On the Stationarity of Multiple Autoregressive Approximants: Theory and Algorithms
1976-08-01
a I (3.4) Hannan and Terrell (1972) consider problems of a similar nature. Efficient estimates A(1),... , A(p) , and i of A(1)... ,A(p) and...34Autoregressive model fitting for control, Ann . Inst. Statist. Math., 23, 163-180. Hannan, E. J. (1970), Multiple Time Series, New York, John Wiley...Hannan, E. J. and Terrell , R. D. (1972), "Time series regression with linear constraints, " International Economic Review, 13, 189-200. Masani, P
Dealing with Multiple Solutions in Structural Vector Autoregressive Models.
Beltz, Adriene M; Molenaar, Peter C M
2016-01-01
Structural vector autoregressive models (VARs) hold great potential for psychological science, particularly for time series data analysis. They capture the magnitude, direction of influence, and temporal (lagged and contemporaneous) nature of relations among variables. Unified structural equation modeling (uSEM) is an optimal structural VAR instantiation, according to large-scale simulation studies, and it is implemented within an SEM framework. However, little is known about the uniqueness of uSEM results. Thus, the goal of this study was to investigate whether multiple solutions result from uSEM analysis and, if so, to demonstrate ways to select an optimal solution. This was accomplished with two simulated data sets, an empirical data set concerning children's dyadic play, and modifications to the group iterative multiple model estimation (GIMME) program, which implements uSEMs with group- and individual-level relations in a data-driven manner. Results revealed multiple solutions when there were large contemporaneous relations among variables. Results also verified several ways to select the correct solution when the complete solution set was generated, such as the use of cross-validation, maximum standardized residuals, and information criteria. This work has immediate and direct implications for the analysis of time series data and for the inferences drawn from those data concerning human behavior.
DEFF Research Database (Denmark)
Fokianos, Konstantinos; Rahbek, Anders Christian; Tjøstheim, Dag
2009-01-01
In this article we consider geometric ergodicity and likelihood-based inference for linear and nonlinear Poisson autoregression. In the linear case, the conditional mean is linked linearly to its past values, as well as to the observed values of the Poisson process. This also applies...... to the conditional variance, making possible interpretation as an integer-valued generalized autoregressive conditional heteroscedasticity process. In a nonlinear conditional Poisson model, the conditional mean is a nonlinear function of its past values and past observations. As a particular example, we consider...... an exponential autoregressive Poisson model for time series. Under geometric ergodicity, the maximum likelihood estimators are shown to be asymptotically Gaussian in the linear model. In addition, we provide a consistent estimator of their asymptotic covariance matrix. Our approach to verifying geometric...
DEFF Research Database (Denmark)
Fokianos, Konstantinos; Rahbek, Anders Christian; Tjøstheim, Dag
This paper considers geometric ergodicity and likelihood based inference for linear and nonlinear Poisson autoregressions. In the linear case the conditional mean is linked linearly to its past values as well as the observed values of the Poisson process. This also applies to the conditional...... variance, implying an interpretation as an integer valued GARCH process. In a nonlinear conditional Poisson model, the conditional mean is a nonlinear function of its past values and a nonlinear function of past observations. As a particular example an exponential autoregressive Poisson model for time...
DEFF Research Database (Denmark)
Fokianos, Konstantinos; Rahbæk, Anders; Tjøstheim, Dag
This paper considers geometric ergodicity and likelihood based inference for linear and nonlinear Poisson autoregressions. In the linear case the conditional mean is linked linearly to its past values as well as the observed values of the Poisson process. This also applies to the conditional...... variance, making an interpretation as an integer valued GARCH process possible. In a nonlinear conditional Poisson model, the conditional mean is a nonlinear function of its past values and a nonlinear function of past observations. As a particular example an exponential autoregressive Poisson model...
Directory of Open Access Journals (Sweden)
Alev Dilek Aydin
2015-01-01
Full Text Available The ANN method has been applied by means of multilayered feedforward neural networks (MLFNs by using different macroeconomic variables such as the exchange rate of USD/TRY, gold prices, and the Borsa Istanbul (BIST 100 index based on monthly data over the period of January 2000 and September 2014 for Turkey. Vector autoregressive (VAR method has also been applied with the same variables for the same period of time. In this study, different from other studies conducted up to the present, ENCOG machine learning framework has been used along with JAVA programming language in order to constitute the ANN. The training of network has been done by resilient propagation method. The ex post and ex ante estimates obtained by the ANN method have been compared with the results obtained by the econometric forecasting method of VAR. Strikingly, our findings based on the ANN method reveal that there is a possibility of financial distress or a financial crisis in Turkey starting from October 2017. The results which were obtained with the method of VAR also support the results of ANN method. Additionally, our results indicate that the ANN approach has more superior prediction performance than the VAR method.
Aydin, Alev Dilek; Caliskan Cavdar, Seyma
2015-01-01
The ANN method has been applied by means of multilayered feedforward neural networks (MLFNs) by using different macroeconomic variables such as the exchange rate of USD/TRY, gold prices, and the Borsa Istanbul (BIST) 100 index based on monthly data over the period of January 2000 and September 2014 for Turkey. Vector autoregressive (VAR) method has also been applied with the same variables for the same period of time. In this study, different from other studies conducted up to the present, ENCOG machine learning framework has been used along with JAVA programming language in order to constitute the ANN. The training of network has been done by resilient propagation method. The ex post and ex ante estimates obtained by the ANN method have been compared with the results obtained by the econometric forecasting method of VAR. Strikingly, our findings based on the ANN method reveal that there is a possibility of financial distress or a financial crisis in Turkey starting from October 2017. The results which were obtained with the method of VAR also support the results of ANN method. Additionally, our results indicate that the ANN approach has more superior prediction performance than the VAR method.
Supporting Multiple Pointing Devices in Microsoft Windows
DEFF Research Database (Denmark)
Westergaard, Michael
2002-01-01
In this paper the implementation of a Microsoft Windows driver including APIs supporting multiple pointing devices is presented. Microsoft Windows does not natively support multiple pointing devices controlling independent cursors, and a number of solutions to this have been implemented by us and...... and others. Here we motivate and describe a general solution, and how user applications can use it by means of a framework. The device driver and the supporting APIs will be made available free of charge. Interested parties can contact the author for more information....
Evaluation of multiple emission point facilities
International Nuclear Information System (INIS)
Miltenberger, R.P.; Hull, A.P.; Strachan, S.; Tichler, J.
1988-01-01
In 1970, the New York State Department of Environmental Conservation (NYSDEC) assumed responsibility for the environmental aspect of the state's regulatory program for by-product, source, and special nuclear material. The major objective of this study was to provide consultation to NYSDEC and the US NRC to assist NYSDEC in determining if broad-based licensed facilities with multiple emission points were in compliance with NYCRR Part 380. Under this contract, BNL would evaluate a multiple emission point facility, identified by NYSDEC, as a case study. The review would be a nonbinding evaluation of the facility to determine likely dispersion characteristics, compliance with specified release limits, and implementation of the ALARA philosophy regarding effluent release practices. From the data collected, guidance as to areas of future investigation and the impact of new federal regulations were to be developed. Reported here is the case study for the University of Rochester, Strong Memorial Medical Center and Riverside Campus
Noncausal Bayesian Vector Autoregression
DEFF Research Database (Denmark)
Lanne, Markku; Luoto, Jani
We propose a Bayesian inferential procedure for the noncausal vector autoregressive (VAR) model that is capable of capturing nonlinearities and incorporating effects of missing variables. In particular, we devise a fast and reliable posterior simulator that yields the predictive distribution...
The Integration Order of Vector Autoregressive Processes
DEFF Research Database (Denmark)
Franchi, Massimo
We show that the order of integration of a vector autoregressive process is equal to the difference between the multiplicity of the unit root in the characteristic equation and the multiplicity of the unit root in the adjoint matrix polynomial. The equivalence with the standard I(1) and I(2...
Generalizing smooth transition autoregressions
DEFF Research Database (Denmark)
Chini, Emilio Zanetti
We introduce a variant of the smooth transition autoregression - the GSTAR model - capable to parametrize the asymmetry in the tails of the transition equation by using a particular generalization of the logistic function. A General-to-Specific modelling strategy is discussed in detail, with part......We introduce a variant of the smooth transition autoregression - the GSTAR model - capable to parametrize the asymmetry in the tails of the transition equation by using a particular generalization of the logistic function. A General-to-Specific modelling strategy is discussed in detail......, with particular emphasis on two different LM-type tests for the null of symmetric adjustment towards a new regime and three diagnostic tests, whose power properties are explored via Monte Carlo experiments. Four classical real datasets illustrate the empirical properties of the GSTAR, jointly to a rolling...
Eta Carinae: Viewed from Multiple Vantage Points
Gull, Theodore
2007-01-01
The central source of Eta Carinae and its ejecta is a massive binary system buried within a massive interacting wind structure which envelops the two stars. However the hot, less massive companion blows a small cavity in the very massive primary wind, plus ionizes a portion of the massive wind just beyond the wind-wind boundary. We gain insight on this complex structure by examining the spatially-resolved Space Telescope Imaging Spectrograph (STIS) spectra of the central source (0.1") with the wind structure which extends out to nearly an arcsecond (2300AU) and the wind-blown boundaries, plus the ejecta of the Little Homunculus. Moreover, the spatially resolved Very Large Telescope/UltraViolet Echelle Spectrograph (VLT/UVES) stellar spectrum (one arcsecond) and spatially sampled spectra across the foreground lobe of the Homunculus provide us vantage points from different angles relative to line of sight. Examples of wind line profiles of Fe II, and the.highly excited [Fe III], [Ne III], [Ar III] and [S III)], plus other lines will be presented.
