Modeling of Volatility with Non-linear Time Series Model

OpenAIRE

Kim Song Yon; Kim Mun Chol

2013-01-01

In this paper, non-linear time series models are used to describe volatility in financial time series data. To describe volatility, two of the non-linear time series are combined into form TAR (Threshold Auto-Regressive Model) with AARCH (Asymmetric Auto-Regressive Conditional Heteroskedasticity) error term and its parameter estimation is studied.

Time series modeling, computation, and inference

CERN Document Server

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

A grid-voltage-sensorless resistive active power filter with series LC-filter

DEFF Research Database (Denmark)

Bai, Haofeng; Wang, Xiongfei; Blaabjerg, Frede

2017-01-01

Voltage-sensorless control has been investigated for Voltage Source Inverters (VSIs) for many years due to the reduced system cost and potentially improved system reliability. The VSI based Resistive Active Power Filters (R-APFs) are now widely used to prevent the harmonic resonance in power...... distribution network, for which the voltage sensors are needed in order to obtain the current reference. In this paper a grid-voltage-sensorless control strategy is proposed for the R-APF with series LC-filter. Unlike the traditional resistance emulation method, this proposed control method re...

A Grid-Voltage-Sensorless Resistive Active Power Filter with Series LC-Filter

DEFF Research Database (Denmark)

Bai, Haofeng; Wang, Xiongfei; Blaabjerg, Frede

2018-01-01

Voltage-sensorless control has been investigated for Voltage Source Inverters (VSIs) for many years due to the reduced system cost and potentially improved system reliability. The VSI based Resistive Active Power Filters (R-APFs) are now widely used to prevent the harmonic resonance in power...... distribution network, for which the voltage sensors are needed in order to obtain the current reference. In this paper a grid-voltage-sensorless control strategy is proposed for the R-APF with series LC-filter. Unlike the traditional resistance emulation method, this proposed control method re...

Accurate calibration of resistance ratios between 1 MΩ and 1 GΩ using series resistors

International Nuclear Information System (INIS)

Yu, Kwang Min; Ihm, G

2011-01-01

As shown in high-resistance key comparisons carried out by the Consultative Committee for Electricity and Magnetism (CCEM), Inter-American Metrology System (SIM) and European Association of National Metrology Institutes (EURAMET), the accuracy of 10 MΩ and 1 GΩ resistances depends on ratio values between the reference resistance and unknown resistance and the accuracy of the reference resistance, which is determined with a quantized Hall resistance standard. This paper presents a method for calibrating 10:1 ratios in a high-resistance bridge using series resistors simply and accurately. By applying the 10:1 ratio errors determined using the presented method, the combined relative standard uncertainty for 1 GΩ resistance measurements using a modified Wheatstone bridge was estimated to be on the 1 × 10 −6 level. The method was also applied to 1 GΩ resistance measurements using a direct-current comparator resistance bridge. It was found that the 1 GΩ resistances determined by the two bridges agreed within 2.4 × 10 −6 Ω/Ω. We expect that the presented method can also be used to calibrate arbitrary resistance ratios

Time-series analysis in imatinib-resistant chronic myeloid leukemia K562-cells under different drug treatments.

Science.gov (United States)

Zhao, Yan-Hong; Zhang, Xue-Fang; Zhao, Yan-Qiu; Bai, Fan; Qin, Fan; Sun, Jing; Dong, Ying

2017-08-01

Chronic myeloid leukemia (CML) is characterized by the accumulation of active BCR-ABL protein. Imatinib is the first-line treatment of CML; however, many patients are resistant to this drug. In this study, we aimed to compare the differences in expression patterns and functions of time-series genes in imatinib-resistant CML cells under different drug treatments. GSE24946 was downloaded from the GEO database, which included 17 samples of K562-r cells with (n=12) or without drug administration (n=5). Three drug treatment groups were considered for this study: arsenic trioxide (ATO), AMN107, and ATO+AMN107. Each group had one sample at each time point (3, 12, 24, and 48 h). Time-series genes with a ratio of standard deviation/average (coefficient of variation) >0.15 were screened, and their expression patterns were revealed based on Short Time-series Expression Miner (STEM). Then, the functional enrichment analysis of time-series genes in each group was performed using DAVID, and the genes enriched in the top ten functional categories were extracted to detect their expression patterns. Different time-series genes were identified in the three groups, and most of them were enriched in the ribosome and oxidative phosphorylation pathways. Time-series genes in the three treatment groups had different expression patterns and functions. Time-series genes in the ATO group (e.g. CCNA2 and DAB2) were significantly associated with cell adhesion, those in the AMN107 group were related to cellular carbohydrate metabolic process, while those in the ATO+AMN107 group (e.g. AP2M1) were significantly related to cell proliferation and antigen processing. In imatinib-resistant CML cells, ATO could influence genes related to cell adhesion, AMN107 might affect genes involved in cellular carbohydrate metabolism, and the combination therapy might regulate genes involved in cell proliferation.

The effect of the series resistance in dye-sensitized solar cells explored by electron transport and back reaction using electrical and optical modulation techniques

International Nuclear Information System (INIS)

Liu Weiqing; Hu Linhua; Dai Songyuan; Guo Lei; Jiang Nianquan; Kou Dongxing

2010-01-01

The influence of the series resistance on the electron transport and recombination processes in dye-sensitized solar cells (DSC) has been investigated. The series resistances induced by some parts of DSC, such as the transparent conductive oxide (TCO), the electrolyte layer and the counter electrode, influence the performance of DSC. By combining three frequency-domain techniques, specifically electrochemical impedance spectroscopy (EIS), intensity modulated photocurrent spectroscopy (IMPS) and intensity modulated photovoltage spectroscopy (IMVS), we studied the relationship between the series resistance and the dynamic response of DSC. The results show that the series resistance induced by the TCO or counter electrode predominantly affects the electron transport under short circuit conditions and has no significant influence on the recombination under open circuit conditions. However, the resistance related to the electrolyte layer not only limits the carrier transport but also influences the recombination. Possible reasons for the influence of the series resistance on the electron transport and recombination processes in DSC are also discussed.

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.

Building Chaotic Model From Incomplete Time Series

Science.gov (United States)

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

Self-organising mixture autoregressive model for non-stationary time series modelling.

Science.gov (United States)

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.

A human model of dietary saturated fatty acid induced insulin resistance.

Science.gov (United States)

Koska, Juraj; Ozias, Marlies K; Deer, James; Kurtz, Julie; Salbe, Arline D; Harman, S Mitchell; Reaven, Peter D

2016-11-01

Increased consumption of high-fat diets is associated with the development of insulin resistance and type 2 diabetes. Current models to study the mechanisms of high-fat diet-induced IR in humans are limited by their long duration or low efficacy. In the present study we developed and characterized an acute dietary model of saturated fatty acid-enriched diet induced insulin resistance. High caloric diets enriched with saturated fatty acids (SFA) or carbohydrates (CARB) were evaluated in subjects with normal and impaired glucose tolerance (NGT or IGT). Both diets were compared to a standard eucaloric American Heart Association (AHA) control diet in a series of crossover studies. Whole body insulin resistance was estimated as steady state plasma glucose (SSPG) concentrations during the last 30min of a 3-h insulin suppression test. SSPG was increased after a 24-h SFA diet (by 83±74% vs. control, n=38) in the entire cohort, which was comprised of participants with NGT (92±82%, n=22) or IGT (65±55%, n=16) (all pinsulin resistance in both NGT and IGT subjects. Insulin resistance persisted overnight after the last SFA meal and was attenuated by one day of a healthy diet. This model offers opportunities for identifying early mechanisms and potential treatments of dietary saturated fat induced insulin resistance. Published by Elsevier Inc.

Photovoltaic module diagnostics by series resistance monitoring and temperature and rated power estimation

DEFF Research Database (Denmark)

Sera, Dezso; Teodorescu, Remus; Rodriguez, Pedro

2008-01-01

One of the most important parameters, which characterize a photovoltaic panel health state, is its series resistance. An increase of this normally indicates bad contacts between cells or panels. Another important property, which characterizes the aging of the panel is the reduction of its MPP power...

An analytic solution for numerical modeling validation in electromagnetics: the resistive sphere

Science.gov (United States)

Swidinsky, Andrei; Liu, Lifei

2017-11-01

We derive the electromagnetic response of a resistive sphere to an electric dipole source buried in a conductive whole space. The solution consists of an infinite series of spherical Bessel functions and associated Legendre polynomials, and follows the well-studied problem of a conductive sphere buried in a resistive whole space in the presence of a magnetic dipole. Our result is particularly useful for controlled-source electromagnetic problems using a grounded electric dipole transmitter and can be used to check numerical methods of calculating the response of resistive targets (such as finite difference, finite volume, finite element and integral equation). While we elect to focus on the resistive sphere in our examples, the expressions in this paper are completely general and allow for arbitrary source frequency, sphere radius, transmitter position, receiver position and sphere/host conductivity contrast so that conductive target responses can also be checked. Commonly used mesh validation techniques consist of comparisons against other numerical codes, but such solutions may not always be reliable or readily available. Alternatively, the response of simple 1-D models can be tested against well-known whole space, half-space and layered earth solutions, but such an approach is inadequate for validating models with curved surfaces. We demonstrate that our theoretical results can be used as a complementary validation tool by comparing analytic electric fields to those calculated through a finite-element analysis; the software implementation of this infinite series solution is made available for direct and immediate application.

Time series modeling in traffic safety research.

Science.gov (United States)

Lavrenz, Steven M; Vlahogianni, Eleni I; Gkritza, Konstantina; Ke, Yue

2018-08-01

The use of statistical models for analyzing traffic safety (crash) data has been well-established. However, time series techniques have traditionally been underrepresented in the corresponding literature, due to challenges in data collection, along with a limited knowledge of proper methodology. In recent years, new types of high-resolution traffic safety data, especially in measuring driver behavior, have made time series modeling techniques an increasingly salient topic of study. Yet there remains a dearth of information to guide analysts in their use. This paper provides an overview of the state of the art in using time series models in traffic safety research, and discusses some of the fundamental techniques and considerations in classic time series modeling. It also presents ongoing and future opportunities for expanding the use of time series models, and explores newer modeling techniques, including computational intelligence models, which hold promise in effectively handling ever-larger data sets. The information contained herein is meant to guide safety researchers in understanding this broad area of transportation data analysis, and provide a framework for understanding safety trends that can influence policy-making. Copyright © 2017 Elsevier Ltd. All rights reserved.

CONTACT RESISTANCE MODELING

Directory of Open Access Journals (Sweden)

S. V. LOSKUTOV

2018-05-01

Full Text Available Purpose. To determine the contribution of the real contact spots distribution in the total conductivity of the conductors contact. Methodology. The electrical contact resistance research was carried out on models. The experimental part of this work was done on paper with a graphite layer with membranes (the first type and conductive liquids with discrete partitions (the second type. Findings. It is shown that the contact electrical resistance is mainly determined by the real area of metal contact. The experimental dependence of the electrical resistance of the second type model on the distance between the electrodes and the potential distribution along the sample surface for the first type model were obtained. The theoretical model based on the principle of electric field superposition was considered. The dependences obtained experimentally and calculated by using the theoretical model are in good agreement. Originality. The regularity of the electrical contact resistance formation on a large number of membranes was researched for the first time. A new model of discrete electrical contact based on the liquid as the conducting environment with nuclear membrane partitions was developed. The conclusions of the additivity of contact and bulk electrical resistance were done. Practical value. Based on these researches, a new experimental method of kinetic macroidentation that as a parameter of the metal surface layer deformation uses the real contact area was developed. This method allows to determine the value of average contact stresses, yield point, change of the stress on the depth of deformation depending on the surface treatment.

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

Dynamic modeling and simulation of an induction motor with adaptive backstepping design of an input-output feedback linearization controller in series hybrid electric vehicle

Directory of Open Access Journals (Sweden)

Jalalifar Mehran

2007-01-01

Full Text Available In this paper using adaptive backstepping approach an adaptive rotor flux observer which provides stator and rotor resistances estimation simultaneously for induction motor used in series hybrid electric vehicle is proposed. The controller of induction motor (IM is designed based on input-output feedback linearization technique. Combining this controller with adaptive backstepping observer the system is robust against rotor and stator resistances uncertainties. In additional, mechanical components of a hybrid electric vehicle are called from the Advanced Vehicle Simulator Software Library and then linked with the electric motor. Finally, a typical series hybrid electric vehicle is modeled and investigated. Various tests, such as acceleration traversing ramp, and fuel consumption and emission are performed on the proposed model of a series hybrid vehicle. Computer simulation results obtained, confirm the validity and performance of the proposed IM control approach using for series hybrid electric vehicle.

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...

Rolling Resistance Measurement and Model Development

DEFF Research Database (Denmark)

Andersen, Lasse Grinderslev; Larsen, Jesper; Fraser, Elsje Sophia

2015-01-01

There is an increased focus worldwide on understanding and modeling rolling resistance because reducing the rolling resistance by just a few percent will lead to substantial energy savings. This paper reviews the state of the art of rolling resistance research, focusing on measuring techniques, s......, surface and texture modeling, contact models, tire models, and macro-modeling of rolling resistance...

Nanofluidic Devices with Two Pores in Series for Resistive-Pulse Sensing of Single Virus Capsids

DEFF Research Database (Denmark)

Harms, Zachary D.; Mogensen, Klaus Bo; Rodrigues de Sousa Nunes, Pedro André

2011-01-01

We report fabrication and characterization of nanochannel devices with two nanopores in series for resistive-pulse sensing of hepatitis B virus (HBV) capsids. The nanochannel and two pores are patterned by electron beam lithography between two microchannels and etched by reactive ion etching....... The two nanopores are 50-nm wide, 50-nm deep, and 40-nm long and are spaced 2.0-μm apart. The nanochannel that brackets the two pores is 20 wider (1 μm) to reduce the electrical resistance adjacent to the two pores and to ensure the current returns to its baseline value between resistive-pulse events...

Incorrectness of conventional one-dimensional parallel thermal resistance circuit model for two-dimensional circular composite pipes

International Nuclear Information System (INIS)

Wong, K.-L.; Hsien, T.-L.; Chen, W.-L.; Yu, S.-J.

2008-01-01

This study is to prove that two-dimensional steady state heat transfer problems of composite circular pipes cannot be appropriately solved by the conventional one-dimensional parallel thermal resistance circuits (PTRC) model because its interface temperatures are not unique. Thus, the PTRC model is definitely different from its conventional recognized analogy, parallel electrical resistance circuits (PERC) model, which has unique node electric voltages. Two typical composite circular pipe examples are solved by CFD software, and the numerical results are compared with those obtained by the PTRC model. This shows that the PTRC model generates large error. Thus, this conventional model, introduced in most heat transfer text books, cannot be applied to two-dimensional composite circular pipes. On the contrary, an alternative one-dimensional separately series thermal resistance circuit (SSTRC) model is proposed and applied to a two-dimensional composite circular pipe with isothermal boundaries, and acceptable results are returned

Forecasting with nonlinear time series models

DEFF Research Database (Denmark)

Kock, Anders Bredahl; Teräsvirta, Timo

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...... 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...... and two versions of a simple artificial neural network model. Techniques for generating multi-period forecasts from nonlinear models recursively are considered, and the direct (non-recursive) method for this purpose is mentioned as well. Forecasting with com- plex dynamic systems, albeit less frequently...

Epidemiology meets econometrics: using time-series analysis to observe the impact of bed occupancy rates on the spread of multidrug-resistant bacteria.

Science.gov (United States)

Kaier, K; Meyer, E; Dettenkofer, M; Frank, U

2010-10-01

Two multivariate time-series analyses were carried out to identify the impact of bed occupancy rates, turnover intervals and the average length of hospital stay on the spread of multidrug-resistant bacteria in a teaching hospital. Epidemiological data on the incidences of meticillin-resistant Staphylococcus aureus (MRSA) and extended-spectrum beta-lactamase (ESBL)-producing bacteria were collected. Time-series of bed occupancy rates, turnover intervals and the average length of stay were tested for inclusion in the models as independent variables. Incidence was defined as nosocomial cases per 1000 patient-days. This included all patients infected or colonised with MRSA/ESBL more than 48h after admission. Between January 2003 and July 2008, a mean incidence of 0.15 nosocomial MRSA cases was identified. ESBL was not included in the surveillance until January 2005. Between January 2005 and July 2008 the mean incidence of nosocomial ESBL was also 0.15 cases per 1000 patient-days. The two multivariate models demonstrate a temporal relationship between bed occupancy rates in general wards and the incidence of nosocomial MRSA and ESBL. Similarly, the temporal relationship between the monthly average length of stay in intensive care units (ICUs) and the incidence of nosocomial MRSA and ESBL was demonstrated. Overcrowding in general wards and long periods of ICU stay were identified as factors influencing the spread of multidrug-resistant bacteria in hospital settings. Copyright 2010 The Hospital Infection Society. Published by Elsevier Ltd. All rights reserved.

Modelling conditional heteroscedasticity in nonstationary series

NARCIS (Netherlands)

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

The inaccuracy of conventional one-dimensional parallel thermal resistance circuit model for two-dimensional composite walls

International Nuclear Information System (INIS)

Wong, K.-L.; Hsien, T.-L.; Hsiao, M.-C.; Chen, W.-L.; Lin, K.-C.

2008-01-01

This investigation is to show that two-dimensional steady state heat transfer problems of composite walls should not be solved by the conventionally one-dimensional parallel thermal resistance circuits (PTRC) model because the interface temperatures are not unique. Thus PTRC model cannot be used like its conventional recognized analogy, parallel electrical resistance circuits (PERC) model which has the unique node electric voltage. Two typical composite wall examples, solved by CFD software, are used to demonstrate the incorrectness. The numerical results are compared with those obtained by PTRC model, and very large differences are observed between their results. This proves that the application of conventional heat transfer PTRC model to two-dimensional composite walls, introduced in most heat transfer text book, is totally incorrect. An alternative one-dimensional separately series thermal resistance circuit (SSTRC) model is proposed and applied to the two-dimensional composite walls with isothermal boundaries. Results with acceptable accuracy can be obtained by the new model

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.

Temperature dependence and effects of series resistance on current and admittance measurements of Al/SnO2/p-Si MIS diode

International Nuclear Information System (INIS)

Altindal, S.; Tekeli, Z.; Karadeniz, S.; Sahingoez, R.

2002-01-01

Temperature dependency and the series resistance effect on I-V, C-V and G-V characteristics of Al/SnO 2 /p-Si MIS diode were investigated in the temperature range 150-350 K. The current-voltage (I-V) analysis in this temperature range gives the saturation current (10''-''9 - 10''-''5 A), the ideality factor (6-1.8), the barrier height Φ B (I-V) (0.3-0.65 eV) the density of interface states D it (8x10''1''3 - 1x10''1''3 eV''-''1cm''-''2) and the series resistance R s (500-100 Ω). The decreases with increasing temperature of density of interface states is the result of molecular restructuring and reordering at the metal-semiconductor interface. The value of series resistance 520 Ω was calculated from the admittance measurement at room temperature and enough high frequency (500 khz) when the diode is biased in strong accumulation region. The admittance frequency (C-V and G-V) measurement confirmed that the measured capacitance (C m ) and conductance (G m ) varies with applied voltage and frequency due to the presence of density of interface states in the MIS diode, interfacial insulator layer and enough high series resistance. Similar results have been observed on MIS type Schottky diodes

RADON CONCENTRATION TIME SERIES MODELING AND APPLICATION DISCUSSION.

Science.gov (United States)

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.

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.

Foundations of Sequence-to-Sequence Modeling for Time Series

OpenAIRE

Kuznetsov, Vitaly; Mariet, Zelda

2018-01-01

The availability of large amounts of time series data, paired with the performance of deep-learning algorithms on a broad class of problems, has recently led to significant interest in the use of sequence-to-sequence models for time series forecasting. We provide the first theoretical analysis of this time series forecasting framework. We include a comparison of sequence-to-sequence modeling to classical time series models, and as such our theory can serve as a quantitative guide for practiti...

An innovation resistance factor model

Directory of Open Access Journals (Sweden)

Siti Salwa Mohd Ishak

2016-09-01

Full Text Available The process and implementation strategy of information technology in construction is generally considered through the limiting prism of theoretical contexts generated from innovation diffusion and acceptance. This research argues that more attention should be given to understanding the positive effects of resistance. The study develops a theoretical framing for the Integrated Resistance Factor Model (IRFM. The framing uses a combination of diffusion of innovation theory, technology acceptance model and social network perspective. The model is tested to identify the most significant resistance factors using Partial Least Square (PLS technique. All constructs proposed in the model are found to be significant, valid and consistent with the theoretical framework. IRFM is shown to be an effective and appropriate model of user resistance factors. The most critical factors to influence technology resistance in the online project information management system (OPIMS context are: support from leaders and peers, complexity of the technology, compatibility with key work practices; and pre-trial of the technology before it is actually deployed. The study provides a new model for further research in technology innovation specific to the construction industry.

Stress corrosion cracking resistance of aluminum alloy 7000 series after two-step aging

Directory of Open Access Journals (Sweden)

Jegdić Bore V.

2015-01-01

Full Text Available The effect of one step-and a new (short two-step aging on the resistance to stress corrosion cracking of an aluminum alloy 7000 series was investigated, using slow strain rate test and fracture mechanics method. Aging level in the tested alloy was evaluated by means of scanning electron microscopy and measurements of electrical resistivity. It was shown that the alloy after the new two-step aging is significantly more resistant to stress corrosion cracking. Values of tensile properties and fracture toughness are similar for both thermal states. Processes that take place at the crack tip have been considered. The effect of the testing solution temperature on the crack growth rate on the plateau was determined. Two values of the apparent activation energy were obtained. These values correspond to different processes that control crack growth rate on the plateau at higher and lower temperatures. [Projekat Ministarstva nauke Republike Srbije, br. TR 34028 i br. TR 34016

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.

Analysis of frequency-dependent series resistance and interface states of In/SiO{sub 2}/p-Si (MIS) structures

Energy Technology Data Exchange (ETDEWEB)

Birkan Selcuk, A. [Department of Nuclear Electronics and Instrumentation, Saraykoey Nuclear Research and Training Center, 06983 Saray, Ankara (Turkey); Tugluoglu, N. [Department of Nuclear Electronics and Instrumentation, Saraykoey Nuclear Research and Training Center, 06983 Saray, Ankara (Turkey)], E-mail: ntuglu@taek.gov.tr; Karadeniz, S.; Bilge Ocak, S. [Department of Nuclear Electronics and Instrumentation, Saraykoey Nuclear Research and Training Center, 06983 Saray, Ankara (Turkey)

2007-11-15

In this work, the investigation of the interface state density and series resistance from capacitance-voltage (C-V) and conductance-voltage (G/{omega}-V) characteristics in In/SiO{sub 2}/p-Si metal-insulator-semiconductor (MIS) structures with thin interfacial insulator layer have been reported. The thickness of SiO{sub 2} film obtained from the measurement of the oxide capacitance corrected for series resistance in the strong accumulation region is 220 A. The forward and reverse bias C-V and G/{omega}-V characteristics of MIS structures have been studied at the frequency range 30 kHz-1 MHz at room temperature. The frequency dispersion in capacitance and conductance can be interpreted in terms of the series resistance (R{sub s}) and interface state density (D{sub it}) values. Both the series resistance R{sub s} and density of interface states D{sub it} are strongly frequency-dependent and decrease with increasing frequency. The distribution profile of R{sub s}-V gives a peak at low frequencies in the depletion region and disappears with increasing frequency. Experimental results show that the interfacial polarization contributes to the improvement of the dielectric properties of In/SiO{sub 2}/p-Si MIS structures. The interface state density value of In/SiO{sub 2}/p-Si MIS diode calculated at strong accumulation region is 1.11x10{sup 12} eV{sup -1} cm{sup -2} at 1 MHz. It is found that the calculated value of D{sub it} ({approx}10{sup 12} eV{sup -1} cm{sup -2}) is not high enough to pin the Fermi level of the Si substrate disrupting the device operation.

Modeling seasonality in bimonthly time series

NARCIS (Netherlands)

Ph.H.B.F. Franses (Philip Hans)

1992-01-01

textabstractA recurring issue in modeling seasonal time series variables is the choice of the most adequate model for the seasonal movements. One selection method for quarterly data is proposed in Hylleberg et al. (1990). Market response models are often constructed for bimonthly variables, and

Adaptive time-variant models for fuzzy-time-series forecasting.

Science.gov (United States)

Wong, Wai-Keung; Bai, Enjian; Chu, Alice Wai-Ching

2010-12-01

A fuzzy time series has been applied to the prediction of enrollment, temperature, stock indices, and other domains. Related studies mainly focus on three factors, namely, the partition of discourse, the content of forecasting rules, and the methods of defuzzification, all of which greatly influence the prediction accuracy of forecasting models. These studies use fixed analysis window sizes for forecasting. In this paper, an adaptive time-variant fuzzy-time-series forecasting model (ATVF) is proposed to improve forecasting accuracy. The proposed model automatically adapts the analysis window size of fuzzy time series based on the prediction accuracy in the training phase and uses heuristic rules to generate forecasting values in the testing phase. The performance of the ATVF model is tested using both simulated and actual time series including the enrollments at the University of Alabama, Tuscaloosa, and the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX). The experiment results show that the proposed ATVF model achieves a significant improvement in forecasting accuracy as compared to other fuzzy-time-series forecasting models.

Modelling road accidents: An approach using structural time series

Science.gov (United States)

Junus, Noor Wahida Md; Ismail, Mohd Tahir

2014-09-01

In this paper, the trend of road accidents in Malaysia for the years 2001 until 2012 was modelled using a structural time series approach. The structural time series model was identified using a stepwise method, and the residuals for each model were tested. The best-fitted model was chosen based on the smallest Akaike Information Criterion (AIC) and prediction error variance. In order to check the quality of the model, a data validation procedure was performed by predicting the monthly number of road accidents for the year 2012. Results indicate that the best specification of the structural time series model to represent road accidents is the local level with a seasonal model.

Trend time-series modeling and forecasting with neural networks.

Science.gov (United States)

Qi, Min; Zhang, G Peter

2008-05-01

Despite its great importance, there has been no general consensus on how to model the trends in time-series data. Compared to traditional approaches, neural networks (NNs) have shown some promise in time-series forecasting. This paper investigates how to best model trend time series using NNs. Four different strategies (raw data, raw data with time index, detrending, and differencing) are used to model various trend patterns (linear, nonlinear, deterministic, stochastic, and breaking trend). We find that with NNs differencing often gives meritorious results regardless of the underlying data generating processes (DGPs). This finding is also confirmed by the real gross national product (GNP) series.

Characterization of Series Resistance and Mobility Degradation Parameter and Optimizing Choice of Oxide Thickness in Thin Oxide N-Channel MOSFET

Directory of Open Access Journals (Sweden)

Noureddine Maouhoub

2011-01-01

Full Text Available We present two methods to extract the series resistance and the mobility degradation parameter in short-channel MOSFETs. The principle of the first method is based on the comparison between the exponential model and the classical model of effective mobility and for the second method is based on directly calculating the two parameters by solving a system of two equations obtained by using two different points in strong inversion at small drain bias from the characteristic (. The results obtained by these techniques have shown a better agreement with data measurements and allowed in the same time to determine the surface roughness amplitude and its influence on the maximum drain current and give the optimal oxide thickness.

Three-dimensional electrical resistivity model of a nuclear waste disposal site

International Nuclear Information System (INIS)

Rucker, Dale F.; Levitt, Marc T.; Greenwood, William J.

2009-01-01

A three-dimensional (3D) modeling study was completed on a very large electrical resistivity survey conducted at a nuclear waste site in eastern Washington. The acquisition included 47 pole-pole two dimensional (2D) resistivity profiles collected along parallel and orthogonal lines over an area of 850 m-570 m. The data were geo-referenced and inverted using EarthImager3D (EI3D). EI3D runs on a Microsoft 32-bit operating system (e.g. WIN-2K, XP) with a maximum usable memory of 2 GB. The memory limits the size of the domain for the inversion model to 200 m-200 m, based on the survey electrode density. Therefore, a series of increasing overlapping models were run to evaluate the effectiveness of dividing the survey area into smaller subdomains. The results of the smaller subdomains were compared to the inversion results of a single domain over a larger area using an upgraded form of EI3D that incorporates multi-processing capabilities and 32 GB of RAM memory. The contours from the smaller subdomains showed discontinuity at the boundaries between the adjacent models, which do not match the hydrogeologic expectations given the nature of disposal at the site. At several boundaries, the contours of the low resistivity areas close, leaving the appearance of disconnected plumes or open contours at boundaries are not met with a continuance of the low resistivity plume into the adjacent subdomain. The model results of the single large domain show a continuous monolithic plume within the central and western portion of the site, directly beneath the elongated trenches. It is recommended that where possible, the domain not be subdivided, but instead include as much of the domain as possible given the memory of available computing resources.

Shunt and series resistance of photovoltaic module evaluated from the I-V curve; I-V tokusei kara hyokashita taiyo denchi no shunt teiko to chokuretsu teiko

Energy Technology Data Exchange (ETDEWEB)

Asano, K; Kawamura, H; Yamanaka, S; Kawamura, H; Ono, H [Meijo University, Nagoya (Japan)

1997-11-25

With an objective of discussing I-V characteristics when a shadow has appeared on part of a photovoltaic module, evaluations were given as a first stage of the study on saturation current, shunt resistance and series resistance for the solar cell module. As a result of measuring change in amount of power generated in a sunny day with a shadow appearing over the solar cell module, reduction in power generation capability of about 23% was verified. In other words, the I-V characteristics of the solar cell module change largely because of existence of the shadow caused on the module. The I-V characteristics curve may be expressed and calculated as a function of the shunt resistance and series resistance. By curve-fitting measurement data for a case of changing insolation without existence of partial shadow, values of the shunt resistance and series resistance were derived. As a result, it was found that the calculations agree well with measurements. It was made also clear that each parameter shows temperature dependence. 6 refs., 10 figs., 1 tab.

Koopman Operator Framework for Time Series Modeling and Analysis

Science.gov (United States)

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.

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

Optimum design of matrix fault current limiters using the series resistance connected with shunt coil

Science.gov (United States)

Chung, D. C.; Choi, H. S.; Lee, N. Y.; Nam, G. Y.; Cho, Y. S.; Sung, T. H.; Han, Y. H.; Kim, B. S.; Lim, S. H.

2007-10-01

In this paper we described the improved design for the matrix fault current limiters (MFCL). To do this, we used thin film-type superconducting elements. therefore it means that we can make the MFCL with minimized size and high switching speed because of the high current density and the high indexing value of superconducting thin film. Also we could minimize the bulky shunt coil using the connection of a series resistance with a shunt coil. Also we could effectively block up a leakage current in shunt coils under no-fault condition and simply control total impedances of a current-limiting part using this method. After we designed an appropriated 1 × 2 basic MFCL module with an applied voltage of 160 V, we enlarged it to a 2 × 2 MFCL module and a 3 × 2 MFCL module where applied voltages were 320 V and 480 V, respectively. Experimental results for our MFCL were reported in terms of various fault currents, variation of series resistance and so on. We think that these methods will be useful in the optimum design of an m × n MFCL.

Optimum design of matrix fault current limiters using the series resistance connected with shunt coil

International Nuclear Information System (INIS)

Chung, D.C.; Choi, H.S.; Lee, N.Y.; Nam, G.Y.; Cho, Y.S.; Sung, T.H.; Han, Y.H.; Kim, B.S.; Lim, S.H.

