes towards labelling system, message framing, and local origin. In addition, high willingness to pay and income level will increase the probability to buy organic food, while the cultural differences and socio-demographic characteristics have no effect on consumer behaviour and attitudes towards organic food products. Policy implications are offered.
Modelization of tritium transfer into the organic compartments of algae
International Nuclear Information System (INIS)
Bonotto, S.; Gerber, G.B.; Arapis, G.; Kirchmann, R.
1982-01-01
Uptake of tritium oxide and its conversion into organic tritium was studied in four different types of algae with widely varying size and growth characteristics (Acetabularia acetabulum, Boergesenia forbesii, two strains of Chlamydomonas and Dunaliella bioculata). Water in the cell and the vacuales equilibrates rapidly with external tritium water. Tritium is actively incorporated into organically bound form as the organisms grow. During the stationary phase, incorporation of tritium is slow. There exists a discrimination against the incorporation of tritium into organically bound form. A model has been elaborated taking in account these different factors. It appears that transfer of organic tritium by algae growing near the sites of release would be significant only for actively growing algae. Algae growing slowly may, however, be useful as cumulative indicators of discontinuous tritium release. (author)
Pan-Arctic TV Series on Inuit wellness: a northern model of communication for social change?
Johnson, Rhonda; Morales, Robin; Leavitt, Doreen; Carry, Catherine; Kinnon, Dianne; Rideout, Denise; Clarida, Kath
2011-06-01
This paper provides highlights of a utilization-focused evaluation of a collaborative Pan-Arctic Inuit Wellness TV Series that was broadcast live in Alaska and Canada in May 2009. This International Polar Year (IPY) communication and outreach project intended to (1) share information on International Polar Year research progress, disseminate findings and explore questions with Inuit in Alaska, Canada and Greenland; (2) provide a forum for Inuit in Alaska, Canada and Greenland to showcase innovative health and wellness projects; (3) ensure Inuit youth and adult engagement throughout; and (4) document and reflect on the overall experience for the purposes of developing and "testing" a participatory communication model. Utilization-focused formative evaluation of the project, with a focus on overall objectives, key messages and lessons learned to facilitate program improvement. Participant observation, surveys, key informant interviews, document review and website tracking. Promising community programs related to 3 themes - men's wellness, maternity care and youth resilience - in diverse circumpolar regions were highlighted, as were current and stillevolving findings from ongoing Arctic research. Multiple media methods were used to effectively deliver and receive key messages determined by both community and academic experts. Local capacity and new regional networks were strengthened. Evidence-based resources for health education and community action were archived in digital formats (websites and DVDs), increasing accessibility to otherwise isolated individuals and remote communities. The Pan-Arctic Inuit Wellness TV Series was an innovative, multi-dimensional communication project that raised both interest and awareness about complex health conditions in the North and stimulated community dialogue and potential for increased collaborative action. Consistent with a communication for social change approach, the project created new networks, increased motivation to act
Zhiqiang Cheng; Jihua Meng; Yanyou Qiao; Yiming Wang; Wenquan Dong; Yanxin Han
2018-01-01
The approach of using multispectral remote sensing (RS) to estimate soil available nutrients (SANs) has been recently developed and shows promising results. This method overcomes the limitations of commonly used methods by building a statistical model that connects RS-based crop growth and nutrient content. However, the stability and accuracy of this model require improvement. In this article, we replaced the statistical model by integrating the World Food Studies (WOFOST) model and time seri...
Dilbert-Peter model of organization effectiveness: computer simulations
Sobkowicz, Pawel
2010-01-01
We describe a computer model of general effectiveness of a hierarchical organization depending on two main aspects: effects of promotion to managerial levels and efforts to self-promote of individual employees, reducing their actual productivity. The combination of judgment by appearance in the promotion to higher levels of hierarchy and the Peter Principle (which states that people are promoted to their level of incompetence) results in fast declines in effectiveness of the organization. The...
Modeling nanostructure-enhanced light trapping in organic solar cells
DEFF Research Database (Denmark)
Adam, Jost
A promising approach for improving the power conversion efficiencies of organic solar cells (OSCs) is by incorporating nanostructures in their thin film architecture to improve the light absorption in the device’s active polymer layers. Here, we present a modelling framework for the prediction....... Diffraction by fractal metallic supergratings. Optics Express, 15(24), 15628–15636 (2007) [3] Goszczak, A. J. et al. Nanoscale Aluminum dimples for light trapping in organic thin films (submitted)...
Immune Organs and Haemopoietic System Under Modelling of the Mission Factors
Sapin, M. R.; Grigoriev, A. I.; Erofeeva, L. M.; Grigorenko, D. E.; Fedorenko, B. S.
1997-07-01
Literary and experimental data on the character of changes in immune organs and lymphoid tissue of respiratory system and digestive system in laboratory animals during the mission factors model are given. Inhibition of reproductive function in bone marrow, thymus and spleen under irradiation of gamma-rays and accelerated carbon ions, tensity of immune response in the lymphoid structures of larynx, trachea and bronchi under the influence of acetaldehyde vapors and decrease of lymphoid tissue square on histological series in spleen and small intestine with an increase of concentration of microbial bodies in the drinking water were estimated.
Linear time series modeling of GPS-derived TEC observations over the Indo-Thailand region
Suraj, Puram Sai; Kumar Dabbakuti, J. R. K.; Chowdhary, V. Rajesh; Tripathi, Nitin K.; Ratnam, D. Venkata
2017-12-01
This paper proposes a linear time series model to represent the climatology of the ionosphere and to investigate the characteristics of hourly averaged total electron content (TEC). The GPS-TEC observation data at the Bengaluru international global navigation satellite system (GNSS) service (IGS) station (geographic 13.02°N , 77.57°E ; geomagnetic latitude 4.4°N ) have been utilized for processing the TEC data during an extended period (2009-2016) in the 24{th} solar cycle. Solar flux F10.7p index, geomagnetic Ap index, and periodic oscillation factors have been considered to construct a linear TEC model. It is evident from the results that solar activity effect on TEC is high. It reaches the maximum value (˜ 40 TECU) during the high solar activity (HSA) year (2014) and minimum value (˜ 15 TECU) during the low solar activity (LSA) year (2009). The larger magnitudes of semiannual variations are observed during the HSA periods. The geomagnetic effect on TEC is relatively low, with the highest being ˜ 4 TECU (March 2015). The magnitude of periodic variations can be seen more significantly during HSA periods (2013-2015) and less during LSA periods (2009-2011). The correlation coefficient of 0.89 between the observations and model-based estimations has been found. The RMSE between the observed TEC and model TEC values is 4.0 TECU (linear model) and 4.21 TECU (IRI2016 Model). Further, the linear TEC model has been validated at different latitudes over the northern low-latitude region. The solar component (F10.7p index) value decreases with an increase in latitude. The magnitudes of the periodic component become less significant with the increase in latitude. The influence of geomagnetic component becomes less significant at Lucknow GNSS station (26.76°N, 80.88°E) when compared to other GNSS stations. The hourly averaged TEC values have been considered and ionospheric features are well recovered with linear TEC model.
Beyond Rating Curves: Time Series Models for in-Stream Turbidity Prediction
Wang, L.; Mukundan, R.; Zion, M.; Pierson, D. C.
2012-12-01
The New York City Department of Environmental Protection (DEP) manages New York City's water supply, which is comprised of over 20 reservoirs and supplies over 1 billion gallons of water per day to more than 9 million customers. DEP's "West of Hudson" reservoirs located in the Catskill Mountains are unfiltered per a renewable filtration avoidance determination granted by the EPA. While water quality is usually pristine, high volume storm events occasionally cause the reservoirs to become highly turbid. A logical strategy for turbidity control is to temporarily remove the turbid reservoirs from service. While effective in limiting delivery of turbid water and reducing the need for in-reservoir alum flocculation, this strategy runs the risk of negatively impacting water supply reliability. Thus, it is advantageous for DEP to understand how long a particular turbidity event will affect their system. In order to understand the duration, intensity and total load of a turbidity event, predictions of future in-stream turbidity values are important. Traditionally, turbidity predictions have been carried out by applying streamflow observations/forecasts to a flow-turbidity rating curve. However, predictions from rating curves are often inaccurate due to inter- and intra-event variability in flow-turbidity relationships. Predictions can be improved by applying an autoregressive moving average (ARMA) time series model in combination with a traditional rating curve. Since 2003, DEP and the Upstate Freshwater Institute have compiled a relatively consistent set of 15-minute turbidity observations at various locations on Esopus Creek above Ashokan Reservoir. Using daily averages of this data and streamflow observations at nearby USGS gauges, flow-turbidity rating curves were developed via linear regression. Time series analysis revealed that the linear regression residuals may be represented using an ARMA(1,2) process. Based on this information, flow-turbidity regressions with
Directory of Open Access Journals (Sweden)
Petrov M.
2007-12-01
Full Text Available An algorithm and programs for modeling, analysis, and prognosis of river quality has been developed, which is a modified method of the time series analysis (TSA. The algorithm and program are used for modeling and prognosis of the river quality of Bulgarian river ecosystems.
Assessment and prediction of road accident injuries trend using time-series models in Kurdistan.
Parvareh, Maryam; Karimi, Asrin; Rezaei, Satar; Woldemichael, Abraha; Nili, Sairan; Nouri, Bijan; Nasab, Nader Esmail
2018-01-01
Road traffic accidents are commonly encountered incidents that can cause high-intensity injuries to the victims and have direct impacts on the members of the society. Iran has one of the highest incident rates of road traffic accidents. The objective of this study was to model the patterns of road traffic accidents leading to injury in Kurdistan province, Iran. A time-series analysis was conducted to characterize and predict the frequency of road traffic accidents that lead to injury in Kurdistan province. The injuries were categorized into three separate groups which were related to the car occupants, motorcyclists and pedestrian road traffic accident injuries. The Box-Jenkins time-series analysis was used to model the injury observations applying autoregressive integrated moving average (ARIMA) and seasonal autoregressive integrated moving average (SARIMA) from March 2009 to February 2015 and to predict the accidents up to 24 months later (February 2017). The analysis was carried out using R-3.4.2 statistical software package. A total of 5199 pedestrians, 9015 motorcyclists, and 28,906 car occupants' accidents were observed. The mean (SD) number of car occupant, motorcyclist and pedestrian accident injuries observed were 401.01 (SD 32.78), 123.70 (SD 30.18) and 71.19 (SD 17.92) per year, respectively. The best models for the pattern of car occupant, motorcyclist, and pedestrian injuries were the ARIMA (1, 0, 0), SARIMA (1, 0, 2) (1, 0, 0) 12 , and SARIMA (1, 1, 1) (0, 0, 1) 12 , respectively. The motorcyclist and pedestrian injuries showed a seasonal pattern and the peak was during summer (August). The minimum frequency for the motorcyclist and pedestrian injuries were observed during the late autumn and early winter (December and January). Our findings revealed that the observed motorcyclist and pedestrian injuries had a seasonal pattern that was explained by air temperature changes overtime. These findings call the need for close monitoring of the
Computer models of dipole magnets of a series 'VULCAN' for the ALICE experiment
International Nuclear Information System (INIS)
Vodop'yanov, A.S.; Shishov, Yu.A.; Yuldasheva, M.B.; Yuldashev, O.I.
1998-01-01
The paper is devoted to a construction of computer models for three magnets of the 'VULCAN' series in the framework of a differential approach for two scalar potentials. The distinctive property of these magnets is that they are 'warm' and their coils are of conic saddle shape. The algorithm of creating a computer model for the coils is suggested. The coil field is computed by Biot-Savart law and a part of the integrals is calculated with the help of analytical formulas. To compute three-dimensional magnetic fields by the finite element method with a local accuracy control, two new algorithms are suggested. The former is based on a comparison of the fields computed by means of linear and quadratic shape functions. The latter is based on a comparison of the field computed with the help of linear shape functions and a local classical solution. The distributions of the local accuracy control characteristics within a working part of the third magnet and the other results of the computations are presented
Ectocarpus: a model organism for the brown algae.
Coelho, Susana M; Scornet, Delphine; Rousvoal, Sylvie; Peters, Nick T; Dartevelle, Laurence; Peters, Akira F; Cock, J Mark
2012-02-01
The brown algae are an interesting group of organisms from several points of view. They are the dominant organisms in many coastal ecosystems, where they often form large, underwater forests. They also have an unusual evolutionary history, being members of the stramenopiles, which are very distantly related to well-studied animal and green plant models. As a consequence of this history, brown algae have evolved many novel features, for example in terms of their cell biology and metabolic pathways. They are also one of only a small number of eukaryotic groups to have independently evolved complex multicellularity. Despite these interesting features, the brown algae have remained a relatively poorly studied group. This situation has started to change over the last few years, however, with the emergence of the filamentous brown alga Ectocarpus as a model system that is amenable to the genomic and genetic approaches that have proved to be so powerful in more classical model organisms such as Drosophila and Arabidopsis.
Modelling the fate of organic micropollutants in stormwater ponds
DEFF Research Database (Denmark)
Vezzaro, Luca; Eriksson, Eva; Ledin, Anna
2011-01-01
Urban water managers need to estimate the potential removal of organic micropollutants (MP) in stormwater treatment systems to support MP pollution control strategies. This study documents how the potential removal of organic MP in stormwater treatment systems can be quantified by using multimedia...... models. The fate of four different MP in a stormwater retention pond was simulated by applying two steady-state multimedia fate models (EPI Suite and SimpleBox) commonly applied in chemical risk assessment and a dynamic multimedia fate model (Stormwater Treatment Unit Model for Micro Pollutants — STUMP...... substance inherent properties to calculate MP fate but differ in their ability to represent the small physical scale and high temporal variability of stormwater treatment systems. Therefore the three models generate different results. A Global Sensitivity Analysis (GSA) highlighted that settling...
Advanced methods for modeling water-levels and estimating drawdowns with SeriesSEE, an Excel add-in
Halford, Keith; Garcia, C. Amanda; Fenelon, Joe; Mirus, Benjamin B.
2012-12-21
Water-level modeling is used for multiple-well aquifer tests to reliably differentiate pumping responses from natural water-level changes in wells, or “environmental fluctuations.” Synthetic water levels are created during water-level modeling and represent the summation of multiple component fluctuations, including those caused by environmental forcing and pumping. Pumping signals are modeled by transforming step-wise pumping records into water-level changes by using superimposed Theis functions. Water-levels can be modeled robustly with this Theis-transform approach because environmental fluctuations and pumping signals are simulated simultaneously. Water-level modeling with Theis transforms has been implemented in the program SeriesSEE, which is a Microsoft® Excel add-in. Moving average, Theis, pneumatic-lag, and gamma functions transform time series of measured values into water-level model components in SeriesSEE. Earth tides and step transforms are additional computed water-level model components. Water-level models are calibrated by minimizing a sum-of-squares objective function where singular value decomposition and Tikhonov regularization stabilize results. Drawdown estimates from a water-level model are the summation of all Theis transforms minus residual differences between synthetic and measured water levels. The accuracy of drawdown estimates is limited primarily by noise in the data sets, not the Theis-transform approach. Drawdowns much smaller than environmental fluctuations have been detected across major fault structures, at distances of more than 1 mile from the pumping well, and with limited pre-pumping and recovery data at sites across the United States. In addition to water-level modeling, utilities exist in SeriesSEE for viewing, cleaning, manipulating, and analyzing time-series data.