Pemodelan Markov Switching Autoregressive
Ariyani, Fiqria Devi; Warsito, Budi; Yasin, Hasbi
2014-01-01
Transition from depreciation to appreciation of exchange rate is one of regime switching that ignored by classic time series model, such as ARIMA, ARCH, or GARCH. Therefore, economic variables are modeled by Markov Switching Autoregressive (MSAR) which consider the regime switching. MLE is not applicable to parameters estimation because regime is an unobservable variable. So that filtering and smoothing process are applied to see the regime probabilities of observation. Using this model, tran...
Tripled Fixed Point in Ordered Multiplicative Metric Spaces
Directory of Open Access Journals (Sweden)
Laishram Shanjit
2017-06-01
Full Text Available In this paper, we present some triple fixed point theorems in partially ordered multiplicative metric spaces depended on another function. Our results generalise the results of [6] and [5].
Multi-lane detection based on multiple vanishing points detection
Li, Chuanxiang; Nie, Yiming; Dai, Bin; Wu, Tao
2015-03-01
Lane detection plays a significant role in Advanced Driver Assistance Systems (ADAS) for intelligent vehicles. In this paper we present a multi-lane detection method based on multiple vanishing points detection. A new multi-lane model assumes that a single lane, which has two approximately parallel boundaries, may not parallel to others on road plane. Non-parallel lanes associate with different vanishing points. A biological plausibility model is used to detect multiple vanishing points and fit lane model. Experimental results show that the proposed method can detect both parallel lanes and non-parallel lanes.
Pilot points method for conditioning multiple-point statistical facies simulation on flow data
Ma, Wei; Jafarpour, Behnam
2018-05-01
We propose a new pilot points method for conditioning discrete multiple-point statistical (MPS) facies simulation on dynamic flow data. While conditioning MPS simulation on static hard data is straightforward, their calibration against nonlinear flow data is nontrivial. The proposed method generates conditional models from a conceptual model of geologic connectivity, known as a training image (TI), by strategically placing and estimating pilot points. To place pilot points, a score map is generated based on three sources of information: (i) the uncertainty in facies distribution, (ii) the model response sensitivity information, and (iii) the observed flow data. Once the pilot points are placed, the facies values at these points are inferred from production data and then are used, along with available hard data at well locations, to simulate a new set of conditional facies realizations. While facies estimation at the pilot points can be performed using different inversion algorithms, in this study the ensemble smoother (ES) is adopted to update permeability maps from production data, which are then used to statistically infer facies types at the pilot point locations. The developed method combines the information in the flow data and the TI by using the former to infer facies values at selected locations away from the wells and the latter to ensure consistent facies structure and connectivity where away from measurement locations. Several numerical experiments are used to evaluate the performance of the developed method and to discuss its important properties.
Mirrored pyramidal wells for simultaneous multiple vantage point microscopy.
Seale, K T; Reiserer, R S; Markov, D A; Ges, I A; Wright, C; Janetopoulos, C; Wikswo, J P
2008-10-01
We report a novel method for obtaining simultaneous images from multiple vantage points of a microscopic specimen using size-matched microscopic mirrors created from anisotropically etched silicon. The resulting pyramidal wells enable bright-field and fluorescent side-view images, and when combined with z-sectioning, provide additional information for 3D reconstructions of the specimen. We have demonstrated the 3D localization and tracking over time of the centrosome of a live Dictyostelium discoideum. The simultaneous acquisition of images from multiple perspectives also provides a five-fold increase in the theoretical collection efficiency of emitted photons, a property which may be useful for low-light imaging modalities such as bioluminescence, or low abundance surface-marker labelling.
Multiple contacts with diversion at the point of arrest.
Riordan, Sharon; Wix, Stuart; Haque, M Sayeed; Humphreys, Martin
2003-04-01
A diversion at the point of arrest (DAPA) scheme was set up in five police stations in South Birmingham in 1992. In a study of all referrals made over a four-year period a sub group of multiple contact individuals was identified. During that time four hundred and ninety-two contacts were recorded in total, of which 130 were made by 58 individuals. The latter group was generally no different from the single contact group but did have a tendency to be younger. This research highlights the need for a re-evaluation of service provision and associated education of police officers and relevant mental health care professionals.
Multiple point statistical simulation using uncertain (soft) conditional data
Hansen, Thomas Mejer; Vu, Le Thanh; Mosegaard, Klaus; Cordua, Knud Skou
2018-05-01
Geostatistical simulation methods have been used to quantify spatial variability of reservoir models since the 80s. In the last two decades, state of the art simulation methods have changed from being based on covariance-based 2-point statistics to multiple-point statistics (MPS), that allow simulation of more realistic Earth-structures. In addition, increasing amounts of geo-information (geophysical, geological, etc.) from multiple sources are being collected. This pose the problem of integration of these different sources of information, such that decisions related to reservoir models can be taken on an as informed base as possible. In principle, though difficult in practice, this can be achieved using computationally expensive Monte Carlo methods. Here we investigate the use of sequential simulation based MPS simulation methods conditional to uncertain (soft) data, as a computational efficient alternative. First, it is demonstrated that current implementations of sequential simulation based on MPS (e.g. SNESIM, ENESIM and Direct Sampling) do not account properly for uncertain conditional information, due to a combination of using only co-located information, and a random simulation path. Then, we suggest two approaches that better account for the available uncertain information. The first make use of a preferential simulation path, where more informed model parameters are visited preferentially to less informed ones. The second approach involves using non co-located uncertain information. For different types of available data, these approaches are demonstrated to produce simulation results similar to those obtained by the general Monte Carlo based approach. These methods allow MPS simulation to condition properly to uncertain (soft) data, and hence provides a computationally attractive approach for integration of information about a reservoir model.
Multiplicative point process as a model of trading activity
Gontis, V.; Kaulakys, B.
2004-11-01
Signals consisting of a sequence of pulses show that inherent origin of the 1/ f noise is a Brownian fluctuation of the average interevent time between subsequent pulses of the pulse sequence. In this paper, we generalize the model of interevent time to reproduce a variety of self-affine time series exhibiting power spectral density S( f) scaling as a power of the frequency f. Furthermore, we analyze the relation between the power-law correlations and the origin of the power-law probability distribution of the signal intensity. We introduce a stochastic multiplicative model for the time intervals between point events and analyze the statistical properties of the signal analytically and numerically. Such model system exhibits power-law spectral density S( f)∼1/ fβ for various values of β, including β= {1}/{2}, 1 and {3}/{2}. Explicit expressions for the power spectra in the low-frequency limit and for the distribution density of the interevent time are obtained. The counting statistics of the events is analyzed analytically and numerically, as well. The specific interest of our analysis is related with the financial markets, where long-range correlations of price fluctuations largely depend on the number of transactions. We analyze the spectral density and counting statistics of the number of transactions. The model reproduces spectral properties of the real markets and explains the mechanism of power-law distribution of trading activity. The study provides evidence that the statistical properties of the financial markets are enclosed in the statistics of the time interval between trades. A multiplicative point process serves as a consistent model generating this statistics.
Phillips, Alan; Fletcher, Chrissie; Atkinson, Gary; Channon, Eddie; Douiri, Abdel; Jaki, Thomas; Maca, Jeff; Morgan, David; Roger, James Henry; Terrill, Paul
2013-01-01
In May 2012, the Committee of Health and Medicinal Products issued a concept paper on the need to review the points to consider document on multiplicity issues in clinical trials. In preparation for the release of the updated guidance document, Statisticians in the Pharmaceutical Industry held a one-day expert group meeting in January 2013. Topics debated included multiplicity and the drug development process, the usefulness and limitations of newly developed strategies to deal with multiplicity, multiplicity issues arising from interim decisions and multiregional development, and the need for simultaneous confidence intervals (CIs) corresponding to multiple test procedures. A clear message from the meeting was that multiplicity adjustments need to be considered when the intention is to make a formal statement about efficacy or safety based on hypothesis tests. Statisticians have a key role when designing studies to assess what adjustment really means in the context of the research being conducted. More thought during the planning phase needs to be given to multiplicity adjustments for secondary endpoints given these are increasing in importance in differentiating products in the market place. No consensus was reached on the role of simultaneous CIs in the context of superiority trials. It was argued that unadjusted intervals should be employed as the primary purpose of the intervals is estimation, while the purpose of hypothesis testing is to formally establish an effect. The opposing view was that CIs should correspond to the test decision whenever possible. Copyright © 2013 John Wiley & Sons, Ltd.
Texture classification using autoregressive filtering
Lawton, W. M.; Lee, M.
1984-01-01
A general theory of image texture models is proposed and its applicability to the problem of scene segmentation using texture classification is discussed. An algorithm, based on half-plane autoregressive filtering, which optimally utilizes second order statistics to discriminate between texture classes represented by arbitrary wide sense stationary random fields is described. Empirical results of applying this algorithm to natural and sysnthesized scenes are presented and future research is outlined.
Autoregressive Moving Average Graph Filtering
Isufi, Elvin; Loukas, Andreas; Simonetto, Andrea; Leus, Geert
2016-01-01
One of the cornerstones of the field of signal processing on graphs are graph filters, direct analogues of classical filters, but intended for signals defined on graphs. This work brings forth new insights on the distributed graph filtering problem. We design a family of autoregressive moving average (ARMA) recursions, which (i) are able to approximate any desired graph frequency response, and (ii) give exact solutions for tasks such as graph signal denoising and interpolation. The design phi...