2007-01-01

In this paper we described the improved design for the matrix fault current limiters (MFCL). To do this, we used thin film-type superconducting elements. therefore it means that we can make the MFCL with minimized size and high switching speed because of the high current density and the high indexing value of superconducting thin film. Also we could minimize the bulky shunt coil using the connection of a series resistance with a shunt coil. Also we could effectively block up a leakage current in shunt coils under no-fault condition and simply control total impedances of a current-limiting part using this method. After we designed an appropriated 1 x 2 basic MFCL module with an applied voltage of 160 V, we enlarged it to a 2 x 2 MFCL module and a 3 x 2 MFCL module where applied voltages were 320 V and 480 V, respectively. Experimental results for our MFCL were reported in terms of various fault currents, variation of series resistance and so on. We think that these methods will be useful in the optimum design of an m x n MFCL

FOURIER SERIES MODELS THROUGH TRANSFORMATION

African Journals Online (AJOL)

DEPT

monthly temperature data (1996 – 2005) collected from the National Root ... KEY WORDS: Fourier series, square transformation, multiplicative model, ... fluctuations or movements are often periodic(Ekpeyong,2005). .... significant trend or not, if the trend is not significant, the grand mean may be used as an estimate of trend.

Distinguishing Antimicrobial Models with Different Resistance Mechanisms via Population Pharmacodynamic Modeling.

Directory of Open Access Journals (Sweden)

Matthieu Jacobs

2016-03-01

Full Text Available Semi-mechanistic pharmacokinetic-pharmacodynamic (PK-PD modeling is increasingly used for antimicrobial drug development and optimization of dosage regimens, but systematic simulation-estimation studies to distinguish between competing PD models are lacking. This study compared the ability of static and dynamic in vitro infection models to distinguish between models with different resistance mechanisms and support accurate and precise parameter estimation. Monte Carlo simulations (MCS were performed for models with one susceptible bacterial population without (M1 or with a resting stage (M2, a one population model with adaptive resistance (M5, models with pre-existing susceptible and resistant populations without (M3 or with (M4 inter-conversion, and a model with two pre-existing populations with adaptive resistance (M6. For each model, 200 datasets of the total bacterial population were simulated over 24h using static antibiotic concentrations (256-fold concentration range or over 48h under dynamic conditions (dosing every 12h; elimination half-life: 1h. Twelve-hundred random datasets (each containing 20 curves for static or four curves for dynamic conditions were generated by bootstrapping. Each dataset was estimated by all six models via population PD modeling to compare bias and precision. For M1 and M3, most parameter estimates were unbiased (<10% and had good imprecision (<30%. However, parameters for adaptive resistance and inter-conversion for M2, M4, M5 and M6 had poor bias and large imprecision under static and dynamic conditions. For datasets that only contained viable counts of the total population, common statistical criteria and diagnostic plots did not support sound identification of the true resistance mechanism. Therefore, it seems advisable to quantify resistant bacteria and characterize their MICs and resistance mechanisms to support extended simulations and translate from in vitro experiments to animal infection models and

Magnetic Field Emission Comparison for Series-Parallel and Series-Series Wireless Power Transfer to Vehicles – PART 2/2

DEFF Research Database (Denmark)

Batra, Tushar; Schaltz, Erik

2014-01-01

Series-series and series-parallel topologies are the most favored topologies for design of wireless power transfer system for vehicle applications. The series-series topology has the advantage of reflecting only the resistive part on the primary side. On the other hand, the current source output...... characteristics of the series-parallel topology are more suited for the battery of the vehicle. This paper compares the two topologies in terms of magnetic emissions to the surroundings for the same input power, primary current, quality factor and inductors. Theoretical and simulation results show that the series...

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 ...

Density of interface states, excess capacitance and series resistance in the metal-insulator-semiconductor (MIS) solar cells

Energy Technology Data Exchange (ETDEWEB)

Altindal, Semsettin; Tataroglu, Adem; Dokme, Ilbilge [Faculty of Arts and Sciences, Physics Department, Gazi University, 06500, Ankara (Turkey)

2005-01-31

Dark and illuminated current-voltage (I-V) characteristics of Al/SiO{sub x}/p-Si metal-insulator-semiconductor (MIS) solar cells were measured at room temperature. In addition to capacitance-voltage (C-V) and conductance-voltage (G-V), characteristics are studied at a wide frequency range of 1kHz-10MHz. The dark I-V characteristics showed non-ideal behavior with an ideal factor of 3.2. The density of interface states distribution profiles as a function of (E{sub ss}-E{sub v}) deduced from the I-V measurements at room temperature for the MIS solar cells on the order of 10{sup 13}cm{sup -2}eV{sup -1}. These interface states were responsible for the non-ideal behavior of I-V, C-V and G-V characteristics. Frequency dispersion in capacitance for MIS solar cells can be interpreted only in terms of interface states. The interface states can follow the a.c. signal and yield an excess capacitance, which depends on the relaxation time of interface states and the frequency of the a.c. signal. It was observed that the excess capacitance C{sub o} caused by an interface state decreases with an increase of frequency. The capacitances characteristics of MIS solar cells are affected not only in interface states but also series resistance. Analysis of this data indicated that the high interface states and series resistance leads to lower values of open-circuit voltage, short-circuit current density, and fill factor. Experimental results show that the location of interface states and series resistance have a significant effect on I-V, C-V and G-V characteristics.

Sensitivity analysis of machine-learning models of hydrologic time series

Science.gov (United States)

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.

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...

Measurements of time average series resonance effect in capacitively coupled radio frequency discharge plasma

International Nuclear Information System (INIS)

Bora, B.; Bhuyan, H.; Favre, M.; Wyndham, E.; Chuaqui, H.; Kakati, M.

2011-01-01

Self-excited plasma series resonance is observed in low pressure capacitvely coupled radio frequency discharges as high-frequency oscillations superimposed on the normal radio frequency current. This high-frequency contribution to the radio frequency current is generated by a series resonance between the capacitive sheath and the inductive and resistive bulk plasma. In this report, we present an experimental method to measure the plasma series resonance in a capacitively coupled radio frequency argon plasma by modifying the homogeneous discharge model. The homogeneous discharge model is modified by introducing a correction factor to the plasma resistance. Plasma parameters are also calculated by considering the plasma series resonances effect. Experimental measurements show that the self-excitation of the plasma series resonance, which arises in capacitive discharge due to the nonlinear interaction of plasma bulk and sheath, significantly enhances both the Ohmic and stochastic heating. The experimentally measured total dissipation, which is the sum of the Ohmic and stochastic heating, is found to increase significantly with decreasing pressure.

A four-stage hybrid model for hydrological time series forecasting.

Science.gov (United States)

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.

A Four-Stage Hybrid Model for Hydrological Time Series Forecasting

Science.gov (United States)

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

A neuro-fuzzy computing technique for modeling hydrological time series

Science.gov (United States)

Nayak, P. C.; Sudheer, K. P.; Rangan, D. M.; Ramasastri, K. S.

2004-05-01

Intelligent computing tools such as artificial neural network (ANN) and fuzzy logic approaches are proven to be efficient when applied individually to a variety of problems. Recently there has been a growing interest in combining both these approaches, and as a result, neuro-fuzzy computing techniques have evolved. This approach has been tested and evaluated in the field of signal processing and related areas, but researchers have only begun evaluating the potential of this neuro-fuzzy hybrid approach in hydrologic modeling studies. This paper presents the application of an adaptive neuro fuzzy inference system (ANFIS) to hydrologic time series modeling, and is illustrated by an application to model the river flow of Baitarani River in Orissa state, India. An introduction to the ANFIS modeling approach is also presented. The advantage of the method is that it does not require the model structure to be known a priori, in contrast to most of the time series modeling techniques. The results showed that the ANFIS forecasted flow series preserves the statistical properties of the original flow series. The model showed good performance in terms of various statistical indices. The results are highly promising, and a comparative analysis suggests that the proposed modeling approach outperforms ANNs and other traditional time series models in terms of computational speed, forecast errors, efficiency, peak flow estimation etc. It was observed that the ANFIS model preserves the potential of the ANN approach fully, and eases the model building process.

Modeling of permeate flux and mass transfer resistances in the reclamation of molasses wastewater by a novel gas-sparged nanofiltration

International Nuclear Information System (INIS)

Patel, Tejal Manish; Nath, Kaushik

2014-01-01

A semi-empirical model has been applied to predict the permeate flux and mass transfer resistances during the cross flow nanofiltration of molasses wastewater in flat-sheet module. The model includes laminar flow regime as well as flow in presence of gas sparging at two different gas velocities. Membrane hydraulic resistance (R m ), osmotic pressure resistance (R osm ) and the concentration polarization resistance (R cp ) were considered in series. The concentration polarization resistance was correlated to the operating conditions, namely, the feed concentration, the trans-membrane pressure difference and the cross flow velocity for a selected range of experiments. There was an appreciable reduction of concentration polarization resistance R cp spar in presence of gas sparging. Both the concentration polarization resistance R cp lam and osmotic pressure resistance R osm decreased with cross-flow velocity, but increased with feed concentration and the operating pressure. Experimental and theoretical permeate flux values as a function of cross flow velocity for both the cases, in the presence and absence of gas sparging, were also compared

Modeling of permeate flux and mass transfer resistances in the reclamation of molasses wastewater by a novel gas-sparged nanofiltration

Energy Technology Data Exchange (ETDEWEB)

Patel, Tejal Manish; Nath, Kaushik [G H Patel College of Engineering and Technology, Gujarat (India)

2014-10-15

A semi-empirical model has been applied to predict the permeate flux and mass transfer resistances during the cross flow nanofiltration of molasses wastewater in flat-sheet module. The model includes laminar flow regime as well as flow in presence of gas sparging at two different gas velocities. Membrane hydraulic resistance (R{sub m}), osmotic pressure resistance (R{sub osm}) and the concentration polarization resistance (R{sub cp}) were considered in series. The concentration polarization resistance was correlated to the operating conditions, namely, the feed concentration, the trans-membrane pressure difference and the cross flow velocity for a selected range of experiments. There was an appreciable reduction of concentration polarization resistance R{sub cp}{sup spar} in presence of gas sparging. Both the concentration polarization resistance R{sub cp}{sup lam} and osmotic pressure resistance R{sub osm} decreased with cross-flow velocity, but increased with feed concentration and the operating pressure. Experimental and theoretical permeate flux values as a function of cross flow velocity for both the cases, in the presence and absence of gas sparging, were also compared.

Multivariate analysis and extraction of parameters in resistive RAMs using the Quantum Point Contact model

Science.gov (United States)

Roldán, J. B.; Miranda, E.; González-Cordero, G.; García-Fernández, P.; Romero-Zaliz, R.; González-Rodelas, P.; Aguilera, A. M.; González, M. B.; Jiménez-Molinos, F.

2018-01-01

A multivariate analysis of the parameters that characterize the reset process in Resistive Random Access Memory (RRAM) has been performed. The different correlations obtained can help to shed light on the current components that contribute in the Low Resistance State (LRS) of the technology considered. In addition, a screening method for the Quantum Point Contact (QPC) current component is presented. For this purpose, the second derivative of the current has been obtained using a novel numerical method which allows determining the QPC model parameters. Once the procedure is completed, a whole Resistive Switching (RS) series of thousands of curves is studied by means of a genetic algorithm. The extracted QPC parameter distributions are characterized in depth to get information about the filamentary pathways associated with LRS in the low voltage conduction regime.

Comparison of annual maximum series and partial duration series methods for modeling extreme hydrologic events

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......). In the case of ML estimation, the PDS model provides the most efficient T-year event estimator. In the cases of MOM and PWM estimation, the PDS model is generally preferable for negative shape parameters, whereas the AMS model yields the most efficient estimator for positive shape parameters. A comparison...... 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...

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

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.

Parametric, nonparametric and parametric modelling of a chaotic circuit time series

Science.gov (United States)

Timmer, J.; Rust, H.; Horbelt, W.; Voss, H. U.

2000-09-01

The determination of a differential equation underlying a measured time series is a frequently arising task in nonlinear time series analysis. In the validation of a proposed model one often faces the dilemma that it is hard to decide whether possible discrepancies between the time series and model output are caused by an inappropriate model or by bad estimates of parameters in a correct type of model, or both. We propose a combination of parametric modelling based on Bock's multiple shooting algorithm and nonparametric modelling based on optimal transformations as a strategy to test proposed models and if rejected suggest and test new ones. We exemplify this strategy on an experimental time series from a chaotic circuit where we obtain an extremely accurate reconstruction of the observed attractor.

Stochastic modeling of hourly rainfall times series in Campania (Italy)

Science.gov (United States)

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

Modelling fourier regression for time series data- a case study: modelling inflation in foods sector in Indonesia

Science.gov (United States)

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%.

Modeling vector nonlinear time series using POLYMARS

NARCIS (Netherlands)

de Gooijer, J.G.; Ray, B.K.

2003-01-01

A modified multivariate adaptive regression splines method for modeling vector nonlinear time series is investigated. The method results in models that can capture certain types of vector self-exciting threshold autoregressive behavior, as well as provide good predictions for more general vector

Forecasting with periodic autoregressive time series models

NARCIS (Netherlands)

Ph.H.B.F. Franses (Philip Hans); R. Paap (Richard)

1999-01-01

textabstractThis paper is concerned with forecasting univariate seasonal time series data using periodic autoregressive models. We show how one should account for unit roots and deterministic terms when generating out-of-sample forecasts. We illustrate the models for various quarterly UK consumption

Outlier Detection in Structural Time Series Models

DEFF Research Database (Denmark)

Marczak, Martyna; Proietti, Tommaso

investigate via Monte Carlo simulations how this approach performs for detecting additive outliers and level shifts in the analysis of nonstationary seasonal time series. The reference model is the basic structural model, featuring a local linear trend, possibly integrated of order two, stochastic seasonality......Structural change affects the estimation of economic signals, like the underlying growth rate or the seasonally adjusted series. An important issue, which has attracted a great deal of attention also in the seasonal adjustment literature, is its detection by an expert procedure. The general......–to–specific approach to the detection of structural change, currently implemented in Autometrics via indicator saturation, has proven to be both practical and effective in the context of stationary dynamic regression models and unit–root autoregressions. By focusing on impulse– and step–indicator saturation, we...

Time Series Modelling of Syphilis Incidence in China from 2005 to 2012.

Science.gov (United States)

Zhang, Xingyu; Zhang, Tao; Pei, Jiao; Liu, Yuanyuan; Li, Xiaosong; Medrano-Gracia, Pau

2016-01-01

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. 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). 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. 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.

Modelling bursty time series

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)

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.

Resistance Fluctuations in GaAs Nanowire Grids

Directory of Open Access Journals (Sweden)

Ivan Marasović

2014-01-01

Full Text Available We present a numerical study on resistance fluctuations in a series of nanowire-based grids. Each grid is made of GaAs nanowires arranged in parallel with metallic contacts crossing all nanowires perpendicularly. Electrical properties of GaAs nanowires known from previous experimental research are used as input parameters in the simulation procedure. Due to the nonhomogeneous doping, the resistivity changes along nanowire. Allowing two possible nanowire orientations (“upwards” or “downwards”, the resulting grid is partially disordered in vertical direction which causes resistance fluctuations. The system is modeled using a two-dimensional random resistor network. Transfer-matrix computation algorithm is used to calculate the total network resistance. It is found that probability density function (PDF of resistance fluctuations for a series of nanowire grids changes from Gaussian behavior towards the Bramwell-Holdsworth-Pinton distribution when both nanowire orientations are equally represented in the grid.

Impact of Nickel silicide Rear Metallization on Series Resistance of Crystalline Silicon Solar Cells

KAUST Repository

Bahabry, Rabab R

2018-01-11

The Silicon-based solar cell is one of the most important enablers toward high efficiency and low-cost clean energy resource. Metallization of silicon-based solar cells typically utilizes screen printed silver-Aluminium (Ag-Al) which affects the optimal electrical performance. To date, metal silicide-based ohmic contacts are occasionally used as an alternative candidate only to the front contact grid lines in crystalline silicon (c-Si) based solar cells. In this paper, we investigate the electrical characteristics of nickel mono-silicide (NiSi)/Cu-Al ohmic contact on the rear side of c-Si solar cells. We observe a significant enhancement in the fill factor of around 6.5% for NiSi/Cu-Al rear contacts leading to increasing the efficiency by 1.2% compared to Ag-Al. This is attributed to the improvement of the parasitic resistance in which the series resistance decreased by 0.737 Ω.cm². Further, we complement experimental observation with a simulation of different contact resistance values, which manifests NiSi/Cu-Al rear contact as a promising low-cost metallization for c-Si solar cells with enhanced efficiency.

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 ... Ugwuowo (2006) proposed asymmetric angular- linear multivariate regression models, ..... Using the parameter estimates in Table 3, the fitted Fourier series model is ..... For the SARIMA model with the stochastic component also being white noise, ...

Time Series Modelling of Syphilis Incidence in China from 2005 to 2012

Science.gov (United States)

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

Hidden Markov Models for Time Series An Introduction Using R

CERN Document Server

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.

Epidemiological models for the spread of anti-malarial resistance

Directory of Open Access Journals (Sweden)

Antia R

2003-02-01

Full Text Available Abstract Background The spread of drug resistance is making malaria control increasingly difficult. Mathematical models for the transmission dynamics of drug sensitive and resistant strains can be a useful tool to help to understand the factors that influence the spread of drug resistance, and they can therefore help in the design of rational strategies for the control of drug resistance. Methods We present an epidemiological framework to investigate the spread of anti-malarial resistance. Several mathematical models, based on the familiar Macdonald-Ross model of malaria transmission, enable us to examine the processes and parameters that are critical in determining the spread of resistance. Results In our simplest model, resistance does not spread if the fraction of infected individuals treated is less than a threshold value; if drug treatment exceeds this threshold, resistance will eventually become fixed in the population. The threshold value is determined only by the rates of infection and the infectious periods of resistant and sensitive parasites in untreated and treated hosts, whereas the intensity of transmission has no influence on the threshold value. In more complex models, where hosts can be infected by multiple parasite strains or where treatment varies spatially, resistance is generally not fixed, but rather some level of sensitivity is often maintained in the population. Conclusions The models developed in this paper are a first step in understanding the epidemiology of anti-malarial resistance and evaluating strategies to reduce the spread of resistance. However, specific recommendations for the management of resistance need to wait until we have more data on the critical parameters underlying the spread of resistance: drug use, spatial variability of treatment and parasite migration among areas, and perhaps most importantly, cost of resistance.

Short-Term Bus Passenger Demand Prediction Based on Time Series Model and Interactive Multiple Model Approach

Directory of Open Access Journals (Sweden)

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.

Small Sample Properties of Bayesian Multivariate Autoregressive Time Series Models

Science.gov (United States)

Price, Larry R.

2012-01-01

The aim of this study was to compare the small sample (N = 1, 3, 5, 10, 15) performance of a Bayesian multivariate vector autoregressive (BVAR-SEM) time series model relative to frequentist power and parameter estimation bias. A multivariate autoregressive model was developed based on correlated autoregressive time series vectors of varying…

Estimating High-Dimensional Time Series Models

DEFF Research Database (Denmark)

Medeiros, Marcelo C.; Mendes, Eduardo F.

We study the asymptotic properties of the Adaptive LASSO (adaLASSO) in sparse, high-dimensional, linear time-series models. We assume both the number of covariates in the model and candidate variables can increase with the number of observations and the number of candidate variables is, possibly......, larger than the number of observations. We show the adaLASSO consistently chooses the relevant variables as the number of observations increases (model selection consistency), and has the oracle property, even when the errors are non-Gaussian and conditionally heteroskedastic. A simulation study shows...

Predicting long-term catchment nutrient export: the use of nonlinear time series models

Science.gov (United States)

Valent, Peter; Howden, Nicholas J. K.; Szolgay, Jan; Komornikova, Magda

2010-05-01

After the Second World War the nitrate concentrations in European water bodies changed significantly as the result of increased nitrogen fertilizer use and changes in land use. However, in the last decades, as a consequence of the implementation of nitrate-reducing measures in Europe, the nitrate concentrations in water bodies slowly decrease. This causes that the mean and variance of the observed time series also changes with time (nonstationarity and heteroscedascity). In order to detect changes and properly describe the behaviour of such time series by time series analysis, linear models (such as autoregressive (AR), moving average (MA) and autoregressive moving average models (ARMA)), are no more suitable. Time series with sudden changes in statistical characteristics can cause various problems in the calibration of traditional water quality models and thus give biased predictions. Proper statistical analysis of these non-stationary and heteroscedastic time series with the aim of detecting and subsequently explaining the variations in their statistical characteristics requires the use of nonlinear time series models. This information can be then used to improve the model building and calibration of conceptual water quality model or to select right calibration periods in order to produce reliable predictions. The objective of this contribution is to analyze two long time series of nitrate concentrations of the rivers Ouse and Stour with advanced nonlinear statistical modelling techniques and compare their performance with traditional linear models of the ARMA class in order to identify changes in the time series characteristics. The time series were analysed with nonlinear models with multiple regimes represented by self-exciting threshold autoregressive (SETAR) and Markov-switching models (MSW). The analysis showed that, based on the value of residual sum of squares (RSS) in both datasets, SETAR and MSW models described the time-series better than models of the

The use of synthetic input sequences in time series modeling

International Nuclear Information System (INIS)

Oliveira, Dair Jose de; Letellier, Christophe; Gomes, Murilo E.D.; Aguirre, Luis A.

2008-01-01

In many situations time series models obtained from noise-like data settle to trivial solutions under iteration. This Letter proposes a way of producing a synthetic (dummy) input, that is included to prevent the model from settling down to a trivial solution, while maintaining features of the original signal. Simulated benchmark models and a real time series of RR intervals from an ECG are used to illustrate the procedure

Time Series Modelling using Proc Varmax

DEFF Research Database (Denmark)

Milhøj, Anders

2007-01-01

In this paper it will be demonstrated how various time series problems could be met using Proc Varmax. The procedure is rather new and hence new features like cointegration, testing for Granger causality are included, but it also means that more traditional ARIMA modelling as outlined by Box...

Time-series modeling: applications to long-term finfish monitoring data

International Nuclear Information System (INIS)

Bireley, L.E.

1985-01-01

The growing concern and awareness that developed during the 1970's over the effects that industry had on the environment caused the electric utility industry in particular to develop monitoring programs. These programs generate long-term series of data that are not very amenable to classical normal-theory statistical analysis. The monitoring data collected from three finfish programs (impingement, trawl and seine) at the Millstone Nuclear Power Station were typical of such series and thus were used to develop methodology that used the full extent of the information in the series. The basis of the methodology was classic Box-Jenkins time-series modeling; however, the models also included deterministic components that involved flow, season and time as predictor variables. Time entered into the models as harmonic regression terms. Of the 32 models fitted to finfish catch data, 19 were found to account for more than 70% of the historical variation. The models were than used to forecast finfish catches a year in advance and comparisons were made to actual data. Usually the confidence intervals associated with the forecasts encompassed most of the observed data. The technique can provide the basis for intervention analysis in future impact assessments

Evaluation of nonlinearity and validity of nonlinear modeling for complex time series.

Science.gov (United States)

Suzuki, Tomoya; Ikeguchi, Tohru; Suzuki, Masuo

2007-10-01

Even if an original time series exhibits nonlinearity, it is not always effective to approximate the time series by a nonlinear model because such nonlinear models have high complexity from the viewpoint of information criteria. Therefore, we propose two measures to evaluate both the nonlinearity of a time series and validity of nonlinear modeling applied to it by nonlinear predictability and information criteria. Through numerical simulations, we confirm that the proposed measures effectively detect the nonlinearity of an observed time series and evaluate the validity of the nonlinear model. The measures are also robust against observational noises. We also analyze some real time series: the difference of the number of chickenpox and measles patients, the number of sunspots, five Japanese vowels, and the chaotic laser. We can confirm that the nonlinear model is effective for the Japanese vowel /a/, the difference of the number of measles patients, and the chaotic laser.

PSO-MISMO modeling strategy for multistep-ahead time series prediction.

Science.gov (United States)

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.

On modeling panels of time series

NARCIS (Netherlands)

Ph.H.B.F. Franses (Philip Hans)

2002-01-01

textabstractThis paper reviews research issues in modeling panels of time series. Examples of this type of data are annually observed macroeconomic indicators for all countries in the world, daily returns on the individual stocks listed in the S&P500, and the sales records of all items in a

A New Six-Parameter Model Based on Chebyshev Polynomials for Solar Cells

Directory of Open Access Journals (Sweden)

Shu-xian Lun

2015-01-01

Full Text Available This paper presents a new current-voltage (I-V model for solar cells. It has been proved that series resistance of a solar cell is related to temperature. However, the existing five-parameter model ignores the temperature dependence of series resistance and then only accurately predicts the performance of monocrystalline silicon solar cells. Therefore, this paper uses Chebyshev polynomials to describe the relationship between series resistance and temperature. This makes a new parameter called temperature coefficient for series resistance introduced into the single-diode model. Then, a new six-parameter model for solar cells is established in this paper. This new model can improve the accuracy of the traditional single-diode model and reflect the temperature dependence of series resistance. To validate the accuracy of the six-parameter model in this paper, five kinds of silicon solar cells with different technology types, that is, monocrystalline silicon, polycrystalline silicon, thin film silicon, and tripe-junction amorphous silicon, are tested at different irradiance and temperature conditions. Experiment results show that the six-parameter model proposed in this paper is an I-V model with moderate computational complexity and high precision.

Mixture model to assess the extent of cross-transmission of multidrug-resistant pathogens in hospitals.

Science.gov (United States)

Mikolajczyk, Rafael T; Kauermann, Göran; Sagel, Ulrich; Kretzschmar, Mirjam

2009-08-01

Creation of a mixture model based on Poisson processes for assessment of the extent of cross-transmission of multidrug-resistant pathogens in the hospital. We propose a 2-component mixture of Poisson processes to describe the time series of detected cases of colonization. The first component describes the admission process of patients with colonization, and the second describes the cross-transmission. The data set used to illustrate the method consists of the routinely collected records for methicillin-resistant Staphylococcus aureus (MRSA), imipenem-resistant Pseudomonas aeruginosa, and multidrug-resistant Acinetobacter baumannii over a period of 3 years in a German tertiary care hospital. For MRSA and multidrug-resistant A. baumannii, cross-transmission was estimated to be responsible for more than 80% of cases; for imipenem-resistant P. aeruginosa, cross-transmission was estimated to be responsible for 59% of cases. For new cases observed within a window of less than 28 days for MRSA and multidrug-resistant A. baumannii or 40 days for imipenem-resistant P. aeruginosa, there was a 50% or greater probability that the cause was cross-transmission. The proposed method offers a solution to assessing of the extent of cross-transmission, which can be of clinical use. The method can be applied using freely available software (the package FlexMix in R) and it requires relatively little data.

Models to Study Colonisation and Colonisation Resistance

OpenAIRE

Boreau, H.; Hartmann, L.; Karjalainen, T.; Rowland, I.; Wilkinson, M. H. F.

2011-01-01

This review describes various in vivo animal models (humans; conventional animals administered antimicrobial agents and animals species used; gnotobiotic and germ-free animals), in vitro models (luminal and mucosal), and in silico and mathematicalmodels which have been developed to study colonisation and colonisation resistance and effects of gut flora on hosts. Where applicable, the advantages and disadvantages of each model are discussed.Keywords: colonisation, colonisation resistance, anim...

Optimization study on inductive-resistive circuit for broadband piezoelectric energy harvesters

Directory of Open Access Journals (Sweden)

Ting Tan

2017-03-01

Full Text Available The performance of cantilever-beam piezoelectric energy harvester is usually analyzed with pure resistive circuit. The optimal performance of such a vibration-based energy harvesting system is limited by narrow bandwidth around its modified natural frequency. For broadband piezoelectric energy harvesting, series and parallel inductive-resistive circuits are introduced. The electromechanical coupled distributed parameter models for such systems under harmonic base excitations are decoupled with modified natural frequency and electrical damping to consider the coupling effect. Analytical solutions of the harvested power and tip displacement for the electromechanical decoupled model are confirmed with numerical solutions for the coupled model. The optimal performance of piezoelectric energy harvesting with inductive-resistive circuits is revealed theoretically as constant maximal power at any excitation frequency. This is achieved by the scenarios of matching the modified natural frequency with the excitation frequency and equating the electrical damping to the mechanical damping. The inductance and load resistance should be simultaneously tuned to their optimal values, which may not be applicable for very high electromechanical coupling systems when the excitation frequency is higher than their natural frequencies. With identical optimal performance, the series inductive-resistive circuit is recommended for relatively small load resistance, while the parallel inductive-resistive circuit is suggested for relatively large load resistance. This study provides a simplified optimization method for broadband piezoelectric energy harvesters with inductive-resistive circuits.

Optimization study on inductive-resistive circuit for broadband piezoelectric energy harvesters

Science.gov (United States)

Tan, Ting; Yan, Zhimiao

2017-03-01

The performance of cantilever-beam piezoelectric energy harvester is usually analyzed with pure resistive circuit. The optimal performance of such a vibration-based energy harvesting system is limited by narrow bandwidth around its modified natural frequency. For broadband piezoelectric energy harvesting, series and parallel inductive-resistive circuits are introduced. The electromechanical coupled distributed parameter models for such systems under harmonic base excitations are decoupled with modified natural frequency and electrical damping to consider the coupling effect. Analytical solutions of the harvested power and tip displacement for the electromechanical decoupled model are confirmed with numerical solutions for the coupled model. The optimal performance of piezoelectric energy harvesting with inductive-resistive circuits is revealed theoretically as constant maximal power at any excitation frequency. This is achieved by the scenarios of matching the modified natural frequency with the excitation frequency and equating the electrical damping to the mechanical damping. The inductance and load resistance should be simultaneously tuned to their optimal values, which may not be applicable for very high electromechanical coupling systems when the excitation frequency is higher than their natural frequencies. With identical optimal performance, the series inductive-resistive circuit is recommended for relatively small load resistance, while the parallel inductive-resistive circuit is suggested for relatively large load resistance. This study provides a simplified optimization method for broadband piezoelectric energy harvesters with inductive-resistive circuits.

New insights into soil temperature time series modeling: linear or nonlinear?

Science.gov (United States)

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

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.

Tumour resistance to cisplatin: a modelling approach

Energy Technology Data Exchange (ETDEWEB)

Marcu, L [School of Chemistry and Physics, University of Adelaide, North Terrace, SA 5000 (Australia); Bezak, E [School of Chemistry and Physics, University of Adelaide, North Terrace, SA 5000 (Australia); Olver, I [Faculty of Medicine, University of Adelaide, North Terrace, SA 5000 (Australia); Doorn, T van [School of Chemistry and Physics, University of Adelaide, North Terrace, SA 5000 (Australia)

2005-01-07

Although chemotherapy has revolutionized the treatment of haematological tumours, in many common solid tumours the success has been limited. Some of the reasons for the limitations are: the timing of drug delivery, resistance to the drug, repopulation between cycles of chemotherapy and the lack of complete understanding of the pharmacokinetics and pharmacodynamics of a specific agent. Cisplatin is among the most effective cytotoxic agents used in head and neck cancer treatments. When modelling cisplatin as a single agent, the properties of cisplatin only have to be taken into account, reducing the number of assumptions that are considered in the generalized chemotherapy models. The aim of the present paper is to model the biological effect of cisplatin and to simulate the consequence of cisplatin resistance on tumour control. The 'treated' tumour is a squamous cell carcinoma of the head and neck, previously grown by computer-based Monte Carlo techniques. The model maintained the biological constitution of a tumour through the generation of stem cells, proliferating cells and non-proliferating cells. Cell kinetic parameters (mean cell cycle time, cell loss factor, thymidine labelling index) were also consistent with the literature. A sensitivity study on the contribution of various mechanisms leading to drug resistance is undertaken. To quantify the extent of drug resistance, the cisplatin resistance factor (CRF) is defined as the ratio between the number of surviving cells of the resistant population and the number of surviving cells of the sensitive population, determined after the same treatment time. It is shown that there is a supra-linear dependence of CRF on the percentage of cisplatin-DNA adducts formed, and a sigmoid-like dependence between CRF and the percentage of cells killed in resistant tumours. Drug resistance is shown to be a cumulative process which eventually can overcome tumour regression leading to treatment failure.