Westenbroek, Stephen M.; Doherty, John; Walker, John F.; Kelson, Victor A.; Hunt, Randall J.; Cera, Timothy B.
2012-01-01
The TSPROC (Time Series PROCessor) computer software uses a simple scripting language to process and analyze time series. It was developed primarily to assist in the calibration of environmental models. The software is designed to perform calculations on time-series data commonly associated with surface-water models, including calculation of flow volumes, transformation by means of basic arithmetic operations, and generation of seasonal and annual statistics and hydrologic indices. TSPROC can also be used to generate some of the key input files required to perform parameter optimization by means of the PEST (Parameter ESTimation) computer software. Through the use of TSPROC, the objective function for use in the model-calibration process can be focused on specific components of a hydrograph.
Transferable Atomic Multipole Machine Learning Models for Small Organic Molecules.
Bereau, Tristan; Andrienko, Denis; von Lilienfeld, O Anatole
2015-07-14
Accurate representation of the molecular electrostatic potential, which is often expanded in distributed multipole moments, is crucial for an efficient evaluation of intermolecular interactions. Here we introduce a machine learning model for multipole coefficients of atom types H, C, O, N, S, F, and Cl in any molecular conformation. The model is trained on quantum-chemical results for atoms in varying chemical environments drawn from thousands of organic molecules. Multipoles in systems with neutral, cationic, and anionic molecular charge states are treated with individual models. The models' predictive accuracy and applicability are illustrated by evaluating intermolecular interaction energies of nearly 1,000 dimers and the cohesive energy of the benzene crystal.
Self-organized Criticality Model for Ocean Internal Waves
International Nuclear Information System (INIS)
Wang Gang; Hou Yijun; Lin Min; Qiao Fangli
2009-01-01
In this paper, we present a simple spring-block model for ocean internal waves based on the self-organized criticality (SOC). The oscillations of the water blocks in the model display power-law behavior with an exponent of -2 in the frequency domain, which is similar to the current and sea water temperature spectra in the actual ocean and the universal Garrett and Munk deep ocean internal wave model [Geophysical Fluid Dynamics 2 (1972) 225; J. Geophys. Res. 80 (1975) 291]. The influence of the ratio of the driving force to the spring coefficient to SOC behaviors in the model is also discussed. (general)
There Is No Simple Model of the Plasma Membrane Organization
Bernardino de la Serna, Jorge; Schütz, Gerhard J.; Eggeling, Christian; Cebecauer, Marek
2016-01-01
Ever since technologies enabled the characterization of eukaryotic plasma membranes, heterogeneities in the distributions of its constituents were observed. Over the years this led to the proposal of various models describing the plasma membrane organization such as lipid shells, picket-and-fences, lipid rafts, or protein islands, as addressed in numerous publications and reviews. Instead of emphasizing on one model we in this review give a brief overview over current models and highlight how current experimental work in one or the other way do not support the existence of a single overarching model. Instead, we highlight the vast variety of membrane properties and components, their influences and impacts. We believe that highlighting such controversial discoveries will stimulate unbiased research on plasma membrane organization and functionality, leading to a better understanding of this essential cellular structure. PMID:27747212
Rummler, Geary A.; Brache, Alan P.
This book offers an integrated framework for achieving competitive advantage by managing organizations, processes, and jobs effectively. Chapter 1 explores the forces driving the needs to be more competitive. Chapter 2 contrasts the traditional functional view of the organization with the systems view. Chapter 3 introduces the three levels of…
Device model investigation of bilayer organic light emitting diodes
International Nuclear Information System (INIS)
Crone, B. K.; Davids, P. S.; Campbell, I. H.; Smith, D. L.
2000-01-01
Organic materials that have desirable luminescence properties, such as a favorable emission spectrum and high luminescence efficiency, are not necessarily suitable for single layer organic light-emitting diodes (LEDs) because the material may have unequal carrier mobilities or contact limited injection properties. As a result, single layer LEDs made from such organic materials are inefficient. In this article, we present device model calculations of single layer and bilayer organic LED characteristics that demonstrate the improvements in device performance that can occur in bilayer devices. We first consider an organic material where the mobilities of the electrons and holes are significantly different. The role of the bilayer structure in this case is to move the recombination away from the electrode that injects the low mobility carrier. We then consider an organic material with equal electron and hole mobilities but where it is not possible to make a good contact for one carrier type, say electrons. The role of a bilayer structure in this case is to prevent the holes from traversing the device without recombining. In both cases, single layer device limitations can be overcome by employing a two organic layer structure. The results are discussed using the calculated spatial variation of the carrier densities, electric field, and recombination rate density in the structures. (c) 2000 American Institute of Physics
Financial incentives: alternatives to the altruistic model of organ donation.
Siminoff, L A; Leonard, M D
1999-12-01
Improvements in transplantation techniques have resulted in a demand for transplantable organs that far outpaces supply. Present efforts to secure organs use an altruistic system designed to appeal to a public that will donate organs because they are needed. Efforts to secure organs under this system have not been as successful as hoped. Many refinements to the altruistic model have been or are currently being proposed, such as "required request," "mandated choice," "routine notification," and "presumed consent." Recent calls for market approaches to organ procurement reflect growing doubts about the efficacy of these refinements. Market approaches generally use a "futures market," with benefits payable either periodically or when or if organs are procured. Lump-sum arrangements could include donations to surviving family or contributions to charities or to funeral costs. Possibilities for a periodic system of payments include reduced premiums for health or life insurance, or a reciprocity system whereby individuals who periodically reaffirm their willingness to donate are given preference if they require a transplant. Market approaches do raise serious ethical issues, including potential exploitation of the poor. Such approaches may also be effectively proscribed by the 1984 National Organ Transplant Act.
Developing a dengue early warning system using time series model: Case study in Tainan, Taiwan
Chen, Xiao-Wei; Jan, Chyan-Deng; Wang, Ji-Shang
2017-04-01
Dengue fever (DF) is a climate-sensitive disease that has been emerging in southern regions of Taiwan over the past few decades, causing a significant health burden to affected areas. This study aims to propose a predictive model to implement an early warning system so as to enhance dengue surveillance and control in Tainan, Taiwan. The Seasonal Autoregressive Integrated Moving Average (SARIMA) model was used herein to forecast dengue cases. Temporal correlation between dengue incidences and climate variables were examined by Pearson correlation analysis and Cross-correlation tests in order to identify key determinants to be included as predictors. The dengue surveillance data between 2000 and 2009, as well as their respective climate variables were then used as inputs for the model. We validated the model by forecasting the number of dengue cases expected to occur each week between January 1, 2010 and December 31, 2015. In addition, we analyzed historical dengue trends and found that 25 cases occurring in one week was a trigger point that often led to a dengue outbreak. This threshold point was combined with the season-based framework put forth by the World Health Organization to create a more accurate epidemic threshold for a Tainan-specific warning system. A Seasonal ARIMA model with the general form: (1,0,5)(1,1,1)52 is identified as the most appropriate model based on lowest AIC, and was proven significant in the prediction of observed dengue cases. Based on the correlation coefficient, Lag-11 maximum 1-hr rainfall (r=0.319, Pdengue surveillance and control in Tainan, Taiwan. We conclude that this timely dengue early warning system will enable public health services to allocate limited resources more effectively, and public health officials to adjust dengue emergency response plans to their maximum capabilities.
Numerical modelling of series-parallel cooling systems in power plant
Regucki, Paweł; Lewkowicz, Marek; Kucięba, Małgorzata
2017-11-01
The paper presents a mathematical model allowing one to study series-parallel hydraulic systems like, e.g., the cooling system of a power boiler's auxiliary devices or a closed cooling system including condensers and cooling towers. The analytical approach is based on a set of non-linear algebraic equations solved using numerical techniques. As a result of the iterative process, a set of volumetric flow rates of water through all the branches of the investigated hydraulic system is obtained. The calculations indicate the influence of changes in the pipeline's geometrical parameters on the total cooling water flow rate in the analysed installation. Such an approach makes it possible to analyse different variants of the modernization of the studied systems, as well as allowing for the indication of its critical elements. Basing on these results, an investor can choose the optimal variant of the reconstruction of the installation from the economic point of view. As examples of such a calculation, two hydraulic installations are described. One is a boiler auxiliary cooling installation including two screw ash coolers. The other is a closed cooling system consisting of cooling towers and condensers.
Testing Homeopathy in Mouse Emotional Response Models: Pooled Data Analysis of Two Series of Studies
Directory of Open Access Journals (Sweden)
Paolo Bellavite
2012-01-01
Full Text Available Two previous investigations were performed to assess the activity of Gelsemium sempervirens (Gelsemium s. in mice, using emotional response models. These two series are pooled and analysed here. Gelsemium s. in various homeopathic centesimal dilutions/dynamizations (4C, 5C, 7C, 9C, and 30C, a placebo (solvent vehicle, and the reference drugs diazepam (1 mg/kg body weight or buspirone (5 mg/kg body weight were delivered intraperitoneally to groups of albino CD1 mice, and their effects on animal behaviour were assessed by the light-dark (LD choice test and the open-field (OF exploration test. Up to 14 separate replications were carried out in fully blind and randomised conditions. Pooled analysis demonstrated highly significant effects of Gelsemium s. 5C, 7C, and 30C on the OF parameter “time spent in central area” and of Gelsemium s. 5C, 9C, and 30C on the LD parameters “time spent in lit area” and “number of light-dark transitions,” without any sedative action or adverse effects on locomotion. This pooled data analysis confirms and reinforces the evidence that Gelsemium s. regulates emotional responses and behaviour of laboratory mice in a nonlinear fashion with dilution/dynamization.
Modeling of the transient mobility in disordered organic semiconductors
Germs, W.C.; Van der Holst, J.M.M.; Van Mensfoort, S.L.M.; Bobbert, P.A.; Coehoorn, R.
2011-01-01
In non-steady-state experiments, the electrical response of devicesbased on disordered organic semiconductors often shows a large transient contribution due to relaxation of the out-of-equilibrium charge-carrier distribution. We have developed a model describing this process, based only on the
There Is No Simple Model of the Plasma Membrane Organization
Czech Academy of Sciences Publication Activity Database
de la serna, J. B.; Schütz, G.; Eggeling, Ch.; Cebecauer, Marek
2016-01-01
Roč. 4, SEP 2016 (2016), 106 ISSN 2296-634X R&D Projects: GA ČR GA15-06989S Institutional support: RVO:61388955 Keywords : plasma membrane * membrane organization models * heterogeneous distribution Subject RIV: CF - Physical ; Theoretical Chemistry
Waste Reduction Model (WARM) Resources for Small Businesses and Organizations
This page provides a brief overview of how EPA’s Waste Reduction Model (WARM) can be used by small businesses and organizations. The page includes a brief summary of uses of WARM for the audience and links to other resources.
Editorial: Plant organ abscission: from models to crops
The shedding of plant organs is a highly coordinated process essential for both vegetative and reproductive development (Addicott, 1982; Sexton and Roberts, 1982; Roberts et al., 2002; Leslie et al., 2007; Roberts and Gonzalez-Carranza, 2007; Estornell et al., 2013). Research with model plants, name...
Modeling growth of specific spoilage organisms in tilapia ...
African Journals Online (AJOL)
enoh
2012-03-29
Mar 29, 2012 ... Tilapia is an important aquatic fish, but severe spoilage of tilapia is most likely related to the global aquaculture. The spoilage is mostly caused by specific spoilage organisms (SSO). Therefore, it is very important to use microbial models to predict the growth of SSO in tilapia. This study firstly verified.
A model of virtual organization for corporate visibility and ...
African Journals Online (AJOL)
This paper considers the existing numerous research in business, Information and Communication Technology (ICT), examines a theoretical framework for value creation in a virtual world. Following a proposed model, a new strategic paradigm is created for corporate value; and virtual organization (VO) apply the use of ...
Modeling growth of specific spoilage organisms in tilapia ...
African Journals Online (AJOL)
Tilapia is an important aquatic fish, but severe spoilage of tilapia is most likely related to the global aquaculture. The spoilage is mostly caused by specific spoilage organisms (SSO). Therefore, it is very important to use microbial models to predict the growth of SSO in tilapia. This study firstly verified Pseudomonas and Vibrio ...
Promoting Representational Competence with Molecular Models in Organic Chemistry
Stull, Andrew T.; Gainer, Morgan; Padalkar, Shamin; Hegarty, Mary
2016-01-01
Mastering the many different diagrammatic representations of molecules used in organic chemistry is challenging for students. This article summarizes recent research showing that manipulating 3-D molecular models can facilitate the understanding and use of these representations. Results indicate that students are more successful in translating…
Wei, Lian-Qiang; Li, Yue; Mao, Li-Yuan; Chen, Qing; Lin, Ning
2018-01-01
A series of porous MOFs with hendecahedron cage-liked cavity has been constructed from the [Cu2(COO)4] secondary building unit, H3L (H3L = [1,1';3',1'']Terphenyl-4,5',4''-tricarboxylic acid) and pyrazine derivatives varied with different sizes; the structural evolving of the hendecahedron cage and the application in drug delivery and controlled release were presented.
Estimating organ doses from tube current modulated CT examinations using a generalized linear model.
Bostani, Maryam; McMillan, Kyle; Lu, Peiyun; Kim, Grace Hyun J; Cody, Dianna; Arbique, Gary; Greenberg, S Bruce; DeMarco, John J; Cagnon, Chris H; McNitt-Gray, Michael F
2017-04-01
Currently, available Computed Tomography dose metrics are mostly based on fixed tube current Monte Carlo (MC) simulations and/or physical measurements such as the size specific dose estimate (SSDE). In addition to not being able to account for Tube Current Modulation (TCM), these dose metrics do not represent actual patient dose. The purpose of this study was to generate and evaluate a dose estimation model based on the Generalized Linear Model (GLM), which extends the ability to estimate organ dose from tube current modulated examinations by incorporating regional descriptors of patient size, scanner output, and other scan-specific variables as needed. The collection of a total of 332 patient CT scans at four different institutions was approved by each institution's IRB and used to generate and test organ dose estimation models. The patient population consisted of pediatric and adult patients and included thoracic and abdomen/pelvis scans. The scans were performed on three different CT scanner systems. Manual segmentation of organs, depending on the examined anatomy, was performed on each patient's image series. In addition to the collected images, detailed TCM data were collected for all patients scanned on Siemens CT scanners, while for all GE and Toshiba patients, data representing z-axis-only TCM, extracted from the DICOM header of the images, were used for TCM simulations. A validated MC dosimetry package was used to perform detailed simulation of CT examinations on all 332 patient models to estimate dose to each segmented organ (lungs, breasts, liver, spleen, and kidneys), denoted as reference organ dose values. Approximately 60% of the data were used to train a dose estimation model, while the remaining 40% was used to evaluate performance. Two different methodologies were explored using GLM to generate a dose estimation model: (a) using the conventional exponential relationship between normalized organ dose and size with regional water equivalent diameter
Energy Technology Data Exchange (ETDEWEB)
Louis, J.P.