Tang, Yunwei; Atkinson, Peter M.; Zhang, Jingxiong
2015-03-01
A cross-scale data integration method was developed and tested based on the theory of geostatistics and multiple-point geostatistics (MPG). The goal was to downscale remotely sensed images while retaining spatial structure by integrating images at different spatial resolutions. During the process of downscaling, a rich spatial correlation model in the form of a training image was incorporated to facilitate reproduction of similar local patterns in the simulated images. Area-to-point cokriging (ATPCK) was used as locally varying mean (LVM) (i.e., soft data) to deal with the change of support problem (COSP) for cross-scale integration, which MPG cannot achieve alone. Several pairs of spectral bands of remotely sensed images were tested for integration within different cross-scale case studies. The experiment shows that MPG can restore the spatial structure of the image at a fine spatial resolution given the training image and conditioning data. The super-resolution image can be predicted using the proposed method, which cannot be realised using most data integration methods. The results show that ATPCK-MPG approach can achieve greater accuracy than methods which do not account for the change of support issue.
Pressure Points in Reading Comprehension: A Quantile Multiple Regression Analysis
Logan, Jessica
2017-01-01
The goal of this study was to examine how selected pressure points or areas of vulnerability are related to individual differences in reading comprehension and whether the importance of these pressure points varies as a function of the level of children's reading comprehension. A sample of 245 third-grade children were given an assessment battery…
La Rosa, Patricio S; Nehorai, Arye; Eswaran, Hari; Lowery, Curtis L; Preissl, Hubert
2008-02-01
We propose a single channel two-stage time-segment discriminator of uterine magnetomyogram (MMG) contractions during pregnancy. We assume that the preprocessed signals are piecewise stationary having distribution in a common family with a fixed number of parameters. Therefore, at the first stage, we propose a model-based segmentation procedure, which detects multiple change-points in the parameters of a piecewise constant time-varying autoregressive model using a robust formulation of the Schwarz information criterion (SIC) and a binary search approach. In particular, we propose a test statistic that depends on the SIC, derive its asymptotic distribution, and obtain closed-form optimal detection thresholds in the sense of the Neyman-Pearson criterion; therefore, we control the probability of false alarm and maximize the probability of change-point detection in each stage of the binary search algorithm. We compute and evaluate the relative energy variation [root mean squares (RMS)] and the dominant frequency component [first order zero crossing (FOZC)] in discriminating between time segments with and without contractions. The former consistently detects a time segment with contractions. Thus, at the second stage, we apply a nonsupervised K-means cluster algorithm to classify the detected time segments using the RMS values. We apply our detection algorithm to real MMG records obtained from ten patients admitted to the hospital for contractions with gestational ages between 31 and 40 weeks. We evaluate the performance of our detection algorithm in computing the detection and false alarm rate, respectively, using as a reference the patients' feedback. We also analyze the fusion of the decision signals from all the sensors as in the parallel distributed detection approach.
Behavioural Pattern of Causality Parameter of Autoregressive ...
African Journals Online (AJOL)
In this paper, a causal form of Autoregressive Moving Average process, ARMA (p, q) of various orders and behaviour of the causality parameter of ARMA model is investigated. It is deduced that the behaviour of causality parameter ψi depends on positive and negative values of autoregressive parameter φ and moving ...
Optimal transformations for categorical autoregressive time series
Buuren, S. van
1996-01-01
This paper describes a method for finding optimal transformations for analyzing time series by autoregressive models. 'Optimal' implies that the agreement between the autoregressive model and the transformed data is maximal. Such transformations help 1) to increase the model fit, and 2) to analyze
Fan, Yuting; Aighobahi, Anthony E; Gomes, Nathan J; Xu, Kun; Li, Jianqiang
2015-03-23
In this paper, we experimentally investigate the throughput of IEEE 802.11n 2x2 multiple-input-multiple-output (MIMO) signals in a radio-over-fiber-based distributed antenna system (DAS) with different fiber lengths and power imbalance. Both a MIMO-supported access point (AP) and a spatial-diversity-supported AP were separately employed in the experiments. Throughput measurements were carried out with wireless users at different locations in a typical office environment. For the different fiber length effect, the results indicate that MIMO signals can maintain high throughput when the fiber length difference between the two remote antenna units (RAUs) is under 100 m and falls quickly when the length difference is greater. For the spatial diversity signals, high throughput can be maintained even when the difference is 150 m. On the other hand, the separation of the MIMO antennas allows additional freedom in placing the antennas in strategic locations for overall improved system performance, although it may also lead to received power imbalance problems. The results show that the throughput performance drops in specific positions when the received power imbalance is above around 13 dB. Hence, there is a trade-off between the extent of the wireless coverage for moderate bit-rates and the area over which peak bit-rates can be achieved.
A MOSUM procedure for the estimation of multiple random change points
Eichinger, Birte; Kirch, Claudia
2018-01-01
In this work, we investigate statistical properties of change point estimators based on moving sum statistics. We extend results for testing in a classical situation with multiple deterministic change points by allowing for random exogenous change points that arise in Hidden Markov or regime switching models among others. To this end, we consider a multiple mean change model with possible time series errors and prove that the number and location of change points are estimated consistently by ...
Multiple Monte Carlo Testing with Applications in Spatial Point Processes
DEFF Research Database (Denmark)
Mrkvička, Tomáš; Myllymäki, Mari; Hahn, Ute
with a function as the test statistic, 3) several Monte Carlo tests with functions as test statistics. The rank test has correct (global) type I error in each case and it is accompanied with a p-value and with a graphical interpretation which shows which subtest or which distances of the used test function......(s) lead to the rejection at the prescribed significance level of the test. Examples of null hypothesis from point process and random set statistics are used to demonstrate the strength of the rank envelope test. The examples include goodness-of-fit test with several test functions, goodness-of-fit test...
Effective communication at the point of multiple sclerosis diagnosis.
Solari, Alessandra
2014-04-01
As a consequence of the current shortened diagnostic workup, people with multiple sclerosis (PwMS) are rapidly confronted with a disease of uncertain prognosis that requires complex treatment decisions. This paper reviews studies that have assessed the experiences of PwMS in the peri-diagnostic period and have evaluated the efficacy of interventions providing information at this critical moment. The studies found that the emotional burden on PwMS at diagnosis was high, and emphasised the need for careful monitoring and management of mood symptoms (chiefly anxiety). Information provision did not affect anxiety symptoms but improved patients' knowledge of their condition, the achievement of 'informed choice', and satisfaction with the diagnosis communication. It is vital to develop and implement information and decision aids for PwMS, but this is resource intensive, and international collaboration may be a way forward. The use of patient self-assessed outcome measures that appraise the quality of diagnosis communication is also important to allow health services to understand and meet the needs and preferences of PwMS.
THE ALLOMETRIC-AUTOREGRESSIVE MODEL IN GENETIC ...
African Journals Online (AJOL)
The application of an allometric-autoregressive model for the quantification of growth and efficiency of feed utilization for purposes of selection for ... be of value in genetic studies. ... mass) gives a fair indication of the cumulative preweaning.
Very-short-term wind power probabilistic forecasts by sparse vector autoregression
DEFF Research Database (Denmark)
Dowell, Jethro; Pinson, Pierre
2016-01-01
A spatio-temporal method for producing very-shortterm parametric probabilistic wind power forecasts at a large number of locations is presented. Smart grids containing tens, or hundreds, of wind generators require skilled very-short-term forecasts to operate effectively, and spatial information...... is highly desirable. In addition, probabilistic forecasts are widely regarded as necessary for optimal power system management as they quantify the uncertainty associated with point forecasts. Here we work within a parametric framework based on the logit-normal distribution and forecast its parameters....... The location parameter for multiple wind farms is modelled as a vector-valued spatiotemporal process, and the scale parameter is tracked by modified exponential smoothing. A state-of-the-art technique for fitting sparse vector autoregressive models is employed to model the location parameter and demonstrates...
Error analysis of dimensionless scaling experiments with multiple points using linear regression
International Nuclear Information System (INIS)
Guercan, Oe.D.; Vermare, L.; Hennequin, P.; Bourdelle, C.
2010-01-01
A general method of error estimation in the case of multiple point dimensionless scaling experiments, using linear regression and standard error propagation, is proposed. The method reduces to the previous result of Cordey (2009 Nucl. Fusion 49 052001) in the case of a two-point scan. On the other hand, if the points follow a linear trend, it explains how the estimated error decreases as more points are added to the scan. Based on the analytical expression that is derived, it is argued that for a low number of points, adding points to the ends of the scanned range, rather than the middle, results in a smaller error estimate. (letter)
History Matching Through a Smooth Formulation of Multiple-Point Statistics
DEFF Research Database (Denmark)
Melnikova, Yulia; Zunino, Andrea; Lange, Katrine
2014-01-01
and the mismatch with multiple-point statistics. As a result, in the framework of the Bayesian approach, such a solution belongs to a high posterior region. The methodology, while applicable to any inverse problem with a training-image-based prior, is especially beneficial for problems which require expensive......We propose a smooth formulation of multiple-point statistics that enables us to solve inverse problems using gradient-based optimization techniques. We introduce a differentiable function that quantifies the mismatch between multiple-point statistics of a training image and of a given model. We...... show that, by minimizing this function, any continuous image can be gradually transformed into an image that honors the multiple-point statistics of the discrete training image. The solution to an inverse problem is then found by minimizing the sum of two mismatches: the mismatch with data...
Estimation of Multiple Point Sources for Linear Fractional Order Systems Using Modulating Functions
Belkhatir, Zehor; Laleg-Kirati, Taous-Meriem
2017-01-01
This paper proposes an estimation algorithm for the characterization of multiple point inputs for linear fractional order systems. First, using polynomial modulating functions method and a suitable change of variables the problem of estimating
Common Fixed Points of Generalized Rational Type Cocyclic Mappings in Multiplicative Metric Spaces
Directory of Open Access Journals (Sweden)
Mujahid Abbas
2015-01-01
Full Text Available The aim of this paper is to present fixed point result of mappings satisfying a generalized rational contractive condition in the setup of multiplicative metric spaces. As an application, we obtain a common fixed point of a pair of weakly compatible mappings. Some common fixed point results of pair of rational contractive types mappings involved in cocyclic representation of a nonempty subset of a multiplicative metric space are also obtained. Some examples are presented to support the results proved herein. Our results generalize and extend various results in the existing literature.