Tumour resistance to cisplatin: a modelling approach

International Nuclear Information System (INIS)

Marcu, L; Bezak, E; Olver, I; Doorn, T van

2005-01-01

Although chemotherapy has revolutionized the treatment of haematological tumours, in many common solid tumours the success has been limited. Some of the reasons for the limitations are: the timing of drug delivery, resistance to the drug, repopulation between cycles of chemotherapy and the lack of complete understanding of the pharmacokinetics and pharmacodynamics of a specific agent. Cisplatin is among the most effective cytotoxic agents used in head and neck cancer treatments. When modelling cisplatin as a single agent, the properties of cisplatin only have to be taken into account, reducing the number of assumptions that are considered in the generalized chemotherapy models. The aim of the present paper is to model the biological effect of cisplatin and to simulate the consequence of cisplatin resistance on tumour control. The 'treated' tumour is a squamous cell carcinoma of the head and neck, previously grown by computer-based Monte Carlo techniques. The model maintained the biological constitution of a tumour through the generation of stem cells, proliferating cells and non-proliferating cells. Cell kinetic parameters (mean cell cycle time, cell loss factor, thymidine labelling index) were also consistent with the literature. A sensitivity study on the contribution of various mechanisms leading to drug resistance is undertaken. To quantify the extent of drug resistance, the cisplatin resistance factor (CRF) is defined as the ratio between the number of surviving cells of the resistant population and the number of surviving cells of the sensitive population, determined after the same treatment time. It is shown that there is a supra-linear dependence of CRF on the percentage of cisplatin-DNA adducts formed, and a sigmoid-like dependence between CRF and the percentage of cells killed in resistant tumours. Drug resistance is shown to be a cumulative process which eventually can overcome tumour regression leading to treatment failure

Thermomechanical Modelling of Resistance Welding

DEFF Research Database (Denmark)

Bay, Niels; Zhang, Wenqi

2007-01-01

The present paper describes a generic programme for analysis, optimization and development of resistance spot and projection welding. The programme includes an electrical model determining electric current and voltage distribution as well as heat generation, a thermal model calculating heat...

Mathematical modeling and computational prediction of cancer drug resistance.

Science.gov (United States)

Sun, Xiaoqiang; Hu, Bin

2017-06-23

Diverse forms of resistance to anticancer drugs can lead to the failure of chemotherapy. Drug resistance is one of the most intractable issues for successfully treating cancer in current clinical practice. Effective clinical approaches that could counter drug resistance by restoring the sensitivity of tumors to the targeted agents are urgently needed. As numerous experimental results on resistance mechanisms have been obtained and a mass of high-throughput data has been accumulated, mathematical modeling and computational predictions using systematic and quantitative approaches have become increasingly important, as they can potentially provide deeper insights into resistance mechanisms, generate novel hypotheses or suggest promising treatment strategies for future testing. In this review, we first briefly summarize the current progress of experimentally revealed resistance mechanisms of targeted therapy, including genetic mechanisms, epigenetic mechanisms, posttranslational mechanisms, cellular mechanisms, microenvironmental mechanisms and pharmacokinetic mechanisms. Subsequently, we list several currently available databases and Web-based tools related to drug sensitivity and resistance. Then, we focus primarily on introducing some state-of-the-art computational methods used in drug resistance studies, including mechanism-based mathematical modeling approaches (e.g. molecular dynamics simulation, kinetic model of molecular networks, ordinary differential equation model of cellular dynamics, stochastic model, partial differential equation model, agent-based model, pharmacokinetic-pharmacodynamic model, etc.) and data-driven prediction methods (e.g. omics data-based conventional screening approach for node biomarkers, static network approach for edge biomarkers and module biomarkers, dynamic network approach for dynamic network biomarkers and dynamic module network biomarkers, etc.). Finally, we discuss several further questions and future directions for the use of

Time series regression model for infectious disease and weather.

Science.gov (United States)

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.

Model for evaluating nuclear strategies with proliferation resistance

International Nuclear Information System (INIS)

Shay, M.R.; Hardie, R.W.; Omberg, R.P.

1979-03-01

A model was developed at HEDL to specifically analyze proliferation resistant strategies. The model was not designed to predict the future, but rather to provide a method for estimating the consequences of decisions affecting proliferation resistance in a rational and plausible manner. The characteristics of the model are described

MODELS OF INSULIN RESISTANCE AND HEART FAILURE

Science.gov (United States)

Velez, Mauricio; Kohli, Smita; Sabbah, Hani N.

2013-01-01

The incidence of heart failure (HF) and diabetes mellitus is rapidly increasing and is associated with poor prognosis. In spite of the advances in therapy, HF remains a major health problem with high morbidity and mortality. When HF and diabetes coexist, clinical outcomes are significantly worse. The relationship between these two conditions has been studied in various experimental models. However, the mechanisms for this interrelationship are complex, incompletely understood, and have become a matter of considerable clinical and research interest. There are only few animal models that manifest both HF and diabetes. However, the translation of results from these models to human disease is limited and new models are needed to expand our current understanding of this clinical interaction. In this review, we discuss mechanisms of insulin signaling and insulin resistance, the clinical association between insulin resistance and HF and its proposed pathophysiologic mechanisms. Finally, we discuss available animal models of insulin resistance and HF and propose requirements for future new models. PMID:23456447

Rotation in the dynamic factor modeling of multivariate stationary time series.

NARCIS (Netherlands)

Molenaar, P.C.M.; Nesselroade, J.R.

2001-01-01

A special rotation procedure is proposed for the exploratory dynamic factor model for stationary multivariate time series. The rotation procedure applies separately to each univariate component series of a q-variate latent factor series and transforms such a component, initially represented as white

Analysis and modeling of resistive switching mechanisms oriented to resistive random-access memory

International Nuclear Information System (INIS)

Huang Da; Wu Jun-Jie; Tang Yu-Hua

2013-01-01

With the progress of the semiconductor industry, the resistive random-access memory (RAM) has drawn increasing attention. The discovery of the memristor has brought much attention to this study. Research has focused on the resistive switching characteristics of different materials and the analysis of resistive switching mechanisms. We discuss the resistive switching mechanisms of different materials in this paper and analyze the differences of those mechanisms from the view point of circuitry to establish their respective circuit models. Finally, simulations are presented. We give the prospect of using different materials in resistive RAM on account of their resistive switching mechanisms, which are applied to explain their resistive switchings

Time series analysis as input for clinical predictive modeling: modeling cardiac arrest in a pediatric ICU.

Science.gov (United States)

Kennedy, Curtis E; Turley, James P

2011-10-24

Thousands of children experience cardiac arrest events every year in pediatric intensive care units. Most of these children die. Cardiac arrest prediction tools are used as part of medical emergency team evaluations to identify patients in standard hospital beds that are at high risk for cardiac arrest. There are no models to predict cardiac arrest in pediatric intensive care units though, where the risk of an arrest is 10 times higher than for standard hospital beds. Current tools are based on a multivariable approach that does not characterize deterioration, which often precedes cardiac arrests. Characterizing deterioration requires a time series approach. The purpose of this study is to propose a method that will allow for time series data to be used in clinical prediction models. Successful implementation of these methods has the potential to bring arrest prediction to the pediatric intensive care environment, possibly allowing for interventions that can save lives and prevent disabilities. We reviewed prediction models from nonclinical domains that employ time series data, and identified the steps that are necessary for building predictive models using time series clinical data. We illustrate the method by applying it to the specific case of building a predictive model for cardiac arrest in a pediatric intensive care unit. Time course analysis studies from genomic analysis provided a modeling template that was compatible with the steps required to develop a model from clinical time series data. The steps include: 1) selecting candidate variables; 2) specifying measurement parameters; 3) defining data format; 4) defining time window duration and resolution; 5) calculating latent variables for candidate variables not directly measured; 6) calculating time series features as latent variables; 7) creating data subsets to measure model performance effects attributable to various classes of candidate variables; 8) reducing the number of candidate features; 9

Forecasting daily meteorological time series using ARIMA and regression models

Science.gov (United States)

Murat, Małgorzata; Malinowska, Iwona; Gos, Magdalena; Krzyszczak, Jaromir

2018-04-01

The daily air temperature and precipitation time series recorded between January 1, 1980 and December 31, 2010 in four European sites (Jokioinen, Dikopshof, Lleida and Lublin) from different climatic zones were modeled and forecasted. In our forecasting we used the methods of the Box-Jenkins and Holt- Winters seasonal auto regressive integrated moving-average, the autoregressive integrated moving-average with external regressors in the form of Fourier terms and the time series regression, including trend and seasonality components methodology with R software. It was demonstrated that obtained models are able to capture the dynamics of the time series data and to produce sensible forecasts.

Hierarchical Hidden Markov Models for Multivariate Integer-Valued Time-Series

DEFF Research Database (Denmark)

Catania, Leopoldo; Di Mari, Roberto

2018-01-01

We propose a new flexible dynamic model for multivariate nonnegative integer-valued time-series. Observations are assumed to depend on the realization of two additional unobserved integer-valued stochastic variables which control for the time-and cross-dependence of the data. An Expectation......-Maximization algorithm for maximum likelihood estimation of the model's parameters is derived. We provide conditional and unconditional (cross)-moments implied by the model, as well as the limiting distribution of the series. A Monte Carlo experiment investigates the finite sample properties of our estimation...

Analysis of interface states and series resistance at MIS structure irradiated under {sup 60}Co {gamma}-rays

Energy Technology Data Exchange (ETDEWEB)

Tataroglu, A. [Department of Physics, Faculty of Arts and Sciences, Gazi University, 06500 Ankara (Turkey)], E-mail: ademt@gazi.edu.tr; Altindal, S. [Department of Physics, Faculty of Arts and Sciences, Gazi University, 06500 Ankara (Turkey)

2007-10-11

In this research, we investigated the effect of {sup 60}Co {gamma}-ray exposure on the electrical properties of Au/SnO{sub 2}/n-Si (MIS) structures using current-voltage (I-V) measurements. The fabricated devices were exposed to {gamma}-ray doses ranging from 0 to 300 kGy at a dose rate of 2.12 kGy h{sup -1} in water at room temperature. The density of interface states N{sub ss} as a function of E{sub c}-E{sub ss} is deduced from the forward bias I-V data for each dose by taking into account the bias dependence effective barrier height and series resistance of device at room temperature. Experimental results show that the {gamma}-irradiation gives rise to an increase in the zero bias barrier height {phi}{sub BO}, as the ideality factor n and N{sub ss} decrease with increasing radiation dose. In addition, the values of series resistance were determined using Cheung's method. The R{sub s} increases with increasing radiation dose. The results show that the main effect of the radiation is the generation of interface states with energy level within the forbidden band gap at the insulator/semiconductor interface.

Modelling Changes in the Unconditional Variance of Long Stock Return Series

DEFF Research Database (Denmark)

Amado, Cristina; Teräsvirta, Timo

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...... 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...... horizons for a subset of the long return series....

Modelling changes in the unconditional variance of long stock return series

DEFF Research Database (Denmark)

Amado, Cristina; Teräsvirta, Timo

2014-01-01

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...... 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...... subsets of the long return series....

Study of self-compliance behaviors and internal filament characteristics in intrinsic SiOx-based resistive switching memory

International Nuclear Information System (INIS)

Chang, Yao-Feng; Zhou, Fei; Chen, Ying-Chen; Lee, Jack C.; Fowler, Burt

2016-01-01

Self-compliance characteristics and reliability optimization are investigated in intrinsic unipolar silicon oxide (SiO x )-based resistive switching (RS) memory using TiW/SiO x /TiW device structures. The program window (difference between SET voltage and RESET voltage) is dependent on external series resistance, demonstrating that the SET process is due to a voltage-triggered mechanism. The program window has been optimized for program/erase disturbance immunity and reliability for circuit-level applications. The SET and RESET transitions have also been characterized using a dynamic conductivity method, which distinguishes the self-compliance behavior due to an internal series resistance effect (filament) in SiO x -based RS memory. By using a conceptual “filament/resistive gap (GAP)” model of the conductive filament and a proton exchange model with appropriate assumptions, the internal filament resistance and GAP resistance can be estimated for high- and low-resistance states (HRS and LRS), and are found to be independent of external series resistance. Our experimental results not only provide insights into potential reliability issues but also help to clarify the switching mechanisms and device operating characteristics of SiO x -based RS memory

Anomaly effects of arrays for 3d geoelectrical resistivity imaging ...

African Journals Online (AJOL)

user

The effectiveness of using a net of orthogonal or parallel sets of two-dimensional (2D) profiles for three- dimensional (3D) geoelectrical resistivity imaging has been evaluated. A series of 2D apparent resistivity data were generated over two synthetic models which represent geological or environmental conditions for a ...

Small-signal model for the series resonant converter

Science.gov (United States)

King, R. J.; Stuart, T. A.

1985-01-01

The results of a previous discrete-time model of the series resonant dc-dc converter are reviewed and from these a small signal dynamic model is derived. This model is valid for low frequencies and is based on the modulation of the diode conduction angle for control. The basic converter is modeled separately from its output filter to facilitate the use of these results for design purposes. Experimental results are presented.

Resistant plasmid profile analysis of multidrug resistant Escherichia ...

African Journals Online (AJOL)

Multiple drug resistance isolates causing UTI has seri- ous implications for the empiric therapy against patho- genic isolates and for the possible co-selection of antimicrobial resistant mediated by multi drug resistant plasmids21,22. E. coli from clinical isolates are known to harbour plasmids of different molecular sizes23.

Rotation in the Dynamic Factor Modeling of Multivariate Stationary Time Series.

Science.gov (United States)

Molenaar, Peter C. M.; Nesselroade, John R.

2001-01-01

Proposes a special rotation procedure for the exploratory dynamic factor model for stationary multivariate time series. The rotation procedure applies separately to each univariate component series of a q-variate latent factor series and transforms such a component, initially represented as white noise, into a univariate moving-average.…

Recursive Bayesian recurrent neural networks for time-series modeling.

Science.gov (United States)

Mirikitani, Derrick T; Nikolaev, Nikolay

2010-02-01

This paper develops a probabilistic approach to recursive second-order training of recurrent neural networks (RNNs) for improved time-series modeling. A general recursive Bayesian Levenberg-Marquardt algorithm is derived to sequentially update the weights and the covariance (Hessian) matrix. The main strengths of the approach are a principled handling of the regularization hyperparameters that leads to better generalization, and stable numerical performance. The framework involves the adaptation of a noise hyperparameter and local weight prior hyperparameters, which represent the noise in the data and the uncertainties in the model parameters. Experimental investigations using artificial and real-world data sets show that RNNs equipped with the proposed approach outperform standard real-time recurrent learning and extended Kalman training algorithms for recurrent networks, as well as other contemporary nonlinear neural models, on time-series modeling.

Model-based Clustering of Categorical Time Series with Multinomial Logit Classification

Science.gov (United States)

Frühwirth-Schnatter, Sylvia; Pamminger, Christoph; Winter-Ebmer, Rudolf; Weber, Andrea

2010-09-01

A common problem in many areas of applied statistics is to identify groups of similar time series in a panel of time series. However, distance-based clustering methods cannot easily be extended to time series data, where an appropriate distance-measure is rather difficult to define, particularly for discrete-valued time series. Markov chain clustering, proposed by Pamminger and Frühwirth-Schnatter [6], is an approach for clustering discrete-valued time series obtained by observing a categorical variable with several states. This model-based clustering method is based on finite mixtures of first-order time-homogeneous Markov chain models. In order to further explain group membership we present an extension to the approach of Pamminger and Frühwirth-Schnatter [6] by formulating a probabilistic model for the latent group indicators within the Bayesian classification rule by using a multinomial logit model. The parameters are estimated for a fixed number of clusters within a Bayesian framework using an Markov chain Monte Carlo (MCMC) sampling scheme representing a (full) Gibbs-type sampler which involves only draws from standard distributions. Finally, an application to a panel of Austrian wage mobility data is presented which leads to an interesting segmentation of the Austrian labour market.

New Models for Forecasting Enrollments: Fuzzy Time Series and Neural Network Approaches.

Science.gov (United States)

Song, Qiang; Chissom, Brad S.

Since university enrollment forecasting is very important, many different methods and models have been proposed by researchers. Two new methods for enrollment forecasting are introduced: (1) the fuzzy time series model; and (2) the artificial neural networks model. Fuzzy time series has been proposed to deal with forecasting problems within 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

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

Science.gov (United States)

Liu, Zitao; Hauskrecht, Milos

2017-11-01

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

Analysis and modeling of resistive switching mechanism oriented to fault tolerance of resistive memory based on memristor

International Nuclear Information System (INIS)

Huang Da; Wu Jun-Jie; Tang Yu-Hua

2014-01-01

With the progress of the semiconductor industry, resistive memories, especially the memristor, have drawn increasing attention. The resistive memory based on memrsitor has not been commercialized mainly because of data error. Currently, there are more studies focused on fault tolerance of resistive memory. This paper studies the resistive switching mechanism which may have time-varying characteristics. Resistive switching mechanism is analyzed and its respective circuit model is established based on the memristor Spice model

Neural network versus classical time series forecasting models

Science.gov (United States)

Nor, Maria Elena; Safuan, Hamizah Mohd; Shab, Noorzehan Fazahiyah Md; Asrul, Mohd; Abdullah, Affendi; Mohamad, Nurul Asmaa Izzati; Lee, Muhammad Hisyam

2017-05-01

Artificial neural network (ANN) has advantage in time series forecasting as it has potential to solve complex forecasting problems. This is because ANN is data driven approach which able to be trained to map past values of a time series. In this study the forecast performance between neural network and classical time series forecasting method namely seasonal autoregressive integrated moving average models was being compared by utilizing gold price data. Moreover, the effect of different data preprocessing on the forecast performance of neural network being examined. The forecast accuracy was evaluated using mean absolute deviation, root mean square error and mean absolute percentage error. It was found that ANN produced the most accurate forecast when Box-Cox transformation was used as data preprocessing.

Modeling financial time series with S-plus

CERN Document Server

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...

Nonlinear Prediction Model for Hydrologic Time Series Based on Wavelet Decomposition

Science.gov (United States)

Kwon, H.; Khalil, A.; Brown, C.; Lall, U.; Ahn, H.; Moon, Y.

2005-12-01

Traditionally forecasting and characterizations of hydrologic systems is performed utilizing many techniques. Stochastic linear methods such as AR and ARIMA and nonlinear ones such as statistical learning theory based tools have been extensively used. The common difficulty to all methods is the determination of sufficient and necessary information and predictors for a successful prediction. Relationships between hydrologic variables are often highly nonlinear and interrelated across the temporal scale. A new hybrid approach is proposed for the simulation of hydrologic time series combining both the wavelet transform and the nonlinear model. The present model employs some merits of wavelet transform and nonlinear time series model. The Wavelet Transform is adopted to decompose a hydrologic nonlinear process into a set of mono-component signals, which are simulated by nonlinear model. The hybrid methodology is formulated in a manner to improve the accuracy of a long term forecasting. The proposed hybrid model yields much better results in terms of capturing and reproducing the time-frequency properties of the system at hand. Prediction results are promising when compared to traditional univariate time series models. An application of the plausibility of the proposed methodology is provided and the results conclude that wavelet based time series model can be utilized for simulating and forecasting of hydrologic variable reasonably well. This will ultimately serve the purpose of integrated water resources planning and management.

Physical model of the contact resistivity of metal-graphene junctions

Energy Technology Data Exchange (ETDEWEB)

Chaves, Ferney A., E-mail: ferneyalveiro.chaves@uab.cat; Jiménez, David [Departament d' Enginyeria Electrònica, Escola d' Enginyeria, Universitat Autònoma de Barcelona, Campus UAB, 08193 Bellaterra, Barcelona (Spain); Cummings, Aron W. [ICN2–Institut Català de Nanociència i Nanotecnologia, Campus UAB, 08193 Bellaterra, Barcelona (Spain); Roche, Stephan [ICN2–Institut Català de Nanociència i Nanotecnologia, Campus UAB, 08193 Bellaterra, Barcelona (Spain); ICREA, Institució Catalana de Recerca i Estudis Avançats, 08070 Barcelona (Spain)

2014-04-28

While graphene-based technology shows great promise for a variety of electronic applications, including radio-frequency devices, the resistance of the metal-graphene contact is a technological bottleneck for the realization of viable graphene electronics. One of the most important factors in determining the resistance of a metal-graphene junction is the contact resistivity. Despite the large number of experimental works that exist in the literature measuring the contact resistivity, a simple model of it is still lacking. In this paper, we present a comprehensive physical model for the contact resistivity of these junctions, based on the Bardeen Transfer Hamiltonian method. This model unveils the role played by different electrical and physical parameters in determining the specific contact resistivity, such as the chemical potential of interaction, the work metal-graphene function difference, and the insulator thickness between the metal and graphene. In addition, our model reveals that the contact resistivity is strongly dependent on the bias voltage across the metal-graphene junction. This model is applicable to a wide variety of graphene-based electronic devices and thus is useful for understanding how to optimize the contact resistance in these systems.

Physical model of the contact resistivity of metal-graphene junctions

International Nuclear Information System (INIS)

Chaves, Ferney A.; Jiménez, David; Cummings, Aron W.; Roche, Stephan

2014-01-01

While graphene-based technology shows great promise for a variety of electronic applications, including radio-frequency devices, the resistance of the metal-graphene contact is a technological bottleneck for the realization of viable graphene electronics. One of the most important factors in determining the resistance of a metal-graphene junction is the contact resistivity. Despite the large number of experimental works that exist in the literature measuring the contact resistivity, a simple model of it is still lacking. In this paper, we present a comprehensive physical model for the contact resistivity of these junctions, based on the Bardeen Transfer Hamiltonian method. This model unveils the role played by different electrical and physical parameters in determining the specific contact resistivity, such as the chemical potential of interaction, the work metal-graphene function difference, and the insulator thickness between the metal and graphene. In addition, our model reveals that the contact resistivity is strongly dependent on the bias voltage across the metal-graphene junction. This model is applicable to a wide variety of graphene-based electronic devices and thus is useful for understanding how to optimize the contact resistance in these systems

Multiband Prediction Model for Financial Time Series with Multivariate Empirical Mode Decomposition

Directory of Open Access Journals (Sweden)

Md. Rabiul Islam

2012-01-01

Full Text Available This paper presents a subband approach to financial time series prediction. Multivariate empirical mode decomposition (MEMD is employed here for multiband representation of multichannel financial time series together. Autoregressive moving average (ARMA model is used in prediction of individual subband of any time series data. Then all the predicted subband signals are summed up to obtain the overall prediction. The ARMA model works better for stationary signal. With multiband representation, each subband becomes a band-limited (narrow band signal and hence better prediction is achieved. The performance of the proposed MEMD-ARMA model is compared with classical EMD, discrete wavelet transform (DWT, and with full band ARMA model in terms of signal-to-noise ratio (SNR and mean square error (MSE between the original and predicted time series. The simulation results show that the MEMD-ARMA-based method performs better than the other methods.

Monitoring groundwater-surface water interaction using time-series and time-frequency analysis of transient three-dimensional electrical resistivity changes

Science.gov (United States)

Johnson, Timothy C.; Slater, Lee D.; Ntarlagiannis, Dimitris; Day-Lewis, Frederick D.; Elwaseif, Mehrez

2012-01-01

Time-lapse resistivity imaging is increasingly used to monitor hydrologic processes. Compared to conventional hydrologic measurements, surface time-lapse resistivity provides superior spatial coverage in two or three dimensions, potentially high-resolution information in time, and information in the absence of wells. However, interpretation of time-lapse electrical tomograms is complicated by the ever-increasing size and complexity of long-term, three-dimensional (3-D) time series conductivity data sets. Here we use 3-D surface time-lapse electrical imaging to monitor subsurface electrical conductivity variations associated with stage-driven groundwater-surface water interactions along a stretch of the Columbia River adjacent to the Hanford 300 near Richland, Washington, USA. We reduce the resulting 3-D conductivity time series using both time-series and time-frequency analyses to isolate a paleochannel causing enhanced groundwater-surface water interactions. Correlation analysis on the time-lapse imaging results concisely represents enhanced groundwater-surface water interactions within the paleochannel, and provides information concerning groundwater flow velocities. Time-frequency analysis using the Stockwell (S) transform provides additional information by identifying the stage periodicities driving groundwater-surface water interactions due to upstream dam operations, and identifying segments in time-frequency space when these interactions are most active. These results provide new insight into the distribution and timing of river water intrusion into the Hanford 300 Area, which has a governing influence on the behavior of a uranium plume left over from historical nuclear fuel processing operations.

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....

Modeling Periodic Impulsive Effects on Online TV Series Diffusion.

Science.gov (United States)

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

Modeling Periodic Impulsive Effects on Online TV Series Diffusion

Science.gov (United States)

Fang, Qiwen; Wang, Xi

2016-01-01

Background 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. Methods 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. Results 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. Conclusion 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

Cell shunt resistance and photovoltaic module performance

Energy Technology Data Exchange (ETDEWEB)

McMahon, T.J.; Basso, T.S.; Rummel, S.R. [National Renewable Energy Lab., Golden, CO (United States)

1996-05-01

Shunt resistance of cells in photovoltaic modules can affect module power output and could indicate flawed manufacturing processes and reliability problems. The authors describe a two-terminal diagnostic method to directly measure the shunt resistance of individual cells in a series-connected module non-intrusively, without deencapsulation. Peak power efficiency vs. light intensity was measured on a 12-cell, series-connected, single crystalline module having relatively high cell shunt resistances. The module was remeasured with 0.5-, 1-, and 2-ohm resistors attached across each cell to simulate shunt resistances of several emerging technologies. Peak power efficiencies decreased dramatically at lower light levels. Using the PSpice circuit simulator, the authors verified that cell shunt and series resistances can indeed be responsible for the observed peak power efficiency vs. intensity behavior. The authors discuss the effect of basic cell diode parameters, i.e., shunt resistance, series resistance, and recombination losses, on PV module performance as a function of light intensity.

Identification of neutral biochemical network models from time series data.

Science.gov (United States)

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.

Analysis of temperature-dependant current–voltage characteristics and extraction of series resistance in Pd/ZnO Schottky barrier diodes

Energy Technology Data Exchange (ETDEWEB)

Mayimele, M A, E-mail: meehleketo@gmail.com; Rensburg, J P van. Janse; Auret, F D; Diale, M

2016-01-01

We report on the analysis of current voltage (I–V) measurements performed on Pd/ZnO Schottky barrier diodes (SBDs) in the 80–320 K temperature range. Assuming thermionic emission (TE) theory, the forward bias I–V characteristics were analysed to extract Pd/ZnO Schottky diode parameters. Comparing Cheung’s method in the extraction of the series resistance with Ohm’s law, it was observed that at lower temperatures (T<180 K) the series resistance decreased with increasing temperature, the absolute minimum was reached near 180 K and increases linearly with temperature at high temperatures (T>200 K). The barrier height and the ideality factor decreased and increased, respectively, with decrease in temperature, attributed to the existence of barrier height inhomogeneity. Such inhomogeneity was explained based on TE with the assumption of Gaussian distribution of barrier heights with a mean barrier height of 0.99 eV and a standard deviation of 0.02 eV. A mean barrier height of 0.11 eV and Richardson constant value of 37 A cm{sup −2} K{sup −2} were determined from the modified Richardson plot that considers the Gaussian distribution of barrier heights.

Bayesian dynamic modeling of time series of dengue disease case counts.

Science.gov (United States)

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

Testing and Modeling of Machine Properties in Resistance Welding

DEFF Research Database (Denmark)

Wu, Pei

The objective of this work has been to test and model the machine properties including the mechanical properties and the electrical properties in resistance welding. The results are used to simulate the welding process more accurately. The state of the art in testing and modeling machine properties...... as real projection welding tests, is easy to realize in industry, since tests may be performed in situ. In part II, an approach of characterizing the electrical properties of AC resistance welding machines is presented, involving testing and mathematical modelling of the weld current, the firing angle...... in resistance welding has been described based on a comprehensive literature study. The present thesis has been subdivided into two parts: Part I: Mechanical properties of resistance welding machines. Part II: Electrical properties of resistance welding machines. In part I, the electrode force in the squeeze...

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.

Six-Dimensional Modeling of Coherent Bunch Instabilities and Related Feedback Systems using Power-Series Maps for the Lattice

Energy Technology Data Exchange (ETDEWEB)

Briggs, D.

2003-07-07

The authors have developed 6-dimensional phase-space code that tracks macroparticles for the study of coherent bunch instabilities and related feedback systems. The model is based on power-series maps to represent the lattice, and allows for straightforward inclusion of effects such as amplitude dependent tune shift, chromaticity, synchrotron oscillations, and synchrotron radiation. It simulates long range wake fields such as resistive-wall effects as well as the higher order modes in cavities. The model has served to study the dynamics relevant to the transverse feedback system currently being commissioned for the Advanced Light Source (ALS). Current work integrates earlier versions into a modular system that includes models for transverse and longitudinal feedback systems. It is designed to provide a modular approach to the dynamics and diagnostics, allowing a user to modify the model of a storage ring at run-time without recompilation.

Salt Concentration Differences Alter Membrane Resistance in Reverse Electrodialysis Stacks

KAUST Repository

Geise, Geoffrey M.

2014-01-14

Membrane ionic resistance is usually measured by immersing the membrane in a salt solution at a single, fixed concentration. While salt concentration is known to affect membrane resistance when the same concentration is used on both sides of the membrane, little is known about membrane resistance when the membrane is placed between solutions of different concentrations, such as in a reverse electrodialysis (RED) stack. Ionic resistance measurements obtained using Selemion CMV and AMV that separated sodium chloride and ammonium bicarbonate solutions of different concentrations were greater than those measured using only the high-concentration solution. Measured RED stack resistances showed good agreement with resistances calculated using an equivalent series resistance model, where the membranes accounted for 46% of the total stack resistance. The high area resistance of the membranes separating different salt concentration solutions has implications for modeling and optimizing membranes used in RED systems.

A model for investigating the influence of road surface texture and tyre tread pattern on rolling resistance

Science.gov (United States)

Hoever, Carsten; Kropp, Wolfgang

2015-09-01

The reduction of rolling resistance is essential for a more environmentally friendly road transportation sector. Both tyre and road design can be utilised to reduce rolling resistance. In both cases a reliable simulation tool is needed which is able to quantify the influence of design parameters on the rolling resistance of a tyre rolling on a specific road surface. In this work a previously developed tyre/road interaction model is extended to account for different tread patterns and for losses due to small-scale tread deformation. Calculated contact forces and tyre vibrations for tyre/road interaction under steady-state rolling are used to predict rolling losses in the tyre. Rolling resistance is calculated for a series of different tyre/road combinations. Results are compared with rolling resistance measurements. The agreement between simulations and measurements is generally very good. It is found that both the tyre structure and small-scale tread deformations contribute to the rolling losses. The small-scale contribution depends mainly on the road roughness profile. The mean profile depth of the road surface is identified to correlate very well with the rolling resistance. Additional calculations are performed for non-traditional rubberised road surfaces, however, with mixed results. This possibly indicates the existence of additional loss mechanisms for these surfaces.

Empirical mode decomposition and k-nearest embedding vectors for timely analyses of antibiotic resistance trends.

Science.gov (United States)

Teodoro, Douglas; Lovis, Christian

2013-01-01

Antibiotic resistance is a major worldwide public health concern. In clinical settings, timely antibiotic resistance information is key for care providers as it allows appropriate targeted treatment or improved empirical treatment when the specific results of the patient are not yet available. To improve antibiotic resistance trend analysis algorithms by building a novel, fully data-driven forecasting method from the combination of trend extraction and machine learning models for enhanced biosurveillance systems. We investigate a robust model for extraction and forecasting of antibiotic resistance trends using a decade of microbiology data. Our method consists of breaking down the resistance time series into independent oscillatory components via the empirical mode decomposition technique. The resulting waveforms describing intrinsic resistance trends serve as the input for the forecasting algorithm. The algorithm applies the delay coordinate embedding theorem together with the k-nearest neighbor framework to project mappings from past events into the future dimension and estimate the resistance levels. The algorithms that decompose the resistance time series and filter out high frequency components showed statistically significant performance improvements in comparison with a benchmark random walk model. We present further qualitative use-cases of antibiotic resistance trend extraction, where empirical mode decomposition was applied to highlight the specificities of the resistance trends. The decomposition of the raw signal was found not only to yield valuable insight into the resistance evolution, but also to produce novel models of resistance forecasters with boosted prediction performance, which could be utilized as a complementary method in the analysis of antibiotic resistance trends.