2004-07-01
The modeling of a system to be automatized is a key step for the determination of the control laws because these laws are based on inverse models deduced from direct models. The ideal example is the DC actuator, the simpleness of which allows to directly shift from the modeling to the control law. For AC actuators, the modeling tools are based on the classical hypotheses: linearity, first harmonics, symmetry. They lead to very efficient models which allow to study the properties in dynamical and permanent regime of the most important actuators: synchronous motors, asynchronous motors, voltage inverters. Some extensions to other kind of machines which does not fulfill the classical hypotheses are also proposed: synchronous machines with non-sinusoidal field distribution and asynchronous machines in saturated regime. (J.S.)
Modeling secondary organic aerosol formation through cloud processing of organic compounds
Directory of Open Access Journals (Sweden)
J. Chen
2007-10-01
Full Text Available Interest in the potential formation of secondary organic aerosol (SOA through reactions of organic compounds in condensed aqueous phases is growing. In this study, the potential formation of SOA from irreversible aqueous-phase reactions of organic species in clouds was investigated. A new proposed aqueous-phase chemistry mechanism (AqChem is coupled with the existing gas-phase Caltech Atmospheric Chemistry Mechanism (CACM and the Model to Predict the Multiphase Partitioning of Organics (MPMPO that simulate SOA formation. AqChem treats irreversible organic reactions that lead mainly to the formation of carboxylic acids, which are usually less volatile than the corresponding aldehydic compounds. Zero-dimensional model simulations were performed for tropospheric conditions with clouds present for three consecutive hours per day. Zero-dimensional model simulations show that 48-h average SOA formation is increased by 27% for a rural scenario with strong monoterpene emissions and 7% for an urban scenario with strong emissions of aromatic compounds, respectively, when irreversible organic reactions in clouds are considered. AqChem was also incorporated into the Community Multiscale Air Quality Model (CMAQ version 4.4 with CACM/MPMPO and applied to a previously studied photochemical episode (3–4 August 2004 focusing on the eastern United States. The CMAQ study indicates that the maximum contribution of SOA formation from irreversible reactions of organics in clouds is 0.28 μg m^{−3} for 24-h average concentrations and 0.60 μg m^{−3} for one-hour average concentrations at certain locations. On average, domain-wide surface SOA predictions for the episode are increased by 9% when irreversible, in-cloud processing of organics is considered. Because aldehydes of carbon number greater than four are assumed to convert fully to the corresponding carboxylic acids upon reaction with OH in cloud droplets and this assumption may overestimate
Directory of Open Access Journals (Sweden)
David E. Allen
2016-03-01
Full Text Available This paper features an analysis of major currency exchange rate movements in relation to the US dollar, as constituted in US dollar terms. Euro, British pound, Chinese yuan, and Japanese yen are modelled using a variety of non-linear models, including smooth transition regression models, logistic smooth transition regressions models, threshold autoregressive models, nonlinear autoregressive models, and additive nonlinear autoregressive models, plus Neural Network models. The models are evaluated on the basis of error metrics for twenty day out-of-sample forecasts using the mean average percentage errors (MAPE. The results suggest that there is no dominating class of time series models, and the different currency pairs relationships with the US dollar are captured best by neural net regression models, over the ten year sample of daily exchange rate returns data, from August 2005 to August 2015.
Ramli, Nazirah; Mutalib, Siti Musleha Ab; Mohamad, Daud
2017-08-01
Fuzzy time series forecasting model has been proposed since 1993 to cater for data in linguistic values. Many improvement and modification have been made to the model such as enhancement on the length of interval and types of fuzzy logical relation. However, most of the improvement models represent the linguistic term in the form of discrete fuzzy sets. In this paper, fuzzy time series model with data in the form of trapezoidal fuzzy numbers and natural partitioning length approach is introduced for predicting the unemployment rate. Two types of fuzzy relations are used in this study which are first order and second order fuzzy relation. This proposed model can produce the forecasted values under different degree of confidence.
Test performance of the QSE series of 5 cm aperture quadrupole model magnets
International Nuclear Information System (INIS)
Archer, B.; Bein, D.; Cunningham, G.; DiMarco, J.; Gathright, T.; Jayakumar, J.; LaBarge, A.; Li, W.; Lambert, D.; Scott, M.
1994-01-01
A 5 cm aperture quadrupole design, the QSE series of magnets were the first to be tested in the Short Magnet and Cable Test Laboratory (SMCTL) at the SSCL. Test performance of the first two magnets of the series are presented, including quench performance, quench localization, strain gage readings, and magnetic measurements. Both magnets behaved reasonably well with no quenches below the collider operating current, four training quenches to plateau, and good training memory between thermal cycles. Future magnets in the QSE series will be used to reduce the initial training and to tune out unwanted magnetic harmonics
Test performance of the QSE series of 5 cm aperture quadrupole model magnets
International Nuclear Information System (INIS)
Archer, B.; Bein, D.; Cunningham, G.; DiMarco, J.; Gathright, T.; Jayakumar, J.; Labarge, A.; Li, W.; Lambert, D.; Scott, M.; Snitchler, G.; Zeigler, R.
1993-04-01
A 5 cm aperture quadrupole design, the QSE series of magnets were the first to be tested in the Short Magnet and Cable Test Laboratory (SMCTL) at the SSCL. Test performance of the first two magnets of the series are presented, including quench performance, quench localization, strain gage readings, and magnetic measurements.Both magnets behaved reasonably well with no quenches below the collider operating current, four training quenches to plateau, and good training memory between thermal cycles. Future magnets in the QSE series will be used to reduce the initial training and to tune out unwanted magnetic harmonics
Modeling of secondary organic aerosol yields from laboratory chamber data
Directory of Open Access Journals (Sweden)
M. N. Chan
2009-08-01
Full Text Available Laboratory chamber data serve as the basis for constraining models of secondary organic aerosol (SOA formation. Current models fall into three categories: empirical two-product (Odum, product-specific, and volatility basis set. The product-specific and volatility basis set models are applied here to represent laboratory data on the ozonolysis of α-pinene under dry, dark, and low-NO_{x} conditions in the presence of ammonium sulfate seed aerosol. Using five major identified products, the model is fit to the chamber data. From the optimal fitting, SOA oxygen-to-carbon (O/C and hydrogen-to-carbon (H/C ratios are modeled. The discrepancy between measured H/C ratios and those based on the oxidation products used in the model fitting suggests the potential importance of particle-phase reactions. Data fitting is also carried out using the volatility basis set, wherein oxidation products are parsed into volatility bins. The product-specific model is most likely hindered by lack of explicit inclusion of particle-phase accretion compounds. While prospects for identification of the majority of SOA products for major volatile organic compounds (VOCs classes remain promising, for the near future empirical product or volatility basis set models remain the approaches of choice.
Pu-239 organ specific dosimetric model applied to non-human biota
Kaspar, Matthew Jason
There are few locations throughout the world, like the Maralinga nuclear test site located in south western Australia, where sufficient plutonium contaminate concentration levels exist that they can be utilized for studies of the long-term radionuclide accumulation in non-human biota. The information obtained will be useful for the potential human users of the site while also keeping with international efforts to better understand doses to non-human biota. In particular, this study focuses primarily on a rabbit sample set collected from the population located within the site. Our approach is intended to employ the same dose and dose rate methods selected by the International Commission on Radiological Protection and adapted by the scientific community for similar research questions. These models rely on a series of simplifying assumptions on biota and their geometry; in particular; organisms are treated as spherical and ellipsoidal representations displaying the animal mass and volume. These simplifications assume homogeneity of all animal tissues. In collaborative efforts between Colorado State University and the Australian Nuclear Science and Technology Organisation (ANSTO), we are expanding current knowledge on radionuclide accumulation in specific organs causing organ-specific dose rates, such as Pu-239 accumulating in bone, liver, and lungs. Organ-specific dose models have been developed for humans; however, little has been developed for the dose assessment to biota, in particular rabbits. This study will determine if it is scientifically valid to use standard software, in particular ERICA Tool, as a means to determine organ-specific dosimetry due to Pu-239 accumulation in organs. ERICA Tool is normally applied to whole organisms as a means to determine radiological risk to whole ecosystems. We will focus on the aquatic model within ERICA Tool, as animal organs, like aquatic organisms, can be assumed to lie within an infinite uniform medium. This model would
National Oceanic and Atmospheric Administration, Department of Commerce — To estimate the carbon chemistry conditions experienced by free-living organisms, we will conduct coupled biological/carbon chemistry sampling for key zooplankton...
On the influence of the exposure model on organ doses
International Nuclear Information System (INIS)
Drexler, G.; Eckerl, H.
1988-01-01
Based on the design characteristics of the MIRD-V phantom, two sex-specific adult phantoms, ADAM and EVA were introduced especially for the calculation of organ doses resulting from external irradiation. Although the body characteristics of all the phantoms are in good agreement with those of the reference man and woman, they have some disadvantages related to the location and shape of organs and the form of the whole body. To overcome these disadvantages related to the location and shape of organs and form of the whole body. To overcome these disadvantages related to the location and shape of organs and the form of the whole body. To overcome these disadvantages and to obtain more realistic phantoms, a technique based on computer tomographic data (voxel-phantom) was developed. This technique allows any physical phantom or real body to be converted into computer files. The improvements are of special importance with regard to the skeleton, because a better modeling of the bone surfaces and separation of hard bone and bone marrow can be achieved. For photon irradiation, the sensitivity of the model on organ doses or the effective dose equivalent is important for operational radiation protection
Sustainable Organic Farming For Environmental Health A Social Development Model
Directory of Open Access Journals (Sweden)
Ijun Rijwan Susanto
2015-05-01
Full Text Available ABSTRACT In this study the researcher attempted 1 to understand the basic features of organic farming in The Paguyuban Pasundans Cianjur 2 to describe and understand how the stakeholders were are able to internalize the challenges of organic farming on their lived experiences in the community 3 to describe and understand how the stakeholders were are able to internalize and applied the values of benefits of organic farming in support of environmental health on their lived experiences in the community 4 The purpose was to describe and understand how the stakeholders who are able to articulate their ideas regarding the model of sustainable organic farming 5 The Policy Recommendation for Organic Farming. The researcher employed triangulation thorough finding that provides breadth and depth to an investigation offering researchers a more accurate picture of the phenomenon. In the implementation of triangulation researchers conducted several interviews to get saturation. After completion of the interview results are written compiled and shown to the participants to check every statement by every participant. In addition researchers also checked the relevant documents and direct observation in the field The participants of this study were the stakeholders namely 1 The leader of Paguyuban Pasundans Organic Farmer Cianjur PPOFC 2 Members of Paguyuban Pasundans Organic FarmersCianjur 3 Leader of NGO 4 Government officials of agriculture 5 Business of organic food 6 and Consumer of organic food. Generally the findings of the study revealed the following 1 PPOFC began to see the reality as the impact of modern agriculture showed in fertility problems due to contaminated soil by residues of agricultural chemicals such as chemical fertilizers and chemical pesticides. So he wants to restore the soil fertility through environmentally friendly of farming practices 2 the challenges of organic farming on their lived experiences in the community farmers did not
Zhai, G.; Shirzaei, M.
2014-12-01
The Kilauea volcano, Hawaii Island, is one of the most active volcanoes worldwide. Its complex system including magma reservoirs and rift zones, provides a unique opportunity to investigate the dynamics of magma transport and supply. The relatively shallow magma reservoir beneath the caldera stores magma prior to eruption at the caldera or migration to the rift zones. Additionally, the temporally variable pressure in the magma reservoir causes changes in the stress field, driving dike propagation and occasional intrusions at the eastern rift zone. Thus constraining the time-dependent evolution of the magma reservoir plays an important role in understanding magma processes such as supply, storage, transport and eruption. The recent development of space-based monitoring technology, InSAR (Interferometric synthetic aperture radar), allows the detection of subtle deformation of the surface at high spatial resolution and accuracy. In order to understand the dynamics of the magma chamber at Kilauea summit area and the associated stress field, we explored SAR data sets acquired in two overlapping tracks of Envisat SAR data during period 2003-2010. The combined InSAR time series includes 100 samples measuring summit deformation at unprecedented spatiotemporal resolutions. To investigate the source of the summit deformation field, we propose a novel time-dependent inverse modelling approach to constrain the dynamics of the reservoir volume change within the summit magma reservoir in three dimensions. In conjunction with seismic and gas data sets, the obtained time-dependent model could resolve the temporally variable relation between shallow and deep reservoirs, as well as their connection to the rift zone via stress changes. The data and model improve the understanding of the Kilauea plumbing system, physics of eruptions, mechanics of rift intrusions, and enhance eruption forecast models.
Directory of Open Access Journals (Sweden)
Z Jalali mola
2011-12-01
Full Text Available The Ising model is one of the simplest models describing the interacting particles. In this work, we calculate the high temperature series expansions of zero field susceptibility of ising model with ferromagnetic, antiferromagnetic and one antiferromagnetic interactions on two dimensional kagome lattice. Using the Pade´ approximation, we calculate the susceptibility of critical exponent of ferromagnetic ising model γ ≈ 1.75, which is consistent with universality hypothesis. However, antiferromagnetic and one antiferromagnetic interaction ising model doesn’t show any transition at finite temperature because of the effect of magnetic frustration.
Branching and self-organization in marine modular colonial organisms: a model.
Sánchez, Juan Armando; Lasker, Howard R; Nepomuceno, Erivelton G; Sánchez, J Dario; Woldenberg, Michael J
2004-03-01
Despite the universality of branching patterns in marine modular colonial organisms, there is neither a clear explanation about the growth of their branching forms nor an understanding of how these organisms conserve their shape during development. This study develops a model of branching and colony growth using parameters and variables related to actual modular structures (e.g., branches) in Caribbean gorgonian corals (Cnidaria). Gorgonians exhibiting treelike networks branch subapically, creating hierarchical mother-daughter relationships among branches. We modeled both the intrinsic subapical branching along with an ecological-physiological limit to growth or maximum number of mother branches (k). Shape is preserved by maintaining a constant ratio (c) between the total number of branches and the mother branches. The size frequency distribution of mother branches follows a scaling power law suggesting self-organized criticality. Differences in branching among species with the same k values are determined by r (branching rate) and c. Species with rr/2 or c>r>0). Ecological/physiological constraints limit growth without altering colony form or the interaction between r and c. The model described the branching dynamics giving the form to colonies and how colony growth declines over time without altering the branching pattern. This model provides a theoretical basis to study branching as a simple function of the number of branches independently of ordering- and bifurcation-based schemes.
Finite-element model of the active organ of Corti
Elliott, Stephen J.; Baumgart, Johannes
2016-01-01
The cochlear amplifier that provides our hearing with its extraordinary sensitivity and selectivity is thought to be the result of an active biomechanical process within the sensory auditory organ, the organ of Corti. Although imaging techniques are developing rapidly, it is not currently possible, in a fully active cochlea, to obtain detailed measurements of the motion of individual elements within a cross section of the organ of Corti. This motion is predicted using a two-dimensional finite-element model. The various solid components are modelled using elastic elements, the outer hair cells (OHCs) as piezoelectric elements and the perilymph and endolymph as viscous and nearly incompressible fluid elements. The model is validated by comparison with existing measurements of the motions within the passive organ of Corti, calculated when it is driven either acoustically, by the fluid pressure or electrically, by excitation of the OHCs. The transverse basilar membrane (BM) motion and the shearing motion between the tectorial membrane and the reticular lamina are calculated for these two excitation modes. The fully active response of the BM to acoustic excitation is predicted using a linear superposition of the calculated responses and an assumed frequency response for the OHC feedback. PMID:26888950
Kuiper, W.E.; Kuwornu, J.K.M.; Pennings, J.M.E.