Mixed Causal-Noncausal Autoregressions with Strictly Exogenous Regressors
Hecq, Alain; Issler, J.V.; Telg, Sean
2017-01-01
The mixed autoregressive causal-noncausal model (MAR) has been proposed to estimate economic relationships involving explosive roots in their autoregressive part, as they have stationary forward solutions. In previous work, possible exogenous variables in economic relationships are substituted into
Optimal Hedging with the Vector Autoregressive Model
L. Gatarek (Lukasz); S.G. Johansen (Soren)
2014-01-01
markdownabstract__Abstract__ We derive the optimal hedging ratios for a portfolio of assets driven by a Cointegrated Vector Autoregressive model with general cointegration rank. Our hedge is optimal in the sense of minimum variance portfolio. We consider a model that allows for the hedges to be
Interval Forecast for Smooth Transition Autoregressive Model ...
African Journals Online (AJOL)
In this paper, we propose a simple method for constructing interval forecast for smooth transition autoregressive (STAR) model. This interval forecast is based on bootstrapping the residual error of the estimated STAR model for each forecast horizon and computing various Akaike information criterion (AIC) function. This new ...
New interval forecast for stationary autoregressive models ...
African Journals Online (AJOL)
In this paper, we proposed a new forecasting interval for stationary Autoregressive, AR(p) models using the Akaike information criterion (AIC) function. Ordinarily, the AIC function is used to determine the order of an AR(p) process. In this study however, AIC forecast interval compared favorably with the theoretical forecast ...
Forecasting nuclear power supply with Bayesian autoregression
International Nuclear Information System (INIS)
Beck, R.; Solow, J.L.
1994-01-01
We explore the possibility of forecasting the quarterly US generation of electricity from nuclear power using a Bayesian autoregression model. In terms of forecasting accuracy, this approach compares favorably with both the Department of Energy's current forecasting methodology and their more recent efforts using ARIMA models, and it is extremely easy and inexpensive to implement. (author)
Oracle Inequalities for High Dimensional Vector Autoregressions
DEFF Research Database (Denmark)
Callot, Laurent; Kock, Anders Bredahl
This paper establishes non-asymptotic oracle inequalities for the prediction error and estimation accuracy of the LASSO in stationary vector autoregressive models. These inequalities are used to establish consistency of the LASSO even when the number of parameters is of a much larger order...
Forecasting with periodic autoregressive time series models
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
Directory of Open Access Journals (Sweden)
Md Selim Hossain
Full Text Available In this paper, we propose a novel parallel architecture for fast hardware implementation of elliptic curve point multiplication (ECPM, which is the key operation of an elliptic curve cryptography processor. The point multiplication over binary fields is synthesized on both FPGA and ASIC technology by designing fast elliptic curve group operations in Jacobian projective coordinates. A novel combined point doubling and point addition (PDPA architecture is proposed for group operations to achieve high speed and low hardware requirements for ECPM. It has been implemented over the binary field which is recommended by the National Institute of Standards and Technology (NIST. The proposed ECPM supports two Koblitz and random curves for the key sizes 233 and 163 bits. For group operations, a finite-field arithmetic operation, e.g. multiplication, is designed on a polynomial basis. The delay of a 233-bit point multiplication is only 3.05 and 3.56 μs, in a Xilinx Virtex-7 FPGA, for Koblitz and random curves, respectively, and 0.81 μs in an ASIC 65-nm technology, which are the fastest hardware implementation results reported in the literature to date. In addition, a 163-bit point multiplication is also implemented in FPGA and ASIC for fair comparison which takes around 0.33 and 0.46 μs, respectively. The area-time product of the proposed point multiplication is very low compared to similar designs. The performance ([Formula: see text] and Area × Time × Energy (ATE product of the proposed design are far better than the most significant studies found in the literature.
Improving the Pattern Reproducibility of Multiple-Point-Based Prior Models Using Frequency Matching
DEFF Research Database (Denmark)
Cordua, Knud Skou; Hansen, Thomas Mejer; Mosegaard, Klaus
2014-01-01
Some multiple-point-based sampling algorithms, such as the snesim algorithm, rely on sequential simulation. The conditional probability distributions that are used for the simulation are based on statistics of multiple-point data events obtained from a training image. During the simulation, data...... events with zero probability in the training image statistics may occur. This is handled by pruning the set of conditioning data until an event with non-zero probability is found. The resulting probability distribution sampled by such algorithms is a pruned mixture model. The pruning strategy leads...... to a probability distribution that lacks some of the information provided by the multiple-point statistics from the training image, which reduces the reproducibility of the training image patterns in the outcome realizations. When pruned mixture models are used as prior models for inverse problems, local re...
Small Sample Properties of Bayesian Multivariate Autoregressive Time Series Models
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…
A feature point identification method for positron emission particle tracking with multiple tracers
Energy Technology Data Exchange (ETDEWEB)
Wiggins, Cody, E-mail: cwiggin2@vols.utk.edu [University of Tennessee-Knoxville, Department of Physics and Astronomy, 1408 Circle Drive, Knoxville, TN 37996 (United States); Santos, Roque [University of Tennessee-Knoxville, Department of Nuclear Engineering (United States); Escuela Politécnica Nacional, Departamento de Ciencias Nucleares (Ecuador); Ruggles, Arthur [University of Tennessee-Knoxville, Department of Nuclear Engineering (United States)
2017-01-21
A novel detection algorithm for Positron Emission Particle Tracking (PEPT) with multiple tracers based on optical feature point identification (FPI) methods is presented. This new method, the FPI method, is compared to a previous multiple PEPT method via analyses of experimental and simulated data. The FPI method outperforms the older method in cases of large particle numbers and fine time resolution. Simulated data show the FPI method to be capable of identifying 100 particles at 0.5 mm average spatial error. Detection error is seen to vary with the inverse square root of the number of lines of response (LORs) used for detection and increases as particle separation decreases. - Highlights: • A new approach to positron emission particle tracking is presented. • Using optical feature point identification analogs, multiple particle tracking is achieved. • Method is compared to previous multiple particle method. • Accuracy and applicability of method is explored.
Reduction of bias in neutron multiplicity assay using a weighted point model
Energy Technology Data Exchange (ETDEWEB)
Geist, W. H. (William H.); Krick, M. S. (Merlyn S.); Mayo, D. R. (Douglas R.)
2004-01-01
Accurate assay of most common plutonium samples was the development goal for the nondestructive assay technique of neutron multiplicity counting. Over the past 20 years the technique has been proven for relatively pure oxides and small metal items. Unfortunately, the technique results in large biases when assaying large metal items. Limiting assumptions, such as unifoh multiplication, in the point model used to derive the multiplicity equations causes these biases for large dense items. A weighted point model has been developed to overcome some of the limitations in the standard point model. Weighting factors are detemiined from Monte Carlo calculations using the MCNPX code. Monte Carlo calculations give the dependence of the weighting factors on sample mass and geometry, and simulated assays using Monte Carlo give the theoretical accuracy of the weighted-point-model assay. Measured multiplicity data evaluated with both the standard and weighted point models are compared to reference values to give the experimental accuracy of the assay. Initial results show significant promise for the weighted point model in reducing or eliminating biases in the neutron multiplicity assay of metal items. The negative biases observed in the assay of plutonium metal samples are caused by variations in the neutron multiplication for neutrons originating in various locations in the sample. The bias depends on the mass and shape of the sample and depends on the amount and energy distribution of the ({alpha},n) neutrons in the sample. When the standard point model is used, this variable-multiplication bias overestimates the multiplication and alpha values of the sample, and underestimates the plutonium mass. The weighted point model potentially can provide assay accuracy of {approx}2% (1 {sigma}) for cylindrical plutonium metal samples < 4 kg with {alpha} < 1 without knowing the exact shape of the samples, provided that the ({alpha},n) source is uniformly distributed throughout the
Directory of Open Access Journals (Sweden)
Zhenxiang Jiang
2016-01-01
Full Text Available The traditional methods of diagnosing dam service status are always suitable for single measuring point. These methods also reflect the local status of dams without merging multisource data effectively, which is not suitable for diagnosing overall service. This study proposes a new method involving multiple points to diagnose dam service status based on joint distribution function. The function, including monitoring data of multiple points, can be established with t-copula function. Therefore, the possibility, which is an important fusing value in different measuring combinations, can be calculated, and the corresponding diagnosing criterion is established with typical small probability theory. Engineering case study indicates that the fusion diagnosis method can be conducted in real time and the abnormal point can be detected, thereby providing a new early warning method for engineering safety.
Testing for Co-integration in Vector Autoregressions with Non-Stationary Volatility
DEFF Research Database (Denmark)
Cavaliere, Guiseppe; Rahbæk, Anders; Taylor, A.M. Robert
Many key macro-economic and financial variables are characterised by permanent changes in unconditional volatility. In this paper we analyse vector autoregressions with non-stationary (unconditional) volatility of a very general form, which includes single and multiple volatility breaks as special...
Testing for Co-integration in Vector Autoregressions with Non-Stationary Volatility
DEFF Research Database (Denmark)
Cavaliere, Giuseppe; Rahbek, Anders Christian; Taylor, A. M. Robert
Many key macro-economic and …nancial variables are characterised by permanent changes in unconditional volatility. In this paper we analyse vector autoregressions with non-stationary (unconditional) volatility of a very general form, which includes single and multiple volatility breaks as special...
Determination of shell correction energies at saddle point using pre-scission neutron multiplicities
International Nuclear Information System (INIS)
Golda, K.S.; Saxena, A.; Mittal, V.K.; Mahata, K.; Sugathan, P.; Jhingan, A.; Singh, V.; Sandal, R.; Goyal, S.; Gehlot, J.; Dhal, A.; Behera, B.R.; Bhowmik, R.K.; Kailas, S.