Testing and Modeling of Contact Problems in Resistance Welding

DEFF Research Database (Denmark)

Song, Quanfeng

together two or three cylindrical parts as well as disc-ring pairs of dissimilar metals. The tests have demonstrated the effectiveness of the model. A theoretical and experimental study is performed on the contact resistance aiming at a more reliable model for numerical simulation of resistance welding......As a part of the efforts towards a professional and reliable numerical tool for resistance welding engineers, this Ph.D. project is dedicated to refining the numerical models related to the interface behavior. An FE algorithm for the contact problems in resistance welding has been developed...... for the formulation, and the interfaces are treated in a symmetric pattern. The frictional sliding contact is also solved employing the constant friction model. The algorithm is incorporated into the finite element code. Verification is carried out in some numerical tests as well as experiments such as upsetting...

Road safety forecasts in five European countries using structural time series models.

Science.gov (United States)

Antoniou, Constantinos; Papadimitriou, Eleonora; Yannis, George

2014-01-01

Modeling road safety development is a complex task and needs to consider both the quantifiable impact of specific parameters as well as the underlying trends that cannot always be measured or observed. The objective of this research is to apply structural time series models for obtaining reliable medium- to long-term forecasts of road traffic fatality risk using data from 5 countries with different characteristics from all over Europe (Cyprus, Greece, Hungary, Norway, and Switzerland). Two structural time series models are considered: (1) the local linear trend model and the (2) latent risk time series model. Furthermore, a structured decision tree for the selection of the applicable model for each situation (developed within the Road Safety Data, Collection, Transfer and Analysis [DaCoTA] research project, cofunded by the European Commission) is outlined. First, the fatality and exposure data that are used for the development of the models are presented and explored. Then, the modeling process is presented, including the model selection process, introduction of intervention variables, and development of mobility scenarios. The forecasts using the developed models appear to be realistic and within acceptable confidence intervals. The proposed methodology is proved to be very efficient for handling different cases of data availability and quality, providing an appropriate alternative from the family of structural time series models in each country. A concluding section providing perspectives and directions for future research is presented.

New series of 3 D lattice integrable models

International Nuclear Information System (INIS)

Mangazeev, V.V.; Sergeev, S.M.; Stroganov, Yu.G.

1993-01-01

In this paper we present a new series of 3-dimensional integrable lattice models with N colors. The weight functions of the models satisfy modified tetrahedron equations with N states and give a commuting family of two-layer transfer-matrices. The dependence on the spectral parameters corresponds to the static limit of the modified tetrahedron equations and weights are parameterized in terms of elliptic functions. The models contain two free parameters: elliptic modulus and additional parameter η. 12 refs

A Sandwich-Type Standard Error Estimator of SEM Models with Multivariate Time Series

Science.gov (United States)

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…

Simulation of variation of apparent resistivity in resistivity surveys using finite difference modelling with Monte Carlo analysis

Science.gov (United States)

Aguirre, E. E.; Karchewski, B.

2017-12-01

DC resistivity surveying is a geophysical method that quantifies the electrical properties of the subsurface of the earth by applying a source current between two electrodes and measuring potential differences between electrodes at known distances from the source. Analytical solutions for a homogeneous half-space and simple subsurface models are well known, as the former is used to define the concept of apparent resistivity. However, in situ properties are heterogeneous meaning that simple analytical models are only an approximation, and ignoring such heterogeneity can lead to misinterpretation of survey results costing time and money. The present study examines the extent to which random variations in electrical properties (i.e. electrical conductivity) affect potential difference readings and therefore apparent resistivities, relative to an assumed homogeneous subsurface model. We simulate the DC resistivity survey using a Finite Difference (FD) approximation of an appropriate simplification of Maxwell's equations implemented in Matlab. Electrical resistivity values at each node in the simulation were defined as random variables with a given mean and variance, and are assumed to follow a log-normal distribution. The Monte Carlo analysis for a given variance of electrical resistivity was performed until the mean and variance in potential difference measured at the surface converged. Finally, we used the simulation results to examine the relationship between variance in resistivity and variation in surface potential difference (or apparent resistivity) relative to a homogeneous half-space model. For relatively low values of standard deviation in the material properties (<10% of mean), we observed a linear correlation between variance of resistivity and variance in apparent resistivity.

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...

Single-Index Additive Vector Autoregressive Time Series Models

KAUST Repository

LI, YEHUA; GENTON, MARC G.

2009-01-01

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

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.

Time-series modeling of long-term weight self-monitoring data.

Science.gov (United States)

Helander, Elina; Pavel, Misha; Jimison, Holly; Korhonen, Ilkka

2015-08-01

Long-term self-monitoring of weight is beneficial for weight maintenance, especially after weight loss. Connected weight scales accumulate time series information over long term and hence enable time series analysis of the data. The analysis can reveal individual patterns, provide more sensitive detection of significant weight trends, and enable more accurate and timely prediction of weight outcomes. However, long term self-weighing data has several challenges which complicate the analysis. Especially, irregular sampling, missing data, and existence of periodic (e.g. diurnal and weekly) patterns are common. In this study, we apply time series modeling approach on daily weight time series from two individuals and describe information that can be extracted from this kind of data. We study the properties of weight time series data, missing data and its link to individuals behavior, periodic patterns and weight series segmentation. Being able to understand behavior through weight data and give relevant feedback is desired to lead to positive intervention on health behaviors.

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

Automated Bayesian model development for frequency detection in biological time series.

Science.gov (United States)

Granqvist, Emma; Oldroyd, Giles E D; Morris, Richard J

2011-06-24

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. 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. 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 the requirement for uniformly sampled data. Biological time

Hybrid model for forecasting time series with trend, seasonal and salendar variation patterns

Science.gov (United States)

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.

Tempered fractional time series model for turbulence in geophysical flows

Science.gov (United States)

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.

Large-scale 3-D modeling by integration of resistivity models and borehole data through inversion

DEFF Research Database (Denmark)

Foged, N.; Marker, Pernille Aabye; Christiansen, A. V.

2014-01-01

resistivity and the clay fraction. Through inversion we use the lithological data and the resistivity data to determine the optimum spatially distributed translator function. Applying the translator function we get a 3-D clay fraction model, which holds information from the resistivity data set...... and the borehole data set in one variable. Finally, we use k-means clustering to generate a 3-D model of the subsurface structures. We apply the procedure to the Norsminde survey in Denmark, integrating approximately 700 boreholes and more than 100 000 resistivity models from an airborne survey...

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

Volterra-series-based nonlinear system modeling and its engineering applications: A state-of-the-art review

Science.gov (United States)

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.

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)

A Novel Method of Modeling the Deformation Resistance for Clad Sheet

International Nuclear Information System (INIS)

Hu Jianliang; Yi Youping; Xie Mantang

2011-01-01

Because of the excellent thermal conductivity, the clad sheet (3003/4004/3003) of aluminum alloy is extensively used in various heat exchangers, such as radiator, motorcar air conditioning, evaporator, and so on. The deformation resistance model plays an important role in designing the process parameters of hot continuous rolling. However, the complex behaviors of the plastic deformation of the clad sheet make the modeling very difficult. In this work, a novel method for modeling the deformation resistance of clad sheet was proposed by combining the finite element analysis with experiments. The deformation resistance model of aluminum 3003 and 4004 was proposed through hot compression test on the Gleeble-1500 thermo-simulation machine. And the deformation resistance model of clad sheet was proposed through finite element analysis using DEFORM-2D software. The relationship between cladding ratio and the deformation resistance was discussed in detail. The results of hot compression simulation demonstrate that the cladding ratio has great effects on the resistance of the clad sheet. Taking the cladding ratio into consideration, the mathematical model of the deformation resistance for clad sheet has been proved to have perfect forecasting precision of different cladding ratio. Therefore, the presented model can be used to predict the rolling force of clad sheet during the hot continuous rolling process.

Preference, resistance to change, and the cumulative decision model.

Science.gov (United States)

Grace, Randolph C

2018-01-01

According to behavioral momentum theory (Nevin & Grace, 2000a), preference in concurrent chains and resistance to change in multiple schedules are independent measures of a common construct representing reinforcement history. Here I review the original studies on preference and resistance to change in which reinforcement variables were manipulated parametrically, conducted by Nevin, Grace and colleagues between 1997 and 2002, as well as more recent research. The cumulative decision model proposed by Grace and colleagues for concurrent chains is shown to provide a good account of both preference and resistance to change, and is able to predict the increased sensitivity to reinforcer rate and magnitude observed with constant-duration components. Residuals from fits of the cumulative decision model to preference and resistance to change data were positively correlated, supporting the prediction of behavioral momentum theory. Although some questions remain, the learning process assumed by the cumulative decision model, in which outcomes are compared against a criterion that represents the average outcome value in the current context, may provide a plausible model for the acquisition of differential resistance to change. © 2018 Society for the Experimental Analysis of Behavior.

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 time series model: First-order integer-valued autoregressive (INAR(1))

Science.gov (United States)

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.

On the maximum-entropy/autoregressive modeling of time series

Science.gov (United States)

Chao, B. F.

1984-01-01

The autoregressive (AR) model of a random process is interpreted in the light of the Prony's relation which relates a complex conjugate pair of poles of the AR process in the z-plane (or the z domain) on the one hand, to the complex frequency of one complex harmonic function in the time domain on the other. Thus the AR model of a time series is one that models the time series as a linear combination of complex harmonic functions, which include pure sinusoids and real exponentials as special cases. An AR model is completely determined by its z-domain pole configuration. The maximum-entropy/autogressive (ME/AR) spectrum, defined on the unit circle of the z-plane (or the frequency domain), is nothing but a convenient, but ambiguous visual representation. It is asserted that the position and shape of a spectral peak is determined by the corresponding complex frequency, and the height of the spectral peak contains little information about the complex amplitude of the complex harmonic functions.

Assimilation of LAI time-series in crop production models

Science.gov (United States)

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

Markov Chain Modelling for Short-Term NDVI Time Series Forecasting

Directory of Open Access Journals (Sweden)

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.

Modeling and Forecasting of Water Demand in Isfahan Using Underlying Trend Concept and Time Series

Directory of Open Access Journals (Sweden)

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

Has the emergence of community-associated methicillin-resistant Staphylococcus aureus increased trimethoprim-sulfamethoxazole use and resistance?: a 10-year time series analysis.

Science.gov (United States)

Wood, Jameson B; Smith, Donald B; Baker, Errol H; Brecher, Stephen M; Gupta, Kalpana

2012-11-01

There are an increasing number of indications for trimethoprim-sulfamethoxazole use, including skin and soft tissue infections due to community-associated methicillin-resistant Staphylococcus aureus (CA-MRSA). Assessing the relationship between rates of use and antibiotic resistance is important for maintaining the expected efficacy of this drug for guideline-recommended conditions. Using interrupted time series analysis, we aimed to determine whether the 2005 emergence of CA-MRSA and recommendations of trimethoprim-sulfamethoxazole as the preferred therapy were associated with changes in trimethoprim-sulfamethoxazole use and susceptibility rates. The data from all VA Boston Health Care System facilities, including 118,863 inpatient admissions, 6,272,661 outpatient clinic visits, and 10,138 isolates were collected over a 10-year period. There was a significant (P = 0.02) increase in trimethoprim-sulfamethoxazole prescriptions in the post-CA-MRSA period (1,605/year) compared to the pre-CA-MRSA period (1,538/year). Although the overall susceptibility of Escherichia coli and Proteus spp. to trimethoprim-sulfamethoxazole decreased over the study period, the rate of change in the pre- versus the post-CA-MRSA period was not significantly different. The changes in susceptibility rates of S. aureus to trimethoprim-sulfamethoxazole and to methicillin were also not significantly different. The CA-MRSA period is associated with a significant increase in use of trimethoprim-sulfamethoxazole but not with significant changes in the rates of susceptibilities among clinical isolates. There is also no evidence for selection of organisms with increased resistance to other antimicrobials in relation to increased trimethoprim-sulfamethoxazole use.

Bayesian near-boundary analysis in basic macroeconomic time series models

NARCIS (Netherlands)

M.D. de Pooter (Michiel); F. Ravazzolo (Francesco); R. Segers (René); H.K. van Dijk (Herman)

2008-01-01

textabstractSeveral lessons learnt from a Bayesian analysis of basic macroeconomic time series models are presented for the situation where some model parameters have substantial posterior probability near the boundary of the parameter region. This feature refers to near-instability within dynamic

forecasting with nonlinear time series model: a monte-carlo

African Journals Online (AJOL)

PUBLICATIONS1

Carlo method of forecasting using a special nonlinear time series model, called logistic smooth transition ... We illustrate this new method using some simulation ..... in MATLAB 7.5.0. ... process (DGP) using the logistic smooth transi-.

The application of time series models to cloud field morphology analysis

Science.gov (United States)

Chin, Roland T.; Jau, Jack Y. C.; Weinman, James A.

1987-01-01

A modeling method for the quantitative description of remotely sensed cloud field images is presented. A two-dimensional texture modeling scheme based on one-dimensional time series procedures is adopted for this purpose. The time series procedure used is the seasonal autoregressive, moving average (ARMA) process in Box and Jenkins. Cloud field properties such as directionality, clustering and cloud coverage can be retrieved by this method. It has been demonstrated that a cloud field image can be quantitatively defined by a small set of parameters and synthesized surrogates can be reconstructed from these model parameters. This method enables cloud climatology to be studied quantitatively.

Development of large area resistive electrodes for ATLAS NSW Micromegas

Science.gov (United States)

Ochi, Atsuhiko

2018-02-01

Micromegas with resistive anodes will be used for the NSW upgrades of the ATLAS experiment at LHC. Resistive electrodes are used in MPGD devices to prevent sparks in high-rate operation. Large-area resistive electrodes for Micromegas have been developed using two different technologies: screen printing and carbon sputtering. The maximum resistive foil size is 45 × 220 cm with a printed pattern of 425-μm pitch strips. These technologies are also suitable for mass production. Prototypes of a production model series have been successfully produced. In this paper, we report the development, the production status, and the test results of resistive Micromegas.

Modeling Philippine Stock Exchange Composite Index Using Time Series Analysis

Science.gov (United States)

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.

Trend and seasonality of community-acquired Escherichia coli antimicrobial resistance and its dynamic relationship with antimicrobial use assessed by ARIMA models.

Science.gov (United States)

Asencio Egea, María Ángeles; Huertas Vaquero, María; Carranza González, Rafael; Herráez Carrera, Óscar; Redondo González, Olga; Arias Arias, Ángel

2017-12-04

We studied the trend and seasonality of community-acquired Escherichia coli resistance and quantified its correlation with the previous use of certain antibiotics. A time series study of resistant community-acquired E. coli isolates and their association with antibiotic use was conducted in a Primary Health Care Area from 2008 to 2012. A Poisson regression model was constructed to estimate the trend and seasonality of E. coli resistance. A significant increasing trend in mean E. coli resistance to cephalosporins, aminoglycosides and nitrofurantoin was observed. Seasonal resistance to ciprofloxacin and amoxicillin-clavulanic acid was significantly higher in autumn-winter. There was a delay of 7, 10 and 12 months between the use of cotrimoxazole (P<0.038), fosfomycin (P<0.024) and amoxicillin-clavulanic acid (P<0.015), respectively, and the occurrence of E. coli resistance. An average delay of 10 months between the previous use of amoxicillin-clavulanic acid, cotrimoxazole and fosfomycin and the appearance of resistant community-acquired E. coli strains was detected. Copyright © 2017 Elsevier España, S.L.U. and Sociedad Española de Enfermedades Infecciosas y Microbiología Clínica. All rights reserved.

Modeling Financial Time Series Based on a Market Microstructure Model with Leverage Effect

OpenAIRE

Yanhui Xi; Hui Peng; Yemei Qin

2016-01-01

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....

Modeling HIV-1 drug resistance as episodic directional selection.

Science.gov (United States)

Murrell, Ben; de Oliveira, Tulio; Seebregts, Chris; Kosakovsky Pond, Sergei L; Scheffler, Konrad

2012-01-01

The evolution of substitutions conferring drug resistance to HIV-1 is both episodic, occurring when patients are on antiretroviral therapy, and strongly directional, with site-specific resistant residues increasing in frequency over time. While methods exist to detect episodic diversifying selection and continuous directional selection, no evolutionary model combining these two properties has been proposed. We present two models of episodic directional selection (MEDS and EDEPS) which allow the a priori specification of lineages expected to have undergone directional selection. The models infer the sites and target residues that were likely subject to directional selection, using either codon or protein sequences. Compared to its null model of episodic diversifying selection, MEDS provides a superior fit to most sites known to be involved in drug resistance, and neither one test for episodic diversifying selection nor another for constant directional selection are able to detect as many true positives as MEDS and EDEPS while maintaining acceptable levels of false positives. This suggests that episodic directional selection is a better description of the process driving the evolution of drug resistance.

Modeling HIV-1 drug resistance as episodic directional selection.

Directory of Open Access Journals (Sweden)

Ben Murrell

Full Text Available The evolution of substitutions conferring drug resistance to HIV-1 is both episodic, occurring when patients are on antiretroviral therapy, and strongly directional, with site-specific resistant residues increasing in frequency over time. While methods exist to detect episodic diversifying selection and continuous directional selection, no evolutionary model combining these two properties has been proposed. We present two models of episodic directional selection (MEDS and EDEPS which allow the a priori specification of lineages expected to have undergone directional selection. The models infer the sites and target residues that were likely subject to directional selection, using either codon or protein sequences. Compared to its null model of episodic diversifying selection, MEDS provides a superior fit to most sites known to be involved in drug resistance, and neither one test for episodic diversifying selection nor another for constant directional selection are able to detect as many true positives as MEDS and EDEPS while maintaining acceptable levels of false positives. This suggests that episodic directional selection is a better description of the process driving the evolution of drug resistance.

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...

Laboratory-scale thyristor controlled series capacitor

Energy Technology Data Exchange (ETDEWEB)

Matsuki, J.; Ikeda, K.; Abe, M. [Kyoto University, Kyoto (Japan)

1996-10-20

This paper describes the results of an experimental study on the characteristics of a thyristor controlled series capacitor (TCSC). At present, there are two major thyristor controlled series compensation projects in the U.S.: the Kayenta ASC and the Slatt TCSC. However, there has been little operating experience and thus further understanding of the characteristics of TCSC is still to be sought. Therefore, a laboratory-scale TCSC was produced and installed in a laboratory power system. The impedance characteristics, waveshapes of voltages and currents in the TCSC circuit, and harmonics, for various thyristor firing angles, and insertion responses were measured and analyzed. In particular, effects of the size of the circuit components, i.e., parasitic resistance, additional damping resistance and series reactor, on the overall TCSC performances were investigated. The results were compared with EMTP simulations. 10 refs., 7 figs.

The Gaussian Graphical Model in Cross-Sectional and Time-Series Data.

Science.gov (United States)

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.

Modeling sports highlights using a time-series clustering framework and model interpretation

Science.gov (United States)

Radhakrishnan, Regunathan; Otsuka, Isao; Xiong, Ziyou; Divakaran, Ajay

2005-01-01

In our past work on sports highlights extraction, we have shown the utility of detecting audience reaction using an audio classification framework. The audio classes in the framework were chosen based on intuition. In this paper, we present a systematic way of identifying the key audio classes for sports highlights extraction using a time series clustering framework. We treat the low-level audio features as a time series and model the highlight segments as "unusual" events in a background of an "usual" process. The set of audio classes to characterize the sports domain is then identified by analyzing the consistent patterns in each of the clusters output from the time series clustering framework. The distribution of features from the training data so obtained for each of the key audio classes, is parameterized by a Minimum Description Length Gaussian Mixture Model (MDL-GMM). We also interpret the meaning of each of the mixture components of the MDL-GMM for the key audio class (the "highlight" class) that is correlated with highlight moments. Our results show that the "highlight" class is a mixture of audience cheering and commentator's excited speech. Furthermore, we show that the precision-recall performance for highlights extraction based on this "highlight" class is better than that of our previous approach which uses only audience cheering as the key highlight class.

Improved time series prediction with a new method for selection of model parameters

International Nuclear Information System (INIS)

Jade, A M; Jayaraman, V K; Kulkarni, B D

2006-01-01

A new method for model selection in prediction of time series is proposed. Apart from the conventional criterion of minimizing RMS error, the method also minimizes the error on the distribution of singularities, evaluated through the local Hoelder estimates and its probability density spectrum. Predictions of two simulated and one real time series have been done using kernel principal component regression (KPCR) and model parameters of KPCR have been selected employing the proposed as well as the conventional method. Results obtained demonstrate that the proposed method takes into account the sharp changes in a time series and improves the generalization capability of the KPCR model for better prediction of the unseen test data. (letter to the editor)

Pin failure modeling of the A series CABRI tests

International Nuclear Information System (INIS)

Young, M.F.; Portugal, J.L.

1978-01-01

The EXPAND pin fialure model, a research tool designed to model pin failure under prompt burst conditions, has been used to predict failure conditions for several of the A series CABRI tests as part of the United States participation in the CABRI Joint Project. The Project is an international program involving France, Germany, England, Japan, and the United States and has the goal of obtaining experimental data relating to the safety of LMFBR's. The A series, designed to simulate high ramp rate TOP conditions, initially utilizes single, fresh UO 2 pins of the PHENIX type in a flowing sodium loop. The pins are preheated at constant power in the CABRI reactor to establish steady state conditions (480 w/cm at the axial peak) and then subjected to a power pulse of 14 ms to 24 ms duration

A Method for Identification of the Equivalent Inductance and Resistance in the Plant Model of Current-Controlled Grid-Tied Converters

DEFF Research Database (Denmark)

Vidal, Ana; Yepes, Alejandro G.; Fernandez, Francisco Daniel Freijedo

2015-01-01

Precise knowledge of the plant time constant L=R is essential to perform a thorough analysis and design of the current control loop in voltage source converters (VSCs). From the perspective of the current controller dynamics in the low frequency range, such plant time constant is also suitable...... for most cases in which an LCL filter is used. As the loop behavior can be significantly influenced by the VSC working conditions, the effects associated to converter losses should be included in the model, through an equivalent series resistance. In addition, the plant inductance may also present...... important uncertainties with respect to the value of the VSC L/LCL interface filter measured at rated conditions. Thus, in this work, a method is presented to estimate both parameters of the plant time constant, i.e., the equivalent inductance and resistance in the plant model of current-controlled VSCs...

Time series modeling by a regression approach based on a latent process.

Science.gov (United States)

Chamroukhi, Faicel; Samé, Allou; Govaert, Gérard; Aknin, Patrice

2009-01-01

Time series are used in many domains including finance, engineering, economics and bioinformatics generally to represent the change of a measurement over time. Modeling techniques may then be used to give a synthetic representation of such data. A new approach for time series modeling is proposed in this paper. It consists of a regression model incorporating a discrete hidden logistic process allowing for activating smoothly or abruptly different polynomial regression models. The model parameters are estimated by the maximum likelihood method performed by a dedicated Expectation Maximization (EM) algorithm. The M step of the EM algorithm uses a multi-class Iterative Reweighted Least-Squares (IRLS) algorithm to estimate the hidden process parameters. To evaluate the proposed approach, an experimental study on simulated data and real world data was performed using two alternative approaches: a heteroskedastic piecewise regression model using a global optimization algorithm based on dynamic programming, and a Hidden Markov Regression Model whose parameters are estimated by the Baum-Welch algorithm. Finally, in the context of the remote monitoring of components of the French railway infrastructure, and more particularly the switch mechanism, the proposed approach has been applied to modeling and classifying time series representing the condition measurements acquired during switch operations.

An Empirical Test of a Model of Resistance to Persuasion.

Science.gov (United States)

And Others; Burgoon, Michael

1978-01-01

Tests a model of resistance to persuasion based upon variables not considered by earlier congruity and inoculation models. Supports the prediction that the kind of critical response set induced and the target of the criticism are mediators of resistance to persuasion. (JMF)

Basic models modeling resistance training: an update for basic scientists interested in study skeletal muscle hypertrophy.

Science.gov (United States)

Cholewa, Jason; Guimarães-Ferreira, Lucas; da Silva Teixeira, Tamiris; Naimo, Marshall Alan; Zhi, Xia; de Sá, Rafaele Bis Dal Ponte; Lodetti, Alice; Cardozo, Mayara Quadros; Zanchi, Nelo Eidy

2014-09-01

Human muscle hypertrophy brought about by voluntary exercise in laboratorial conditions is the most common way to study resistance exercise training, especially because of its reliability, stimulus control and easy application to resistance training exercise sessions at fitness centers. However, because of the complexity of blood factors and organs involved, invasive data is difficult to obtain in human exercise training studies due to the integration of several organs, including adipose tissue, liver, brain and skeletal muscle. In contrast, studying skeletal muscle remodeling in animal models are easier to perform as the organs can be easily obtained after euthanasia; however, not all models of resistance training in animals displays a robust capacity to hypertrophy the desired muscle. Moreover, some models of resistance training rely on voluntary effort, which complicates the results observed when animal models are employed since voluntary capacity is something theoretically impossible to measure in rodents. With this information in mind, we will review the modalities used to simulate resistance training in animals in order to present to investigators the benefits and risks of different animal models capable to provoke skeletal muscle hypertrophy. Our second objective is to help investigators analyze and select the experimental resistance training model that best promotes the research question and desired endpoints. © 2013 Wiley Periodicals, Inc.

Detection of increased series losses in PV arrays using Fuzzy Inference Systems

DEFF Research Database (Denmark)

Spataru, Sergiu; Sera, Dezso; Kerekes, Tamas

2012-01-01

There are well-defined methods to measure the (increased) series resistance of PV panels in controlled laboratory conditions. However, the presence of various irradiance levels and partial shadows, in case of an outdoor installation, may affect the series resistance estimation. This paper focuses...

Scenario Evaluator for Electrical Resistivity survey pre-modeling tool

Science.gov (United States)

Terry, Neil; Day-Lewis, Frederick D.; Robinson, Judith L.; Slater, Lee D.; Halford, Keith J.; Binley, Andrew; Lane, John W.; Werkema, Dale D.

2017-01-01

Geophysical tools have much to offer users in environmental, water resource, and geotechnical fields; however, techniques such as electrical resistivity imaging (ERI) are often oversold and/or overinterpreted due to a lack of understanding of the limitations of the techniques, such as the appropriate depth intervals or resolution of the methods. The relationship between ERI data and resistivity is nonlinear; therefore, these limitations depend on site conditions and survey design and are best assessed through forward and inverse modeling exercises prior to field investigations. In this approach, proposed field surveys are first numerically simulated given the expected electrical properties of the site, and the resulting hypothetical data are then analyzed using inverse models. Performing ERI forward/inverse modeling, however, requires substantial expertise and can take many hours to implement. We present a new spreadsheet-based tool, the Scenario Evaluator for Electrical Resistivity (SEER), which features a graphical user interface that allows users to manipulate a resistivity model and instantly view how that model would likely be interpreted by an ERI survey. The SEER tool is intended for use by those who wish to determine the value of including ERI to achieve project goals, and is designed to have broad utility in industry, teaching, and research.

A COMPARATIVE STUDY OF FORECASTING MODELS FOR TREND AND SEASONAL TIME SERIES DOES COMPLEX MODEL ALWAYS YIELD BETTER FORECAST THAN SIMPLE MODELS

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.

Modeling and experimental study of resistive switching in vertically aligned carbon nanotubes

Science.gov (United States)

Ageev, O. A.; Blinov, Yu F.; Ilina, M. V.; Ilin, O. I.; Smirnov, V. A.

2016-08-01

Model of the resistive switching in vertically aligned carbon nanotube (VA CNT) taking into account the processes of deformation, polarization and piezoelectric charge accumulation have been developed. Origin of hysteresis in VA CNT-based structure is described. Based on modeling results the VACNTs-based structure has been created. The ration resistance of high-resistance to low-resistance states of the VACNTs-based structure amounts 48. The correlation the modeling results with experimental studies is shown. The results can be used in the development nanoelectronics devices based on VA CNTs, including the nonvolatile resistive random-access memory.

Modeling and experimental study of resistive switching in vertically aligned carbon nanotubes

International Nuclear Information System (INIS)

Ageev, O A; Blinov, Yu F; Ilina, M V; Ilin, O I; Smirnov, V A

2016-01-01

Model of the resistive switching in vertically aligned carbon nanotube (VA CNT) taking into account the processes of deformation, polarization and piezoelectric charge accumulation have been developed. Origin of hysteresis in VA CNT-based structure is described. Based on modeling results the VACNTs-based structure has been created. The ration resistance of high-resistance to low-resistance states of the VACNTs-based structure amounts 48. The correlation the modeling results with experimental studies is shown. The results can be used in the development nanoelectronics devices based on VA CNTs, including the nonvolatile resistive random-access memory. (paper)

Disease management with ARIMA model in time series.

Science.gov (United States)

Sato, Renato Cesar

2013-01-01

The evaluation of infectious and noninfectious disease management can be done through the use of a time series analysis. In this study, we expect to measure the results and prevent intervention effects on the disease. Clinical studies have benefited from the use of these techniques, particularly for the wide applicability of the ARIMA model. This study briefly presents the process of using the ARIMA model. This analytical tool offers a great contribution for researchers and healthcare managers in the evaluation of healthcare interventions in specific populations.

Modeling the full-bridge series-resonant power converter

Science.gov (United States)

King, R. J.; Stuart, T. A.

1982-01-01

A steady state model is derived for the full-bridge series-resonant power converter. Normalized parametric curves for various currents and voltages are then plotted versus the triggering angle of the switching devices. The calculations are compared with experimental measurements made on a 50 kHz converter and a discussion of certain operating problems is presented.

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...... by an optimization process to facilitate the extraction of the model’s parameters in a more automated way relying on a small number of experimental waveforms. Through extensive experimental work, it is shown that the model accurately predicts both static and dynamic performances. The series connection of two Si......C power modules has been investigated through the validation of the static and dynamic conditions. Thanks to the developed model, a better understanding of the challenges introduced by uneven voltage balance sharing among series connected devices is possible....

Development of large area resistive electrodes for ATLAS NSW MicroMEGAS

CERN Document Server

Ochi, Atsuhiko; The ATLAS collaboration

2015-01-01

MicroMegas with resistive anode will be used for the NSW upgrade of the ATLAS experiment at LHC. The resistive electrode is one of key technology for MPGDs to prevent sparks. Large area resistive electrodes for the MM have been developed using two different technology; screen printing and carbon sputtering. Maximum size of each resistive foil is 45cm x 220cm with printed pattern of 425 micron pitch strips. Those technologies are also suitable to mass production. The prototypes of series production model have been produced successfully. We will report the development and production status and test results of the resistive MicroMegas.

Effect of electroless nickel on the series resistance of high-efficiency inkjet printed passivated emitter rear contacted solar cells

Energy Technology Data Exchange (ETDEWEB)

Lenio, Martha A.T. [REC Technology US, Inc., 1159 Triton Dr., Foster City, CA 94301 (United States); Lennon, A.J.; Ho-Baillie, A.; Wenham, S.R. [ARC Photovoltaics Centre of Excellence, University of NSW, Sydney, NSW 2052 (Australia)

2010-12-15

Many existing and emerging solar cell technologies rely on plated metal to form the front surface contacts, and aluminium to form the rear contact. Interactions between the metal plating solutions and the aluminium rear can have a significant impact on cell performance. This paper describes non-uniform nickel deposition on the sintered aluminium rear surface of passivated emitter and rear contacted (PERC) cells patterned using an inkjet printing technique. Rather than being plated homogeneously over the entire rear surface as is observed on an alloyed aluminium rear, the nickel is plated only in the vicinity of the point openings in the rear surface silicon dioxide dielectric layer. Furthermore, this non-uniform nickel deposition was shown to increase the contact resistance of the rear point contacts by an order of magnitude, resulting in higher series resistance values for these fabricated PERC cells. (author)

Modelos de gestión de conflictos en serie de ficción televisiva (Conflict management models in television fiction series

Directory of Open Access Journals (Sweden)

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.