2003-01-01
We apply the classic agency model to investigate risk shifting in an agricultural marketing channel, using time series analysis. We show that if the principal is risk-neutral and the agent is risk-averse instead of risk-neutral, then a linear contract can still be optimal if the fixed payment is
Directory of Open Access Journals (Sweden)
Subanar Subanar
2006-01-01
Full Text Available Recently, one of the central topics for the neural networks (NN community is the issue of data preprocessing on the use of NN. In this paper, we will investigate this topic particularly on the effect of Decomposition method as data processing and the use of NN for modeling effectively time series with both trend and seasonal patterns. Limited empirical studies on seasonal time series forecasting with neural networks show that some find neural networks are able to model seasonality directly and prior deseasonalization is not necessary, and others conclude just the opposite. In this research, we study particularly on the effectiveness of data preprocessing, including detrending and deseasonalization by applying Decomposition method on NN modeling and forecasting performance. We use two kinds of data, simulation and real data. Simulation data are examined on multiplicative of trend and seasonality patterns. The results are compared to those obtained from the classical time series model. Our result shows that a combination of detrending and deseasonalization by applying Decomposition method is the effective data preprocessing on the use of NN for forecasting trend and seasonal time series.
The scale mismatch between remotely sensed observations and crop growth models simulated state variables decreases the reliability of crop yield estimates. To overcome this problem, we used a two-step data assimilation phases: first we generated a complete leaf area index (LAI) time series by combin...
2010-09-13
... should address the following actions. (1) Permanently drain auxiliary fuel tanks, and clear them of fuel... them at the pneumatic source, and secure them. (4) Disconnect all fuel feed and fuel vent plumbing... Airworthiness Directives; The Boeing Company Model 737-700 (IGW) Series Airplanes Equipped With Auxiliary Fuel...
Energy Technology Data Exchange (ETDEWEB)
Schulze-Halberg, Axel, E-mail: axgeschu@iun.edu, E-mail: xbataxel@gmail.com [Department of Mathematics and Actuarial Science and Department of Physics, Indiana University Northwest, 3400 Broadway, Gary, Indiana 46408 (United States); Wang, Jie, E-mail: wangjie@iun.edu [Department of Computer Information Systems, Indiana University Northwest, 3400 Broadway, Gary, Indiana 46408 (United States)
2015-07-15
We obtain series solutions, the discrete spectrum, and supersymmetric partners for a quantum double-oscillator system. Its potential features a superposition of the one-parameter Mathews-Lakshmanan interaction and a one-parameter harmonic or inverse harmonic oscillator contribution. Furthermore, our results are transferred to a generalized Pöschl-Teller model that is isospectral to the double-oscillator system.
2011-01-28
..., will have a novel or unusual design features associated with the pilot lower lobe crew rest module (CRM... people to take part in this rulemaking by sending written comments, data, or views. The most helpful...) for installation of a lower lobe pilot crew rest module (CRM) in Boeing Model 767-300 series airplanes...
International Nuclear Information System (INIS)
Schulze-Halberg, Axel; Wang, Jie
2015-01-01
We obtain series solutions, the discrete spectrum, and supersymmetric partners for a quantum double-oscillator system. Its potential features a superposition of the one-parameter Mathews-Lakshmanan interaction and a one-parameter harmonic or inverse harmonic oscillator contribution. Furthermore, our results are transferred to a generalized Pöschl-Teller model that is isospectral to the double-oscillator system
Romaguera, M.; Vaughan, R. G.; Ettema, J.; Izquierdo-Verdiguier, E.; Hecker, C. A.; van der Meer, F. D.
2017-01-01
This paper explores for the first time the possibilities to use two land surface temperature (LST) time series of different origins (geostationary Meteosat Second Generation satellite data and Noah land surface modelling, LSM), to detect geothermal anomalies and extract the geothermal component of
2013-12-17
....dot.gov/ . Docket: Background documents or comments received may be read at http://www.regulations.gov.... Background On August 25, 2008, Airbus applied for a type certificate for their new Model A350-900 series... corruption of data and systems critical to the safety and maintenance of the airplane. The existing...
2010-12-28
... Directives; The Boeing Company Model 777-200, - 200LR, -300, and -300ER Series Airplanes AGENCY: Federal... installing new panels in the main equipment center, making certain wiring changes, installing new GFI relays... other airplanes, this proposed AD would require doing certain bond resistance measurements, and...
Durbin, J.; Koopman, S.J.M.
1998-01-01
The analysis of non-Gaussian time series using state space models is considered from both classical and Bayesian perspectives. The treatment in both cases is based on simulation using importance sampling and antithetic variables; Monte Carlo Markov chain methods are not employed. Non-Gaussian
Invertebrates as model organisms for research on aging biology.
Murthy, Mahadev; Ram, Jeffrey L
2015-01-30
Invertebrate model systems, such as nematodes and fruit flies, have provided valuable information about the genetics and cellular biology involved in aging. However, limitations of these simple, genetically tractable organisms suggest the need for other model systems, some of them invertebrate, to facilitate further advances in the understanding of mechanisms of aging and longevity in mammals, including humans. This paper introduces 10 review articles about the use of invertebrate model systems for the study of aging by authors who participated in an 'NIA-NIH symposium on aging in invertebrate model systems' at the 2013 International Congress for Invertebrate Reproduction and Development. In contrast to the highly derived characteristics of nematodes and fruit flies as members of the superphylum Ecdysozoa, cnidarians, such as Hydra, are more 'basal' organisms that have a greater number of genetic orthologs in common with humans. Moreover, some other new model systems, such as the urochordate Botryllus schlosseri , the tunicate Ciona , and the sea urchins (Echinodermata) are members of the Deuterostomia, the same superphylum that includes all vertebrates, and thus have mechanisms that are likely to be more closely related to those occurring in humans. Additional characteristics of these new model systems, such as the recent development of new molecular and genetic tools and a more similar pattern to humans of regeneration and stem cell function suggest that these new model systems may have unique advantages for the study of mechanisms of aging and longevity.
Organizing the space and behavior of semantic models.
Rubin, Timothy N; Kievit-Kylar, Brent; Willits, Jon A; Jones, Michael N
Semantic models play an important role in cognitive science. These models use statistical learning to model word meanings from co-occurrences in text corpora. A wide variety of semantic models have been proposed, and the literature has typically emphasized situations in which one model outperforms another. However, because these models often vary with respect to multiple sub-processes (e.g., their normalization or dimensionality-reduction methods), it can be difficult to delineate which of these processes are responsible for observed performance differences. Furthermore, the fact that any two models may vary along multiple dimensions makes it difficult to understand where these models fall within the space of possible psychological theories. In this paper, we propose a general framework for organizing the space of semantic models. We then illustrate how this framework can be used to understand model comparisons in terms of individual manipulations along sub-processes. Using several artificial datasets we show how both representational structure and dimensionality-reduction influence a model's ability to pick up on different types of word relationships.
IT Business Value Model for Information Intensive Organizations
Directory of Open Access Journals (Sweden)
Antonio Carlos Gastaud Maçada
2012-01-01
Full Text Available Many studies have highlighted the capacity Information Technology (IT has for generating value for organizations. Investments in IT made by organizations have increased each year. Therefore, the purpose of the present study is to analyze the IT Business Value for Information Intensive Organizations (IIO - e.g. banks, insurance companies and securities brokers. The research method consisted of a survey that used and combined the models from Weill and Broadbent (1998 and Gregor, Martin, Fernandez, Stern and Vitale (2006. Data was gathered using an adapted instrument containing 5 dimensions (Strategic, Informational, Transactional, Transformational and Infra-structure with 27 items. The instrument was refined by employing statistical techniques such as Exploratory and Confirmatory Factorial Analysis through Structural Equations (first and second order Model Measurement. The final model is composed of four factors related to IT Business Value: Strategic, Informational, Transactional and Transformational, arranged in 15 items. The dimension Infra-structure was excluded during the model refinement process because it was discovered during interviews that managers were unable to perceive it as a distinct dimension of IT Business Value.
Minimal agent based model for financial markets I. Origin and self-organization of stylized facts
Alfi, V.; Cristelli, M.; Pietronero, L.; Zaccaria, A.
2009-02-01
We introduce a minimal agent based model for financial markets to understand the nature and self-organization of the stylized facts. The model is minimal in the sense that we try to identify the essential ingredients to reproduce the most important deviations of price time series from a random walk behavior. We focus on four essential ingredients: fundamentalist agents which tend to stabilize the market; chartist agents which induce destabilization; analysis of price behavior for the two strategies; herding behavior which governs the possibility of changing strategy. Bubbles and crashes correspond to situations dominated by chartists, while fundamentalists provide a long time stability (on average). The stylized facts are shown to correspond to an intermittent behavior which occurs only for a finite value of the number of agents N. Therefore they correspond to finite size effects which, however, can occur at different time scales. We propose a new mechanism for the self-organization of this state which is linked to the existence of a threshold for the agents to be active or not active. The feedback between price fluctuations and number of active agents represents a crucial element for this state of self-organized intermittency. The model can be easily generalized to consider more realistic variants.
Mobility dependent recombination models for organic solar cells
Wagenpfahl, Alexander
2017-09-01
Modern solar cell technologies are driven by the effort to enhance power conversion efficiencies. A main mechanism limiting power conversion efficiencies is charge carrier recombination which is a direct function of the encounter probability of both recombination partners. In inorganic solar cells with rather high charge carrier mobilities, charge carrier recombination is often dominated by energetic states which subsequently trap both recombination partners for recombination. Free charge carriers move fast enough for Coulomb attraction to be irrelevant for the encounter probability. Thus, charge carrier recombination is independent of charge carrier mobilities. In organic semiconductors charge carrier mobilities are much lower. Therefore, electrons and holes have more time react to mutual Coulomb-forces. This results in the strong charge carrier mobility dependencies of the observed charge carrier recombination rates. In 1903 Paul Langevin published a fundamental model to describe the recombination of ions in gas-phase or aqueous solutions, known today as Langevin recombination. During the last decades this model was used to interpret and model recombination in organic semiconductors. However, certain experiments especially with bulk-heterojunction solar cells reveal much lower recombination rates than predicted by Langevin. In search of an explanation, many material and device properties such as morphology and energetic properties have been examined in order to extend the validity of the Langevin model. A key argument for most of these extended models is, that electron and hole must find each other at a mutual spatial location. This encounter may be limited for instance by trapping of charges in trap states, by selective electrodes separating electrons and holes, or simply by the morphology of the involved semiconductors, making it impossible for electrons and holes to recombine at high rates. In this review, we discuss the development of mobility limited
A taxonomy of nursing care organization models in hospitals.
Dubois, Carl-Ardy; D'Amour, Danielle; Tchouaket, Eric; Rivard, Michèle; Clarke, Sean; Blais, Régis
2012-08-28
Over the last decades, converging forces in hospital care, including cost-containment policies, rising healthcare demands and nursing shortages, have driven the search for new operational models of nursing care delivery that maximize the use of available nursing resources while ensuring safe, high-quality care. Little is known, however, about the distinctive features of these emergent nursing care models. This article contributes to filling this gap by presenting a theoretically and empirically grounded taxonomy of nursing care organization models in the context of acute care units in Quebec and comparing their distinctive features. This study was based on a survey of 22 medical units in 11 acute care facilities in Quebec. Data collection methods included questionnaire, interviews, focus groups and administrative data census. The analytical procedures consisted of first generating unit profiles based on qualitative and quantitative data collected at the unit level, then applying hierarchical cluster analysis to the units' profile data. The study identified four models of nursing care organization: two professional models that draw mainly on registered nurses as professionals to deliver nursing services and reflect stronger support to nurses' professional practice, and two functional models that draw more significantly on licensed practical nurses (LPNs) and assistive staff (orderlies) to deliver nursing services and are characterized by registered nurses' perceptions that the practice environment is less supportive of their professional work. This study showed that medical units in acute care hospitals exhibit diverse staff mixes, patterns of skill use, work environment design, and support for innovation. The four models reflect not only distinct approaches to dealing with the numerous constraints in the nursing care environment, but also different degrees of approximations to an "ideal" nursing professional practice model described by some leaders in the
Modeling regional secondary organic aerosol using the Master Chemical Mechanism
Li, Jingyi; Cleveland, Meredith; Ziemba, Luke D.; Griffin, Robert J.; Barsanti, Kelley C.; Pankow, James F.; Ying, Qi
2015-02-01
A modified near-explicit Master Chemical Mechanism (MCM, version 3.2) with 5727 species and 16,930 reactions and an equilibrium partitioning module was incorporated into the Community Air Quality Model (CMAQ) to predict the regional concentrations of secondary organic aerosol (SOA) from volatile organic compounds (VOCs) in the eastern United States (US). In addition to the semi-volatile SOA from equilibrium partitioning, reactive surface uptake processes were used to simulate SOA formation due to isoprene epoxydiol, glyoxal and methylglyoxal. The CMAQ-MCM-SOA model was applied to simulate SOA formation during a two-week episode from August 28 to September 7, 2006. The southeastern US has the highest SOA, with a maximum episode-averaged concentration of ∼12 μg m-3. Primary organic aerosol (POA) and SOA concentrations predicted by CMAQ-MCM-SOA agree well with AMS-derived hydrocarbon-like organic aerosol (HOA) and oxygenated organic aerosol (OOA) urban concentrations at the Moody Tower at the University of Houston. Predicted molecular properties of SOA (O/C, H/C, N/C and OM/OC ratios) at the site are similar to those reported in other urban areas, and O/C values agree with measured O/C at the same site. Isoprene epoxydiol is predicted to be the largest contributor to total SOA concentration in the southeast US, followed by methylglyoxal and glyoxal. The semi-volatile SOA components are dominated by products from β-caryophyllene oxidation, but the major species and their concentrations are sensitive to errors in saturation vapor pressure estimation. A uniform decrease of saturation vapor pressure by a factor of 100 for all condensable compounds can lead to a 150% increase in total SOA. A sensitivity simulation with UNIFAC-calculated activity coefficients (ignoring phase separation and water molecule partitioning into the organic phase) led to a 10% change in the predicted semi-volatile SOA concentrations.
Zhou, Fuqun; Zhang, Aining
2016-10-25
Nowadays, various time-series Earth Observation data with multiple bands are freely available, such as Moderate Resolution Imaging Spectroradiometer (MODIS) datasets including 8-day composites from NASA, and 10-day composites from the Canada Centre for Remote Sensing (CCRS). It is challenging to efficiently use these time-series MODIS datasets for long-term environmental monitoring due to their vast volume and information redundancy. This challenge will be greater when Sentinel 2-3 data become available. Another challenge that researchers face is the lack of in-situ data for supervised modelling, especially for time-series data analysis. In this study, we attempt to tackle the two important issues with a case study of land cover mapping using CCRS 10-day MODIS composites with the help of Random Forests' features: variable importance, outlier identification. The variable importance feature is used to analyze and select optimal subsets of time-series MODIS imagery for efficient land cover mapping, and the outlier identification feature is utilized for transferring sample data available from one year to an adjacent year for supervised classification modelling. The results of the case study of agricultural land cover classification at a regional scale show that using only about a half of the variables we can achieve land cover classification accuracy close to that generated using the full dataset. The proposed simple but effective solution of sample transferring could make supervised modelling possible for applications lacking sample data.