2013-01-01
Pre-scission neutron multiplicities have been measured for 12 C + 194, 198 Pt systems at matching excitation energies at near Coulomb barrier region. Statistical model analysis with a modified fission barrier and level density prescription have been carried out to fit the measured pre-scission neutron multiplicities and the available evaporation residue and fission cross sections simultaneously to constrain statistical model parameters. Simultaneous fitting of the pre-scission neutron multiplicities and cross section data requires shell correction at the saddle point
2012-06-11
... DEPARTMENT OF TRANSPORTATION Federal Aviation Administration 14 CFR Part 71 [Docket No. FAA-2012-0130; Airspace Docket No. 12-AWA-2] RIN 2120-AA66 Modification of Multiple Compulsory Reporting Points; Continental United States, Alaska and Hawaii AGENCY: Federal Aviation Administration (FAA), DOT. ACTION: Final...
Chen, Wen-Jie; Xiao, Meng; Chan, C. T.
2016-01-01
Weyl points, as monopoles of Berry curvature in momentum space, have captured much attention recently in various branches of physics. Realizing topological materials that exhibit such nodal points is challenging and indeed, Weyl points have been found experimentally in transition metal arsenide and phosphide and gyroid photonic crystal whose structure is complex. If realizing even the simplest type of single Weyl nodes with a topological charge of 1 is difficult, then making a real crystal carrying higher topological charges may seem more challenging. Here we design, and fabricate using planar fabrication technology, a photonic crystal possessing single Weyl points (including type-II nodes) and multiple Weyl points with topological charges of 2 and 3. We characterize this photonic crystal and find nontrivial 2D bulk band gaps for a fixed kz and the associated surface modes. The robustness of these surface states against kz-preserving scattering is experimentally observed for the first time. PMID:27703140
Gschwind, Michael K
2013-04-16
Mechanisms for generating and executing programs for a floating point (FP) only single instruction multiple data (SIMD) instruction set architecture (ISA) are provided. A computer program product comprising a computer recordable medium having a computer readable program recorded thereon is provided. The computer readable program, when executed on a computing device, causes the computing device to receive one or more instructions and execute the one or more instructions using logic in an execution unit of the computing device. The logic implements a floating point (FP) only single instruction multiple data (SIMD) instruction set architecture (ISA), based on data stored in a vector register file of the computing device. The vector register file is configured to store both scalar and floating point values as vectors having a plurality of vector elements.
International Nuclear Information System (INIS)
Tweardy, Matthew C.; McConchie, Seth; Hayward, Jason P.
2017-01-01
An extension of the point kinetics model is developed in this paper to describe the neutron multiplicity response of a bare uranium object under interrogation by an associated particle imaging deuterium-tritium (D-T) measurement system. This extended model is used to estimate the total neutron multiplication of the uranium. Both MCNPX-PoliMi simulations and data from active interrogation measurements of highly enriched and depleted uranium geometries are used to evaluate the potential of this method and to identify the sources of systematic error. The detection efficiency correction for measured coincidence response is identified as a large source of systematic error. If the detection process is not considered, results suggest that the method can estimate total multiplication to within 13% of the simulated value. Values for multiplicity constants in the point kinetics equations are sensitive to enrichment due to (n, xn) interactions by D-T neutrons and can introduce another significant source of systematic bias. This can theoretically be corrected if isotopic composition is known a priori. Finally, the spatial dependence of multiplication is also suspected of introducing further systematic bias for high multiplication uranium objects.
Tweardy, Matthew C.; McConchie, Seth; Hayward, Jason P.
2017-07-01
An extension of the point kinetics model is developed to describe the neutron multiplicity response of a bare uranium object under interrogation by an associated particle imaging deuterium-tritium (D-T) measurement system. This extended model is used to estimate the total neutron multiplication of the uranium. Both MCNPX-PoliMi simulations and data from active interrogation measurements of highly enriched and depleted uranium geometries are used to evaluate the potential of this method and to identify the sources of systematic error. The detection efficiency correction for measured coincidence response is identified as a large source of systematic error. If the detection process is not considered, results suggest that the method can estimate total multiplication to within 13% of the simulated value. Values for multiplicity constants in the point kinetics equations are sensitive to enrichment due to (n, xn) interactions by D-T neutrons and can introduce another significant source of systematic bias. This can theoretically be corrected if isotopic composition is known a priori. The spatial dependence of multiplication is also suspected of introducing further systematic bias for high multiplication uranium objects.
Multistage Stochastic Programming via Autoregressive Sequences
Czech Academy of Sciences Publication Activity Database
Kaňková, Vlasta
2007-01-01
Roč. 15, č. 4 (2007), s. 99-110 ISSN 0572-3043 R&D Projects: GA ČR GA402/07/1113; GA ČR(CZ) GA402/06/0990; GA ČR GD402/03/H057 Institutional research plan: CEZ:AV0Z10750506 Keywords : Economic proceses * Multistage stochastic programming * autoregressive sequences * individual probability constraints Subject RIV: BB - Applied Statistics, Operational Research
Zlinszky, András; Schroiff, Anke; Otepka, Johannes; Mandlburger, Gottfried; Pfeifer, Norbert
2014-05-01
LIDAR point clouds hold valuable information for land cover and vegetation analysis, not only in the spatial distribution of the points but also in their various attributes. However, LIDAR point clouds are rarely used for visual interpretation, since for most users, the point cloud is difficult to interpret compared to passive optical imagery. Meanwhile, point cloud viewing software is available allowing interactive 3D interpretation, but typically only one attribute at a time. This results in a large number of points with the same colour, crowding the scene and often obscuring detail. We developed a scheme for mapping information from multiple LIDAR point attributes to the Red, Green, and Blue channels of a widely used LIDAR data format, which are otherwise mostly used to add information from imagery to create "photorealistic" point clouds. The possible combinations of parameters are therefore represented in a wide range of colours, but relative differences in individual parameter values of points can be well understood. The visualization was implemented in OPALS software, using a simple and robust batch script, and is viewer independent since the information is stored in the point cloud data file itself. In our case, the following colour channel assignment delivered best results: Echo amplitude in the Red, echo width in the Green and normalized height above a Digital Terrain Model in the Blue channel. With correct parameter scaling (but completely without point classification), points belonging to asphalt and bare soil are dark red, low grassland and crop vegetation are bright red to yellow, shrubs and low trees are green and high trees are blue. Depending on roof material and DTM quality, buildings are shown from red through purple to dark blue. Erroneously high or low points, or points with incorrect amplitude or echo width usually have colours contrasting from terrain or vegetation. This allows efficient visual interpretation of the point cloud in planar
FireProt: Energy- and Evolution-Based Computational Design of Thermostable Multiple-Point Mutants.
Bednar, David; Beerens, Koen; Sebestova, Eva; Bendl, Jaroslav; Khare, Sagar; Chaloupkova, Radka; Prokop, Zbynek; Brezovsky, Jan; Baker, David; Damborsky, Jiri
2015-11-01
There is great interest in increasing proteins' stability to enhance their utility as biocatalysts, therapeutics, diagnostics and nanomaterials. Directed evolution is a powerful, but experimentally strenuous approach. Computational methods offer attractive alternatives. However, due to the limited reliability of predictions and potentially antagonistic effects of substitutions, only single-point mutations are usually predicted in silico, experimentally verified and then recombined in multiple-point mutants. Thus, substantial screening is still required. Here we present FireProt, a robust computational strategy for predicting highly stable multiple-point mutants that combines energy- and evolution-based approaches with smart filtering to identify additive stabilizing mutations. FireProt's reliability and applicability was demonstrated by validating its predictions against 656 mutations from the ProTherm database. We demonstrate that thermostability of the model enzymes haloalkane dehalogenase DhaA and γ-hexachlorocyclohexane dehydrochlorinase LinA can be substantially increased (ΔTm = 24°C and 21°C) by constructing and characterizing only a handful of multiple-point mutants. FireProt can be applied to any protein for which a tertiary structure and homologous sequences are available, and will facilitate the rapid development of robust proteins for biomedical and biotechnological applications.
FireProt: Energy- and Evolution-Based Computational Design of Thermostable Multiple-Point Mutants.
Directory of Open Access Journals (Sweden)
David Bednar
2015-11-01
Full Text Available There is great interest in increasing proteins' stability to enhance their utility as biocatalysts, therapeutics, diagnostics and nanomaterials. Directed evolution is a powerful, but experimentally strenuous approach. Computational methods offer attractive alternatives. However, due to the limited reliability of predictions and potentially antagonistic effects of substitutions, only single-point mutations are usually predicted in silico, experimentally verified and then recombined in multiple-point mutants. Thus, substantial screening is still required. Here we present FireProt, a robust computational strategy for predicting highly stable multiple-point mutants that combines energy- and evolution-based approaches with smart filtering to identify additive stabilizing mutations. FireProt's reliability and applicability was demonstrated by validating its predictions against 656 mutations from the ProTherm database. We demonstrate that thermostability of the model enzymes haloalkane dehalogenase DhaA and γ-hexachlorocyclohexane dehydrochlorinase LinA can be substantially increased (ΔTm = 24°C and 21°C by constructing and characterizing only a handful of multiple-point mutants. FireProt can be applied to any protein for which a tertiary structure and homologous sequences are available, and will facilitate the rapid development of robust proteins for biomedical and biotechnological applications.