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

Time-series models on somatic cell score improve detection of matistis

DEFF Research Database (Denmark)

Norberg, E; Korsgaard, I R; Sloth, K H M N

2008-01-01

In-line detection of mastitis using frequent milk sampling was studied in 241 cows in a Danish research herd. Somatic cell scores obtained at a daily basis were analyzed using a mixture of four time-series models. Probabilities were assigned to each model for the observations to belong to a normal...... "steady-state" development, change in "level", change of "slope" or "outlier". Mastitis was indicated from the sum of probabilities for the "level" and "slope" models. Time-series models were based on the Kalman filter. Reference data was obtained from veterinary assessment of health status combined...... with bacteriological findings. At a sensitivity of 90% the corresponding specificity was 68%, which increased to 83% using a one-step back smoothing. It is concluded that mixture models based on Kalman filters are efficient in handling in-line sensor data for detection of mastitis and may be useful for similar...

Insulin Resistance in Alzheimer's Disease

Science.gov (United States)

Dineley, Kelly T; Jahrling, Jordan B; Denner, Larry

2014-01-01

Insulin is a key hormone regulating metabolism. Insulin binding to cell surface insulin receptors engages many signaling intermediates operating in parallel and in series to control glucose, energy, and lipids while also regulating mitogenesis and development. Perturbations in the function of any of these intermediates, which occur in a variety of diseases, cause reduced sensitivity to insulin and insulin resistance with consequent metabolic dysfunction. Chronic inflammation ensues which exacerbates compromised metabolic homeostasis. Since insulin has a key role in learning and memory as well as directly regulating ERK, a kinase required for the type of learning and memory compromised in early Alzheimer's disease (AD), insulin resistance has been identified as a major risk factor for the onset of AD. Animal models of AD or insulin resistance or both demonstrate that AD pathology and impaired insulin signaling form a reciprocal relationship. Of note are human and animal model studies geared toward improving insulin resistance that have led to the identification of the nuclear receptor and transcription factor, peroxisome proliferator-activated receptor gamma (PPARγ) as an intervention tool for early AD. Strategic targeting of alternate nodes within the insulin signaling network has revealed disease-stage therapeutic windows in animal models that coalesce with previous and ongoing clinical trial approaches. Thus, exploiting the connection between insulin resistance and AD provides powerful opportunities to delineate therapeutic interventions that slow or block the pathogenesis of AD. PMID:25237037

Time Series Analysis, Modeling and Applications A Computational Intelligence Perspective

CERN Document Server

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...

Evaluating an Automated Number Series Item Generator Using Linear Logistic Test Models

Directory of Open Access Journals (Sweden)

Bao Sheng Loe

2018-04-01

Full Text Available This study investigates the item properties of a newly developed Automatic Number Series Item Generator (ANSIG. The foundation of the ANSIG is based on five hypothesised cognitive operators. Thirteen item models were developed using the numGen R package and eleven were evaluated in this study. The 16-item ICAR (International Cognitive Ability Resource1 short form ability test was used to evaluate construct validity. The Rasch Model and two Linear Logistic Test Model(s (LLTM were employed to estimate and predict the item parameters. Results indicate that a single factor determines the performance on tests composed of items generated by the ANSIG. Under the LLTM approach, all the cognitive operators were significant predictors of item difficulty. Moderate to high correlations were evident between the number series items and the ICAR test scores, with high correlation found for the ICAR Letter-Numeric-Series type items, suggesting adequate nomothetic span. Extended cognitive research is, nevertheless, essential for the automatic generation of an item pool with predictable psychometric properties.

Time Series Modeling of Nano-Gold Immunochromatographic Assay via Expectation Maximization Algorithm.

Science.gov (United States)

Zeng, Nianyin; Wang, Zidong; Li, Yurong; Du, Min; Cao, Jie; Liu, Xiaohui

2013-12-01

In this paper, the expectation maximization (EM) algorithm is applied to the modeling of the nano-gold immunochromatographic assay (nano-GICA) via available time series of the measured signal intensities of the test and control lines. The model for the nano-GICA is developed as the stochastic dynamic model that consists of a first-order autoregressive stochastic dynamic process and a noisy measurement. By using the EM algorithm, the model parameters, the actual signal intensities of the test and control lines, as well as the noise intensity can be identified simultaneously. Three different time series data sets concerning the target concentrations are employed to demonstrate the effectiveness of the introduced algorithm. Several indices are also proposed to evaluate the inferred models. It is shown that the model fits the data very well.

75 FR 47199 - Airworthiness Directives; McDonnell Douglas Corporation Model DC-9-10 Series Airplanes, DC-9-30...

Science.gov (United States)

2010-08-05

... Airworthiness Directives; McDonnell Douglas Corporation Model DC- 9-10 Series Airplanes, DC-9-30 Series... existing airworthiness directive (AD), which applies to all McDonnell Douglas Model DC-9-10 series..., 2010). That AD applies to all McDonnell Douglas Corporation Model DC-9-10 series airplanes, DC-9-30...

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.

Big Data impacts on stochastic Forecast Models: Evidence from FX time series

Directory of Open Access Journals (Sweden)

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";}

Modeling multivariate time series on manifolds with skew radial basis functions.

Science.gov (United States)

Jamshidi, Arta A; Kirby, Michael J

2011-01-01

We present an approach for constructing nonlinear empirical mappings from high-dimensional domains to multivariate ranges. We employ radial basis functions and skew radial basis functions for constructing a model using data that are potentially scattered or sparse. The algorithm progresses iteratively, adding a new function at each step to refine the model. The placement of the functions is driven by a statistical hypothesis test that accounts for correlation in the multivariate range variables. The test is applied on training and validation data and reveals nonstatistical or geometric structure when it fails. At each step, the added function is fit to data contained in a spatiotemporally defined local region to determine the parameters--in particular, the scale of the local model. The scale of the function is determined by the zero crossings of the autocorrelation function of the residuals. The model parameters and the number of basis functions are determined automatically from the given data, and there is no need to initialize any ad hoc parameters save for the selection of the skew radial basis functions. Compactly supported skew radial basis functions are employed to improve model accuracy, order, and convergence properties. The extension of the algorithm to higher-dimensional ranges produces reduced-order models by exploiting the existence of correlation in the range variable data. Structure is tested not just in a single time series but between all pairs of time series. We illustrate the new methodologies using several illustrative problems, including modeling data on manifolds and the prediction of chaotic time series.

Single-Index Additive Vector Autoregressive Time Series Models

KAUST Repository

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.

Incorporating Satellite Time-Series Data into Modeling

Science.gov (United States)

Gregg, Watson

2008-01-01

In situ time series observations have provided a multi-decadal view of long-term changes in ocean biology. These observations are sufficiently reliable to enable discernment of even relatively small changes, and provide continuous information on a host of variables. Their key drawback is their limited domain. Satellite observations from ocean color sensors do not suffer the drawback of domain, and simultaneously view the global oceans. This attribute lends credence to their use in global and regional model validation and data assimilation. We focus on these applications using the NASA Ocean Biogeochemical Model. The enhancement of the satellite data using data assimilation is featured and the limitation of tongterm satellite data sets is also discussed.

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.

Six-dimensional modeling of coherent bunch instabilities and related freedback systems in storage rings with power-series maps for the lattice

International Nuclear Information System (INIS)

Bengtsson, J.; Briggs, D.; Meddahi, M.

1994-06-01

The authors have developed 6-dimensional phase-space code that tracks macroparticles for the study of coherent bunch instabilities and related feedback systems. The model is based on power-series maps to represent the lattice, and allows for straightforward inclusion of effects such as amplitude dependent tune shift, chromaticity, synchrotron oscillations, and synchrotron radiation. It simulates long range wake fields such as resistive-wall effects as well as the higher order modes in cavities. The model has served to study the dynamics relevant to the transverse feedback system currently being commissioned for the Advanced Light Source (ALS). Current work integrates earlier versions into a modular system that includes models for transverse and longitudinal feedback systems. It is designed to provide a modular approach to the dynamics and diagnostics, allowing a user to modify the model of a storage ring at run-time without recompilation

Receiver Expectations: Toward a New Model of Resistance to Persuasion.

Science.gov (United States)

Miller, Michael D.; Burgoon, Michael

Communication research long has noted how pretreatment strategies ("inoculations") induce resistance to persuasion, but a new model proposes that resistance is an integral part of the persuasion process. Using the inoculation framework, researchers showed the importance of threats to an individual's attitudes in developing resistance to…

The `L' Array, a method to model 3D Electrical Resistivity Tomography (ERT) data

Science.gov (United States)

Chavez Segura, R. E.; Chavez-Hernandez, G.; Delgado, C.; Tejero-Andrade, A.

2010-12-01

The electrical resistivity tomography (ERT) is a method designed to calculate the distribution of apparent electrical resistivities in the subsoil by means of a great number of observations with the aim of determining an electrical image displaying the distribution of true resistivities in the subsoil. Such process can be carried out to define 2D or 3D models of the subsurface. For a 3D ERT, usually, the electrodes are placed in a squared grid keeping the distance between adjacent electrodes constant in the x and y directions. Another design employed, consists of a series of parallel lines whose space inter-lines must be smaller or equal to four times the electrode separation. The most common electrode arrays frequently employed for this type of studies are the pole-pole, pole-dipole and dipole-dipole. Unfortunately, ERT surface sampling schemes are limited by physical conditions or obstacles, like buildings, highly populated urban zones, and geologic/topographic features, where the lines of electrodes cannot be set. However, it is always necessary to characterize the subsoil beneath such anthropogenic or natural features. The ‘L’ shaped array has the main purpose to overcome such difficulties by surrounding the study area with a square of electrode lines. The measurements are obtained by switching automatically current and potential electrodes from one line to the other. Each observation adds a level of information, from one profile to the other. Once the total levels of data are completed, the opposite ‘L’ array can be measured following the same process. The complete square is computed after the parallel profiles are observed as well. At the end, the computed resistivities are combined to form a 3D matrix of observations. Such set of data can be inverted to obtain the true resistivity distribution at depth in the form of a working cube, which can be interpreted. The method was tested with theoretical models, which included a set of two resistive cubes

An Illustration of Generalised Arma (garma) Time Series Modeling of Forest Area in Malaysia

Science.gov (United States)

Pillai, Thulasyammal Ramiah; Shitan, Mahendran

Forestry is the art and science of managing forests, tree plantations, and related natural resources. The main goal of forestry is to create and implement systems that allow forests to continue a sustainable provision of environmental supplies and services. Forest area is land under natural or planted stands of trees, whether productive or not. Forest area of Malaysia has been observed over the years and it can be modeled using time series models. A new class of GARMA models have been introduced in the time series literature to reveal some hidden features in time series data. For these models to be used widely in practice, we illustrate the fitting of GARMA (1, 1; 1, δ) model to the Annual Forest Area data of Malaysia which has been observed from 1987 to 2008. The estimation of the model was done using Hannan-Rissanen Algorithm, Whittle's Estimation and Maximum Likelihood Estimation.

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.

ATHENS SEASONAL VARIATION OF GROUND RESISTANCE PREDICTION USING NEURAL NETWORKS

Directory of Open Access Journals (Sweden)

S. Anbazhagan

2015-10-01

Full Text Available The objective in ground resistance is to attain the most minimal ground safety esteem conceivable that bodes well monetarily and physically. An application of artificial neural networks (ANN to presage and relegation has been growing rapidly due to sundry unique characteristics of ANN models. A decent forecast is able to capture the dubiousness associated with those ground resistance. A portion of the key instabilities are soil composition, moisture content, temperature, ground electrodes and spacing of the electrodes. Propelled by this need, this paper endeavors to develop a generalized regression neural network (GRNN to predict the ground resistance. The GRNN has a single design parameter and expeditious learning and efficacious modeling for nonlinear time series. The precision of the forecast is applied to the Athens seasonal variation of ground resistance that shows the efficacy of the proposed approach.

Empirical intrinsic geometry for nonlinear modeling and time series filtering.

Science.gov (United States)

Talmon, Ronen; Coifman, Ronald R

2013-07-30

In this paper, we present a method for time series analysis based on empirical intrinsic geometry (EIG). EIG enables one to reveal the low-dimensional parametric manifold as well as to infer the underlying dynamics of high-dimensional time series. By incorporating concepts of information geometry, this method extends existing geometric analysis tools to support stochastic settings and parametrizes the geometry of empirical distributions. However, the statistical models are not required as priors; hence, EIG may be applied to a wide range of real signals without existing definitive models. We show that the inferred model is noise-resilient and invariant under different observation and instrumental modalities. In addition, we show that it can be extended efficiently to newly acquired measurements in a sequential manner. These two advantages enable us to revisit the Bayesian approach and incorporate empirical dynamics and intrinsic geometry into a nonlinear filtering framework. We show applications to nonlinear and non-Gaussian tracking problems as well as to acoustic signal localization.

Series-NonUniform Rational B-Spline (S-NURBS) model: a geometrical interpolation framework for chaotic data.

Science.gov (United States)

Shao, Chenxi; Liu, Qingqing; Wang, Tingting; Yin, Peifeng; Wang, Binghong

2013-09-01

Time series is widely exploited to study the innate character of the complex chaotic system. Existing chaotic models are weak in modeling accuracy because of adopting either error minimization strategy or an acceptable error to end the modeling process. Instead, interpolation can be very useful for solving differential equations with a small modeling error, but it is also very difficult to deal with arbitrary-dimensional series. In this paper, geometric theory is considered to reduce the modeling error, and a high-precision framework called Series-NonUniform Rational B-Spline (S-NURBS) model is developed to deal with arbitrary-dimensional series. The capability of the interpolation framework is proved in the validation part. Besides, we verify its reliability by interpolating Musa dataset. The main improvement of the proposed framework is that we are able to reduce the interpolation error by properly adjusting weights series step by step if more information is given. Meanwhile, these experiments also demonstrate that studying the physical system from a geometric perspective is feasible.

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...

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)

Effect of calibration data series length on performance and optimal parameters of hydrological model

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Chuan-zhe Li

2010-12-01

Full Text Available In order to assess the effects of calibration data series length on the performance and optimal parameter values of a hydrological model in ungauged or data-limited catchments (data are non-continuous and fragmental in some catchments, we used non-continuous calibration periods for more independent streamflow data for SIMHYD (simple hydrology model calibration. Nash-Sutcliffe efficiency and percentage water balance error were used as performance measures. The particle swarm optimization (PSO method was used to calibrate the rainfall-runoff models. Different lengths of data series ranging from one year to ten years, randomly sampled, were used to study the impact of calibration data series length. Fifty-five relatively unimpaired catchments located all over Australia with daily precipitation, potential evapotranspiration, and streamflow data were tested to obtain more general conclusions. The results show that longer calibration data series do not necessarily result in better model performance. In general, eight years of data are sufficient to obtain steady estimates of model performance and parameters for the SIMHYD model. It is also shown that most humid catchments require fewer calibration data to obtain a good performance and stable parameter values. The model performs better in humid and semi-humid catchments than in arid catchments. Our results may have useful and interesting implications for the efficiency of using limited observation data for hydrological model calibration in different climates.

Stochastic model stationarization by eliminating the periodic term and its effect on time series prediction

Science.gov (United States)

Moeeni, Hamid; Bonakdari, Hossein; Fatemi, Seyed Ehsan

2017-04-01

Because time series stationarization has a key role in stochastic modeling results, three methods are analyzed in this study. The methods are seasonal differencing, seasonal standardization and spectral analysis to eliminate the periodic effect on time series stationarity. First, six time series including 4 streamflow series and 2 water temperature series are stationarized. The stochastic term for these series obtained with ARIMA is subsequently modeled. For the analysis, 9228 models are introduced. It is observed that seasonal standardization and spectral analysis eliminate the periodic term completely, while seasonal differencing maintains seasonal correlation structures. The obtained results indicate that all three methods present acceptable performance overall. However, model accuracy in monthly streamflow prediction is higher with seasonal differencing than with the other two methods. Another advantage of seasonal differencing over the other methods is that the monthly streamflow is never estimated as negative. Standardization is the best method for predicting monthly water temperature although it is quite similar to seasonal differencing, while spectral analysis performed the weakest in all cases. It is concluded that for each monthly seasonal series, seasonal differencing is the best stationarization method in terms of periodic effect elimination. Moreover, the monthly water temperature is predicted with more accuracy than monthly streamflow. The criteria of the average stochastic term divided by the amplitude of the periodic term obtained for monthly streamflow and monthly water temperature were 0.19 and 0.30, 0.21 and 0.13, and 0.07 and 0.04 respectively. As a result, the periodic term is more dominant than the stochastic term for water temperature in the monthly water temperature series compared to streamflow series.

Intuitionistic Fuzzy Time Series Forecasting Model Based on Intuitionistic Fuzzy Reasoning

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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.

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

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

State-space prediction model for chaotic time series

Science.gov (United States)

Alparslan, A. K.; Sayar, M.; Atilgan, A. R.

1998-08-01

A simple method for predicting the continuation of scalar chaotic time series ahead in time is proposed. The false nearest neighbors technique in connection with the time-delayed embedding is employed so as to reconstruct the state space. A local forecasting model based upon the time evolution of the topological neighboring in the reconstructed phase space is suggested. A moving root-mean-square error is utilized in order to monitor the error along the prediction horizon. The model is tested for the convection amplitude of the Lorenz model. The results indicate that for approximately 100 cycles of the training data, the prediction follows the actual continuation very closely about six cycles. The proposed model, like other state-space forecasting models, captures the long-term behavior of the system due to the use of spatial neighbors in the state space.

The role of {sup 60}Co gamma-ray irradiation on the interface states and series resistance in MIS structures

Energy Technology Data Exchange (ETDEWEB)

Tascioglu, Ilke [Department of Physics, Faculty of Science and Arts, Gazi University, 06500 Ankara (Turkey); Tataroglu, Adem, E-mail: ademt@gazi.edu.t [Department of Physics, Faculty of Science and Arts, Gazi University, 06500 Ankara (Turkey); Ozbay, Akif; Altindal, Semsettin [Department of Physics, Faculty of Science and Arts, Gazi University, 06500 Ankara (Turkey)

2010-04-15

The effect of gamma-ray exposure on the metal-insulator-semiconductor (MIS) structures has been investigated using the electrical characteristics at room temperature. The MIS structures are irradiated with {sup 60}Co gamma-ray source. The energy distribution of interface states was determined from the forward bias I-V characteristics by taking into account the bias dependence of the effective barrier height and ideality factor. The value of series resistance decreases with increasing dose. Experimental results confirmed that gamma-ray irradiation have a significant effect on electrical characteristics of MIS structures.

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 ...

A Seasonal Time-Series Model Based on Gene Expression Programming for Predicting Financial Distress.

Science.gov (United States)

Cheng, Ching-Hsue; Chan, Chia-Pang; Yang, Jun-He

2018-01-01

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.

Identification of human operator performance models utilizing time series analysis

Science.gov (United States)

Holden, F. M.; Shinners, S. M.

1973-01-01

The results of an effort performed by Sperry Systems Management Division for AMRL in applying time series analysis as a tool for modeling the human operator are presented. This technique is utilized for determining the variation of the human transfer function under various levels of stress. The human operator's model is determined based on actual input and output data from a tracking experiment.

Cointegration and Error Correction Modelling in Time-Series Analysis: A Brief Introduction

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Helmut Thome

2015-07-01

Full Text Available Criminological research is often based on time-series data showing some type of trend movement. Trending time-series may correlate strongly even in cases where no causal relationship exists (spurious causality. To avoid this problem researchers often apply some technique of detrending their data, such as by differencing the series. This approach, however, may bring up another problem: that of spurious non-causality. Both problems can, in principle, be avoided if the series under investigation are “difference-stationary” (if the trend movements are stochastic and “cointegrated” (if the stochastically changing trendmovements in different variables correspond to each other. The article gives a brief introduction to key instruments and interpretative tools applied in cointegration modelling.

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...... and efficient framework for estimation. These advantages are used to for instance estimate stochastic volatility models with leverage effect or with Student-t distributed errors. We also model changing time series characteristics of the US inflation rate by considering a heteroskedastic ARFIMA model where...

Applications of soft computing in time series forecasting simulation and modeling techniques

CERN Document Server

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...

Application of the Laplace transform method for computational modelling of radioactive decay series

Energy Technology Data Exchange (ETDEWEB)

Oliveira, Deise L.; Damasceno, Ralf M.; Barros, Ricardo C. [Univ. do Estado do Rio de Janeiro (IME/UERJ) (Brazil). Programa de Pos-graduacao em Ciencias Computacionais

2012-03-15

It is well known that when spent fuel is removed from the core, it is still composed of considerable amount of radioactive elements with significant half-lives. Most actinides, in particular plutonium, fall into this category, and have to be safely disposed of. One solution is to store the long-lived spent fuel as it is, by encasing and burying it deep underground in a stable geological formation. This implies estimating the transmutation of these radioactive elements with time. Therefore, we describe in this paper the application of the Laplace transform technique in matrix formulation to analytically solve initial value problems that mathematically model radioactive decay series. Given the initial amount of each type of radioactive isotopes in the decay series, the computer code generates the amount at a given time of interest, or may plot a graph of the evolution in time of the amount of each type of isotopes in the series. This computer code, that we refer to as the LTRad{sub L} code, where L is the number of types of isotopes belonging to the series, was developed using the Scilab free platform for numerical computation and can model one segment or the entire chain of any of the three radioactive series existing on Earth today. Numerical results are given to typical model problems to illustrate the computer code efficiency and accuracy. (orig.)

Application of the Laplace transform method for computational modelling of radioactive decay series

International Nuclear Information System (INIS)

Oliveira, Deise L.; Damasceno, Ralf M.; Barros, Ricardo C.

2012-01-01

It is well known that when spent fuel is removed from the core, it is still composed of considerable amount of radioactive elements with significant half-lives. Most actinides, in particular plutonium, fall into this category, and have to be safely disposed of. One solution is to store the long-lived spent fuel as it is, by encasing and burying it deep underground in a stable geological formation. This implies estimating the transmutation of these radioactive elements with time. Therefore, we describe in this paper the application of the Laplace transform technique in matrix formulation to analytically solve initial value problems that mathematically model radioactive decay series. Given the initial amount of each type of radioactive isotopes in the decay series, the computer code generates the amount at a given time of interest, or may plot a graph of the evolution in time of the amount of each type of isotopes in the series. This computer code, that we refer to as the LTRad L code, where L is the number of types of isotopes belonging to the series, was developed using the Scilab free platform for numerical computation and can model one segment or the entire chain of any of the three radioactive series existing on Earth today. Numerical results are given to typical model problems to illustrate the computer code efficiency and accuracy. (orig.)

Effect of Rolling Resistance in Dem Models With Spherical Bodies

Directory of Open Access Journals (Sweden)

Dubina Radek

2016-12-01

Full Text Available The rolling resistance is an artificial moment arising on the contact of two discrete elements which mimics resistance of two grains of complex shape in contact rolling relatively to each other. The paper investigates the influence of rolling resistance on behaviour of an assembly of spherical discrete elements. Besides the resistance to rolling, the contacts between spherical particles obey the Hertzian law in normal straining and Coulomb model of friction in shear.

Time series modeling and forecasting using memetic algorithms for regime-switching models.

Science.gov (United States)

Bergmeir, Christoph; Triguero, Isaac; Molina, Daniel; Aznarte, José Luis; Benitez, José Manuel

2012-11-01

In this brief, we present a novel model fitting procedure for the neuro-coefficient smooth transition autoregressive model (NCSTAR), as presented by Medeiros and Veiga. The model is endowed with a statistically founded iterative building procedure and can be interpreted in terms of fuzzy rule-based systems. The interpretability of the generated models and a mathematically sound building procedure are two very important properties of forecasting models. The model fitting procedure employed by the original NCSTAR is a combination of initial parameter estimation by a grid search procedure with a traditional local search algorithm. We propose a different fitting procedure, using a memetic algorithm, in order to obtain more accurate models. An empirical evaluation of the method is performed, applying it to various real-world time series originating from three forecasting competitions. The results indicate that we can significantly enhance the accuracy of the models, making them competitive to models commonly used in the field.

Nonlinear Fluctuation Behavior of Financial Time Series Model by Statistical Physics System

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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 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.

Evaluation of a series of 2-napthamide derivatives as inhibitors of the drug efflux pump AcrB for the reversal of antimicrobial resistance.

Science.gov (United States)

Wang, Yinhu; Mowla, Rumana; Guo, Liwei; Ogunniyi, Abiodun D; Rahman, Taufiq; De Barros Lopes, Miguel A; Ma, Shutao; Venter, Henrietta

2017-02-15

Drug efflux pumps confer multidrug resistance to dangerous pathogens which makes these pumps important drug targets. We have synthesised a novel series of compounds based on a 2-naphthamide pharmacore aimed at inhibiting the efflux pumps from Gram-negative bacteria. The archeatypical transporter AcrB from Escherichia coli was used as model efflux pump as AcrB is widely conserved throughout Gram-negative organisms. The compounds were tested for their antibacterial action, ability to potentiate the action of antibiotics and for their ability to inhibit Nile Red efflux by AcrB. None of the compounds were antimicrobial against E. coli wild type cells. Most of the compounds were able to inhibit Nile Red efflux indicating that they are substrates of the AcrB efflux pump. Three compounds were able to synergise with antibiotics and reverse resistance in the resistant phenotype. Compound A3, 4-(isopentyloxy)-2-naphthamide, reduced the MICs of erythromycin and chloramphenicol to the MIC levels of the drug sensitive strain that lacks an efflux pump. A3 had no effect on the MIC of the non-substrate rifampicin indicating that this compound acts specifically through the AcrB efflux pump. A3 also does not act through non-specific mechanisms such as outer membrane or inner membrane permeabilisation and is not cytotoxic against mammalian cell lines. Therefore, we have designed and synthesised a novel chemical compound with great potential to further optimisation as inhibitor of drug efflux pumps. Copyright © 2017 Elsevier Ltd. All rights reserved.

Neural network modeling of nonlinear systems based on Volterra series extension of a linear model

Science.gov (United States)

Soloway, Donald I.; Bialasiewicz, Jan T.

1992-01-01

A Volterra series approach was applied to the identification of nonlinear systems which are described by a neural network model. A procedure is outlined by which a mathematical model can be developed from experimental data obtained from the network structure. Applications of the results to the control of robotic systems are discussed.

Small-scale electrical resistivity tomography of wet fractured rocks.

Science.gov (United States)

LaBrecque, Douglas J; Sharpe, Roger; Wood, Thomas; Heath, Gail

2004-01-01

This paper describes a series of experiments that tested the ability of the electrical resistivity tomography (ERT) method to locate correctly wet and dry fractures in a meso-scale model. The goal was to develop a method of monitoring the flow of water through a fractured rock matrix. The model was a four by six array of limestone blocks equipped with 28 stainless steel electrodes. Dry fractures were created by placing pieces of vinyl between one or more blocks. Wet fractures were created by injecting tap water into a joint between blocks. In electrical terms, the dry fractures are resistive and the wet fractures are conductive. The quantities measured by the ERT system are current and voltage around the outside edge of the model. The raw ERT data were translated to resistivity values inside the model using a three-dimensional Occam's inversion routine. This routine was one of the key components of ERT being tested. The model presented several challenges. First, the resistivity of both the blocks and the joints was highly variable. Second, the resistive targets introduced extreme changes the software could not precisely quantify. Third, the abrupt changes inherent in a fracture system were contrary to the smoothly varying changes expected by the Occam's inversion routine. Fourth, the response of the conductive fractures was small compared to the background variability. In general, ERT was able to locate correctly resistive fractures. Problems occurred, however, when the resistive fracture was near the edges of the model or when multiple fractures were close together. In particular, ERT tended to position the fracture closer to the model center than its true location. Conductive fractures yielded much smaller responses than the resistive case. A difference-inversion method was able to correctly locate these targets.

Accurate measurement of junctional conductance between electrically coupled cells with dual whole-cell voltage-clamp under conditions of high series resistance.

Science.gov (United States)

Hartveit, Espen; Veruki, Margaret Lin

2010-03-15

Accurate measurement of the junctional conductance (G(j)) between electrically coupled cells can provide important information about the functional properties of coupling. With the development of tight-seal, whole-cell recording, it became possible to use dual, single-electrode voltage-clamp recording from pairs of small cells to measure G(j). Experiments that require reduced perturbation of the intracellular environment can be performed with high-resistance pipettes or the perforated-patch technique, but an accompanying increase in series resistance (R(s)) compromises voltage-clamp control and reduces the accuracy of G(j) measurements. Here, we present a detailed analysis of methodologies available for accurate determination of steady-state G(j) and related parameters under conditions of high R(s), using continuous or discontinuous single-electrode voltage-clamp (CSEVC or DSEVC) amplifiers to quantify the parameters of different equivalent electrical circuit model cells. Both types of amplifiers can provide accurate measurements of G(j), with errors less than 5% for a wide range of R(s) and G(j) values. However, CSEVC amplifiers need to be combined with R(s)-compensation or mathematical correction for the effects of nonzero R(s) and finite membrane resistance (R(m)). R(s)-compensation is difficult for higher values of R(s) and leads to instability that can damage the recorded cells. Mathematical correction for R(s) and R(m) yields highly accurate results, but depends on accurate estimates of R(s) throughout an experiment. DSEVC amplifiers display very accurate measurements over a larger range of R(s) values than CSEVC amplifiers and have the advantage that knowledge of R(s) is unnecessary, suggesting that they are preferable for long-duration experiments and/or recordings with high R(s). Copyright (c) 2009 Elsevier B.V. All rights reserved.

Adaptive Anchoring Model: How Static and Dynamic Presentations of Time Series Influence Judgments and Predictions.

Science.gov (United States)

Kusev, Petko; van Schaik, Paul; Tsaneva-Atanasova, Krasimira; Juliusson, Asgeir; Chater, Nick

2018-01-01

When attempting to predict future events, people commonly rely on historical data. One psychological characteristic of judgmental forecasting of time series, established by research, is that when people make forecasts from series, they tend to underestimate future values for upward trends and overestimate them for downward ones, so-called trend-damping (modeled by anchoring on, and insufficient adjustment from, the average of recent time series values). Events in a time series can be experienced sequentially (dynamic mode), or they can also be retrospectively viewed simultaneously (static mode), not experienced individually in real time. In one experiment, we studied the influence of presentation mode (dynamic and static) on two sorts of judgment: (a) predictions of the next event (forecast) and (b) estimation of the average value of all the events in the presented series (average estimation). Participants' responses in dynamic mode were anchored on more recent events than in static mode for all types of judgment but with different consequences; hence, dynamic presentation improved prediction accuracy, but not estimation. These results are not anticipated by existing theoretical accounts; we develop and present an agent-based model-the adaptive anchoring model (ADAM)-to account for the difference between processing sequences of dynamically and statically presented stimuli (visually presented data). ADAM captures how variation in presentation mode produces variation in responses (and the accuracy of these responses) in both forecasting and judgment tasks. ADAM's model predictions for the forecasting and judgment tasks fit better with the response data than a linear-regression time series model. Moreover, ADAM outperformed autoregressive-integrated-moving-average (ARIMA) and exponential-smoothing models, while neither of these models accounts for people's responses on the average estimation task. Copyright © 2017 The Authors. Cognitive Science published by Wiley

On the profile of frequency and voltage dependent interface states and series resistance in MIS structures

Energy Technology Data Exchange (ETDEWEB)

Doekme, Ilbilge [Science Education Department, Faculty of Kirsehir Education, Gazi University, Kirsehir (Turkey)]. E-mail: ilbilgedokme@gazi.edu.tr; Altindal, Semsettin [Physics Department, Faculty of Arts and Sciences, Gazi University, 06500, Teknikokullar, Ankara (Turkey)

2007-04-30

The variation in the capacitance-voltage (C-V) and conductance-voltage (G/{omega}-V) characteristics of Au/SiO{sub 2}/n-Si metal-insulator-semiconductor (MIS) structure have been systematically investigated as a function of frequencies in the frequency range 0.5 kHz-10 MHz at room temperature. In addition, the forward and reverse bias current-voltage (I-V) characteristics of this structure were measured at room temperature. The high value of ideality factor was attributed to the high density of interface states localized at Si/SiO{sub 2} interface and interfacial oxide layer. The density of interface states (N{sub ss}) and the series resistance (R{sub ss}) were calculated from I-V and C-V measurements using different methods and the effect of them on C-V and G/{omega}-V characteristics were deeply researched. At the same energy position near the top of valance band, the calculated N{sub ss} values, obtained without taking into account the series resistance of the devices almost one order of magnitude larger than N{sub ss} values obtained by taking into account R{sub ss} values. It is found that the C-V and G/{omega}-V curves exhibit a peak at low frequencies and the peak values of C and G/{omega} decrease with increasing frequency. Also, the plots of R {sub s} as a function of bias give two peaks in the certain voltage range at low frequencies. These observations indicate that at low frequencies, the charges at interface states can easily follow an AC signal and the number of them increases with decreasing frequency. The I-V, C-V and G/{omega}-V characteristics of the MIS structure are affected not only with R {sub s} but also N {sub ss}. Experimental results show that both the R{sub s} and C{sub o} values should be taken into account in determining frequency-dependent electrical characteristics.