Zhou, Ya-Tong; Fan, Yu; Chen, Zi-Yi; Sun, Jian-Cheng
2017-05-01
The contribution of this work is twofold: (1) a multimodality prediction method of chaotic time series with the Gaussian process mixture (GPM) model is proposed, which employs a divide and conquer strategy. It automatically divides the chaotic time series into multiple modalities with different extrinsic patterns and intrinsic characteristics, and thus can more precisely fit the chaotic time series. (2) An effective sparse hard-cut expectation maximization (SHC-EM) learning algorithm for the GPM model is proposed to improve the prediction performance. SHC-EM replaces a large learning sample set with fewer pseudo inputs, accelerating model learning based on these pseudo inputs. Experiments on Lorenz and Chua time series demonstrate that the proposed method yields not only accurate multimodality prediction, but also the prediction confidence interval. SHC-EM outperforms the traditional variational learning in terms of both prediction accuracy and speed. In addition, SHC-EM is more robust and insusceptible to noise than variational learning. Supported by the National Natural Science Foundation of China under Grant No 60972106, the China Postdoctoral Science Foundation under Grant No 2014M561053, the Humanity and Social Science Foundation of Ministry of Education of China under Grant No 15YJA630108, and the Hebei Province Natural Science Foundation under Grant No E2016202341.
Directory of Open Access Journals (Sweden)
Y. S. La
2016-02-01
Full Text Available Recent studies have shown that low volatility gas-phase species can be lost onto the smog chamber wall surfaces. Although this loss of organic vapors to walls could be substantial during experiments, its effect on secondary organic aerosol (SOA formation has not been well characterized and quantified yet. Here the potential impact of chamber walls on the loss of gaseous organic species and SOA formation has been explored using the Generator for Explicit Chemistry and Kinetics of the Organics in the Atmosphere (GECKO-A modeling tool, which explicitly represents SOA formation and gas–wall partitioning. The model was compared with 41 smog chamber experiments of SOA formation under OH oxidation of alkane and alkene series (linear, cyclic and C12-branched alkanes and terminal, internal and 2-methyl alkenes with 7 to 17 carbon atoms under high NOx conditions. Simulated trends match observed trends within and between homologous series. The loss of organic vapors to the chamber walls is found to affect SOA yields as well as the composition of the gas and the particle phases. Simulated distributions of the species in various phases suggest that nitrates, hydroxynitrates and carbonylesters could substantially be lost onto walls. The extent of this process depends on the rate of gas–wall mass transfer, the vapor pressure of the species and the duration of the experiments. This work suggests that SOA yields inferred from chamber experiments could be underestimated up a factor of 2 due to the loss of organic vapors to chamber walls.
Lean construction as an effective organization model in Arctic
Directory of Open Access Journals (Sweden)
Balashova Elena S.
2017-01-01
Full Text Available In recent time, due to the sharp climatic changes, the Arctic attracts an increased interest of the world powers as a strategically important object. In 2013, the development strategy of the Arctic zone of the Russian Federation and national security for the period up to 2020 was approved by the President. In this strategy, the socio-economic development of the region in terms of improving the quality of life, expressed in the implementation of housing and civil engineering is very important. The goal of the study is to identify effective organization model of construction in the Arctic zone of the Russian Federation. Lean construction as a dynamically developing methodology abroad is analyzed. Characteristics of this organization model of construction meet the necessary requirements for the construction of various infrastructure objects in the Arctic. Therefore, the concept of lean construction can be an effective strategy of development of the Arctic regions of Russia as well as other Arctic countries.
Khan, A.; Belluzzi, L.; Landi Degl'Innocenti, E.; Fineschi, S.; Romoli, M.
2011-05-01
Context. The presence and importance of the coronal magnetic field is illustrated by a wide range of phenomena, such as the abnormally high temperatures of the coronal plasma, the existence of a slow and fast solar wind, the triggering of explosive events such as flares and CMEs. Aims: We investigate the possibility of using the Hanle effect to diagnose the coronal magnetic field by analysing its influence on the linear polarisation, i.e. the rotation of the plane of polarisation and depolarisation. Methods: We analyse the polarisation characteristics of the first three lines of the hydrogen Lyman-series using an axisymmetric, self-consistent, minimum-corona MHD model with relatively low values of the magnetic field (a few Gauss). Results: We find that the Hanle effect in the above-mentioned lines indeed seems to be a valuable tool for analysing the coronal magnetic field. However, great care must be taken when analysing the spectropolarimetry of the Lα line, given that a non-radial solar wind and active regions on the solar disk can mimic the effects of the magnetic field, and, in some cases, even mask them. Similar drawbacks are not found for the Lβ and Lγ lines because they are more sensitive to the magnetic field. We also briefly consider the instrumental requirements needed to perform polarimetric observations for diagnosing the coronal magnetic fields. Conclusions: The combined analysis of the three aforementioned lines could provide an important step towards better constrainting the value of solar coronal magnetic fields.
Understanding rare disease pathogenesis: a grand challenge for model organisms.
Hieter, Philip; Boycott, Kym M
2014-10-01
In this commentary, Philip Hieter and Kym Boycott discuss the importance of model organisms for understanding pathogenesis of rare human genetic diseases, and highlight the work of Brooks et al., "Dysfunction of 60S ribosomal protein L10 (RPL10) disrupts neurodevelopment and causes X-linked microcephaly in humans," published in this issue of GENETICS. Copyright © 2014 by the Genetics Society of America.
Quasi-dynamic model for an organic Rankine cycle
International Nuclear Information System (INIS)
Bamgbopa, Musbaudeen O.; Uzgoren, Eray
2013-01-01
Highlights: • Study presents a simplified transient modeling approach for an ORC under variable heat input. • The ORC model is presented as a synthesis of its models of its sub-components. • The model is compared to benchmark numerical simulations and experimental data at different stages. - Abstract: When considering solar based thermal energy input to an organic Rankine cycle (ORC), intermittent nature of the heat input does not only adversely affect the power output but also it may prevent ORC to operate under steady state conditions. In order to identify reliability and efficiency of such systems, this paper presents a simplified transient modeling approach for an ORC operating under variable heat input. The approach considers that response of the system to heat input variations is mainly dictated by the evaporator. Consequently, overall system is assembled using dynamic models for the heat exchangers (evaporator and condenser) and static models of the pump and the expander. In addition, pressure drop within heat exchangers is neglected. The model is compared to benchmark numerical and experimental data showing that the underlying assumptions are reasonable for cases where thermal input varies in time. Furthermore, the model is studied on another configuration and mass flow rates of both the working fluid and hot water and hot water’s inlet temperature to the ORC unit are shown to have direct influence on the system’s response
Ng, Kar Yong; Awang, Norhashidah
2018-01-06
Frequent haze occurrences in Malaysia have made the management of PM 10 (particulate matter with aerodynamic less than 10 μm) pollution a critical task. This requires knowledge on factors associating with PM 10 variation and good forecast of PM 10 concentrations. Hence, this paper demonstrates the prediction of 1-day-ahead daily average PM 10 concentrations based on predictor variables including meteorological parameters and gaseous pollutants. Three different models were built. They were multiple linear regression (MLR) model with lagged predictor variables (MLR1), MLR model with lagged predictor variables and PM 10 concentrations (MLR2) and regression with time series error (RTSE) model. The findings revealed that humidity, temperature, wind speed, wind direction, carbon monoxide and ozone were the main factors explaining the PM 10 variation in Peninsular Malaysia. Comparison among the three models showed that MLR2 model was on a same level with RTSE model in terms of forecasting accuracy, while MLR1 model was the worst.
Turbulence and Self-Organization Modeling Astrophysical Objects
Marov, Mikhail Ya
2013-01-01
This book focuses on the development of continuum models of natural turbulent media. It provides a theoretical approach to the solutions of different problems related to the formation, structure and evolution of astrophysical and geophysical objects. A stochastic modeling approach is used in the mathematical treatment of these problems, which reflects self-organization processes in open dissipative systems. The authors also consider examples of ordering for various objects in space throughout their evolutionary processes. This volume is aimed at graduate students and researchers in the fields of mechanics, astrophysics, geophysics, planetary and space science.
AGRICULTURAL COOPERATION IN RUSSIA: THE PROBLEM OF ORGANIZATION MODEL CHOICE
Directory of Open Access Journals (Sweden)
J. Nilsson
2008-09-01
Full Text Available In today's Russia many agricultural co-operatives are established from the top downwards. The national project "Development of Agroindustrial Complex" and other governmental programs initiate the formation of cooperative societies. These cooperatives are organized in accordance with the traditional cooperative model. Many of them do, however, not have any real business activities. The aim of this paper to investigate if traditional cooperatives (following principles such as collective ownership, one member one vote, equal treatment, and solidarity, etc. constitute the best organizational model for cooperatives societies under the present conditions in the Russian agriculture.
Ward-Garrison, Christian; Markstrom, Steven L.; Hay, Lauren E.
2009-01-01
The U.S. Geological Survey Downsizer is a computer application that selects, downloads, verifies, and formats station-based time-series data for environmental-resource models, particularly the Precipitation-Runoff Modeling System. Downsizer implements the client-server software architecture. The client presents a map-based, graphical user interface that is intuitive to modelers; the server provides streamflow and climate time-series data from over 40,000 measurement stations across the United States. This report is the Downsizer user's manual and provides (1) an overview of the software design, (2) installation instructions, (3) a description of the graphical user interface, (4) a description of selected output files, and (5) troubleshooting information.
International Nuclear Information System (INIS)
Zhang Yu; Sprecher, Alicia J.; Zhao Zongxi; Jiang, Jack J.
2011-01-01
Highlights: → The VWK method effectively detects the nonlinearity of a discrete map. → The method describes the chaotic time series of a biomechanical vocal fold model. → Nonlinearity in laryngeal pathology is detected from short and noisy time series. - Abstract: In this paper, we apply the Volterra-Wiener-Korenberg (VWK) model method to detect nonlinearity in disordered voice productions. The VWK method effectively describes the nonlinearity of a third-order nonlinear map. It allows for the analysis of short and noisy data sets. The extracted VWK model parameters show an agreement with the original nonlinear map parameters. Furthermore, the VWK mode method is applied to successfully assess the nonlinearity of a biomechanical voice production model simulating irregular vibratory dynamics of vocal folds with a unilateral vocal polyp. Finally, we show the clinical applicability of this nonlinear detection method to analyze the electroglottographic data generated by 14 patients with vocal nodules or polyps. The VWK model method shows potential in describing the nonlinearity inherent in disordered voice productions from short and noisy time series that are common in the clinical setting.
Fruit tree model for uptake of organic compounds from soil
DEFF Research Database (Denmark)
Trapp, Stefan; Rasmussen, D.; Samsoe-Petersen, L.
2003-01-01
rences: 20 [ view related records ] Citation Map Abstract: Apples and other fruits are frequently cultivated in gardens and are part of our daily diet. Uptake of pollutants into apples may therefore contribute to the human daily intake of toxic substances. In current risk assessment of polluted...... soils, regressions or models are in use, which were not intended to be used for tree fruits. A simple model for uptake of neutral organic contaminants into fruits is developed. It considers xylem and phloem transport to fruits through the stem. The mass balance is solved for the steady......-state, and an example calculation is given. The Fruit Tree Model is compared to the empirical equation of Travis and Arms (T&A), and to results from fruits, collected in contaminated areas. For polar compounds, both T&A and the Fruit Tree Model predict bioconcentration factors fruit to soil (BCF, wet weight based...
Modeling Taylor series approximations for prompt neutron kinetics with lab view simulations
International Nuclear Information System (INIS)
Adzri, E. P.
2012-09-01
The reactor point kinetics equations have been subjected to intense research in an effort to find simple yet accurate numerical solutions methods. The equations are very stiff numerically, meaning that there is a wide variation in the decay constants, so that using a particular time step in the numerical solution may provide sufficient accuracy for the group, but not for another. Several solutions techniques have been presented on the point kinetics equations with varying degrees of complexity. These include Power Series Solutions, CORE, PCA, Genapol and Taylor series methods. In this research, algorithms were developed based on the first and second order Taylor series expansion and simulated in LabVIEW to solve the Reactor Point Kinetics equations using block diagram nodes implemented within stacked sequences. The algorithms developed were fast,accurate and simple to code. Several reactivity insertions were used to simulate the change in neutron population with time. The LabVIEW- Taylor series solutions were compared with other solution techniques such as Power Series Solutions, CORE, PCA, Genapol and McMahon and Pierson's Taylor series approximation. The results of LabVIEW-Taylor series technique used by McMahon and Pearson The LabVIEW-implemented techniques were found to agree very well with these other methods. At 1x10 -8 s the neutron population was 1.000220 neutrons / cm 3 , at 1 x 10 -2 s it was 2.007681 neutrons / cm 3 and at 1x10 -1 s it was 2.075317 neutrons / cm 3 ; same results reported by Genapol for a fast reactor, it produced good and accurate results and compared very favorably with other methods found in the literature. Using much smaller time steps to the order or 10 -8 s commensurate with fast reactor parameters also produced very satisfactory results, indicating that the LabVIEW-based Taylor series technique is suitable for simulating the kinetics of fast reactors as well as thermal reactors. Algorithms developed that included second order terms
Ohkubo, Jun
2011-12-01
A scheme is developed for estimating state-dependent drift and diffusion coefficients in a stochastic differential equation from time-series data. The scheme does not require to specify parametric forms for the drift and diffusion coefficients in advance. In order to perform the nonparametric estimation, a maximum likelihood method is combined with a concept based on a kernel density estimation. In order to deal with discrete observation or sparsity of the time-series data, a local linearization method is employed, which enables a fast estimation.
International Nuclear Information System (INIS)
Keles, Dogan; Genoese, Massimo; Möst, Dominik; Fichtner, Wolf
2012-01-01
This paper evaluates different financial price and time series models, such as mean reversion, autoregressive moving average (ARMA), integrated ARMA (ARIMA) and general autoregressive conditional heteroscedasticity (GARCH) process, usually applied for electricity price simulations. However, as these models are developed to describe the stochastic behaviour of electricity prices, they are extended by a separate data treatment for the deterministic components (trend, daily, weekly and annual cycles) of electricity spot prices. Furthermore price jumps are considered and implemented within a regime-switching model. Since 2008 market design allows for negative prices at the European Energy Exchange, which also occurred for several hours in the last years. Up to now, only a few financial and time series approaches exist, which are able to capture negative prices. This paper presents a new approach incorporating negative prices. The evaluation of the different approaches presented points out that the mean reversion and the ARMA models deliver the lowest mean root square error between simulated and historical electricity spot prices gained from the European Energy Exchange. These models posses also lower mean average errors than GARCH models. Hence, they are more suitable to simulate well-fitting price paths. Furthermore it is shown that the daily structure of historical price curves is better captured applying ARMA or ARIMA processes instead of mean-reversion or GARCH models. Another important outcome of the paper is that the regime-switching approach and the consideration of negative prices via the new proposed approach lead to a significant improvement of the electricity price simulation. - Highlights: ► Considering negative prices improves the results of time-series and financial models for electricity prices. ► Regime-switching approach captures the jumps and base prices quite well. ► Removing and separate modelling of deterministic annual, weekly and daily
Modelling erosion and its interaction with soil organic carbon.