Directory of Open Access Journals (Sweden)
A. Afghan-Toloee
2013-09-01
Full Text Available The problem of specifying the minimum number of sensors to deploy in a certain area to face multiple targets has been generally studied in the literatures. In this paper, we are arguing the multi-sensors deployment problem (MDP. The Multi-sensor placement problem can be clarified as minimizing the cost required to cover the multi target points in the area. We propose a more feasible method for the multi-sensor placement problem. Our method makes provision the high coverage of grid based placements while minimizing the cost as discovered in perimeter placement techniques. The NICA algorithm as improved ICA (Imperialist Competitive Algorithm is used to decrease the performance time to explore an enough solution compared to other meta-heuristic schemes such as GA, PSO and ICA. A three dimensional area is used for clarify the multiple target and placement points, making provision x, y, and z computations in the observation algorithm. A structure of model for the multi-sensor placement problem is proposed: The problem is constructed as an optimization problem with the objective to minimize the cost while covering all multiple target points upon a given probability of observation tolerance.
REGIONAL FIRST ORDER PERIODIC AUTOREGRESSIVE MODELS FOR MONTHLY FLOWS
Directory of Open Access Journals (Sweden)
Ceyhun ÖZÇELİK
2008-01-01
Full Text Available First order periodic autoregressive models is of mostly used models in modeling of time dependency of hydrological flow processes. In these models, periodicity of the correlogram is preserved as well as time dependency of processes. However, the parameters of these models, namely, inter-monthly lag-1 autocorrelation coefficients may be often estimated erroneously from short samples, since they are statistics of high order moments. Therefore, to constitute a regional model may be a solution that can produce more reliable and decisive estimates, and derive models and model parameters in any required point of the basin considered. In this study, definitions of homogeneous region for lag-1 autocorrelation coefficients are made; five parametric and non parametric models are proposed to set regional models of lag-1 autocorrelation coefficients. Regional models are applied on 30 stream flow gauging stations in Seyhan and Ceyhan basins, and tested by criteria of relative absolute bias, simple and relative root of mean square errors.
Chain binomial models and binomial autoregressive processes.
Weiss, Christian H; Pollett, Philip K
2012-09-01
We establish a connection between a class of chain-binomial models of use in ecology and epidemiology and binomial autoregressive (AR) processes. New results are obtained for the latter, including expressions for the lag-conditional distribution and related quantities. We focus on two types of chain-binomial model, extinction-colonization and colonization-extinction models, and present two approaches to parameter estimation. The asymptotic distributions of the resulting estimators are studied, as well as their finite-sample performance, and we give an application to real data. A connection is made with standard AR models, which also has implications for parameter estimation. © 2011, The International Biometric Society.
on the performance of Autoregressive Moving Average Polynomial
African Journals Online (AJOL)
Timothy Ademakinwa
Distributed Lag (PDL) model, Autoregressive Polynomial Distributed Lag ... Moving Average Polynomial Distributed Lag (ARMAPDL) model. ..... Global Journal of Mathematics and Statistics. Vol. 1. ... Business and Economic Research Center.
MULTIPLE ACCESS POINTS WITHIN THE ONLINE CLASSROOM: WHERE STUDENTS LOOK FOR INFORMATION
Directory of Open Access Journals (Sweden)
John STEELE
2017-01-01
Full Text Available The purpose of this study is to examine the impact of information placement within the confines of the online classroom architecture. Also reviewed was the impact of other variables such as course design, teaching presence and student patterns in looking for information. The sample population included students from a major online university in their first year course sequence. Students were tasked with completing a survey at the end of the course, indicating their preference for accessing information within the online classroom. The qualitative data indicated that student preference is to receive information from multiple access points and sources within the online classroom architecture. Students also expressed a desire to have information delivered through the usage of technology such as email and text messaging. In addition to receiving information from multiple sources, the qualitative data indicated students were satisfied overall, with the current ways in which they received and accessed information within the online classroom setting. Major findings suggest that instructors teaching within the online classroom should have multiple data access points within the classroom architecture. Furthermore, instructors should use a variety of communication venues to enhance the ability for students to access and receive information pertinent to the course.
MACROECONOMIC FORECASTING USING BAYESIAN VECTOR AUTOREGRESSIVE APPROACH
Directory of Open Access Journals (Sweden)
D. Tutberidze
2017-04-01
Full Text Available There are many arguments that can be advanced to support the forecasting activities of business entities. The underlying argument in favor of forecasting is that managerial decisions are significantly dependent on proper evaluation of future trends as market conditions are constantly changing and require a detailed analysis of future dynamics. The article discusses the importance of using reasonable macro-econometric tool by suggesting the idea of conditional forecasting through a Vector Autoregressive (VAR modeling framework. Under this framework, a macroeconomic model for Georgian economy is constructed with the few variables believed to be shaping business environment. Based on the model, forecasts of macroeconomic variables are produced, and three types of scenarios are analyzed - a baseline and two alternative ones. The results of the study provide confirmatory evidence that suggested methodology is adequately addressing the research phenomenon and can be used widely by business entities in responding their strategic and operational planning challenges. Given this set-up, it is shown empirically that Bayesian Vector Autoregressive approach provides reasonable forecasts for the variables of interest.
Kepler AutoRegressive Planet Search (KARPS)
Caceres, Gabriel
2018-01-01
One of the main obstacles in detecting faint planetary transits is the intrinsic stellar variability of the host star. The Kepler AutoRegressive Planet Search (KARPS) project implements statistical methodology associated with autoregressive processes (in particular, ARIMA and ARFIMA) to model stellar lightcurves in order to improve exoplanet transit detection. We also develop a novel Transit Comb Filter (TCF) applied to the AR residuals which provides a periodogram analogous to the standard Box-fitting Least Squares (BLS) periodogram. We train a random forest classifier on known Kepler Objects of Interest (KOIs) using select features from different stages of this analysis, and then use ROC curves to define and calibrate the criteria to recover the KOI planet candidates with high fidelity. These statistical methods are detailed in a contributed poster (Feigelson et al., this meeting).These procedures are applied to the full DR25 dataset of NASA’s Kepler mission. Using the classification criteria, a vast majority of known KOIs are recovered and dozens of new KARPS Candidate Planets (KCPs) discovered, including ultra-short period exoplanets. The KCPs will be briefly presented and discussed.
Methods of fast, multiple-point in vivo T1 determination
International Nuclear Information System (INIS)
Zhang, Y.; Spigarelli, M.; Fencil, L.E.; Yeung, H.N.
1989-01-01
Two methods of rapid, multiple-point determination of T1 in vivo have been evaluated with a phantom consisting of vials of gel in different Mn + + concentrations. The first method was an inversion-recovery- on-the-fly technique, and the second method used a variable- tip-angle (α) progressive saturation with two sub- sequences of different repetition times. In the first method, 1/T1 was evaluated by an exponential fit. In the second method, 1/T1 was obtained iteratively with a linear fit and then readjusted together with α to a model equation until self-consistency was reached
Coexistence of different vacua in the effective quantum field theory and multiple point principle
International Nuclear Information System (INIS)
Volovik, G.E.
2004-01-01
According to the multiple point principle our Universe in on the coexistence curve of two or more phases of the quantum vacuum. The coexistence of different quantum vacua can be regulated by the exchange of the global fermionic charges between the vacua. If the coexistence is regulated by the baryonic charge, all the coexisting vacua exhibit the baryonic asymmetry. Due to the exchange of the baryonic charge between the vacuum and matter which occurs above the electroweak transition, the baryonic asymmetry of the vacuum induces the baryonic asymmetry of matter in our Standard-Model phase of the quantum vacuum [ru
Robust set-point regulation for ecological models with multiple management goals.
Guiver, Chris; Mueller, Markus; Hodgson, Dave; Townley, Stuart
2016-05-01
Population managers will often have to deal with problems of meeting multiple goals, for example, keeping at specific levels both the total population and population abundances in given stage-classes of a stratified population. In control engineering, such set-point regulation problems are commonly tackled using multi-input, multi-output proportional and integral (PI) feedback controllers. Building on our recent results for population management with single goals, we develop a PI control approach in a context of multi-objective population management. We show that robust set-point regulation is achieved by using a modified PI controller with saturation and anti-windup elements, both described in the paper, and illustrate the theory with examples. Our results apply more generally to linear control systems with positive state variables, including a class of infinite-dimensional systems, and thus have broader appeal.
Estimation of Multiple Point Sources for Linear Fractional Order Systems Using Modulating Functions
Belkhatir, Zehor
2017-06-28
This paper proposes an estimation algorithm for the characterization of multiple point inputs for linear fractional order systems. First, using polynomial modulating functions method and a suitable change of variables the problem of estimating the locations and the amplitudes of a multi-pointwise input is decoupled into two algebraic systems of equations. The first system is nonlinear and solves for the time locations iteratively, whereas the second system is linear and solves for the input’s amplitudes. Second, closed form formulas for both the time location and the amplitude are provided in the particular case of single point input. Finally, numerical examples are given to illustrate the performance of the proposed technique in both noise-free and noisy cases. The joint estimation of pointwise input and fractional differentiation orders is also presented. Furthermore, a discussion on the performance of the proposed algorithm is provided.
Kepler AutoRegressive Planet Search
Caceres, Gabriel Antonio; Feigelson, Eric
2016-01-01
The Kepler AutoRegressive Planet Search (KARPS) project uses statistical methodology associated with autoregressive (AR) processes to model Kepler lightcurves in order to improve exoplanet transit detection in systems with high stellar variability. We also introduce a planet-search algorithm to detect transits in time-series residuals after application of the AR models. One of the main obstacles in detecting faint planetary transits is the intrinsic stellar variability of the host star. The variability displayed by many stars may have autoregressive properties, wherein later flux values are correlated with previous ones in some manner. Our analysis procedure consisting of three steps: pre-processing of the data to remove discontinuities, gaps and outliers; AR-type model selection and fitting; and transit signal search of the residuals using a new Transit Comb Filter (TCF) that replaces traditional box-finding algorithms. The analysis procedures of the project are applied to a portion of the publicly available Kepler light curve data for the full 4-year mission duration. Tests of the methods have been made on a subset of Kepler Objects of Interest (KOI) systems, classified both as planetary `candidates' and `false positives' by the Kepler Team, as well as a random sample of unclassified systems. We find that the ARMA-type modeling successfully reduces the stellar variability, by a factor of 10 or more in active stars and by smaller factors in more quiescent stars. A typical quiescent Kepler star has an interquartile range (IQR) of ~10 e-/sec, which may improve slightly after modeling, while those with IQR ranging from 20 to 50 e-/sec, have improvements from 20% up to 70%. High activity stars (IQR exceeding 100) markedly improve. A periodogram based on the TCF is constructed to concentrate the signal of these periodic spikes. When a periodic transit is found, the model is displayed on a standard period-folded averaged light curve. Our findings to date on real
Incorporating measurement error in n=1 psychological autoregressive modeling
Schuurman, Noemi K.; Houtveen, Jan H.; Hamaker, Ellen L.