Apparent resistivity for transient electromagnetic induction logging and its correction in radial layer identification

Science.gov (United States)

Meng, Qingxin; Hu, Xiangyun; Pan, Heping; Xi, Yufei

2018-04-01

We propose an algorithm for calculating all-time apparent resistivity from transient electromagnetic induction logging. The algorithm is based on the whole-space transient electric field expression of the uniform model and Halley's optimisation. In trial calculations for uniform models, the all-time algorithm is shown to have high accuracy. We use the finite-difference time-domain method to simulate the transient electromagnetic field in radial two-layer models without wall rock and convert the simulation results to apparent resistivity using the all-time algorithm. The time-varying apparent resistivity reflects the radially layered geoelectrical structure of the models and the apparent resistivity of the earliest time channel follows the true resistivity of the inner layer; however, the apparent resistivity at larger times reflects the comprehensive electrical characteristics of the inner and outer layers. To accurately identify the outer layer resistivity based on the series relationship model of the layered resistance, the apparent resistivity and diffusion depth of the different time channels are approximately replaced by related model parameters; that is, we propose an apparent resistivity correction algorithm. By correcting the time-varying apparent resistivity of radial two-layer models, we show that the correction results reflect the radially layered electrical structure and the corrected resistivities of the larger time channels follow the outer layer resistivity. The transient electromagnetic fields of radially layered models with wall rock are simulated to obtain the 2D time-varying profiles of the apparent resistivity and corrections. The results suggest that the time-varying apparent resistivity and correction results reflect the vertical and radial geoelectrical structures. For models with small wall-rock effect, the correction removes the effect of the low-resistance inner layer on the apparent resistivity of the larger time channels.

Physics constrained nonlinear regression models for time series

International Nuclear Information System (INIS)

Majda, Andrew J; Harlim, John

2013-01-01

A central issue in contemporary science is the development of data driven statistical nonlinear dynamical models for time series of partial observations of nature or a complex physical model. It has been established recently that ad hoc quadratic multi-level regression (MLR) models can have finite-time blow up of statistical solutions and/or pathological behaviour of their invariant measure. Here a new class of physics constrained multi-level quadratic regression models are introduced, analysed and applied to build reduced stochastic models from data of nonlinear systems. These models have the advantages of incorporating memory effects in time as well as the nonlinear noise from energy conserving nonlinear interactions. The mathematical guidelines for the performance and behaviour of these physics constrained MLR models as well as filtering algorithms for their implementation are developed here. Data driven applications of these new multi-level nonlinear regression models are developed for test models involving a nonlinear oscillator with memory effects and the difficult test case of the truncated Burgers–Hopf model. These new physics constrained quadratic MLR models are proposed here as process models for Bayesian estimation through Markov chain Monte Carlo algorithms of low frequency behaviour in complex physical data. (paper)

A Seasonal Time-Series Model Based on Gene Expression Programming for Predicting Financial Distress

Science.gov (United States)

2018-01-01

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. PMID:29765399

A Seasonal Time-Series Model Based on Gene Expression Programming for Predicting Financial Distress

Directory of Open Access Journals (Sweden)

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.

Mathematical Modeling of Contact Resistance in Silicon Photovoltaic Cells

KAUST Repository

Black, J. P.

2013-10-22

In screen-printed silicon-crystalline solar cells, the contact resistance of a thin interfacial glass layer between the silicon and the silver electrode plays a limiting role for electron transport. We analyze a simple model for electron transport across this layer, based on the driftdiffusion equations. We utilize the size of the current/Debye length to conduct asymptotic techniques to simplify the model; we solve the model numerically to find that the effective contact resistance may be a monotonic increasing, monotonic decreasing, or nonmonotonic function of the electron flux, depending on the values of the physical parameters. © 2013 Society for Industrial and Applied Mathematics.

Electrical Resistance Based Damage Modeling of Multifunctional Carbon Fiber Reinforced Polymer Matrix Composites

Science.gov (United States)

Hart, Robert James

In the current thesis, the 4-probe electrical resistance of carbon fiber-reinforced polymer (CFRP) composites is utilized as a metric for sensing low-velocity impact damage. A robust method has been developed for recovering the directionally dependent electrical resistivities using an experimental line-type 4-probe resistance method. Next, the concept of effective conducting thickness was uniquely applied in the development of a brand new point-type 4-probe method for applications with electrically anisotropic materials. An extensive experimental study was completed to characterize the 4-probe electrical resistance of CFRP specimens using both the traditional line-type and new point-type methods. Leveraging the concept of effective conducting thickness, a novel method was developed for building 4-probe electrical finite element (FE) models in COMSOL. The electrical models were validated against experimental resistance measurements and the FE models demonstrated predictive capabilities when applied to CFRP specimens with varying thickness and layup. These new models demonstrated a significant improvement in accuracy compared to previous literature and could provide a framework for future advancements in FE modeling of electrically anisotropic materials. FE models were then developed in ABAQUS for evaluating the influence of prescribed localized damage on the 4-probe resistance. Experimental data was compiled on the impact response of various CFRP laminates, and was used in the development of quasi- static FE models for predicting presence of impact-induced delamination. The simulation-based delamination predictions were then integrated into the electrical FE models for the purpose of studying the influence of realistic damage patterns on electrical resistance. When the size of the delamination damage was moderate compared to the electrode spacing, the electrical resistance increased by less than 1% due to the delamination damage. However, for a specimen with large

Module Five: Relationships of Current, Voltage, and Resistance; Basic Electricity and Electronics Individualized Learning System.

Science.gov (United States)

Bureau of Naval Personnel, Washington, DC.

This module covers the relationships between current and voltage; resistance in a series circuit; how to determine the values of current, voltage, resistance, and power in resistive series circuits; the effects of source internal resistance; and an introduction to the troubleshooting of series circuits. This module is divided into five lessons:…

Time series models of environmental exposures: Good predictions or good understanding.

Science.gov (United States)

Barnett, Adrian G; Stephen, Dimity; Huang, Cunrui; Wolkewitz, Martin

2017-04-01

Time series data are popular in environmental epidemiology as they make use of the natural experiment of how changes in exposure over time might impact on disease. Many published time series papers have used parameter-heavy models that fully explained the second order patterns in disease to give residuals that have no short-term autocorrelation or seasonality. This is often achieved by including predictors of past disease counts (autoregression) or seasonal splines with many degrees of freedom. These approaches give great residuals, but add little to our understanding of cause and effect. We argue that modelling approaches should rely more on good epidemiology and less on statistical tests. This includes thinking about causal pathways, making potential confounders explicit, fitting a limited number of models, and not over-fitting at the cost of under-estimating the true association between exposure and disease. Copyright © 2017 Elsevier Inc. All rights reserved.

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

as the explanatory variables in the regional and temporal domain, respectively. Further analysis of partial duration series with nonstationary and regional thresholds shows that the mean exceedances also exhibit a significant variation in space and time for some rainfall durations, while the shape parameter is found...... 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...

Proliferation resistance modeling

International Nuclear Information System (INIS)

Bari, R.; Peterson, P.; Roglans, J.; Mladineo, S.; Nuclear Engineering Division; BNL; Univ. of California at Berkely; PNNL

2004-01-01

The National Nuclear Security Administration is developing methods for nonproliferation assessments. A working group on Nonproliferation Assessment Methodology (NPAM) assembled a toolbox of methods for various applications in the nonproliferation arena. One application of this methodology is to the evaluation of the proliferation resistance of Generation IV nuclear energy systems. This paper first summarizes the key results of the NPAM program and then provides results obtained thus far in the ongoing application, which is co-sponsored by the DOE Office of Nuclear Energy Science and Technology. In NPAM, a top-level measure of proliferation resistance for a fuel cycle system is developed from a hierarchy of metrics. The problem is decomposed into: metrics to be computed, barriers to proliferation, and a finite set of threats. The analyst models the process undertaken by the proliferant to overcome barriers to proliferation and evaluates the outcomes. In addition to proliferation resistance (PR) evaluation, the application also addresses physical protection (PP) evaluation against sabotage and theft. The Generation IV goal for future nuclear energy systems is to assure that they are very unattractive and the least desirable route for diversion or theft of weapons-usable materials, and provide increased physical protection against terrorism. An Expert Group, addressing this application, has identified six high-level measures for the PR goals (six measures have also been identified for the PP goals). Combined together, the complete set of measures provides information for program policy makers and system designers to compare specific system design features and integral system characteristics and to make choices among alternative options. The Group has developed a framework for a phased evaluation approach to analyzing PR and PP of system characteristics and to quantifying metrics and measures. This approach allows evaluations to become more detailed and representative

Book Review: "Hidden Markov Models for Time Series: An ...

African Journals Online (AJOL)

Hidden Markov Models for Time Series: An Introduction using R. by Walter Zucchini and Iain L. MacDonald. Chapman & Hall (CRC Press), 2009. Full Text: EMAIL FULL TEXT EMAIL FULL TEXT · DOWNLOAD FULL TEXT DOWNLOAD FULL TEXT · http://dx.doi.org/10.4314/saaj.v10i1.61717 · AJOL African Journals Online.

A New Nonlinear Model of Body Resistance in Nanometer PD SOI MOSFETs

Directory of Open Access Journals (Sweden)

Arash Daghighi

2011-01-01

Full Text Available In this paper, a nonlinear model for the body resistance of a 45nm PD SOI MOSFET is developed. This model verified on the base of the small signal three-dimensional simulation results. In this paper by using the three-dimensional simulation of ISE-TCAD software, the indicating factors of body resistance in nanometer transistors and then are shown, using the surface potential model. A mathematical relation to calculat the body resistance incorporating device width and body potential was derived. Excellent agreement was obtained by comparing the model outputs and three-dimensional simulation results.

Analysis of interface states and series resistance for Al/PVA:n-CdS nanocomposite metal-semiconductor and metal-insulator-semiconductor diode structures

Energy Technology Data Exchange (ETDEWEB)

Sharma, Mamta; Tripathi, S.K. [Panjab University, Centre of Advanced Study in Physics, Department of Physics, Chandigarh (India)

2013-11-15

This paper presents the fabrication and characterization of Al/PVA:n-CdS (MS) and Al/Al{sub 2}O{sub 3}/PVA:n-CdS (MIS) diode. The effects of interfacial insulator layer, interface states (N{sub ss}) and series resistance (R{sub s}) on the electrical characteristics of Al/PVA:n-CdS structures have been investigated using forward and reverse bias I-V, C-V, and G/w-V characteristics at room temperature. Al/PVA:n-CdS diode is fabricated with and without insulator Al{sub 2}O{sub 3} layer to explain the effect of insulator layer on main electrical parameters. The values of the ideality factor (n), series resistance (R{sub s}) and barrier height ({phi} {sub b}) are calculated from ln(I) vs. V plots, by the Cheung and Norde methods. The energy density distribution profile of the interface states is obtained from the forward bias I-V data by taking into account the bias dependence ideality factor (n(V)) and effective barrier height ({phi} {sub e}) for MS and MIS diode. The N{sub ss} values increase from mid-gap energy of CdS to the bottom of the conductance band edge for both MS and MIS diode. (orig.)

Mathematical modeling for corrosion environment estimation based on concrete resistivity measurement directly above reinforcement

International Nuclear Information System (INIS)

Lim, Young-Chul; Lee, Han-Seung; Noguchi, Takafumi

2009-01-01

This study aims to formulate a resistivity model whereby the concrete resistivity expressing the environment of steel reinforcement can be directly estimated and evaluated based on measurement immediately above reinforcement as a method of evaluating corrosion deterioration in reinforced concrete structures. It also aims to provide a theoretical ground for the feasibility of durability evaluation by electric non-destructive techniques with no need for chipping of cover concrete. This Resistivity Estimation Model (REM), which is a mathematical model using the mirror method, combines conventional four-electrode measurement of resistivity with geometric parameters including cover depth, bar diameter, and electrode intervals. This model was verified by estimation using this model at areas directly above reinforcement and resistivity measurement at areas unaffected by reinforcement in regard to the assessment of the concrete resistivity. Both results strongly correlated, proving the validity of this model. It is expected to be applicable to laboratory study and field diagnosis regarding reinforcement corrosion. (author)

Comparison of ARIMA and Random Forest time series models for prediction of avian influenza H5N1 outbreaks.

Science.gov (United States)

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.

Multi-Step Time Series Forecasting with an Ensemble of Varied Length Mixture Models.

Science.gov (United States)

Ouyang, Yicun; Yin, Hujun

2018-05-01

Many real-world problems require modeling and forecasting of time series, such as weather temperature, electricity demand, stock prices and foreign exchange (FX) rates. Often, the tasks involve predicting over a long-term period, e.g. several weeks or months. Most existing time series models are inheritably for one-step prediction, that is, predicting one time point ahead. Multi-step or long-term prediction is difficult and challenging due to the lack of information and uncertainty or error accumulation. The main existing approaches, iterative and independent, either use one-step model recursively or treat the multi-step task as an independent model. They generally perform poorly in practical applications. In this paper, as an extension of the self-organizing mixture autoregressive (AR) model, the varied length mixture (VLM) models are proposed to model and forecast time series over multi-steps. The key idea is to preserve the dependencies between the time points within the prediction horizon. Training data are segmented to various lengths corresponding to various forecasting horizons, and the VLM models are trained in a self-organizing fashion on these segments to capture these dependencies in its component AR models of various predicting horizons. The VLM models form a probabilistic mixture of these varied length models. A combination of short and long VLM models and an ensemble of them are proposed to further enhance the prediction performance. The effectiveness of the proposed methods and their marked improvements over the existing methods are demonstrated through a number of experiments on synthetic data, real-world FX rates and weather temperatures.

Hybrid perturbation methods based on statistical time series models

Science.gov (United States)

San-Juan, Juan Félix; San-Martín, Montserrat; Pérez, Iván; López, Rosario

2016-04-01

In this work we present a new methodology for orbit propagation, the hybrid perturbation theory, based on the combination of an integration method and a prediction technique. The former, which can be a numerical, analytical or semianalytical theory, generates an initial approximation that contains some inaccuracies derived from the fact that, in order to simplify the expressions and subsequent computations, not all the involved forces are taken into account and only low-order terms are considered, not to mention the fact that mathematical models of perturbations not always reproduce physical phenomena with absolute precision. The prediction technique, which can be based on either statistical time series models or computational intelligence methods, is aimed at modelling and reproducing missing dynamics in the previously integrated approximation. This combination results in the precision improvement of conventional numerical, analytical and semianalytical theories for determining the position and velocity of any artificial satellite or space debris object. In order to validate this methodology, we present a family of three hybrid orbit propagators formed by the combination of three different orders of approximation of an analytical theory and a statistical time series model, and analyse their capability to process the effect produced by the flattening of the Earth. The three considered analytical components are the integration of the Kepler problem, a first-order and a second-order analytical theories, whereas the prediction technique is the same in the three cases, namely an additive Holt-Winters method.

Characterisation of Dynamic Mechanical Properties of Resistance Welding Machines

DEFF Research Database (Denmark)

Wu, Pei; Zhang, Wenqi; Bay, Niels

2005-01-01

characterizing the dynamic mechanical characteristics of resistance welding machines is suggested, and a test set-up is designed determining the basic, independent machine parameters required in the model. The model is verified by performing a series of mechanical tests as well as real projection welds.......The dynamic mechanical properties of a resistance welding machine have significant influence on weld quality, which must be considered when simulating the welding process numerically. However, due to the complexity of the machine structure and the mutual coupling of components of the machine system......, it is very difficult to measure or calculate the basic, independent machine parameters required in a mathematical model of the machine dynamics, and no test method has so far been presented in literature, which can be applied directly in an industrial environment. In this paper, a mathematical model...

Use of mathematical modelling to assess the impact of vaccines on antibiotic resistance.

Science.gov (United States)

Atkins, Katherine E; Lafferty, Erin I; Deeny, Sarah R; Davies, Nicholas G; Robotham, Julie V; Jit, Mark

2017-11-13

Antibiotic resistance is a major global threat to the provision of safe and effective health care. To control antibiotic resistance, vaccines have been proposed as an essential intervention, complementing improvements in diagnostic testing, antibiotic stewardship, and drug pipelines. The decision to introduce or amend vaccination programmes is routinely based on mathematical modelling. However, few mathematical models address the impact of vaccination on antibiotic resistance. We reviewed the literature using PubMed to identify all studies that used an original mathematical model to quantify the impact of a vaccine on antibiotic resistance transmission within a human population. We reviewed the models from the resulting studies in the context of a new framework to elucidate the pathways through which vaccination might impact antibiotic resistance. We identified eight mathematical modelling studies; the state of the literature highlighted important gaps in our understanding. Notably, studies are limited in the range of pathways represented, their geographical scope, and the vaccine-pathogen combinations assessed. Furthermore, to translate model predictions into public health decision making, more work is needed to understand how model structure and parameterisation affects model predictions and how to embed these predictions within economic frameworks. Copyright © 2017 Elsevier Ltd. All rights reserved.

High-order fuzzy time-series based on multi-period adaptation model for forecasting stock markets

Science.gov (United States)

Chen, Tai-Liang; Cheng, Ching-Hsue; Teoh, Hia-Jong

2008-02-01

Stock investors usually make their short-term investment decisions according to recent stock information such as the late market news, technical analysis reports, and price fluctuations. To reflect these short-term factors which impact stock price, this paper proposes a comprehensive fuzzy time-series, which factors linear relationships between recent periods of stock prices and fuzzy logical relationships (nonlinear relationships) mined from time-series into forecasting processes. In empirical analysis, the TAIEX (Taiwan Stock Exchange Capitalization Weighted Stock Index) and HSI (Heng Seng Index) are employed as experimental datasets, and four recent fuzzy time-series models, Chen’s (1996), Yu’s (2005), Cheng’s (2006) and Chen’s (2007), are used as comparison models. Besides, to compare with conventional statistic method, the method of least squares is utilized to estimate the auto-regressive models of the testing periods within the databases. From analysis results, the performance comparisons indicate that the multi-period adaptation model, proposed in this paper, can effectively improve the forecasting performance of conventional fuzzy time-series models which only factor fuzzy logical relationships in forecasting processes. From the empirical study, the traditional statistic method and the proposed model both reveal that stock price patterns in the Taiwan stock and Hong Kong stock markets are short-term.

Model for nuclear proliferation resistance analysis using decision making tools

International Nuclear Information System (INIS)

Ko, Won Il; Kim, Ho Dong; Yang, Myung Seung

2003-06-01

The nuclear proliferation risks of nuclear fuel cycles is being considered as one of the most important factors in assessing advanced and innovative nuclear systems in GEN IV and INPRO program. They have been trying to find out an appropriate and reasonable method to evaluate quantitatively several nuclear energy system alternatives. Any reasonable methodology for integrated analysis of the proliferation resistance, however, has not yet been come out at this time. In this study, several decision making methods, which have been used in the situation of multiple objectives, are described in order to see if those can be appropriately used for proliferation resistance evaluation. Especially, the AHP model for quantitatively evaluating proliferation resistance is dealt with in more detail. The theoretical principle of the method and some examples for the proliferation resistance problem are described. For more efficient applications, a simple computer program for the AHP model is developed, and the usage of the program is introduced here in detail. We hope that the program developed in this study could be useful for quantitative analysis of the proliferation resistance involving multiple conflict criteria

Model for nuclear proliferation resistance analysis using decision making tools

Energy Technology Data Exchange (ETDEWEB)

Ko, Won Il; Kim, Ho Dong; Yang, Myung Seung

2003-06-01

The nuclear proliferation risks of nuclear fuel cycles is being considered as one of the most important factors in assessing advanced and innovative nuclear systems in GEN IV and INPRO program. They have been trying to find out an appropriate and reasonable method to evaluate quantitatively several nuclear energy system alternatives. Any reasonable methodology for integrated analysis of the proliferation resistance, however, has not yet been come out at this time. In this study, several decision making methods, which have been used in the situation of multiple objectives, are described in order to see if those can be appropriately used for proliferation resistance evaluation. Especially, the AHP model for quantitatively evaluating proliferation resistance is dealt with in more detail. The theoretical principle of the method and some examples for the proliferation resistance problem are described. For more efficient applications, a simple computer program for the AHP model is developed, and the usage of the program is introduced here in detail. We hope that the program developed in this study could be useful for quantitative analysis of the proliferation resistance involving multiple conflict criteria.

Analysis of Data from a Series of Events by a Geometric Process Model

Institute of Scientific and Technical Information of China (English)

Yeh Lam; Li-xing Zhu; Jennifer S. K. Chan; Qun Liu

2004-01-01

Geometric process was first introduced by Lam[10,11]. A stochastic process {Xi, i = 1, 2,…} is called a geometric process (GP) if, for some a > 0, {ai-1Xi, i = 1, 2,…} forms a renewal process. In thispaper, the GP is used to analyze the data from a series of events. A nonparametric method is introduced forthe estimation of the three parameters in the GP. The limiting distributions of the three estimators are studied.Through the analysis of some real data sets, the GP model is compared with other three homogeneous andnonhomogeneous Poisson models. It seems that on average the GP model is the best model among these fourmodels in analyzing the data from a series of events.

CVD-graphene for low equivalent series resistance in rGO/CVD-graphene/Ni-based supercapacitors

Science.gov (United States)

Kwon, Young Hwi; Kumar, Sunil; Bae, Joonho; Seo, Yongho

2018-05-01

Reduced equivalent series resistance (ESR) is necessary, particularly at a high current density, for high performance supercapacitors, and the interface resistance between the current collector and electrode material is one of the main components of ESR. In this report, we have optimized chemical vapor deposition-grown graphene (CVD-G) on a current collector (Ni-foil) using reduced graphene oxide as an active electrode material to fabricate an electric double layer capacitor with reduced ESR. The CVD-G was grown at different cooling rates—20 °C min‑1, 40 °C min‑1 and 100 °C min‑1—to determine the optimum conditions. The lowest ESR, 0.38 Ω, was obtained for a cell with a 100 °C min‑1 cooling rate, while the sample without a CVD-G interlayer exhibited 0.80 Ω. The CVD-G interlayer-based supercapacitors exhibited fast CD characteristics with high scan rates up to 10 Vs‑1 due to low ESR. The specific capacitances deposited with CVD-G were in the range of 145.6 F g‑1–213.8 F g‑1 at a voltage scan rate of 0.05 V s‑1. A quasi-rectangular behavior was observed in the cyclic voltammetry curves, even at very high scan rates of 50 and 100 V s‑1, for the cell with optimized CVD-G at higher cooling rates, i.e. 100 °C min‑1.

Towards predictive resistance models for agrochemicals by combining chemical and protein similarity via proteochemometric modelling.

Science.gov (United States)

van Westen, Gerard J P; Bender, Andreas; Overington, John P

2014-10-01

Resistance to pesticides is an increasing problem in agriculture. Despite practices such as phased use and cycling of 'orthogonally resistant' agents, resistance remains a major risk to national and global food security. To combat this problem, there is a need for both new approaches for pesticide design, as well as for novel chemical entities themselves. As summarized in this opinion article, a technique termed 'proteochemometric modelling' (PCM), from the field of chemoinformatics, could aid in the quantification and prediction of resistance that acts via point mutations in the target proteins of an agent. The technique combines information from both the chemical and biological domain to generate bioactivity models across large numbers of ligands as well as protein targets. PCM has previously been validated in prospective, experimental work in the medicinal chemistry area, and it draws on the growing amount of bioactivity information available in the public domain. Here, two potential applications of proteochemometric modelling to agrochemical data are described, based on previously published examples from the medicinal chemistry literature.

Modeling pollen time series using seasonal-trend decomposition procedure based on LOESS smoothing.

Science.gov (United States)

Rojo, Jesús; Rivero, Rosario; Romero-Morte, Jorge; Fernández-González, Federico; Pérez-Badia, Rosa

2017-02-01

Analysis of airborne pollen concentrations provides valuable information on plant phenology and is thus a useful tool in agriculture-for predicting harvests in crops such as the olive and for deciding when to apply phytosanitary treatments-as well as in medicine and the environmental sciences. Variations in airborne pollen concentrations, moreover, are indicators of changing plant life cycles. By modeling pollen time series, we can not only identify the variables influencing pollen levels but also predict future pollen concentrations. In this study, airborne pollen time series were modeled using a seasonal-trend decomposition procedure based on LOcally wEighted Scatterplot Smoothing (LOESS) smoothing (STL). The data series-daily Poaceae pollen concentrations over the period 2006-2014-was broken up into seasonal and residual (stochastic) components. The seasonal component was compared with data on Poaceae flowering phenology obtained by field sampling. Residuals were fitted to a model generated from daily temperature and rainfall values, and daily pollen concentrations, using partial least squares regression (PLSR). This method was then applied to predict daily pollen concentrations for 2014 (independent validation data) using results for the seasonal component of the time series and estimates of the residual component for the period 2006-2013. Correlation between predicted and observed values was r = 0.79 (correlation coefficient) for the pre-peak period (i.e., the period prior to the peak pollen concentration) and r = 0.63 for the post-peak period. Separate analysis of each of the components of the pollen data series enables the sources of variability to be identified more accurately than by analysis of the original non-decomposed data series, and for this reason, this procedure has proved to be a suitable technique for analyzing the main environmental factors influencing airborne pollen concentrations.

A stochastic HMM-based forecasting model for fuzzy time series.

Science.gov (United States)

Li, Sheng-Tun; Cheng, Yi-Chung

2010-10-01

Recently, fuzzy time series have attracted more academic attention than traditional time series due to their capability of dealing with the uncertainty and vagueness inherent in the data collected. The formulation of fuzzy relations is one of the key issues affecting forecasting results. Most of the present works adopt IF-THEN rules for relationship representation, which leads to higher computational overhead and rule redundancy. Sullivan and Woodall proposed a Markov-based formulation and a forecasting model to reduce computational overhead; however, its applicability is limited to handling one-factor problems. In this paper, we propose a novel forecasting model based on the hidden Markov model by enhancing Sullivan and Woodall's work to allow handling of two-factor forecasting problems. Moreover, in order to make the nature of conjecture and randomness of forecasting more realistic, the Monte Carlo method is adopted to estimate the outcome. To test the effectiveness of the resulting stochastic model, we conduct two experiments and compare the results with those from other models. The first experiment consists of forecasting the daily average temperature and cloud density in Taipei, Taiwan, and the second experiment is based on the Taiwan Weighted Stock Index by forecasting the exchange rate of the New Taiwan dollar against the U.S. dollar. In addition to improving forecasting accuracy, the proposed model adheres to the central limit theorem, and thus, the result statistically approximates to the real mean of the target value being forecast.

Mathematical Model of Thyristor Inverter Including a Series-parallel Resonant Circuit

OpenAIRE

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.

[Matematical modeling of antibiotic resistance: perspectives from a meta-analysys].

Science.gov (United States)

Fresnadillo-Martínez, M J; García-Sánchez, E; García-Merino, E; Martín-Del-Rey, A; Rodríguez-Encinas, A; Rodríguez-Sánchez, G; García-Sánchez, J E

2012-09-01

The antibiotic resistance is one of the greatest challenges of the international health community. The study of antibiotic resistance must be a multidisciplinary task and, in this sense, the main goal of this work is to analyze the role that Mathematical Modeling can play in this scenario. A qualitative and cuantitative analysis of the works published in the scientific literature is done by means of a search in the most important databases: MEDLINE, SCOPUS and ISI Web of Science. Consequently, there are few papers related to our topic but the existing works have been published in high-quality and impact international journals. Moreover, we can state that mathematical models are a very important and useful tool to analyze and study both the treatments protocols for resistance prevention and the assesment of control strategies in hospital environtment, or the prediction of the evolution of diseases due to resistant strains.

75 FR 6865 - Airworthiness Directives; The Boeing Company Model 737-700 (IGW) Series Airplanes Equipped With...

Science.gov (United States)

2010-02-12

... replacing aging float level switch conduit assemblies, periodically inspecting the external dry bay system... Model 737-700 (IGW) Series Airplanes Equipped With Auxiliary Fuel Tanks Installed in Accordance With... airworthiness directive (AD) for certain Model 737-700 (IGW) series airplanes. This proposed AD would require...

Univaried models in the series of temperature of the air

International Nuclear Information System (INIS)

Leon Aristizabal Gloria esperanza

2000-01-01

The theoretical framework for the study of the air's temperature time series is the theory of stochastic processes, particularly those known as ARIMA, that make it possible to carry out a univaried analysis. ARIMA models are built in order to explain the structure of the monthly temperatures corresponding to the mean, the absolute maximum, absolute minimum, maximum mean and minimum mean temperatures, for four stations in Colombia. By means of those models, the possible evolution of the latter variables is estimated with predictive aims in mind. The application and utility of the models is discussed

From 1 Sun to 10 Suns c-Si Cells by Optimizing Metal Grid, Metal Resistance, and Junction Depth

International Nuclear Information System (INIS)

Chaudhari, V.A.; Solanki, C.S.

2009-01-01

Use of a solar cell in concentrator PV technology requires reduction in its series resistance in order to minimize the resistive power losses. The present paper discusses a methodology of reducing the series resistance of a commercial c-Si solar cell for concentrator applications, in the range of 2 to 10 suns. Step by step optimization of commercial cell in terms of grid geometry, junction depth, and electroplating of the front metal contacts is proposed. A model of resistance network of solar cell is developed and used for the optimization. Efficiency of un optimized commercial cell at 10 suns drops by 30% of its 1 sun value corresponding to resistive power loss of about 42%. The optimized cell with grid optimization, junction optimization, electroplating, and junction optimized with electroplated contacts cell gives resistive power loss of 20%, 16%, 11%, and 8%, respectively. An efficiency gain of 3% at 10 suns for fully optimized cell is estimated

Mathematical model of thyristor inverter including a series-parallel resonant circuit

OpenAIRE

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.

High-performance carbon nanotube-implanted mesoporous carbon spheres for supercapacitors with low series resistance

Energy Technology Data Exchange (ETDEWEB)

Yi, Bin [College of Materials Science and Engineering, Hunan University, Changsha 410082 (China); Chen, Xiaohua, E-mail: hudacxh62@yahoo.com.cn [College of Materials Science and Engineering, Hunan University, Changsha 410082 (China); Guo, Kaimin [College of Physics and Electronic Science, Changsha University of Science and Technology (China); Xu, Longshan [Department of Mechanical Engineering, Xiamen University of Technology, Xiamen 361024 (China); Chen, Chuansheng [College of Physics and Electronic Science, Changsha University of Science and Technology (China); Yan, Haimei; Chen, Jianghua [College of Materials Science and Engineering, Hunan University, Changsha 410082 (China)

2011-11-15

Research highlights: {yields} CNTs-implanted porous carbon spheres are prepared by using gelatin as soft template. {yields} Homogeneously distributed CNTs form a well-develop network in carbon spheres. {yields} CNTs act as a reinforcing backbone assisting the formation of pore structure. {yields} CNTs improve electrical conductivity and specific capacitance of supercapacitor. -- Abstract: Carbon nanotube-implanted mesoporous carbon spheres were prepared by an easy polymerization-induced colloid aggregation method using gelatin as a soft template. Scanning electron microscopy, transmission electron microscopy and nitrogen adsorption-desorption measurements reveal that the materials are mesoporous carbon spheres, with a diameter of {approx}0.5-1.0 {mu}m, a specific surface area of 284 m{sup 2}/g and average pore size of 3.9 nm. Using the carbon nanotube-implanted mesoporous carbon spheres as electrode material for supercapacitors in an aqueous electrolyte solution, a low equivalent series resistance of 0.83 {Omega} cm{sup 2} and a maximum specific capacitance of 189 F/g with a measured power density of 8.7 kW/kg at energy density of 6.6 Wh/kg are obtained.