Oyesiku-Blakemore, Joseph; Verrot, Lucile; Geris, Josie; Zhang, Ganlin; Peng, Xinhua; Hallett, Paul; Smith, Jo
2017-04-01
Water driven soil erosion removes and relocates a significant quantity of soil organic carbon. In China the quantity of carbon removed from the soil through water erosion has been reported to be 180+/-80 Mt y-1 (Yue et al., 2011). Being able to effectively model the movement of such a large quantity of carbon is important for the assessment of soil quality and carbon storage in the region and further afield. A large selection of erosion models are available and much work has been done on evaluating the performance of these in developed countries (Merritt et al., 2006). Fewer studies have evaluated the application of these models on soils in developing countries. Here we evaluate and compare the performance of two of these models, WEPP (Laflen et al., 1997) and RUSLE (Renard et al., 1991), for simulations of soil erosion and deposition at the slope scale on a Chinese Red Soil under cultivation using measurements taken at the site. We also describe work to dynamically couple the movement of carbon presented in WEPP to a model of soil organic matter and nutrient turnover, ECOSSE (Smith et al., 2010). This aims to improve simulations of both erosion and carbon cycling by using the simulated rates of erosion to alter the distribution of soil carbon, the depth of soil and the clay content across the slopes, changing the simulated rate of carbon turnover. This, in turn, affects the soil carbon available to be eroded in the next timestep, so improving estimates of carbon erosion. We compare the simulations of this coupled modelling approach with those of the unaltered ECOSSE and WEPP models to determine the importance of coupling erosion and turnover models on the simulation of carbon losses at catchment scale.
Modeling of U-series Radionuclide Transport Through Soil at Pena Blanca, Chihuahua, Mexico
Pekar, K. E.; Goodell, P. C.; Walton, J. C.; Anthony, E. Y.; Ren, M.
2007-05-01
The Nopal I uranium deposit is located at Pena Blanca in Chihuahua, Mexico. Mining of high-grade uranium ore occurred in the early 1980s, with the ore stockpiled nearby. The stockpile was mostly cleared in the 1990s; however, some of the high-grade boulders have remained there, creating localized sources of radioactivity for a period of 25-30 years. This provides a unique opportunity to study radionuclide transport, because the study area did not have any uranium contamination predating the stockpile in the 1980s. One high-grade boulder was selected for study based upon its shape, location, and high activity. The presumed drip-line off of the boulder was marked, samples from the boulder surface were taken, and then the boulder was moved several feet away. Soil samples were taken from directly beneath the boulder, around the drip-line, and down slope. Eight of these samples were collected in a vertical profile directly beneath the boulder. Visible flakes of boulder material were removed from the surficial soil samples, because they would have higher concentrations of U-series radionuclides and cause the activities in the soil samples to be excessively high. The vertical sampling profile used 2-inch thicknesses for each sample. The soil samples were packaged into thin plastic containers to minimize the attenuation and to standardize sample geometry, and then they were analyzed by gamma-ray spectroscopy with a Ge(Li) detector for Th-234, Pa-234, U-234, Th-230, Ra-226, Pb-214, Bi-214, and Pb-210. The raw counts were corrected for self-attenuation and normalized using BL-5, a uranium standard from Beaverlodge, Saskatchewan. BL-5 allowed the counts obtained on the Ge(Li) to be referenced to a known concentration or activity, which was then applied to the soil unknowns for a reliable calculation of their concentrations. Gamma ray spectra of five soil samples from the vertical profile exhibit decreasing activities with increasing depth for the selected radionuclides
Technical Report Series on Global Modeling and Data Assimilation, Volume 41 : GDIS Workshop Report
Koster, Randal D. (Editor); Schubert, Siegfried; Pozzi, Will; Mo, Kingtse; Wood, Eric F.; Stahl, Kerstin; Hayes, Mike; Vogt, Juergen; Seneviratne, Sonia; Stewart, Ron;
2015-01-01
The workshop "An International Global Drought Information System Workshop: Next Steps" was held on 10-13 December 2014 in Pasadena, California. The more than 60 participants from 15 countries spanned the drought research community and included select representatives from applications communities as well as providers of regional and global drought information products. The workshop was sponsored and supported by the US National Integrated Drought Information System (NIDIS) program, the World Climate Research Program (WCRP: GEWEX, CLIVAR), the World Meteorological Organization (WMO), the Group on Earth Observations (GEO), the European Commission Joint Research Centre (JRC), the US Climate Variability and Predictability (CLIVAR) program, and the US National Oceanic and Atmospheric Administration (NOAA) programs on Modeling, Analysis, Predictions and Projections (MAPP) and Climate Variability & Predictability (CVP). NASA/JPL hosted the workshop with logistical support provided by the GEWEX program office. The goal of the workshop was to build on past Global Drought Information System (GDIS) progress toward developing an experimental global drought information system. Specific goals were threefold: (i) to review recent research results focused on understanding drought mechanisms and their predictability on a wide range of time scales and to identify gaps in understanding that could be addressed by coordinated research; (ii) to help ensure that WRCP research priorities mesh with efforts to build capacity to address drought at the regional level; and (iii) to produce an implementation plan for a short duration pilot project to demonstrate current GDIS capabilities. See http://www.wcrp-climate.org/gdis-wkshp-2014-objectives for more information.
Ecotoxicological modelling of cosmetics for aquatic organisms: A QSTR approach.
Khan, K; Roy, K
2017-07-01
In this study, externally validated quantitative structure-toxicity relationship (QSTR) models were developed for toxicity of cosmetic ingredients on three different ecotoxicologically relevant organisms, namely Pseudokirchneriella subcapitata, Daphnia magna and Pimephales promelas following the OECD guidelines. The final models were developed by partial least squares (PLS) regression technique, which is more robust than multiple linear regression. The obtained model for P. subcapitata shows that molecular size and complexity have significant impacts on the toxicity of cosmetics. In case of P. promelas and D. magna, we found that the largest contribution to the toxicity was shown by hydrophobicity and van der Waals surface area, respectively. All models were validated using both internal and test compounds employing multiple strategies. For each QSTR model, applicability domain studies were also performed using the "Distance to Model in X-space" method. A comparison was made with the ECOSAR predictions in order to prove the good predictive performances of our developed models. Finally, individual models were applied to predict toxicity for an external set of 596 personal care products having no experimental data for at least one of the endpoints, and the compounds were ranked based on a decreasing order of toxicity using a scaling approach.
An Instructional Development Model for Global Organizations: The GOaL Model.
Hara, Noriko; Schwen, Thomas M.
1999-01-01
Presents an instructional development model, GOaL (Global Organization Localization), for use by global organizations. Topics include gaps in language, culture, and needs; decentralized processes; collaborative efforts; predetermined content; multiple perspectives; needs negotiation; learning within context; just-in-time training; and bilingual…
Bakire, Serge; Yang, Xinya; Ma, Guangcai; Wei, Xiaoxuan; Yu, Haiying; Chen, Jianrong; Lin, Hongjun
2018-01-01
Organic chemicals in the aquatic ecosystem may inhibit algae growth and subsequently lead to the decline of primary productivity. Growth inhibition tests are required for ecotoxicological assessments for regulatory purposes. In silico study is playing an important role in replacing or reducing animal tests and decreasing experimental expense due to its efficiency. In this work, a series of theoretical models was developed for predicting algal growth inhibition (log EC 50 ) after 72 h exposure to diverse chemicals. In total 348 organic compounds were classified into five modes of toxic action using the Verhaar Scheme. Each model was established by using molecular descriptors that characterize electronic and structural properties. The external validation and leave-one-out cross validation proved the statistical robustness of the derived models. Thus they can be used to predict log EC 50 values of chemicals that lack authorized algal growth inhibition values (72 h). This work systematically studied algal growth inhibition according to toxic modes and the developed model suite covers all five toxic modes. The outcome of this research will promote toxic mechanism analysis and be made applicable to structural diversity. Copyright © 2017 Elsevier Ltd. All rights reserved.
Rajaprasad, Sunku Venkata Siva; Chalapathi, Pasupulati Venkata
2015-09-01
Construction activity has made considerable breakthroughs in the past two decades on the back of increases in development activities, government policies, and public demand. At the same time, occupational health and safety issues have become a major concern to construction organizations. The unsatisfactory safety performance of the construction industry has always been highlighted since the safety management system is neglected area and not implemented systematically in Indian construction organizations. Due to a lack of enforcement of the applicable legislation, most of the construction organizations are forced to opt for the implementation of Occupational Health Safety Assessment Series (OHSAS) 18001 to improve safety performance. In order to better understand factors influencing the implementation of OHSAS 18001, an interpretive structural modeling approach has been applied and the factors have been classified using matrice d'impacts croises-multiplication appliqué a un classement (MICMAC) analysis. The study proposes the underlying theoretical framework to identify factors and to help management of Indian construction organizations to understand the interaction among factors influencing in implementation of OHSAS 18001. Safety culture, continual improvement, morale of employees, and safety training have been identified as dependent variables. Safety performance, sustainable construction, and conducive working environment have been identified as linkage variables. Management commitment and safety policy have been identified as the driver variables. Management commitment has the maximum driving power and the most influential factor is safety policy, which states clearly the commitment of top management towards occupational safety and health.
Directory of Open Access Journals (Sweden)
Chih-Chieh Young
2015-01-01
Full Text Available Accurate prediction of water level fluctuation is important in lake management due to its significant impacts in various aspects. This study utilizes four model approaches to predict water levels in the Yuan-Yang Lake (YYL in Taiwan: a three-dimensional hydrodynamic model, an artificial neural network (ANN model (back propagation neural network, BPNN, a time series forecasting (autoregressive moving average with exogenous inputs, ARMAX model, and a combined hydrodynamic and ANN model. Particularly, the black-box ANN model and physically based hydrodynamic model are coupled to more accurately predict water level fluctuation. Hourly water level data (a total of 7296 observations was collected for model calibration (training and validation. Three statistical indicators (mean absolute error, root mean square error, and coefficient of correlation were adopted to evaluate model performances. Overall, the results demonstrate that the hydrodynamic model can satisfactorily predict hourly water level changes during the calibration stage but not for the validation stage. The ANN and ARMAX models better predict the water level than the hydrodynamic model does. Meanwhile, the results from an ANN model are superior to those by the ARMAX model in both training and validation phases. The novel proposed concept using a three-dimensional hydrodynamic model in conjunction with an ANN model has clearly shown the improved prediction accuracy for the water level fluctuation.
Factor models in high-dimensional time series : A time-domain approach
Hallin, M.; Lippi, M.
2013-01-01
High-dimensional time series may well be the most common type of dataset in the so-called “big data” revolution, and have entered current practice in many areas, including meteorology, genomics, chemometrics, connectomics, complex physics simulations, biological and environmental research, finance
Linear genetic programming for time-series modelling of daily flow rate
Indian Academy of Sciences (India)
two versions of Neural Networks (NNs) are used in predicting time-series of daily flow rates at a station on Schuylkill River at Berne, PA, .... function estimate directly from the training data. (Cigizoglu and Alp 2006). ..... Note: Qmean – mean observed discharge, Sx – standard deviation, Qmin – mini- mum observed discharge ...
Harmonic analysis of dense time series of landsat imagery for modeling change in forest conditions
Barry Tyler. Wilson
2015-01-01
This study examined the utility of dense time series of Landsat imagery for small area estimation and mapping of change in forest conditions over time. The study area was a region in north central Wisconsin for which Landsat 7 ETM+ imagery and field measurements from the Forest Inventory and Analysis program are available for the decade of 2003 to 2012. For the periods...
SAX-VSM: Interpretable Time Series Classification Using SAX and Vector Space Model
2013-01-01
satisfactory partitioning on short synthetic data sets. Further, we evaluated our technique on the long time series from PhysioNet archive [40]. We...PhysioBank, PhysioToolkit, and PhysioNet : Circulation. Discovery 101(23), 1(3), 215–220 (1997) [41] Gavrilov, M., Anguelov, D., Indyk, P., Motwani, R
Directory of Open Access Journals (Sweden)
J D Velásquez
2012-06-01
Full Text Available Many time series with trend and seasonal pattern are successfully modeled and forecasted by the airline model of Box and Jenkins; however, this model neglects the presence of nonlinearity on data. In this paper, we propose a new nonlinear version of the airline model; for this, we replace the moving average linear component by a multilayer perceptron neural network. The proposedmodel is used for forecasting two benchmark time series; we found that theproposed model is able to forecast the time series with more accuracy that other traditional approaches.Muchas series de tiempo con tendencia y ciclos estacionales son exitosamente modeladas y pronosticadas usando el modelo airline de Box y Jenkins; sin embargo, la presencia de no linealidades en los datos son despreciadas por este modelo. En este artículo, se propone una nueva versión no lineal del modelo airline; para esto, se reemplaza la componente lineal de promedios móviles por un perceptrón multicapa. El modelo propuesto es usado para pronosticar dos series de tiempo benchmark; se encontró que el modelo propuesto es capaz de pronosticar las series de tiempo con mayor precisión que otras aproximaciones tradicionales.
Spatiotemporal Organization of Spin-Coated Supported Model Membranes
Simonsen, Adam Cohen
All cells of living organisms are separated from their surroundings and organized internally by means of flexible lipid membranes. In fact, there is consensus that the minimal requirements for self-replicating life processes include the following three features: (1) information carriers (DNA, RNA), (2) a metabolic system, and (3) encapsulation in a container structure [1]. Therefore, encapsulation can be regarded as an essential part of life itself. In nature, membranes are highly diverse interfacial structures that compartmentalize cells [2]. While prokaryotic cells only have an outer plasma membrane and a less-well-developed internal membrane structure, eukaryotic cells have a number of internal membranes associated with the organelles and the nucleus. Many of these membrane structures, including the plasma membrane, are complex layered systems, but with the basic structure of a lipid bilayer. Biomembranes contain hundreds of different lipid species in addition to embedded or peripherally associated membrane proteins and connections to scaffolds such as the cytoskeleton. In vitro, lipid bilayers are spontaneously self-organized structures formed by a large group of amphiphilic lipid molecules in aqueous suspensions. Bilayer formation is driven by the entropic properties of the hydrogen bond network in water in combination with the amphiphilic nature of the lipids. The molecular shapes of the lipid constituents play a crucial role in bilayer formation, and only lipids with approximately cylindrical shapes are able to form extended bilayers. The bilayer structure of biomembranes was discovered by Gorter and Grendel in 1925 [3] using monolayer studies of lipid extracts from red blood cells. Later, a number of conceptual models were developed to rationalize the organization of lipids and proteins in biological membranes. One of the most celebrated is the fluid-mosaic model by Singer and Nicolson (1972) [4]. According to this model, the lipid bilayer component of
OBJECT ORIENTED MODELLING, A MODELLING METHOD OF AN ECONOMIC ORGANIZATION ACTIVITY
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TĂNĂSESCU ANA
2014-05-01
Full Text Available Now, most economic organizations use different information systems types in order to facilitate their activity. There are different methodologies, methods and techniques that can be used to design information systems. In this paper, I propose to present the advantages of using the object oriented modelling at the information system design of an economic organization. Thus, I have modelled the activity of a photo studio, using Visual Paradigm for UML as a modelling tool. For this purpose, I have identified the use cases for the analyzed system and I have presented the use case diagram. I have, also, realized the system static and dynamic modelling, through the most known UML diagrams.