2015-01-01
Measurement error is omnipresent in psychological data. However, the vast majority of applications of autoregressive time series analyses in psychology do not take measurement error into account. Disregarding measurement error when it is present in the data results in a bias of the autoregressive
Mariethoz, Gregoire; Lefebvre, Sylvain
2014-05-01
Multiple-Point Simulations (MPS) is a family of geostatistical tools that has received a lot of attention in recent years for the characterization of spatial phenomena in geosciences. It relies on the definition of training images to represent a given type of spatial variability, or texture. We show that the algorithmic tools used are similar in many ways to techniques developed in computer graphics, where there is a need to generate large amounts of realistic textures for applications such as video games and animated movies. Similarly to MPS, these texture synthesis methods use training images, or exemplars, to generate realistic-looking graphical textures. Both domains of multiple-point geostatistics and example-based texture synthesis present similarities in their historic development and share similar concepts. These disciplines have however remained separated, and as a result significant algorithmic innovations in each discipline have not been universally adopted. Texture synthesis algorithms present drastically increased computational efficiency, patterns reproduction and user control. At the same time, MPS developed ways to condition models to spatial data and to produce 3D stochastic realizations, which have not been thoroughly investigated in the field of texture synthesis. In this paper we review the possible links between these disciplines and show the potential and limitations of using concepts and approaches from texture synthesis in MPS. We also provide guidelines on how recent developments could benefit both fields of research, and what challenges remain open.
Tacina, K. M.; Hicks, Y. R.
2017-01-01
The combustion dynamics of multiple 7-point lean direct injection (LDI) combustor configurations are compared. LDI is a fuel-lean combustor concept for aero gas turbine engines in which multiple small fuel-air mixers replace one traditionally-sized fuel-air mixer. This 7-point LDI configuration has a circular cross section, with a center (pilot) fuel-air mixer surrounded by six outer (main) fuel-air mixers. Each fuel-air mixer consists of an axial air swirler followed by a converging-diverging venturi. A simplex fuel injector is inserted through the center of the air swirler, with the fuel injector tip located near the venturi throat. All 7 fuel-air mixers are identical except for the swirler blade angle, which varies with the configuration. Testing was done in a 5-atm flame tube with inlet air temperatures from 600 to 800 F and equivalence ratios from 0.4 to 0.7. Combustion dynamics were measured using a cooled PCB pressure transducer flush-mounted in the wall of the combustor test section.
Bias-correction in vector autoregressive models
DEFF Research Database (Denmark)
Engsted, Tom; Pedersen, Thomas Quistgaard
2014-01-01
We analyze the properties of various methods for bias-correcting parameter estimates in both stationary and non-stationary vector autoregressive models. First, we show that two analytical bias formulas from the existing literature are in fact identical. Next, based on a detailed simulation study......, we show that when the model is stationary this simple bias formula compares very favorably to bootstrap bias-correction, both in terms of bias and mean squared error. In non-stationary models, the analytical bias formula performs noticeably worse than bootstrapping. Both methods yield a notable...... improvement over ordinary least squares. We pay special attention to the risk of pushing an otherwise stationary model into the non-stationary region of the parameter space when correcting for bias. Finally, we consider a recently proposed reduced-bias weighted least squares estimator, and we find...
Model reduction methods for vector autoregressive processes
Brüggemann, Ralf
2004-01-01
1. 1 Objective of the Study Vector autoregressive (VAR) models have become one of the dominant research tools in the analysis of macroeconomic time series during the last two decades. The great success of this modeling class started with Sims' (1980) critique of the traditional simultaneous equation models (SEM). Sims criticized the use of 'too many incredible restrictions' based on 'supposed a priori knowledge' in large scale macroeconometric models which were popular at that time. Therefore, he advo cated largely unrestricted reduced form multivariate time series models, unrestricted VAR models in particular. Ever since his influential paper these models have been employed extensively to characterize the underlying dynamics in systems of time series. In particular, tools to summarize the dynamic interaction between the system variables, such as impulse response analysis or forecast error variance decompo sitions, have been developed over the years. The econometrics of VAR models and related quantities i...
DEFF Research Database (Denmark)
Cordua, Knud Skou; Hansen, Thomas Mejer; Lange, Katrine
In order to move beyond simplified covariance based a priori models, which are typically used for inverse problems, more complex multiple-point-based a priori models have to be considered. By means of marginal probability distributions ‘learned’ from a training image, sequential simulation has...... proven to be an efficient way of obtaining multiple realizations that honor the same multiple-point statistics as the training image. The frequency matching method provides an alternative way of formulating multiple-point-based a priori models. In this strategy the pattern frequency distributions (i.......e. marginals) of the training image and a subsurface model are matched in order to obtain a solution with the same multiple-point statistics as the training image. Sequential Gibbs sampling is a simulation strategy that provides an efficient way of applying sequential simulation based algorithms as a priori...
Point spread function due to multiple scattering of light in the atmosphere
International Nuclear Information System (INIS)
Pękala, J.; Wilczyński, H.
2013-01-01
The atmospheric scattering of light has a significant influence on the results of optical observations of air showers. It causes attenuation of direct light from the shower, but also contributes a delayed signal to the observed light. The scattering of light therefore should be accounted for, both in simulations of air shower detection and reconstruction of observed events. In this work a Monte Carlo simulation of multiple scattering of light has been used to determine the contribution of the scattered light in observations of a point source of light. Results of the simulations and a parameterization of the angular distribution of the scattered light contribution to the observed signal (the point spread function) are presented. -- Author-Highlights: •Analysis of atmospheric scattering of light from an isotropic point source. •Different geometries and atmospheric conditions were investigated. •A parameterization of scattered light distribution has been developed. •The parameterization allows one to easily account for the light scattering in air. •The results will be useful in analyses of observations of extensive air shower
The shooting method and multiple solutions of two/multi-point BVPs of second-order ODE
Directory of Open Access Journals (Sweden)
Man Kam Kwong
2006-06-01
Full Text Available Within the last decade, there has been growing interest in the study of multiple solutions of two- and multi-point boundary value problems of nonlinear ordinary differential equations as fixed points of a cone mapping. Undeniably many good results have emerged. The purpose of this paper is to point out that, in the special case of second-order equations, the shooting method can be an effective tool, sometimes yielding better results than those obtainable via fixed point techniques.
Li, Dan; Zheng, Binghui; Liu, Yan; Chu, Zhaosheng; He, Yan; Huang, Minsheng
2018-05-02
Multiple free water surface flow constructed wetlands (multi-FWS CWs) are a variety of conventional water treatment plants for the interception of pollutants. This review encapsulated the characteristics and applications in the field of ecological non-point source water pollution control technology. The roles of in-series design and operation parameters (hydraulic residence time, hydraulic load rate, water depth and aspect ratio, composition of influent, and plant species) for performance intensification were also analyzed, which were crucial to achieve sustainable and effective contaminants removal, especially the retention of nutrient. The mechanism study of design and operation parameters for the removal of nitrogen and phosphorus was also highlighted. Conducive perspectives for further research on optimizing its design/operation parameters and advanced technologies of ecological restoration were illustrated to possibly interpret the functions of multi-FWS CWs.
Multiple-point statistical prediction on fracture networks at Yucca Mountain
International Nuclear Information System (INIS)
Liu, X.Y; Zhang, C.Y.; Liu, Q.S.; Birkholzer, J.T.
2009-01-01
In many underground nuclear waste repository systems, such as at Yucca Mountain, water flow rate and amount of water seepage into the waste emplacement drifts are mainly determined by hydrological properties of fracture network in the surrounding rock mass. Natural fracture network system is not easy to describe, especially with respect to its connectivity which is critically important for simulating the water flow field. In this paper, we introduced a new method for fracture network description and prediction, termed multi-point-statistics (MPS). The process of the MPS method is to record multiple-point statistics concerning the connectivity patterns of a fracture network from a known fracture map, and to reproduce multiple-scale training fracture patterns in a stochastic manner, implicitly and directly. It is applied to fracture data to study flow field behavior at the Yucca Mountain waste repository system. First, the MPS method is used to create a fracture network with an original fracture training image from Yucca Mountain dataset. After we adopt a harmonic and arithmetic average method to upscale the permeability to a coarse grid, THM simulation is carried out to study near-field water flow in the surrounding waste emplacement drifts. Our study shows that connectivity or patterns of fracture networks can be grasped and reconstructed by MPS methods. In theory, it will lead to better prediction of fracture system characteristics and flow behavior. Meanwhile, we can obtain variance from flow field, which gives us a way to quantify model uncertainty even in complicated coupled THM simulations. It indicates that MPS can potentially characterize and reconstruct natural fracture networks in a fractured rock mass with advantages of quantifying connectivity of fracture system and its simulation uncertainty simultaneously.
Systems near a critical point under multiplicative noise and the concept of effective potential
Shapiro, V. E.