Advanced methods for modeling water-levels and estimating drawdowns with SeriesSEE, an Excel add-in

Science.gov (United States)

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.

Model development for quantitative evaluation of proliferation resistance of nuclear fuel cycles

Energy Technology Data Exchange (ETDEWEB)

Ko, Won Il; Kim, Ho Dong; Yang, Myung Seung

2000-07-01

This study addresses the quantitative evaluation of the proliferation resistance which is important factor of the alternative nuclear fuel cycle system. In this study, model was developed to quantitatively evaluate the proliferation resistance of the nuclear fuel cycles. The proposed models were then applied to Korean environment as a sample study to provide better references for the determination of future nuclear fuel cycle system in Korea. In order to quantify the proliferation resistance of the nuclear fuel cycle, the proliferation resistance index was defined in imitation of an electrical circuit with an electromotive force and various electrical resistance components. The analysis on the proliferation resistance of nuclear fuel cycles has shown that the resistance index as defined herein can be used as an international measure of the relative risk of the nuclear proliferation if the motivation index is appropriately defined. It has also shown that the proposed model can include political issues as well as technical ones relevant to the proliferation resistance, and consider all facilities and activities in a specific nuclear fuel cycle (from mining to disposal). In addition, sensitivity analyses on the sample study indicate that the direct disposal option in a country with high nuclear propensity may give rise to a high risk of the nuclear proliferation than the reprocessing option in a country with low nuclear propensity.

Model development for quantitative evaluation of proliferation resistance of nuclear fuel cycles

International Nuclear Information System (INIS)

Ko, Won Il; Kim, Ho Dong; Yang, Myung Seung

2000-07-01

This study addresses the quantitative evaluation of the proliferation resistance which is important factor of the alternative nuclear fuel cycle system. In this study, model was developed to quantitatively evaluate the proliferation resistance of the nuclear fuel cycles. The proposed models were then applied to Korean environment as a sample study to provide better references for the determination of future nuclear fuel cycle system in Korea. In order to quantify the proliferation resistance of the nuclear fuel cycle, the proliferation resistance index was defined in imitation of an electrical circuit with an electromotive force and various electrical resistance components. The analysis on the proliferation resistance of nuclear fuel cycles has shown that the resistance index as defined herein can be used as an international measure of the relative risk of the nuclear proliferation if the motivation index is appropriately defined. It has also shown that the proposed model can include political issues as well as technical ones relevant to the proliferation resistance, and consider all facilities and activities in a specific nuclear fuel cycle (from mining to disposal). In addition, sensitivity analyses on the sample study indicate that the direct disposal option in a country with high nuclear propensity may give rise to a high risk of the nuclear proliferation than the reprocessing option in a country with low nuclear propensity

Applying ARIMA model for annual volume time series of the Magdalena River

OpenAIRE

Gloria Amaris; Humberto Ávila; Thomas Guerrero

2017-01-01

Context: Climate change effects, human interventions, and river characteristics are factors that increase the risk on the population and the water resources. However, negative impacts such as flooding, and river droughts may be previously identified using appropriate numerical tools. Objectives: The annual volume (Millions of m3/year) time series of the Magdalena River was analyzed by an ARIMA model, using the historical time series of the Calamar station (Instituto de Hidrología, Meteoro...

A Course in Time Series Analysis

CERN Document Server

Peña, Daniel; Tsay, Ruey S

2011-01-01

New statistical methods and future directions of research in time series A Course in Time Series Analysis demonstrates how to build time series models for univariate and multivariate time series data. It brings together material previously available only in the professional literature and presents a unified view of the most advanced procedures available for time series model building. The authors begin with basic concepts in univariate time series, providing an up-to-date presentation of ARIMA models, including the Kalman filter, outlier analysis, automatic methods for building ARIMA models, a

A Novel Series Connected Batteries State of High Voltage Safety Monitor System for Electric Vehicle Application

Directory of Open Access Journals (Sweden)

Qiang Jiaxi

2013-01-01

Full Text Available Batteries, as the main or assistant power source of EV (Electric Vehicle, are usually connected in series with high voltage to improve the drivability and energy efficiency. Today, more and more batteries are connected in series with high voltage, if there is any fault in high voltage system (HVS, the consequence is serious and dangerous. Therefore, it is necessary to monitor the electric parameters of HVS to ensure the high voltage safety and protect personal safety. In this study, a high voltage safety monitor system is developed to solve this critical issue. Four key electric parameters including precharge, contact resistance, insulation resistance, and remaining capacity are monitored and analyzed based on the equivalent models presented in this study. The high voltage safety controller which integrates the equivalent models and control strategy is developed. By the help of hardware-in-loop system, the equivalent models integrated in the high voltage safety controller are validated, and the online electric parameters monitor strategy is analyzed and discussed. The test results indicate that the high voltage safety monitor system designed in this paper is suitable for EV application.

A novel series connected batteries state of high voltage safety monitor system for electric vehicle application.

Science.gov (United States)

Jiaxi, Qiang; Lin, Yang; Jianhui, He; Qisheng, Zhou

2013-01-01

Batteries, as the main or assistant power source of EV (Electric Vehicle), are usually connected in series with high voltage to improve the drivability and energy efficiency. Today, more and more batteries are connected in series with high voltage, if there is any fault in high voltage system (HVS), the consequence is serious and dangerous. Therefore, it is necessary to monitor the electric parameters of HVS to ensure the high voltage safety and protect personal safety. In this study, a high voltage safety monitor system is developed to solve this critical issue. Four key electric parameters including precharge, contact resistance, insulation resistance, and remaining capacity are monitored and analyzed based on the equivalent models presented in this study. The high voltage safety controller which integrates the equivalent models and control strategy is developed. By the help of hardware-in-loop system, the equivalent models integrated in the high voltage safety controller are validated, and the online electric parameters monitor strategy is analyzed and discussed. The test results indicate that the high voltage safety monitor system designed in this paper is suitable for EV application.

Within-host selection of drug resistance in a mouse model reveals dose-dependent selection of atovaquone resistance mutations

NARCIS (Netherlands)

Nuralitha, Suci; Murdiyarso, Lydia S.; Siregar, Josephine E.; Syafruddin, Din; Roelands, Jessica; Verhoef, Jan; Hoepelman, Andy I.M.; Marzuki, Sangkot

2017-01-01

The evolutionary selection of malaria parasites within an individual host plays a critical role in the emergence of drug resistance. We have compared the selection of atovaquone resistance mutants in mouse models reflecting two different causes of failure of malaria treatment, an inadequate

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.

Combining Different Conceptual Change Methods within Four-Step Constructivist Teaching Model: A Sample Teaching of Series and Parallel Circuits

Science.gov (United States)

Ipek, Hava; Calik, Muammer

2008-01-01

Based on students' alternative conceptions of the topics "electric circuits", "electric charge flows within an electric circuit", "how the brightness of bulbs and the resistance changes in series and parallel circuits", the current study aims to present a combination of different conceptual change methods within a four-step constructivist teaching…

Empirical validation of landscape resistance models: insights from the Greater Sage-Grouse (Centrocercus urophasianus)

Science.gov (United States)

Andrew J. Shirk; Michael A. Schroeder; Leslie A. Robb; Samuel A. Cushman

2015-01-01

The ability of landscapes to impede species’ movement or gene flow may be quantified by resistance models. Few studies have assessed the performance of resistance models parameterized by expert opinion. In addition, resistance models differ in terms of spatial and thematic resolution as well as their focus on the ecology of a particular species or more generally on the...

Antibiotic Resistances in Livestock: A Comparative Approach to Identify an Appropriate Regression Model for Count Data

Directory of Open Access Journals (Sweden)

Anke Hüls

2017-05-01

Full Text Available Antimicrobial resistance in livestock is a matter of general concern. To develop hygiene measures and methods for resistance prevention and control, epidemiological studies on a population level are needed to detect factors associated with antimicrobial resistance in livestock holdings. In general, regression models are used to describe these relationships between environmental factors and resistance outcome. Besides the study design, the correlation structures of the different outcomes of antibiotic resistance and structural zero measurements on the resistance outcome as well as on the exposure side are challenges for the epidemiological model building process. The use of appropriate regression models that acknowledge these complexities is essential to assure valid epidemiological interpretations. The aims of this paper are (i to explain the model building process comparing several competing models for count data (negative binomial model, quasi-Poisson model, zero-inflated model, and hurdle model and (ii to compare these models using data from a cross-sectional study on antibiotic resistance in animal husbandry. These goals are essential to evaluate which model is most suitable to identify potential prevention measures. The dataset used as an example in our analyses was generated initially to study the prevalence and associated factors for the appearance of cefotaxime-resistant Escherichia coli in 48 German fattening pig farms. For each farm, the outcome was the count of samples with resistant bacteria. There was almost no overdispersion and only moderate evidence of excess zeros in the data. Our analyses show that it is essential to evaluate regression models in studies analyzing the relationship between environmental factors and antibiotic resistances in livestock. After model comparison based on evaluation of model predictions, Akaike information criterion, and Pearson residuals, here the hurdle model was judged to be the most appropriate

Time series ARIMA models for daily price of palm oil

Science.gov (United States)

Ariff, Noratiqah Mohd; Zamhawari, Nor Hashimah; Bakar, Mohd Aftar Abu

2015-02-01

Palm oil is deemed as one of the most important commodity that forms the economic backbone of Malaysia. Modeling and forecasting the daily price of palm oil is of great interest for Malaysia's economic growth. In this study, time series ARIMA models are used to fit the daily price of palm oil. The Akaike Infromation Criterion (AIC), Akaike Infromation Criterion with a correction for finite sample sizes (AICc) and Bayesian Information Criterion (BIC) are used to compare between different ARIMA models being considered. It is found that ARIMA(1,2,1) model is suitable for daily price of crude palm oil in Malaysia for the year 2010 to 2012.

Stochastic series expansion simulation of the t -V model

Science.gov (United States)

Wang, Lei; Liu, Ye-Hua; Troyer, Matthias

2016-04-01

We present an algorithm for the efficient simulation of the half-filled spinless t -V model on bipartite lattices, which combines the stochastic series expansion method with determinantal quantum Monte Carlo techniques widely used in fermionic simulations. The algorithm scales linearly in the inverse temperature, cubically with the system size, and is free from the time-discretization error. We use it to map out the finite-temperature phase diagram of the spinless t -V model on the honeycomb lattice and observe a suppression of the critical temperature of the charge-density-wave phase in the vicinity of a fermionic quantum critical point.

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...

Error modelling of quantum Hall array resistance standards

Science.gov (United States)

Marzano, Martina; Oe, Takehiko; Ortolano, Massimo; Callegaro, Luca; Kaneko, Nobu-Hisa

2018-04-01

Quantum Hall array resistance standards (QHARSs) are integrated circuits composed of interconnected quantum Hall effect elements that allow the realization of virtually arbitrary resistance values. In recent years, techniques were presented to efficiently design QHARS networks. An open problem is that of the evaluation of the accuracy of a QHARS, which is affected by contact and wire resistances. In this work, we present a general and systematic procedure for the error modelling of QHARSs, which is based on modern circuit analysis techniques and Monte Carlo evaluation of the uncertainty. As a practical example, this method of analysis is applied to the characterization of a 1 MΩ QHARS developed by the National Metrology Institute of Japan. Software tools are provided to apply the procedure to other arrays.

Mathematical Modeling of Contact Resistance in Silicon Photovoltaic Cells

KAUST Repository

Black, J. P.; Breward, C. J. W.; Howell, P. D.; Young, R. J. S.

2013-01-01

across this layer, based on the driftdiffusion equations. We utilize the size of the current/Debye length to conduct asymptotic techniques to simplify the model; we solve the model numerically to find that the effective contact resistance may be a

Ecological modeling from time-series inference: insight into dynamics and stability of intestinal microbiota.

Directory of Open Access Journals (Sweden)

Richard R Stein

Full Text Available The intestinal microbiota is a microbial ecosystem of crucial importance to human health. Understanding how the microbiota confers resistance against enteric pathogens and how antibiotics disrupt that resistance is key to the prevention and cure of intestinal infections. We present a novel method to infer microbial community ecology directly from time-resolved metagenomics. This method extends generalized Lotka-Volterra dynamics to account for external perturbations. Data from recent experiments on antibiotic-mediated Clostridium difficile infection is analyzed to quantify microbial interactions, commensal-pathogen interactions, and the effect of the antibiotic on the community. Stability analysis reveals that the microbiota is intrinsically stable, explaining how antibiotic perturbations and C. difficile inoculation can produce catastrophic shifts that persist even after removal of the perturbations. Importantly, the analysis suggests a subnetwork of bacterial groups implicated in protection against C. difficile. Due to its generality, our method can be applied to any high-resolution ecological time-series data to infer community structure and response to external stimuli.

Ecological modeling from time-series inference: insight into dynamics and stability of intestinal microbiota.

Science.gov (United States)

Stein, Richard R; Bucci, Vanni; Toussaint, Nora C; Buffie, Charlie G; Rätsch, Gunnar; Pamer, Eric G; Sander, Chris; Xavier, João B

2013-01-01

The intestinal microbiota is a microbial ecosystem of crucial importance to human health. Understanding how the microbiota confers resistance against enteric pathogens and how antibiotics disrupt that resistance is key to the prevention and cure of intestinal infections. We present a novel method to infer microbial community ecology directly from time-resolved metagenomics. This method extends generalized Lotka-Volterra dynamics to account for external perturbations. Data from recent experiments on antibiotic-mediated Clostridium difficile infection is analyzed to quantify microbial interactions, commensal-pathogen interactions, and the effect of the antibiotic on the community. Stability analysis reveals that the microbiota is intrinsically stable, explaining how antibiotic perturbations and C. difficile inoculation can produce catastrophic shifts that persist even after removal of the perturbations. Importantly, the analysis suggests a subnetwork of bacterial groups implicated in protection against C. difficile. Due to its generality, our method can be applied to any high-resolution ecological time-series data to infer community structure and response to external stimuli.

Analysis of the equalizing holes resistance in fuel assembly spike for lead-based reactor

International Nuclear Information System (INIS)

Zhang, Guangyu; Jin, Ming; Wang, Jianye; Song, Yong

2017-01-01

Highlights: • A RELAP5 model for a 10 MWth lead-based reactor was built to study the hydrodynamic characteristics between the equalizing holes in the fuel assembly spike. • Different fuel assembly total blockage scenarios and different resistances for different fuel assemblies were examined. • The inherent safety characteristics of the lead-based reactor was improved by optimizing the configuration of equalizing holes in the fuel assembly spike. - Abstract: To avoid the damage of the fuel rod cladding when a fuel assembly (FA) is totally blocked, a special configuration of the fuel assembly spike was designed with some equalizing holes in the center region which can let the coolant to flow during the totally blockage scenarios of FA. To study the hydrodynamic characteristics between the equalizing holes and an appropriate resistance, a RELAP5 model was developed for a 10 MWth lead-based reactor which used lead-bismuth as coolant. Several FA total blockage and partial core blockage scenarios were selected. The simulation results indicated that when all the FA spike equalizing holes had the same hydraulic resistance, only a narrow range of suitable equalizing holes resistances could be chosen when a FA was blocked. However, in the two or more FA blockage scenarios, there were no appropriate resistances to meet the requirement. In addition, with different FA spike equalizing holes with different resistances, a large range of suitable equalizing hole resistances could be chosen. Especially a series of suitable resistances were selected when the small power FA resistance was 1/2, 1/4, 1/8 of the large one. Under these circumstances, one, two or three FA blockages would not damage the core. These demonstrated that selecting a series of suitable hydraulic resistances for the equalizing holes could improve the safety characteristics of the reactor effectively.

Evaluation of the autoregression time-series model for analysis of a noisy signal

International Nuclear Information System (INIS)

Allen, J.W.

1977-01-01

The autoregression (AR) time-series model of a continuous noisy signal was statistically evaluated to determine quantitatively the uncertainties of the model order, the model parameters, and the model's power spectral density (PSD). The result of such a statistical evaluation enables an experimenter to decide whether an AR model can adequately represent a continuous noisy signal and be consistent with the signal's frequency spectrum, and whether it can be used for on-line monitoring. Although evaluations of other types of signals have been reported in the literature, no direct reference has been found to AR model's uncertainties for continuous noisy signals; yet the evaluation is necessary to decide the usefulness of AR models of typical reactor signals (e.g., neutron detector output or thermocouple output) and the potential of AR models for on-line monitoring applications. AR and other time-series models for noisy data representation are being investigated by others since such models require fewer parameters than the traditional PSD model. For this study, the AR model was selected for its simplicity and conduciveness to uncertainty analysis, and controlled laboratory bench signals were used for continuous noisy data. (author)

Grain-Boundary Resistance in Copper Interconnects: From an Atomistic Model to a Neural Network

Science.gov (United States)

Valencia, Daniel; Wilson, Evan; Jiang, Zhengping; Valencia-Zapata, Gustavo A.; Wang, Kuang-Chung; Klimeck, Gerhard; Povolotskyi, Michael

2018-04-01

Orientation effects on the specific resistance of copper grain boundaries are studied systematically with two different atomistic tight-binding methods. A methodology is developed to model the specific resistance of grain boundaries in the ballistic limit using the embedded atom model, tight- binding methods, and nonequilibrium Green's functions. The methodology is validated against first-principles calculations for thin films with a single coincident grain boundary, with 6.4% deviation in the specific resistance. A statistical ensemble of 600 large, random structures with grains is studied. For structures with three grains, it is found that the distribution of specific resistances is close to normal. Finally, a compact model for grain-boundary-specific resistance is constructed based on a neural network.

Neural Network Models for Time Series Forecasts

OpenAIRE

Tim Hill; Marcus O'Connor; William Remus

1996-01-01

Neural networks have been advocated as an alternative to traditional statistical forecasting methods. In the present experiment, time series forecasts produced by neural networks are compared with forecasts from six statistical time series methods generated in a major forecasting competition (Makridakis et al. [Makridakis, S., A. Anderson, R. Carbone, R. Fildes, M. Hibon, R. Lewandowski, J. Newton, E. Parzen, R. Winkler. 1982. The accuracy of extrapolation (time series) methods: Results of a ...

Some Remarks on the Accuracy of Wave Resistance Determination From Wave Measurements along a Parallel Cut

OpenAIRE

Moriconi, Alessandro; Lalli, Francesco; Di Felice, Fabio; Esposito, Pier Giorgio; Piscopia, Rodolfo

1998-01-01

In the present work some of the main error sources in the wave pattern resistance determination were investigated. The experimental data obtained at the Italian Ship Model Basin (longitudinal wave profiles generated by the steady motion of the Series 60 model and a hard chine Catamaran) were analyzed. It was found that, within the range of Froude numbers tested (.225 ≤ Fr ≤ .345 for the Series 60 and .5 ≤ Fr ≤ 1 for the Catamaran) two sources of uncertainty play a sign...

Statistical models of a gas diffusion electrode: II. Current resistent

Energy Technology Data Exchange (ETDEWEB)

Proksch, D B; Winsel, O W

1965-07-01

The authors describe an apparatus for measuring the flow resistance of gas diffusion electrodes which is a mechanical analog of the Wheatstone bridge for measuring electric resistance. The flow resistance of a circular DSK electrode sheet, consisting of two covering layers and a working layer between them, was measured as a function of the gas pressure. While the pressure first was increased and then decreased, a hysteresis occurred, which is discussed and explained by a statistical model of a porous electrode.

Novel series of 1,2,4-trioxane derivatives as antimalarial agents.

Science.gov (United States)

Rudrapal, Mithun; Chetia, Dipak; Singh, Vineeta

2017-12-01

Among three series of 1,2,4-trioxane derivatives, five compounds showed good in vitro antimalarial activity, three compounds of which exhibited better activity against P. falciparum resistant (RKL9) strain than the sensitive (3D7) one. Two best compounds were one from aryl series and the other from heteroaryl series with IC 50 values of 1.24 µM and 1.24 µM and 1.06 µM and 1.17 µM, against sensitive and resistant strains, respectively. Further, trioxane derivatives exhibited good binding affinity for the P. falciparum cysteine protease falcipain 2 receptor (PDB id: 3BPF) with well defined drug-like and pharmacokinetic properties based on Lipinski's rule of five with additional physicochemical and ADMET parameters. In view of having antimalarial potential, 1,2,4-trioxane derivative(s) reported herein may be useful as novel antimalarial lead(s) in the discovery and development of future antimalarial drug candidates as P. falciparum falcipain 2 inhibitors against resistant malaria.

A model of antibiotic-resistant bacterial epidemics in hospitals

OpenAIRE

Webb, Glenn F.; D'Agata, Erika M. C.; Magal, Pierre; Ruan, Shigui

2005-01-01

The emergence of drug-resistant strains of bacteria is an increasing threat to society, especially in hospital settings. Many antibiotics that were formerly effective in combating bacterial infections in hospital patients are no longer effective because of the evolution of resistant strains, which compromises medical care worldwide. In this article, we formulate a two-level population model to quantify key elements in nosocomial (hospital-acquired) infections. At the bacteria level, patients ...

Modeling of plasma chemistry in a corona streamer pulse series in air

International Nuclear Information System (INIS)

Nowakowska, H.; Stanco, J.; Dors, M.; Mizeraczyk, J.

2002-01-01

The aim of this study is to analyse the chemistry in air treated by a series of corona discharge streamers. Attention is focused on the conversion of ozone and nitrogen oxides. In the model it is assumed that the streamer head of relatively small geometrical dimensions propagates from the anode to the cathode, leaving the streamer channel behind. Any elemental gas volume in the streamer path is subjected first to the conditions of the streamer head, and next to those of the streamer channel. The kinetics of plasma-chemical processes occurring in the gas is modeled numerically for a single streamer and a series of streamers. The temporal evolution of 25 chemical compounds initially present or produced in air is calculated. (author)

Fisher information framework for time series modeling

Science.gov (United States)

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.

Time series modelling to forecast prehospital EMS demand for diabetic emergencies.

Science.gov (United States)

Villani, Melanie; Earnest, Arul; Nanayakkara, Natalie; Smith, Karen; de Courten, Barbora; Zoungas, Sophia

2017-05-05

Acute diabetic emergencies are often managed by prehospital Emergency Medical Services (EMS). The projected growth in prevalence of diabetes is likely to result in rising demand for prehospital EMS that are already under pressure. The aims of this study were to model the temporal trends and provide forecasts of prehospital attendances for diabetic emergencies. A time series analysis on monthly cases of hypoglycemia and hyperglycemia was conducted using data from the Ambulance Victoria (AV) electronic database between 2009 and 2015. Using the seasonal autoregressive integrated moving average (SARIMA) modelling process, different models were evaluated. The most parsimonious model with the highest accuracy was selected. Forty-one thousand four hundred fifty-four prehospital diabetic emergencies were attended over a seven-year period with an increase in the annual median monthly caseload between 2009 (484.5) and 2015 (549.5). Hypoglycemia (70%) and people with type 1 diabetes (48%) accounted for most attendances. The SARIMA (0,1,0,12) model provided the best fit, with a MAPE of 4.2% and predicts a monthly caseload of approximately 740 by the end of 2017. Prehospital EMS demand for diabetic emergencies is increasing. SARIMA time series models are a valuable tool to allow forecasting of future caseload with high accuracy and predict increasing cases of prehospital diabetic emergencies into the future. The model generated by this study may be used by service providers to allow appropriate planning and resource allocation of EMS for diabetic emergencies.

Modelling of series of types of automated trenchless works tunneling

Science.gov (United States)

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.

Time series modeling of live-cell shape dynamics for image-based phenotypic profiling.

Science.gov (United States)

Gordonov, Simon; Hwang, Mun Kyung; Wells, Alan; Gertler, Frank B; Lauffenburger, Douglas A; Bathe, Mark

2016-01-01

Live-cell imaging can be used to capture spatio-temporal aspects of cellular responses that are not accessible to fixed-cell imaging. As the use of live-cell imaging continues to increase, new computational procedures are needed to characterize and classify the temporal dynamics of individual cells. For this purpose, here we present the general experimental-computational framework SAPHIRE (Stochastic Annotation of Phenotypic Individual-cell Responses) to characterize phenotypic cellular responses from time series imaging datasets. Hidden Markov modeling is used to infer and annotate morphological state and state-switching properties from image-derived cell shape measurements. Time series modeling is performed on each cell individually, making the approach broadly useful for analyzing asynchronous cell populations. Two-color fluorescent cells simultaneously expressing actin and nuclear reporters enabled us to profile temporal changes in cell shape following pharmacological inhibition of cytoskeleton-regulatory signaling pathways. Results are compared with existing approaches conventionally applied to fixed-cell imaging datasets, and indicate that time series modeling captures heterogeneous dynamic cellular responses that can improve drug classification and offer additional important insight into mechanisms of drug action. The software is available at http://saphire-hcs.org.

Spread of anti-malarial drug resistance: Mathematical model with implications for ACT drug policies

Directory of Open Access Journals (Sweden)

Dondorp Arjen M

2008-11-01

Full Text Available Abstract Background Most malaria-endemic countries are implementing a change in anti-malarial drug policy to artemisinin-based combination therapy (ACT. The impact of different drug choices and implementation strategies is uncertain. Data from many epidemiological studies in different levels of malaria endemicity and in areas with the highest prevalence of drug resistance like borders of Thailand are certainly valuable. Formulating an appropriate dynamic data-driven model is a powerful predictive tool for exploring the impact of these strategies quantitatively. Methods A comprehensive model was constructed incorporating important epidemiological and biological factors of human, mosquito, parasite and treatment. The iterative process of developing the model, identifying data needed, and parameterization has been taken to strongly link the model to the empirical evidence. The model provides quantitative measures of outcomes, such as malaria prevalence/incidence and treatment failure, and illustrates the spread of resistance in low and high transmission settings. The model was used to evaluate different anti-malarial policy options focusing on ACT deployment. Results The model predicts robustly that in low transmission settings drug resistance spreads faster than in high transmission settings, and treatment failure is the main force driving the spread of drug resistance. In low transmission settings, ACT slows the spread of drug resistance to a partner drug, especially at high coverage rates. This effect decreases exponentially with increasing delay in deploying the ACT and decreasing rates of coverage. In the high transmission settings, however, drug resistance is driven by the proportion of the human population with a residual drug level, which gives resistant parasites some survival advantage. The spread of drug resistance could be slowed down by controlling presumptive drug use and avoiding the use of combination therapies containing drugs with

A multiple-field coupled resistive transition model for superconducting Nb3Sn

Science.gov (United States)

Yang, Lin; Ding, He; Zhang, Xin; Qiao, Li

2016-12-01

A study on the superconducting transition width as functions of the applied magnetic field and strain is performed in superconducting Nb3Sn. A quantitative, yet universal phenomenological resistivity model is proposed. The numerical simulation by the proposed model shows predicted resistive transition characteristics under variable magnetic fields and strain, which in good agreement with the experimental observations. Furthermore, a temperature-modulated magnetoresistance transition behavior in filamentary Nb3Sn conductors can also be well described by the given model. The multiple-field coupled resistive transition model is helpful for making objective determinations of the high-dimensional critical surface of Nb3Sn in the multi-parameter space, offering some preliminary information about the basic vortex-pinning mechanisms, and guiding the design of the quench protection system of Nb3Sn superconducting magnets.

A time series modeling approach in risk appraisal of violent and sexual recidivism.

Science.gov (United States)

Bani-Yaghoub, Majid; Fedoroff, J Paul; Curry, Susan; Amundsen, David E

2010-10-01

For over half a century, various clinical and actuarial methods have been employed to assess the likelihood of violent recidivism. Yet there is a need for new methods that can improve the accuracy of recidivism predictions. This study proposes a new time series modeling approach that generates high levels of predictive accuracy over short and long periods of time. The proposed approach outperformed two widely used actuarial instruments (i.e., the Violence Risk Appraisal Guide and the Sex Offender Risk Appraisal Guide). Furthermore, analysis of temporal risk variations based on specific time series models can add valuable information into risk assessment and management of violent offenders.

Forecast models for suicide: Time-series analysis with data from Italy.

Science.gov (United States)

Preti, Antonio; Lentini, Gianluca

2016-01-01

The prediction of suicidal behavior is a complex task. To fine-tune targeted preventative interventions, predictive analytics (i.e. forecasting future risk of suicide) is more important than exploratory data analysis (pattern recognition, e.g. detection of seasonality in suicide time series). This study sets out to investigate the accuracy of forecasting models of suicide for men and women. A total of 101 499 male suicides and of 39 681 female suicides - occurred in Italy from 1969 to 2003 - were investigated. In order to apply the forecasting model and test its accuracy, the time series were split into a training set (1969 to 1996; 336 months) and a test set (1997 to 2003; 84 months). The main outcome was the accuracy of forecasting models on the monthly number of suicides. These measures of accuracy were used: mean absolute error; root mean squared error; mean absolute percentage error; mean absolute scaled error. In both male and female suicides a change in the trend pattern was observed, with an increase from 1969 onwards to reach a maximum around 1990 and decrease thereafter. The variances attributable to the seasonal and trend components were, respectively, 24% and 64% in male suicides, and 28% and 41% in female ones. Both annual and seasonal historical trends of monthly data contributed to forecast future trends of suicide with a margin of error around 10%. The finding is clearer in male than in female time series of suicide. The main conclusion of the study is that models taking seasonality into account seem to be able to derive information on deviation from the mean when this occurs as a zenith, but they fail to reproduce it when it occurs as a nadir. Preventative efforts should concentrate on the factors that influence the occurrence of increases above the main trend in both seasonal and cyclic patterns of suicides.

Time Series with Long Memory

OpenAIRE

西埜, 晴久

2004-01-01

The paper investigates an application of long-memory processes to economic time series. We show properties of long-memory processes, which are motivated to model a long-memory phenomenon in economic time series. An FARIMA model is described as an example of long-memory model in statistical terms. The paper explains basic limit theorems and estimation methods for long-memory processes in order to apply long-memory models to economic time series.

Generation of Natural Runoff Monthly Series at Ungauged Sites Using a Regional Regressive Model

Directory of Open Access Journals (Sweden)

Dario Pumo

2016-05-01

Full Text Available Many hydrologic applications require reliable estimates of runoff in river basins to face the widespread lack of data, both in time and in space. A regional method for the reconstruction of monthly runoff series is here developed and applied to Sicily (Italy. A simple modeling structure is adopted, consisting of a regression-based rainfall–runoff model with four model parameters, calibrated through a two-step procedure. Monthly runoff estimates are based on precipitation, temperature, and exploiting the autocorrelation with runoff at the previous month. Model parameters are assessed by specific regional equations as a function of easily measurable physical and climate basin descriptors. The first calibration step is aimed at the identification of a set of parameters optimizing model performances at the level of single basin. Such “optimal” sets are used at the second step, part of a regional regression analysis, to establish the regional equations for model parameters assessment as a function of basin attributes. All the gauged watersheds across the region have been analyzed, selecting 53 basins for model calibration and using the other six basins exclusively for validation. Performances, quantitatively evaluated by different statistical indexes, demonstrate relevant model ability in reproducing the observed hydrological time-series at both the monthly and coarser time resolutions. The methodology, which is easily transferable to other arid and semi-arid areas, provides a reliable tool for filling/reconstructing runoff time series at any gauged or ungauged basin of a region.