MIANN models in medicinal, physical and organic chemistry.
González-Díaz, Humberto; Arrasate, Sonia; Sotomayor, Nuria; Lete, Esther; Munteanu, Cristian R; Pazos, Alejandro; Besada-Porto, Lina; Ruso, Juan M
2013-01-01
Reducing costs in terms of time, animal sacrifice, and material resources with computational methods has become a promising goal in Medicinal, Biological, Physical and Organic Chemistry. There are many computational techniques that can be used in this sense. In any case, almost all these methods focus on few fundamental aspects including: type (1) methods to quantify the molecular structure, type (2) methods to link the structure with the biological activity, and others. In particular, MARCH-INSIDE (MI), acronym for Markov Chain Invariants for Networks Simulation and Design, is a well-known method for QSAR analysis useful in step (1). In addition, the bio-inspired Artificial-Intelligence (AI) algorithms called Artificial Neural Networks (ANNs) are among the most powerful type (2) methods. We can combine MI with ANNs in order to seek QSAR models, a strategy which is called herein MIANN (MI & ANN models). One of the first applications of the MIANN strategy was in the development of new QSAR models for drug discovery. MIANN strategy has been expanded to the QSAR study of proteins, protein-drug interactions, and protein-protein interaction networks. In this paper, we review for the first time many interesting aspects of the MIANN strategy including theoretical basis, implementation in web servers, and examples of applications in Medicinal and Biological chemistry. We also report new applications of the MIANN strategy in Medicinal chemistry and the first examples in Physical and Organic Chemistry, as well. In so doing, we developed new MIANN models for several self-assembly physicochemical properties of surfactants and large reaction networks in organic synthesis. In some of the new examples we also present experimental results which were not published up to date.
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Farshad Fathian
2017-01-01
Full Text Available Introduction: Time series models are generally categorized as a data-driven method or mathematically-based method. These models are known as one of the most important tools in modeling and forecasting of hydrological processes, which are used to design and scientific management of water resources projects. On the other hand, a better understanding of the river flow process is vital for appropriate streamflow modeling and forecasting. One of the main concerns of hydrological time series modeling is whether the hydrologic variable is governed by the linear or nonlinear models through time. Although the linear time series models have been widely applied in hydrology research, there has been some recent increasing interest in the application of nonlinear time series approaches. The threshold autoregressive (TAR method is frequently applied in modeling the mean (first order moment of financial and economic time series. Thise type of the model has not received considerable attention yet from the hydrological community. The main purposes of this paper are to analyze and to discuss stochastic modeling of daily river flow time series of the study area using linear (such as ARMA: autoregressive integrated moving average and non-linear (such as two- and three- regime TAR models. Material and Methods: The study area has constituted itself of four sub-basins namely, Saghez Chai, Jighato Chai, Khorkhoreh Chai and Sarogh Chai from west to east, respectively, which discharge water into the Zarrineh Roud dam reservoir. River flow time series of 6 hydro-gauge stations located on upstream basin rivers of Zarrineh Roud dam (located in the southern part of Urmia Lake basin were considered to model purposes. All the data series used here to start from January 1, 1997, and ends until December 31, 2011. In this study, the daily river flow data from January 01 1997 to December 31 2009 (13 years were chosen for calibration and data for January 01 2010 to December 31 2011
Genome Editing and Its Applications in Model Organisms
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Dongyuan Ma
2015-12-01
Full Text Available Technological advances are important for innovative biological research. Development of molecular tools for DNA manipulation, such as zinc finger nucleases (ZFNs, transcription activator-like effector nucleases (TALENs, and the clustered regularly-interspaced short palindromic repeat (CRISPR/CRISPR-associated (Cas, has revolutionized genome editing. These approaches can be used to develop potential therapeutic strategies to effectively treat heritable diseases. In the last few years, substantial progress has been made in CRISPR/Cas technology, including technical improvements and wide application in many model systems. This review describes recent advancements in genome editing with a particular focus on CRISPR/Cas, covering the underlying principles, technological optimization, and its application in zebrafish and other model organisms, disease modeling, and gene therapy used for personalized medicine.
Genome Editing and Its Applications in Model Organisms.
Ma, Dongyuan; Liu, Feng
2015-12-01
Technological advances are important for innovative biological research. Development of molecular tools for DNA manipulation, such as zinc finger nucleases (ZFNs), transcription activator-like effector nucleases (TALENs), and the clustered regularly-interspaced short palindromic repeat (CRISPR)/CRISPR-associated (Cas), has revolutionized genome editing. These approaches can be used to develop potential therapeutic strategies to effectively treat heritable diseases. In the last few years, substantial progress has been made in CRISPR/Cas technology, including technical improvements and wide application in many model systems. This review describes recent advancements in genome editing with a particular focus on CRISPR/Cas, covering the underlying principles, technological optimization, and its application in zebrafish and other model organisms, disease modeling, and gene therapy used for personalized medicine. Copyright © 2016 The Authors. Production and hosting by Elsevier Ltd.. All rights reserved.
DEFF Research Database (Denmark)
Wiechowski, Wojciech Tomasz; Lykkegaard, Jan; Bak, Claus Leth
2007-01-01
In this paper two methods of validation of transmission network harmonic models are introduced. The methods were developed as a result of the work presented in [1]. The first method allows calculating the transfer harmonic impedance between two nodes of a network. Switching a linear, series network......, as for example a transmission line. Both methods require that harmonic measurements performed at two ends of the disconnected element are precisely synchronized....... are used for calculation of the transfer harmonic impedance between the nodes. The determined transfer harmonic impedance can be used to validate a computer model of the network. The second method is an extension of the fist one. It allows switching a series element that contains a shunt branch...
Onisko, Agnieszka; Druzdzel, Marek J; Austin, R Marshall
2016-01-01
Classical statistics is a well-established approach in the analysis of medical data. While the medical community seems to be familiar with the concept of a statistical analysis and its interpretation, the Bayesian approach, argued by many of its proponents to be superior to the classical frequentist approach, is still not well-recognized in the analysis of medical data. The goal of this study is to encourage data analysts to use the Bayesian approach, such as modeling with graphical probabilistic networks, as an insightful alternative to classical statistical analysis of medical data. This paper offers a comparison of two approaches to analysis of medical time series data: (1) classical statistical approach, such as the Kaplan-Meier estimator and the Cox proportional hazards regression model, and (2) dynamic Bayesian network modeling. Our comparison is based on time series cervical cancer screening data collected at Magee-Womens Hospital, University of Pittsburgh Medical Center over 10 years. The main outcomes of our comparison are cervical cancer risk assessments produced by the three approaches. However, our analysis discusses also several aspects of the comparison, such as modeling assumptions, model building, dealing with incomplete data, individualized risk assessment, results interpretation, and model validation. Our study shows that the Bayesian approach is (1) much more flexible in terms of modeling effort, and (2) it offers an individualized risk assessment, which is more cumbersome for classical statistical approaches.
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Jamal Shamsara
2014-01-01
Full Text Available MMP-12 is a member of matrix metalloproteinases (MMPs family involved in pathogenesis of some inflammatory based diseases. Design of selective matrix MMPs inhibitors is still challenging because of binding pocket similarities among MMPs family. We tried to generate a HQSAR (hologram quantitative structure activity relationship model for a series of MMP-12 inhibitors. Compounds in the series of inhibitors with reported biological activity against MMP-12 were used to construct a predictive HQSAR model for their inhibitory activity against MMP-12. The HQSAR model had statistically excellent properties and possessed good predictive ability for test set compounds. The HQSAR model was obtained for the 26 training set compounds showing cross-validated q2 value of 0.697 and conventional r2 value of 0.986. The model was then externally validated using a test set of 9 compounds and the predicted values were in good agreement with the experimental results (rpred2=0.8733. Then, the external validity of the model was confirmed by Golbraikh-Tropsha and rm2 metrics. The color code analysis based on the obtained HQSAR model provided useful insights into the structural features of the training set for their bioactivity against MMP-12 and was useful for the design of some new not yet synthesized MMP-12 inhibitors.
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Patrícia Ramos
2016-11-01
Full Text Available In this work, a cross-validation procedure is used to identify an appropriate Autoregressive Integrated Moving Average model and an appropriate state space model for a time series. A minimum size for the training set is specified. The procedure is based on one-step forecasts and uses different training sets, each containing one more observation than the previous one. All possible state space models and all ARIMA models where the orders are allowed to range reasonably are fitted considering raw data and log-transformed data with regular differencing (up to second order differences and, if the time series is seasonal, seasonal differencing (up to first order differences. The value of root mean squared error for each model is calculated averaging the one-step forecasts obtained. The model which has the lowest root mean squared error value and passes the Ljung–Box test using all of the available data with a reasonable significance level is selected among all the ARIMA and state space models considered. The procedure is exemplified in this paper with a case study of retail sales of different categories of women’s footwear from a Portuguese retailer, and its accuracy is compared with three reliable forecasting approaches. The results show that our procedure consistently forecasts more accurately than the other approaches and the improvements in the accuracy are significant.
Developing an Enzyme Mediated Soil Organic Carbon Decomposition Model
Mayes, M. A.; Post, W. M.; Wang, G.; Jagadamma, S.; Steinweg, J. M.; Schadt, C. W.
2012-12-01
We developed the Microbial-ENzyme-mediated Decomposition (MEND) model in order to mechanistically model the decomposition of soil organic carbon (C). This presentation is an overview of the concept and development of the model and of the design of complementary lab-scale experiments. The model divides soil C into five pools of particulate, mineral-associated, dissolved, microbial, and enzyme organic C (Wang et al. 2012). There are three input types - cellulose, lignin, and dissolved C. Decomposition is mediated via microbial extracellular enzymes using the Michaelis-Menten equation, resulting in the production of a common pool of dissolved organic C. Parameters for the Michaelis-Menten equation are obtained through a literature review (Wang and Post, 2012a). The dissolved C is taken up by microbial biomass and proportioned according to microbial maintenance and growth, which were recalculated according to Wang and Post (2012b). The model allows dissolved C to undergo adsorption and desorption reactions with the mineral-associated C, which was also parameterized based upon a literature review and complementary laboratory experiments. In the lab, four 14C-labeled substrates (cellulose, fatty acid, glucose, and lignin-like) were incubated with either the particulate C pool, the mineral-associated C pool, or to bulk soils. The rate of decomposition was measured via the production of 14CO2 over time, along with incorporation into microbial biomass, production of dissolved C, and estimation of sorbed C. We performed steady-state and dynamic simulations and sensitivity analyses under temperature increases of 1-5°C for a period of 100 y. Simulations indicated an initial decrease in soil organic C consisting of both cellulose and lignin pools. Over longer time intervals (> 6 y), however, a shrinking microbial population, a concomitant decrease in enzyme production, and a decrease in microbial carbon use efficiency together decreased CO2 production and resulted in greater
Morteza Hatami; Mitra Mohammadi Mohammadi; Reza Esmaeli; Mandana Mohammadi
2017-01-01
Epidemiological studies conducted in the past two decades indicate that air pollution causes increase in cardiovascular, breathing and chronic bronchitis disorders and even causes cardiovascular mortality. Therefore, the aim of this study was to investigate the relationship between meteorological parameters, air pollution and cardiovascular mortality in the city of Mashhad in 2014 by a time series model. Data on mortality from cardiovascular disease, meteorological parameters and air pollutio...
Zhang, Hong; Zhang, Sheng; Wang, Ping; Qin, Yuzhe; Wang, Huifeng
2017-07-01
Particulate matter with aerodynamic diameter below 10 μm (PM 10 ) forecasting is difficult because of the uncertainties in describing the emission and meteorological fields. This paper proposed a wavelet-ARMA/ARIMA model to forecast the short-term series of the PM 10 concentrations. It was evaluated by experiments using a 10-year data set of daily PM 10 concentrations from 4 stations located in Taiyuan, China. The results indicated the following: (1) PM 10 concentrations of Taiyuan had a decreasing trend during 2005 to 2012 but increased in 2013. PM 10 concentrations had an obvious seasonal fluctuation related to coal-fired heating in winter and early spring. (2) Spatial differences among the four stations showed that the PM 10 concentrations in industrial and heavily trafficked areas were higher than those in residential and suburb areas. (3) Wavelet analysis revealed that the trend variation and the changes of the PM 10 concentration of Taiyuan were complicated. (4) The proposed wavelet-ARIMA model could be efficiently and successfully applied to the PM 10 forecasting field. Compared with the traditional ARMA/ARIMA methods, this wavelet-ARMA/ARIMA method could effectively reduce the forecasting error, improve the prediction accuracy, and realize multiple-time-scale prediction. Wavelet analysis can filter noisy signals and identify the variation trend and the fluctuation of the PM 10 time-series data. Wavelet decomposition and reconstruction reduce the nonstationarity of the PM 10 time-series data, and thus improve the accuracy of the prediction. This paper proposed a wavelet-ARMA/ARIMA model to forecast the PM 10 time series. Compared with the traditional ARMA/ARIMA method, this wavelet-ARMA/ARIMA method could effectively reduce the forecasting error, improve the prediction accuracy, and realize multiple-time-scale prediction. The proposed model could be efficiently and successfully applied to the PM 10 forecasting field.
Thermodynamic Modeling of Organic-Inorganic Aerosols with the Group-Contribution Model AIOMFAC
Zuend, A.; Marcolli, C.; Luo, B. P.; Peter, T.
2009-04-01
Liquid aerosol particles are - from a physicochemical viewpoint - mixtures of inorganic salts, acids, water and a large variety of organic compounds (Rogge et al., 1993; Zhang et al., 2007). Molecular interactions between these aerosol components lead to deviations from ideal thermodynamic behavior. Strong non-ideality between organics and dissolved ions may influence the aerosol phases at equilibrium by means of liquid-liquid phase separations into a mainly polar (aqueous) and a less polar (organic) phase. A number of activity models exists to successfully describe the thermodynamic equilibrium of aqueous electrolyte solutions. However, the large number of different, often multi-functional, organic compounds in mixed organic-inorganic particles is a challenging problem for the development of thermodynamic models. The group-contribution concept as introduced in the UNIFAC model by Fredenslund et al. (1975), is a practical method to handle this difficulty and to add a certain predictability for unknown organic substances. We present the group-contribution model AIOMFAC (Aerosol Inorganic-Organic Mixtures Functional groups Activity Coefficients), which explicitly accounts for molecular interactions between solution constituents, both organic and inorganic, to calculate activities, chemical potentials and the total Gibbs energy of mixed systems (Zuend et al., 2008). This model enables the computation of vapor-liquid (VLE), liquid-liquid (LLE) and solid-liquid (SLE) equilibria within one framework. Focusing on atmospheric applications we considered eight different cations, five anions and a wide range of alcohols/polyols as organic compounds. With AIOMFAC, the activities of the components within an aqueous electrolyte solution are very well represented up to high ionic strength. We show that the semi-empirical middle-range parametrization of direct organic-inorganic interactions in alcohol-water-salt solutions enables accurate computations of vapor-liquid and liquid
Modelling and Simulation of Single-Phase Series Active Compensator for Power Quality Improvement
Verma, Arun Kumar; Mathuria, Kirti; Singh, Bhim; Bhuvaneshwari, G.
2017-10-01
A single-phase active series compensator is proposed in this work to reduce harmonic currents at the ac mains and to regulate the dc link voltage of a diode bridge rectifier (DBR) that acts as the front end converter for a voltage source inverter feeding an ac motor. This ac motor drive is used in any of the domestic, commercial or industrial appliances. Under fluctuating ac mains voltages, the dc link voltage of the DBR depicts wide variations and hence the ac motor is used at reduced rating as compared to its name-plate rating. The active series compensator proposed here provides dual functions of improving the power quality at the ac mains and regulating the dc link voltage thus averting the need for derating of the ac motor.
Development of the Croatian model of organ donation and transplantation
Živčić-Ćosić, Stela; Bušić, Mirela; Župan, Željko; Pelčić, Gordana; Anušić Juričić, Martina; Jurčić, Željka; Ivanovski, Mladen; Rački, Sanjin
2013-01-01
During the past ten years, the efforts to improve and organize the national transplantation system in Croatia have resulted in a steadily growing donor rate, which reached its highest level in 2011, with 33.6 utilized donors per million population (p.m.p.). Nowadays, Croatia is one of the leading countries in the world according to deceased donation and transplantation rates. Between 2008 and 2011, the waiting list for kidney transplantation decreased by 37.2% (from 430 to 270 persons waiting for a transplant) and the median waiting time decreased from 46 to 24 months. The Croatian model has been internationally recognized as successful and there are plans for its implementation in other countries. We analyzed the key factors that contributed to the development of this successful model for organ donation and transplantation. These are primarily the appointment of hospital and national transplant coordinators, implementation of a new financial model with donor hospital reimbursement, public awareness campaign, international cooperation, adoption of new legislation, and implementation of a donor quality assurance program. The selection of key factors is based on the authors' opinions; we are open for further discussion and propose systematic research into the issue. PMID:23444248
Integrated approaches to modeling the organic and inorganic atmospheric aerosol components
Koo, Bonyoung; Ansari, Asif S.; Pandis, Spyros N.
A series of modeling approaches for the description of the dynamic behavior of secondary organic aerosol (SOA) components and their interactions with inorganics is presented. The models employ a lumped species approach based on available smog chamber studies and the UNIquac Functional-group Activity Coefficient (UNIFAC) method to estimate SOA water absorption. The additional water due to SOA species can change the partitioning behavior of the semi-volatile inorganics. Primary organic particles significantly influence the SOA partitioning between gas and aerosol phases. The SOA size distribution predicted by a bulk equilibrium approach is biased toward smaller sizes compared with that of a fully dynamic model. An improved weighting scheme for the bulk equilibrium approach is proposed in this work and is shown to minimize this discrepancy. SOA is predicted to increase the total aerosol water in Southern California by 2-13% depending on conditions. However, the effect of SOA water absorption on aerosol nitrate is insignificant for all the cases studied in Southern California.
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Hao Yu
2018-01-01
Full Text Available This study introduces a data-driven modeling strategy for smart grid power quality (PQ coupling assessment based on time series pattern matching to quantify the influence of single and integrated disturbance among nodes in different pollution patterns. Periodic and random PQ patterns are constructed by using multidimensional frequency-domain decomposition for all disturbances. A multidimensional piecewise linear representation based on local extreme points is proposed to extract the patterns features of single and integrated disturbance in consideration of disturbance variation trend and severity. A feature distance of pattern (FDP is developed to implement pattern matching on univariate PQ time series (UPQTS and multivariate PQ time series (MPQTS to quantify the influence of single and integrated disturbance among nodes in the pollution patterns. Case studies on a 14-bus distribution system are performed and analyzed; the accuracy and applicability of the FDP in the smart grid PQ coupling assessment are verified by comparing with other time series pattern matching methods.
Modeling of Electronic Properties in Organic Semiconductor Device Structures
Chang, Hsiu-Chuang
Organic semiconductors (OSCs) have recently become viable for a wide range of electronic devices, some of which have already been commercialized. With the mechanical flexibility of organic materials and promising performance of organic field effect transistors (OFETs) and organic bulk heterojunction devices, OSCs have been demonstrated in applications such as radio frequency identification tags, flexible displays, and photovoltaic cells. Transient phenomena play decisive roles in the performance of electronic devices and OFETs in particular. The dynamics of the establishment and depletion of the conducting channel in OFETs are investigated theoretically. The device structures explored resemble typical organic thin-film transistors with one of the channel contacts removed. By calculating the displacement current associated with charging and discharging of the channel in these capacitors, transient effects on the carrier transport in OSCs may be studied. In terms of the relevant models it is shown that the non-linearity of the process plays a key role. The non-linearity arises in the simplest case from the fact that channel resistance varies during the charging and discharging phases. Traps can be introduced into the models and their effects examined in some detail. When carriers are injected into the device, a conducting channel is established with traps that are initially empty. Gradual filling of the traps then modifies the transport characteristics of the injected charge carriers. In contrast, dc measurements as they are typically performed to characterize the transport properties of organic semiconductor channels investigate a steady state with traps partially filled. Numerical and approximate analytical models of the formation of the conducting channel and the resulting displacement currents are presented. For the process of transient carrier extraction, it is shown that if the channel capacitance is partially or completely discharged through the channel
A Revised Iranian Model of Organ Donation as an Answer to the Current Organ Shortage Crisis.
Hamidian Jahromi, Alireza; Fry-Revere, Sigrid; Bastani, Bahar
2015-09-01
Kidney transplantation has become the treatment of choice for patients with end-stage renal disease. Six decades of success in the field of transplantation have made it possible to save thousands of lives every year. Unfortunately, in recent years success has been overshadowed by an ever-growing shortage of organs. In the United States, there are currently more than 100 000 patients waiting for kidneys. However, the supply of kidneys (combined cadaveric and live donations) has stagnated around 17 000 per year. The ever-widening gap between demand and supply has resulted in an illegal black market and unethical transplant tourism of global proportions. While we believe there is much room to improve the Iranian model of regulated incentivized live kidney donation, with some significant revisions, the Iranian Model could serve as an example for how other countries could make significant strides to lessening their own organ shortage crises.
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Mahammad A. Hannan
2017-09-01
Full Text Available This study aims to develop an accurate model of a charge equalization controller (CEC that manages individual cell monitoring and equalizing by charging and discharging series-connected lithium-ion (Li-ion battery cells. In this concept, an intelligent control algorithm is developed to activate bidirectional cell switches and control direct current (DC–DC converter switches along with pulse width modulation (PWM generation. Individual models of an electric vehicle (EV-sustainable Li-ion battery, optimal power rating, a bidirectional flyback DC–DC converter, and charging and discharging controllers are integrated to develop a small-scale CEC model that can be implemented for 10 series-connected Li-ion battery cells. Results show that the charge equalization controller operates at 91% efficiency and performs well in equalizing both overdischarged and overcharged cells on time. Moreover, the outputs of the CEC model show that the desired balancing level occurs at 2% of state of charge difference and that all cells are operated within a normal range. The configuration, execution, control, power loss, cost, size, and efficiency of the developed CEC model are compared with those of existing controllers. The proposed model is proven suitable for high-tech storage systems toward the advancement of sustainable EV technologies and renewable source of applications.
A Multiagent Modeling Environment for Simulating Work Practice in Organizations
Sierhuis, Maarten; Clancey, William J.; vanHoof, Ron
2004-01-01
In this paper we position Brahms as a tool for simulating organizational processes. Brahms is a modeling and simulation environment for analyzing human work practice, and for using such models to develop intelligent software agents to support the work practice in organizations. Brahms is the result of more than ten years of research at the Institute for Research on Learning (IRL), NYNEX Science & Technology (the former R&D institute of the Baby Bell telephone company in New York, now Verizon), and for the last six years at NASA Ames Research Center, in the Work Systems Design and Evaluation group, part of the Computational Sciences Division (Code IC). Brahms has been used on more than ten modeling and simulation research projects, and recently has been used as a distributed multiagent development environment for developing work practice support tools for human in-situ science exploration on planetary surfaces, in particular a human mission to Mars. Brahms was originally conceived of as a business process modeling and simulation tool that incorporates the social systems of work, by illuminating how formal process flow descriptions relate to people s actual located activities in the workplace. Our research started in the early nineties as a reaction to experiences with work process modeling and simulation . Although an effective tool for convincing management of the potential cost-savings of the newly designed work processes, the modeling and simulation environment was only able to describe work as a normative workflow. However, the social systems, uncovered in work practices studied by the design team played a significant role in how work actually got done-actual lived work. Multi- tasking, informal assistance and circumstantial work interactions could not easily be represented in a tool with a strict workflow modeling paradigm. In response, we began to develop a tool that would have the benefits of work process modeling and simulation, but be distinctively able to
Modeling the adsorption of weak organic acids on goethite : the ligand and charge distribution model
Filius, J.D.
2001-01-01
A detailed study is presented in which the CD-MUSIC modeling approach is used in a new modeling approach that can describe the binding of large organic molecules by metal (hydr)oxides taking the full speciation of the adsorbed molecule into account. Batch equilibration experiments were
Organic polyaromatic hydrocarbons as sensitizing model dyes for semiconductor nanoparticles.
Zhang, Yongyi; Galoppini, Elena
2010-04-26
The study of interfacial charge-transfer processes (sensitization) of a dye bound to large-bandgap nanostructured metal oxide semiconductors, including TiO(2), ZnO, and SnO(2), is continuing to attract interest in various areas of renewable energy, especially for the development of dye-sensitized solar cells (DSSCs). The scope of this Review is to describe how selected model sensitizers prepared from organic polyaromatic hydrocarbons have been used over the past 15 years to elucidate, through a variety of techniques, fundamental aspects of heterogeneous charge transfer at the surface of a semiconductor. This Review does not focus on the most recent or efficient dyes, but rather on how model dyes prepared from aromatic hydrocarbons have been used, over time, in key fundamental studies of heterogeneous charge transfer. In particular, we describe model chromophores prepared from anthracene, pyrene, perylene, and azulene. As the level of complexity of the model dye-bridge-anchor group compounds has increased, the understanding of some aspects of very complex charge transfer events has improved. The knowledge acquired from the study of the described model dyes is of importance not only for DSSC development but also to other fields of science for which electronic processes at the molecule/semiconductor interface are relevant.
International Nuclear Information System (INIS)
Dominguez Catasus, Judith; Abreu Diaz, Aida Mary; Mc Calla, Rogelio; Ortueta Milan, Marvic; Perez Machado, Esperanza; Borroto Portela, Jorge
1998-01-01
Presently work settles down the fitness of the tanks-in-series with recycle model for describing the blending in a 100 liters anchor-agitated batch reactor. This model is used to establish the relationship between the mixing-rate number and the Reynolds number The basic information needed was obtained from the curves that record the counting rate variations of the 99mT c with time, during the mixing process. The mixing-rate number shows a tendency to a constant value of 7,8 within the Reynolds range between 4,78x10 4 and 2,68x10 5
International Nuclear Information System (INIS)
Bu Lin; Wang Kun; Zhao Qingliang; Wei Liangliang; Zhang Jing; Yang Junchen
2010-01-01
Landfill leachate is generally characterized as a complex recalcitrant wastewater containing high concentration of dissolved organic matter (DOM). A combination of sequencing batch reactor (SBR) + aeration corrosive cell-Fenton (ACF) + granular activated carbon (GAC) adsorption in series was proposed for the purpose of removing pollutants in the leachate. Fractionation was also performed to investigate the composition changes and characteristics of the leachate DOM in each treatment process. Experimental results showed that organic matter, in terms of chemical oxygen demand (COD), 5-day biological oxygen demand (BOD 5 ), and dissolved organic carbon (DOC), was reduced by 97.2%, 99.1%, and 98.7%, respectively. To differentiate the DOM portions, leachates were separated into five fractions by XAD-8 and XAD-4 resins: hydrophobic acid (HPO-A), hydrophobic neutral (HPO-N), transphilic acid (TPI-A), transphilic neutral (TPI-N), and hydrophilic fraction (HPI). The predominant fraction in the raw leachate was HPO-A (36% of DOC), while the dominant fraction in the final effluent was HPI (53% of DOC). Accordingly, macromolecules were degraded to simpler ones in a relatively narrow range below 1000 Da. Spectral and chromatographic analyses also showed that most humic-like substances in all fractions were effectively removed during the treatments and led to a simultaneous decrease in aromaticity.
Modeling financial markets by self-organized criticality
Biondo, Alessio Emanuele; Pluchino, Alessandro; Rapisarda, Andrea
2015-10-01
We present a financial market model, characterized by self-organized criticality, that is able to generate endogenously a realistic price dynamics and to reproduce well-known stylized facts. We consider a community of heterogeneous traders, composed by chartists and fundamentalists, and focus on the role of informative pressure on market participants, showing how the spreading of information, based on a realistic imitative behavior, drives contagion and causes market fragility. In this model imitation is not intended as a change in the agent's group of origin, but is referred only to the price formation process. We introduce in the community also a variable number of random traders in order to study their possible beneficial role in stabilizing the market, as found in other studies. Finally, we also suggest some counterintuitive policy strategies able to dampen fluctuations by means of a partial reduction of information.
Models of charge pair generation in organic solar cells.
Few, Sheridan; Frost, Jarvist M; Nelson, Jenny
2015-01-28
Efficient charge pair generation is observed in many organic photovoltaic (OPV) heterojunctions, despite nominal electron-hole binding energies which greatly exceed the average thermal energy. Empirically, the efficiency of this process appears to be related to the choice of donor and acceptor materials, the resulting sequence of excited state energy levels and the structure of the interface. In order to establish a suitable physical model for the process, a range of different theoretical studies have addressed the nature and energies of the interfacial states, the energetic profile close to the heterojunction and the dynamics of excited state transitions. In this paper, we review recent developments underpinning the theory of charge pair generation and phenomena, focussing on electronic structure calculations, electrostatic models and approaches to excited state dynamics. We discuss the remaining challenges in achieving a predictive approach to charge generation efficiency.
Self-Organized Criticality Theory Model of Thermal Sandpile
International Nuclear Information System (INIS)
Peng Xiao-Dong; Qu Hong-Peng; Xu Jian-Qiang; Han Zui-Jiao
2015-01-01
A self-organized criticality model of a thermal sandpile is formulated for the first time to simulate the dynamic process with interaction between avalanche events on the fast time scale and diffusive transports on the slow time scale. The main characteristics of the model are that both particle and energy avalanches of sand grains are considered simultaneously. Properties of intermittent transport and improved confinement are analyzed in detail. The results imply that the intermittent phenomenon such as blobs in the low confinement mode as well as edge localized modes in the high confinement mode observed in tokamak experiments are not only determined by the edge plasma physics, but also affected by the core plasma dynamics. (paper)