Homology modelling of Drosophila cytochrome P450 enzymes associated with insecticide resistance.

Science.gov (United States)

Jones, Robert T; Bakker, Saskia E; Stone, Deborah; Shuttleworth, Sally N; Boundy, Sam; McCart, Caroline; Daborn, Phillip J; ffrench-Constant, Richard H; van den Elsen, Jean M H

2010-10-01

Overexpression of the cytochrome P450 gene Cyp6g1 confers resistance against DDT and a broad range of other insecticides in Drosophila melanogaster Meig. In the absence of crystal structures of CYP6G1 or complexes with its substrates, structural studies rely on homology modelling and ligand docking to understand P450-substrate interactions. Homology models are presented for CYP6G1, a P450 associated with resistance to DDT and neonicotinoids, and two other enzymes associated with insecticide resistance in D. melanogaster, CYP12D1 and CYP6A2. The models are based on a template of the X-ray structure of the phylogenetically related human CYP3A4, which is known for its broad substrate specificity. The model of CYP6G1 has a much smaller active site cavity than the template. The cavity is also 'V'-shaped and is lined with hydrophobic residues, showing high shape and chemical complementarity with the molecular characteristics of DDT. Comparison of the DDT-CYP6G1 complex and a non-resistant CYP6A2 homology model implies that tight-fit recognition of this insecticide is important in CYP6G1. The active site can accommodate differently shaped substrates ranging from imidacloprid to malathion but not the pyrethroids permethrin and cyfluthrin. The CYP6G1, CYP12D1 and CYP6A2 homology models can provide a structural insight into insecticide resistance in flies overexpressing P450 enzymes with broad substrate specificities.

An approach to ductile fracture resistance modelling in pipeline steels

Energy Technology Data Exchange (ETDEWEB)

Pussegoda, L.N.; Fredj, A. [BMT Fleet Technology Ltd., Kanata (Canada)

2009-07-01

Ductile fracture resistance studies of high grade steels in the pipeline industry often included analyses of the crack tip opening angle (CTOA) parameter using 3-point bend steel specimens. The CTOA is a function of specimen ligament size in high grade materials. Other resistance measurements may include steady state fracture propagation energy, critical fracture strain, and the adoption of damage mechanisms. Modelling approaches for crack propagation were discussed in this abstract. Tension tests were used to calibrate damage model parameters. Results from the tests were then applied to the crack propagation in a 3-point bend specimen using modern 1980 vintage steels. Limitations and approaches to overcome the difficulties associated with crack propagation modelling were discussed.

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.

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...... estimator is superior to the at-site estimator even in extremely heterogenous regions, the performance of the regional estimator being relatively better in regions with a negative shape parameter. When the record length increases, the relative performance of the regional estimator decreases, but it is still...

Resistance and support to electronic government, building a model of innovation

NARCIS (Netherlands)

Ebbers, Wolfgang E.; van Dijk, Johannes A.G.M.

2007-01-01

In several countries forces that resist e-government innovations apparently override those that support them. A first step is taken in order to identify organizational processes of resistance and support to e-government innovations. A multi-disciplinary and non-linear innovation model is proposed

Modeling the Responses to Resistance Training in an Animal Experiment Study

Directory of Open Access Journals (Sweden)

Antony G. Philippe

2015-01-01

Full Text Available The aim of the present study was to test whether systems models of training effects on performance in athletes can be used to explore the responses to resistance training in rats. 11 Wistar Han rats (277 ± 15 g underwent 4 weeks of resistance training consisting in climbing a ladder with progressive loads. Training amount and performance were computed from total work and mean power during each training session. Three systems models relating performance to cumulated training bouts have been tested: (i with a single component for adaptation to training, (ii with two components to distinguish the adaptation and fatigue produced by exercise bouts, and (iii with an additional component to account for training-related changes in exercise-induced fatigue. Model parameters were fitted using a mixed-effects modeling approach. The model with two components was found to be the most suitable to analyze the training responses (R2=0.53; P<0.001. In conclusion, the accuracy in quantifying training loads and performance in a rodent experiment makes it possible to model the responses to resistance training. This modeling in rodents could be used in future studies in combination with biological tools for enhancing our understanding of the adaptive processes that occur during physical training.

A multiple-field coupled resistive transition model for superconducting Nb3Sn

Directory of Open Access Journals (Sweden)

Lin Yang

2016-12-01

Full Text Available A study on the superconducting transition width as functions of the applied magnetic field and strain is performed in superconducting Nb3Sn. A quantitative, yet universal phenomenological resistivity model is proposed. The numerical simulation by the proposed model shows predicted resistive transition characteristics under variable magnetic fields and strain, which in good agreement with the experimental observations. Furthermore, a temperature-modulated magnetoresistance transition behavior in filamentary Nb3Sn conductors can also be well described by the given model. The multiple-field coupled resistive transition model is helpful for making objective determinations of the high-dimensional critical surface of Nb3Sn in the multi-parameter space, offering some preliminary information about the basic vortex-pinning mechanisms, and guiding the design of the quench protection system of Nb3Sn superconducting magnets.

Validation of the inverse pulse wave transit time series as surrogate of systolic blood pressure in MVAR modeling.

Science.gov (United States)

Giassi, Pedro; Okida, Sergio; Oliveira, Maurício G; Moraes, Raimes

2013-11-01

Short-term cardiovascular regulation mediated by the sympathetic and parasympathetic branches of the autonomic nervous system has been investigated by multivariate autoregressive (MVAR) modeling, providing insightful analysis. MVAR models employ, as inputs, heart rate (HR), systolic blood pressure (SBP) and respiratory waveforms. ECG (from which HR series is obtained) and respiratory flow waveform (RFW) can be easily sampled from the patients. Nevertheless, the available methods for acquisition of beat-to-beat SBP measurements during exams hamper the wider use of MVAR models in clinical research. Recent studies show an inverse correlation between pulse wave transit time (PWTT) series and SBP fluctuations. PWTT is the time interval between the ECG R-wave peak and photoplethysmography waveform (PPG) base point within the same cardiac cycle. This study investigates the feasibility of using inverse PWTT (IPWTT) series as an alternative input to SBP for MVAR modeling of the cardiovascular regulation. For that, HR, RFW, and IPWTT series acquired from volunteers during postural changes and autonomic blockade were used as input of MVAR models. Obtained results show that IPWTT series can be used as input of MVAR models, replacing SBP measurements in order to overcome practical difficulties related to the continuous sampling of the SBP during clinical exams.

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.

Report on series 3 reflood experiment

International Nuclear Information System (INIS)

Murao, Yoshio; Iguchi, Tadashi; Sudoh, Takashi; Sudo, Yukio; Sugimoto, Jun

1977-03-01

Series 3 reflood experiment was carried out from December 1975 to January 1976. The purpose was to confirm temperature response and durability of the improved thermocouple installation and to examine system effect with parameters, flow housing temperature and primary loop flow resistance. The results are : 1) The improved thermocouples installation still has some problems, but is generally satisfactory up to 1000 0 C. 2) The flow housing temperature has large influence on the reflood phenomena, especially oscillation. 3) The primary loop resistance determines the flooding rate, and so influences the reflood phenomena. (auth.)

A multivariate time series approach to modeling and forecasting demand in the emergency department.

Science.gov (United States)

Jones, Spencer S; Evans, R Scott; Allen, Todd L; Thomas, Alun; Haug, Peter J; Welch, Shari J; Snow, Gregory L

2009-02-01

The goals of this investigation were to study the temporal relationships between the demands for key resources in the emergency department (ED) and the inpatient hospital, and to develop multivariate forecasting models. Hourly data were collected from three diverse hospitals for the year 2006. Descriptive analysis and model fitting were carried out using graphical and multivariate time series methods. Multivariate models were compared to a univariate benchmark model in terms of their ability to provide out-of-sample forecasts of ED census and the demands for diagnostic resources. Descriptive analyses revealed little temporal interaction between the demand for inpatient resources and the demand for ED resources at the facilities considered. Multivariate models provided more accurate forecasts of ED census and of the demands for diagnostic resources. Our results suggest that multivariate time series models can be used to reliably forecast ED patient census; however, forecasts of the demands for diagnostic resources were not sufficiently reliable to be useful in the clinical setting.

Leveraging Resistance to Change and the Skunk Works Model of Innovation

DEFF Research Database (Denmark)

Fosfuri, Andrea; Rønde, Thomas

We study a situation in which an R&D department promotes the introduction of an innovation, which results in costly re-adjustments for production workers. In response, the production department tries to resist change by improving the existing technology. We show that firms balancing the strengths...... of the two departments perform better. This principle is employed to derive several implications concerning the hiring of talents, monetary incentives, and technology investment policies. As a negative effect, resistance to change might distort the R&D department's effort away from radical innovations....... The firm can solve this problem by implementing the so-called "skunk works model" of innovation where the R&D department is isolated from the rest of the organization. Resistance to change, innovation, skunk works model, contest....

An ETP model (exclusion-tolerance-progression for multi drug resistance

Directory of Open Access Journals (Sweden)

Kannan Subburaj

2005-04-01

Full Text Available Abstract Background It is known that sensitivity or resistance of tumor cells to a given chemotherapeutic agent is an acquired characteristic(s, depending on the heterogeneity of the tumor mass subjected to the treatment. The clinical success of a chemotherapeutic regimen depends on the ratio of sensitive to resistant cell populations. Results Based on findings from clinical and experimental studies, a unifying model is proposed to delineate the potential mechanism by which tumor cells progress towards multi drug resistance, resulting in failure of chemotherapy. Conclusion It is suggested that the evolution of multi drug resistance is a developmentally orchestrated event. Identifying stage-specific time windows during this process would help to identify valid therapeutic targets for the effective elimination of malignancy.

The establishment of insulin resistance model in FL83B and L6 cell

Science.gov (United States)

Liu, Lanlan; Han, Jizhong; Li, Haoran; Liu, Mengmeng; Zeng, Bin

2017-10-01

The insulin resistance models of mouse liver epithelial and rat myoblasts cells were induced by three kinds of inducers: dexamethasone, high insulin and high glucose. The purpose is to select the optimal insulin resistance model, to provide a simple and reliable TR cell model for the study of the pathogenesis of TR and the improvement of TR drugs and functional foods. The MTT method is used for toxicity screening of three compounds, selecting security and suitable concentration. We performed a Glucose oxidase peroxidase (GOD-POD) method involving FL83B and L6 cell with dexamethasone, high insulin and high glucose-induced insulin resistance. Results suggested that FL83B cells with dexamethasone-induced (0.25uM) were established insulin resistance and L6 cells with high-glucose (30mM) and dexamethasone-induced (0.25uM) were established insulin resistance.

Radiation Resistance and Life Time Estimates at Cryogenic Temperatures of Series Produced By-Pass Diodes for the LHC Magnet Protection

Science.gov (United States)

Denz, R.; Gharib, A.; Hagedorn, D.

2004-06-01

For the protection of the LHC superconducting magnets about 2100 specially developed by-pass diodes have been manufactured in industry and more than one thousand of these diodes have been mounted into stacks and tested in liquid helium. By-pass diode samples, taken from the series production, have been submitted to irradiation tests at cryogenic temperatures together with some prototype diodes up to an accumulated dose of about 2 kGy and neutron fluences up to about 3.0 1013 n cm-2 with and without intermediate warm up to 300 K. The device characteristics of the diodes under forward bias and reverse bias have been measured at 77 K and ambient versus dose and the results are presented. Using a thermo-electrical model and new estimates for the expected dose in the LHC, the expected lifetime of the by-pass diodes has been estimated for various positions in the LHC arcs. It turns out that for all of the by-pass diodes across the arc elements the radiation resistance is largely sufficient. In the dispersion suppresser regions of the LHC, on a few diodes annual annealing during the shut down of the LHC must be applied or those diodes may need to be replaced after some time.

Characteristics of the LeRC/Hughes J-series 30-cm engineering model thruster

Science.gov (United States)

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.

Image reconstruction method for electrical capacitance tomography based on the combined series and parallel normalization model

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

Modeling physiological resistance in bacterial biofilms.

Science.gov (United States)

Cogan, N G; Cortez, Ricardo; Fauci, Lisa

2005-07-01

A mathematical model of the action of antimicrobial agents on bacterial biofilms is presented. The model includes the fluid dynamics in and around the biofilm, advective and diffusive transport of two chemical constituents and the mechanism of physiological resistance. Although the mathematical model applies in three dimensions, we present two-dimensional simulations for arbitrary biofilm domains and various dosing strategies. The model allows the prediction of the spatial evolution of bacterial population and chemical constituents as well as different dosing strategies based on the fluid motion. We find that the interaction between the nutrient and the antimicrobial agent can reproduce survival curves which are comparable to other model predictions as well as experimental results. The model predicts that exposing the biofilm to low concentration doses of antimicrobial agent for longer time is more effective than short time dosing with high antimicrobial agent concentration. The effects of flow reversal and the roughness of the fluid/biofilm are also investigated. We find that reversing the flow increases the effectiveness of dosing. In addition, we show that overall survival decreases with increasing surface roughness.

Series expansions without diagrams

International Nuclear Information System (INIS)

Bhanot, G.; Creutz, M.; Horvath, I.; Lacki, J.; Weckel, J.

1994-01-01

We discuss the use of recursive enumeration schemes to obtain low- and high-temperature series expansions for discrete statistical systems. Using linear combinations of generalized helical lattices, the method is competitive with diagrammatic approaches and is easily generalizable. We illustrate the approach using Ising and Potts models. We present low-temperature series results in up to five dimensions and high-temperature series in three dimensions. The method is general and can be applied to any discrete model

Regression and regression analysis time series prediction modeling on climate data of quetta, pakistan

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)

75 FR 28480 - Airworthiness Directives; Airbus Model A300 Series Airplanes; Model A300 B4-600, B4-600R, F4-600R...

Science.gov (United States)

2010-05-21

... Airworthiness Directives; Airbus Model A300 Series Airplanes; Model A300 B4-600, B4-600R, F4-600R Series..., B4-622, B4- 605R, B4-622R, F4-605R, F4-622R, and C4-605R Variant F airplanes; and Model A310-203...

Constructing the reduced dynamical models of interannual climate variability from spatial-distributed time series

Science.gov (United States)

Mukhin, Dmitry; Gavrilov, Andrey; Loskutov, Evgeny; Feigin, Alexander

2016-04-01

We suggest a method for empirical forecast of climate dynamics basing on the reconstruction of reduced dynamical models in a form of random dynamical systems [1,2] derived from observational time series. The construction of proper embedding - the set of variables determining the phase space the model works in - is no doubt the most important step in such a modeling, but this task is non-trivial due to huge dimension of time series of typical climatic fields. Actually, an appropriate expansion of observational time series is needed yielding the number of principal components considered as phase variables, which are to be efficient for the construction of low-dimensional evolution operator. We emphasize two main features the reduced models should have for capturing the main dynamical properties of the system: (i) taking into account time-lagged teleconnections in the atmosphere-ocean system and (ii) reflecting the nonlinear nature of these teleconnections. In accordance to these principles, in this report we present the methodology which includes the combination of a new way for the construction of an embedding by the spatio-temporal data expansion and nonlinear model construction on the basis of artificial neural networks. The methodology is aplied to NCEP/NCAR reanalysis data including fields of sea level pressure, geopotential height, and wind speed, covering Northern Hemisphere. Its efficiency for the interannual forecast of various climate phenomena including ENSO, PDO, NAO and strong blocking event condition over the mid latitudes, is demonstrated. Also, we investigate the ability of the models to reproduce and predict the evolution of qualitative features of the dynamics, such as spectral peaks, critical transitions and statistics of extremes. This research was supported by the Government of the Russian Federation (Agreement No. 14.Z50.31.0033 with the Institute of Applied Physics RAS) [1] Y. I. Molkov, E. M. Loskutov, D. N. Mukhin, and A. M. Feigin, "Random

Construction of the exact Fisher information matrix of Gaussian time series models by means of matrix differential rules

NARCIS (Netherlands)

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

Modeling Time Series Data for Supervised Learning

Science.gov (United States)

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…

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

Energy Technology Data Exchange (ETDEWEB)

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

1999-07-01

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

Statistical models and time series forecasting of sulfur dioxide: a case study Tehran.

Science.gov (United States)

Hassanzadeh, S; Hosseinibalam, F; Alizadeh, R

2009-08-01

This study performed a time-series analysis, frequency distribution and prediction of SO(2) levels for five stations (Pardisan, Vila, Azadi, Gholhak and Bahman) in Tehran for the period of 2000-2005. Most sites show a quite similar characteristic with highest pollution in autumn-winter time and least pollution in spring-summer. The frequency distributions show higher peaks at two residential sites. The potential for SO(2) problems is high because of high emissions and the close geographical proximity of the major industrial and urban centers. The ACF and PACF are nonzero for several lags, indicating a mixed (ARMA) model, then at Bahman station an ARMA model was used for forecasting SO(2). The partial autocorrelations become close to 0 after about 5 lags while the autocorrelations remain strong through all the lags shown. The results proved that ARMA (2,2) model can provides reliable, satisfactory predictions for time series.

Extracting Knowledge From Time Series An Introduction to Nonlinear Empirical Modeling

CERN Document Server

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.

Global Current Circuit Structure in a Resistive Pulsar Magnetosphere Model

Science.gov (United States)

Kato, Yugo. E.

2017-12-01

Pulsar magnetospheres have strong magnetic fields and large amounts of plasma. The structures of these magnetospheres are studied using force-free electrodynamics. To understand pulsar magnetospheres, discussions must include their outer region. However, force-free electrodynamics is limited in it does not handle dissipation. Therefore, a resistive pulsar magnetic field model is needed. To break the ideal magnetohydrodynamic (MHD) condition E\\cdot B=0, Ohm’s law is used. This work introduces resistivity depending upon the distance from the star and obtain a self-consistent steady state by time integration. Poloidal current circuits form in the magnetosphere while the toroidal magnetic field region expands beyond the light cylinder and the Poynting flux radiation appears. High electric resistivity causes a large space scale poloidal current circuit and the magnetosphere radiates a larger Poynting flux than the linear increase outside of the light cylinder radius. The formed poloidal-current circuit has width, which grows with the electric conductivity. This result contributes to a more concrete dissipative pulsar magnetosphere model.

Simple and accurate model for voltage-dependent resistance of metallic carbon nanotube interconnects: An ab initio study

International Nuclear Information System (INIS)

Yamacli, Serhan; Avci, Mutlu

2009-01-01

In this work, development of a voltage dependent resistance model for metallic carbon nanotubes is aimed. Firstly, the resistance of metallic carbon nanotube interconnects are obtained from ab initio simulations and then the voltage dependence of the resistance is modeled through regression. Self-consistent non-equilibrium Green's function formalism combined with density functional theory is used for calculating the voltage dependent resistance of metallic carbon nanotubes. It is shown that voltage dependent resistances of carbon nanotubes can be accurately modeled as a polynomial function which enables rapid integration of carbon nanotube interconnect models into electronic design automation tools.

Modeling the electrical resistance of gold film conductors on uniaxially stretched elastomeric substrates

Science.gov (United States)

Cao, Wenzhe; Görrn, Patrick; Wagner, Sigurd

2011-05-01

The electrical resistance of gold film conductors on polydimethyl siloxane substrates at stages of uniaxial stretching is measured and modeled. The surface area of a gold conductor is assumed constant during stretching so that the exposed substrate takes up all strain. Sheet resistances are calculated from frames of scanning electron micrographs by numerically solving for the electrical potentials of all pixels in a frame. These sheet resistances agree sufficiently well with values measured on the same conductors to give credence to the model of a stretchable network of gold links defined by microcracks.

Modeling the impact of forecast-based regime switches on macroeconomic time series

NARCIS (Netherlands)

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

Clustering of financial time series

Science.gov (United States)

D'Urso, Pierpaolo; Cappelli, Carmela; Di Lallo, Dario; Massari, Riccardo

2013-05-01

This paper addresses the topic of classifying financial time series in a fuzzy framework proposing two fuzzy clustering models both based on GARCH models. In general clustering of financial time series, due to their peculiar features, needs the definition of suitable distance measures. At this aim, the first fuzzy clustering model exploits the autoregressive representation of GARCH models and employs, in the framework of a partitioning around medoids algorithm, the classical autoregressive metric. The second fuzzy clustering model, also based on partitioning around medoids algorithm, uses the Caiado distance, a Mahalanobis-like distance, based on estimated GARCH parameters and covariances that takes into account the information about the volatility structure of time series. In order to illustrate the merits of the proposed fuzzy approaches an application to the problem of classifying 29 time series of Euro exchange rates against international currencies is presented and discussed, also comparing the fuzzy models with their crisp version.

Prediction of traffic-related nitrogen oxides concentrations using Structural Time-Series models

Science.gov (United States)

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.

The rising impact of mathematical modelling in epidemiology: antibiotic resistance research as a case study

Science.gov (United States)

TEMIME, L.; HEJBLUM, G.; SETBON, M.; VALLERON, A. J.

2008-01-01

SUMMARY Mathematical modelling of infectious diseases has gradually become part of public health decision-making in recent years. However, the developing status of modelling in epidemiology and its relationship with other relevant scientific approaches have never been assessed quantitatively. Herein, using antibiotic resistance as a case study, 60 published models were analysed. Their interactions with other scientific fields are reported and their citation impact evaluated, as well as temporal trends. The yearly number of antibiotic resistance modelling publications increased significantly between 1990 and 2006. This rise cannot be explained by the surge of interest in resistance phenomena alone. Moreover, modelling articles are, on average, among the most frequently cited third of articles from the journal in which they were published. The results of this analysis, which might be applicable to other emerging public health problems, demonstrate the growing interest in mathematical modelling approaches to evaluate antibiotic resistance. PMID:17767792

Trend Estimation and Regression Analysis in Climatological Time Series: An Application of Structural Time Series Models and the Kalman Filter.

Science.gov (United States)

Visser, H.; Molenaar, J.

1995-05-01

The detection of trends in climatological data has become central to the discussion on climate change due to the enhanced greenhouse effect. To prove detection, a method is needed (i) to make inferences on significant rises or declines in trends, (ii) to take into account natural variability in climate series, and (iii) to compare output from GCMs with the trends in observed climate data. To meet these requirements, flexible mathematical tools are needed. A structural time series model is proposed with which a stochastic trend, a deterministic trend, and regression coefficients can be estimated simultaneously. The stochastic trend component is described using the class of ARIMA models. The regression component is assumed to be linear. However, the regression coefficients corresponding with the explanatory variables may be time dependent to validate this assumption. The mathematical technique used to estimate this trend-regression model is the Kaiman filter. The main features of the filter are discussed.Examples of trend estimation are given using annual mean temperatures at a single station in the Netherlands (1706-1990) and annual mean temperatures at Northern Hemisphere land stations (1851-1990). The inclusion of explanatory variables is shown by regressing the latter temperature series on four variables: Southern Oscillation index (SOI), volcanic dust index (VDI), sunspot numbers (SSN), and a simulated temperature signal, induced by increasing greenhouse gases (GHG). In all analyses, the influence of SSN on global temperatures is found to be negligible. The correlations between temperatures and SOI and VDI appear to be negative. For SOI, this correlation is significant, but for VDI it is not, probably because of a lack of volcanic eruptions during the sample period. The relation between temperatures and GHG is positive, which is in agreement with the hypothesis of a warming climate because of increasing levels of greenhouse gases. The prediction performance of

Creation and evaluation of a database of renewable production time series and other data for energy system modelling

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.

On the interface trap density and series resistance of tin oxide film prepared on n-type Si (1 1 1) substrate: Frequency dependent effects before and after 60Co γ-ray irradiation

International Nuclear Information System (INIS)

Karadeniz, S.; Selcuk, A. Birkan; Tugluoglu, N.; Ocak, S. Bilge

2007-01-01

We report the first investigation of the frequency dependent effects of gamma irradiation on interface state density and series resistance determined from capacitance-voltage (C-V) and conductance-voltage (G-V) characteristics in SnO 2 /n-Si structures prepared by spray deposition method. The samples were irradiated using a 60 Co γ-ray source at 500 kGy at room temperature. The C-V and G-V measurements of the samples were performed in the voltage range -6 V to 2 V and at 10 kHz, 100 kHz, 500 kHz and 1 MHz at room temperature before and after 500 kGy irradiation. The measurement capacitance and conductance are corrected for series resistance. It has been seen that the value of the series resistance R s of sample decreases from 204 Ω to 55.4 Ω with increasing the frequency before irradiation while it decreases from 248 Ω to 60 Ω with increasing frequency at 500 kGy irradiation. It has been found that and D it values of MOS structure increases up to 100 kHz and then decreases up to 1 MHz while the R s increases with increasing irradiation dose for our sample. The interface state density D it ranges from 1.83 x 10 13 cm -2 eV -1 for before irradiation to 1.54 x 10 13 cm -2 eV -1 for 500 kGy irradiation dose at 500 kHz and decreases with increasing frequency

Linear series of stellar models. Pt. 4. Helium-carbon stars of 3.5Msub(o) and 1Msub(o)

International Nuclear Information System (INIS)

Kozlowski, M.; Paczynski, B.; Popova, K.

1973-01-01

One linear series of models for a star of 3.5Msub(o) and two linear series of models for a star of 1Msub(o) are constructed. Models consist of helium rich envelopes (Y = 0.97, Z = 0.03) and pure carbon cores, and they have a rectangular helium profile, Y(Msub(r)). The linear series for a star of 3.5Msub(o) begins on the normal branch of the helium main sequence and terminates on the normal branch of the carbon main sequence. This series has eight turning points at which the core mass attains a local extremum. One of the two linear series for a star of 1Msub(o) begins on the normal branch of the helium main sequence, terminates on the high density branch of the helium main sequence, and has one turning point. The second linear series for a star of 1Msub(o) begins on the normal branch of the carbon main sequence, terminates on the high density branch of the carbon main sequence, and has three turning points. Two such linear series may have a common bifurcation point for a star of about 1.26Msub(o). (author)

Quantitative trait loci associated with anthracnose resistance in sorghum

Science.gov (United States)

With an aim to develop a durable resistance to the fungal disease anthracnose, two unique genetic sources of resistance were selected to create genetic mapping populations to identify regions of the sorghum genome that encode anthracnose resistance. A series of quantitative trait loci were identifi...

Predictions of fire behavior and resistance to control: for use with photo series for the ponderosa pine type, ponderosa pine and associated species type, and lodgepole pine type.

Science.gov (United States)

Franklin R. Ward; David V. Sandberg

1981-01-01

This publication presents tables on the behavior of fire and the resistance of fuels to control. The information is to be used with the publication, "Photo Series for Quantifying Forest Residues in the Ponderosa Pine Type, Ponderosa Pine and Associated Species Type, Lodgepole Pine Type" (Maxwell, Wayne G.; Ward, Franklin R. 1976. Gen. Tech. Rep. PNW-GTR-052....

Hot electron transport modelling in fast ignition relevant targets with non-Spitzer resistivity

Energy Technology Data Exchange (ETDEWEB)

Chapman, D A; Hoarty, D J; Swatton, D J R [Plasma Physics Department, AWE, Aldermaston, Reading, Berkshire, RG7 4PR (United Kingdom); Hughes, S J, E-mail: david.chapman@awe.co.u [Computational Physics Group, AWE, Aldermaston, Reading, Berkshire, RG7 4PR (United Kingdom)

2010-08-01

The simple Lee-More model for electrical resistivity is implemented in the hybrid fast electron transport code THOR. The model is shown to reproduce experimental data across a wide range of temperatures using a small number of parameters. The effect of this model on the heating of simple Al targets by a short-pulse laser is studied and compared to the predictions of the classical Spitzer-Haerm resistivity. The model is then used in simulations of hot electron transport experiments using buried layer targets.

Accelerating resistance breeding in wheat by integrating marker ...

African Journals Online (AJOL)

Genetic resistance is the simplest and most cost-effective way to guard against disease in plants. The pyramiding of resistance genes is a useful practice in bringing about durable resistance. This study aimed to develop a series of doubled haploid (DH) wheat lines containing combinations of wild species genes for rust ...

Cisplatin Resistant Spheroids Model Clinically Relevant Survival Mechanisms in Ovarian Tumors.

Directory of Open Access Journals (Sweden)

Winyoo Chowanadisai

Full Text Available The majority of ovarian tumors eventually recur in a drug resistant form. Using cisplatin sensitive and resistant cell lines assembled into 3D spheroids we profiled gene expression and identified candidate mechanisms and biological pathways associated with cisplatin resistance. OVCAR-8 human ovarian carcinoma cells were exposed to sub-lethal concentrations of cisplatin to create a matched cisplatin-resistant cell line, OVCAR-8R. Genome-wide gene expression profiling of sensitive and resistant ovarian cancer spheroids identified 3,331 significantly differentially expressed probesets coding for 3,139 distinct protein-coding genes (Fc >2, FDR < 0.05 (S2 Table. Despite significant expression changes in some transporters including MDR1, cisplatin resistance was not associated with differences in intracellular cisplatin concentration. Cisplatin resistant cells were significantly enriched for a mesenchymal gene expression signature. OVCAR-8R resistance derived gene sets were significantly more biased to patients with shorter survival. From the most differentially expressed genes, we derived a 17-gene expression signature that identifies ovarian cancer patients with shorter overall survival in three independent datasets. We propose that the use of cisplatin resistant cell lines in 3D spheroid models is a viable approach to gain insight into resistance mechanisms relevant to ovarian tumors in patients. Our data support the emerging concept that ovarian cancers can acquire drug resistance through an epithelial-to-mesenchymal transition.

A novel model for Time-Series Data Clustering Based on piecewise SVD and BIRCH for Stock Data Analysis on Hadoop Platform

Directory of Open Access Journals (Sweden)

Ibgtc Bowala

2017-06-01

Full Text Available With the rapid growth of financial markets, analyzers are paying more attention on predictions. Stock data are time series data, with huge amounts. Feasible solution for handling the increasing amount of data is to use a cluster for parallel processing, and Hadoop parallel computing platform is a typical representative. There are various statistical models for forecasting time series data, but accurate clusters are a pre-requirement. Clustering analysis for time series data is one of the main methods for mining time series data for many other analysis processes. However, general clustering algorithms cannot perform clustering for time series data because series data has a special structure and a high dimensionality has highly co-related values due to high noise level. A novel model for time series clustering is presented using BIRCH, based on piecewise SVD, leading to a novel dimension reduction approach. Highly co-related features are handled using SVD with a novel approach for dimensionality reduction in order to keep co-related behavior optimal and then use BIRCH for clustering. The algorithm is a novel model that can handle massive time series data. Finally, this new model is successfully applied to real stock time series data of Yahoo finance with satisfactory results.

Extended causal modeling to assess Partial Directed Coherence in multiple time series with significant instantaneous interactions.

Science.gov (United States)

Faes, Luca; Nollo, Giandomenico

2010-11-01

The Partial Directed Coherence (PDC) and its generalized formulation (gPDC) are popular tools for investigating, in the frequency domain, the concept of Granger causality among multivariate (MV) time series. PDC and gPDC are formalized in terms of the coefficients of an MV autoregressive (MVAR) model which describes only the lagged effects among the time series and forsakes instantaneous effects. However, instantaneous effects are known to affect linear parametric modeling, and are likely to occur in experimental time series. In this study, we investigate the impact on the assessment of frequency domain causality of excluding instantaneous effects from the model underlying PDC evaluation. Moreover, we propose the utilization of an extended MVAR model including both instantaneous and lagged effects. This model is used to assess PDC either in accordance with the definition of Granger causality when considering only lagged effects (iPDC), or with an extended form of causality, when we consider both instantaneous and lagged effects (ePDC). The approach is first evaluated on three theoretical examples of MVAR processes, which show that the presence of instantaneous correlations may produce misleading profiles of PDC and gPDC, while ePDC and iPDC derived from the extended model provide here a correct interpretation of extended and lagged causality. It is then applied to representative examples of cardiorespiratory and EEG MV time series. They suggest that ePDC and iPDC are better interpretable than PDC and gPDC in terms of the known cardiovascular and neural physiologies.