1993-07-01
This paper presents a general approach to and elucidates the main features of the effective potential, friction, and diffusion exerted by systems near a critical point due to nonlinear influence of noise. The model is that of a general many-dimensional system of coupled nonlinear oscillators of finite damping under frequently alternating influences, multiplicative or additive, and arbitrary form of the power spectrum, provided the time scales of the system's drift due to noise are large compared to the scales of unperturbed relaxation behavior. The conventional statistical approach and the widespread deterministic effective potential concept use the assumptions about a small parameter which are particular cases of the considered. We show close correspondence between the asymptotic methods of these approaches and base the analysis on this. The results include an analytical treatment of the system's long-time behavior as a function of the noise covering all the range of its table- and bell-shaped spectra, from the monochromatic limit to white noise. The trend is considered both in the coordinate momentum and in the coordinate system's space. Particular attention is paid to the stabilization behavior forced by multiplicative noise. An intermittency, in a broad area of the control parameter space, is shown to be an intrinsic feature of these phenomena.
Multiple types of motives don't multiply the motivation of West Point cadets.
Wrzesniewski, Amy; Schwartz, Barry; Cong, Xiangyu; Kane, Michael; Omar, Audrey; Kolditz, Thomas
2014-07-29
Although people often assume that multiple motives for doing something will be more powerful and effective than a single motive, research suggests that different types of motives for the same action sometimes compete. More specifically, research suggests that instrumental motives, which are extrinsic to the activities at hand, can weaken internal motives, which are intrinsic to the activities at hand. We tested whether holding both instrumental and internal motives yields negative outcomes in a field context in which various motives occur naturally and long-term educational and career outcomes are at stake. We assessed the impact of the motives of over 10,000 West Point cadets over the period of a decade on whether they would become commissioned officers, extend their officer service beyond the minimum required period, and be selected for early career promotions. For each outcome, motivation internal to military service itself predicted positive outcomes; a relationship that was negatively affected when instrumental motives were also in evidence. These results suggest that holding multiple motives damages persistence and performance in educational and occupational contexts over long periods of time.
Penalised Complexity Priors for Stationary Autoregressive Processes
Sø rbye, Sigrunn Holbek; Rue, Haavard
2017-01-01
The autoregressive (AR) process of order p(AR(p)) is a central model in time series analysis. A Bayesian approach requires the user to define a prior distribution for the coefficients of the AR(p) model. Although it is easy to write down some prior, it is not at all obvious how to understand and interpret the prior distribution, to ensure that it behaves according to the users' prior knowledge. In this article, we approach this problem using the recently developed ideas of penalised complexity (PC) priors. These prior have important properties like robustness and invariance to reparameterisations, as well as a clear interpretation. A PC prior is computed based on specific principles, where model component complexity is penalised in terms of deviation from simple base model formulations. In the AR(1) case, we discuss two natural base model choices, corresponding to either independence in time or no change in time. The latter case is illustrated in a survival model with possible time-dependent frailty. For higher-order processes, we propose a sequential approach, where the base model for AR(p) is the corresponding AR(p-1) model expressed using the partial autocorrelations. The properties of the new prior distribution are compared with the reference prior in a simulation study.
Circular Conditional Autoregressive Modeling of Vector Fields.
Modlin, Danny; Fuentes, Montse; Reich, Brian
2012-02-01
As hurricanes approach landfall, there are several hazards for which coastal populations must be prepared. Damaging winds, torrential rains, and tornadoes play havoc with both the coast and inland areas; but, the biggest seaside menace to life and property is the storm surge. Wind fields are used as the primary forcing for the numerical forecasts of the coastal ocean response to hurricane force winds, such as the height of the storm surge and the degree of coastal flooding. Unfortunately, developments in deterministic modeling of these forcings have been hindered by computational expenses. In this paper, we present a multivariate spatial model for vector fields, that we apply to hurricane winds. We parameterize the wind vector at each site in polar coordinates and specify a circular conditional autoregressive (CCAR) model for the vector direction, and a spatial CAR model for speed. We apply our framework for vector fields to hurricane surface wind fields for Hurricane Floyd of 1999 and compare our CCAR model to prior methods that decompose wind speed and direction into its N-S and W-E cardinal components.
Penalised Complexity Priors for Stationary Autoregressive Processes
Sørbye, Sigrunn Holbek
2017-05-25
The autoregressive (AR) process of order p(AR(p)) is a central model in time series analysis. A Bayesian approach requires the user to define a prior distribution for the coefficients of the AR(p) model. Although it is easy to write down some prior, it is not at all obvious how to understand and interpret the prior distribution, to ensure that it behaves according to the users\\' prior knowledge. In this article, we approach this problem using the recently developed ideas of penalised complexity (PC) priors. These prior have important properties like robustness and invariance to reparameterisations, as well as a clear interpretation. A PC prior is computed based on specific principles, where model component complexity is penalised in terms of deviation from simple base model formulations. In the AR(1) case, we discuss two natural base model choices, corresponding to either independence in time or no change in time. The latter case is illustrated in a survival model with possible time-dependent frailty. For higher-order processes, we propose a sequential approach, where the base model for AR(p) is the corresponding AR(p-1) model expressed using the partial autocorrelations. The properties of the new prior distribution are compared with the reference prior in a simulation study.
On robust forecasting of autoregressive time series under censoring
Kharin, Y.; Badziahin, I.
2009-01-01
Problems of robust statistical forecasting are considered for autoregressive time series observed under distortions generated by interval censoring. Three types of robust forecasting statistics are developed; meansquare risk is evaluated for the developed forecasting statistics. Numerical results are given.
vector bilinear autoregressive time series model and its superiority
African Journals Online (AJOL)
KEYWORDS: Linear time series, Autoregressive process, Autocorrelation function, Partial autocorrelation function,. Vector time .... important result on matrix algebra with respect to the spectral ..... application to covariance analysis of super-.
Screening of point mutations by multiple SSCP analysis in the dystrophin gene
Energy Technology Data Exchange (ETDEWEB)
Lasa, A.; Baiget, M.; Gallano, P. [Hospital Sant Pau, Barcelona (Spain)
1994-09-01
Duchenne muscular dystrophy (DMD) is a lethal, X-linked neuromuscular disorder. The population frequency of DMD is one in approximately 3500 boys, of which one third is thought to be a new mutant. The DMD gene is the largest known to date, spanning over 2,3 Mb in band Xp21.2; 79 exons are transcribed into a 14 Kb mRNA coding for a protein of 427 kD which has been named dystrophin. It has been shown that about 65% of affected boys have a gene deletion with a wide variation in localization and size. The remaining affected individuals who have no detectable deletions or duplications would probably carry more subtle mutations that are difficult to detect. These mutations occur in several different exons and seem to be unique to single patients. Their identification represents a formidable goal because of the large size and complexity of the dystrophin gene. SSCP is a very efficient method for the detection of point mutations if the parameters that affect the separation of the strands are optimized for a particular DNA fragment. The multiple SSCP allows the simultaneous study of several exons, and implies the use of different conditions because no single set of conditions will be optimal for all fragments. Seventy-eight DMD patients with no deletion or duplication in the dystrophin gene were selected for the multiple SSCP analysis. Genomic DNA from these patients was amplified using the primers described for the diagnosis procedure (muscle promoter and exons 3, 8, 12, 16, 17, 19, 32, 45, 48 and 51). We have observed different mobility shifts in bands corresponding to exons 8, 12, 43 and 51. In exons 17 and 45, altered electrophoretic patterns were found in different samples identifying polymorphisms already described.
Association of a novel point mutation in MSH2 gene with familial multiple primary cancers
Directory of Open Access Journals (Sweden)
Hai Hu
2017-10-01
Full Text Available Abstract Background Multiple primary cancers (MPC have been identified as two or more cancers without any subordinate relationship that occur either simultaneously or metachronously in the same or different organs of an individual. Lynch syndrome is an autosomal dominant genetic disorder that increases the risk of many types of cancers. Lynch syndrome patients who suffer more than two cancers can also be considered as MPC; patients of this kind provide unique resources to learn how genetic mutation causes MPC in different tissues. Methods We performed a whole genome sequencing on blood cells and two tumor samples of a Lynch syndrome patient who was diagnosed with five primary cancers. The mutational landscape of the tumors, including somatic point mutations and copy number alternations, was characterized. We also compared Lynch syndrome with sporadic cancers and proposed a model to illustrate the mutational process by which Lynch syndrome progresses to MPC. Results We revealed a novel pathologic mutation on the MSH2 gene (G504 splicing that associates with Lynch syndrome. Systematical comparison of the mutation landscape revealed that multiple cancers in the proband were evolutionarily independent. Integrative analysis showed that truncating mutations of DNA mismatch repair (MMR genes were significantly enriched in the patient. A mutation progress model that included germline mutations of MMR genes, double hits of MMR system, mutations in tissue-specific driver genes, and rapid accumulation of additional passenger mutations was proposed to illustrate how MPC occurs in Lynch syndrome patients. Conclusion Our findings demonstrate that both germline and somatic alterations are driving forces of carcinogenesis, which may resolve the carcinogenic theory of Lynch syndrome.
Zheng, Guanglou; Fang, Gengfa; Shankaran, Rajan; Orgun, Mehmet A; Zhou, Jie; Qiao, Li; Saleem, Kashif
2017-05-01
Generating random binary sequences (BSes) is a fundamental requirement in cryptography. A BS is a sequence of N bits, and each bit has a value of 0 or 1. For securing sensors within wireless body area networks (WBANs), electrocardiogram (ECG)-based BS generation methods have been widely investigated in which interpulse intervals (IPIs) from each heartbeat cycle are processed to produce BSes. Using these IPI-based methods to generate a 128-bit BS in real time normally takes around half a minute. In order to improve the time efficiency of such methods, this paper presents an ECG multiple fiducial-points based binary sequence generation (MFBSG) algorithm. The technique of discrete wavelet transforms is employed to detect arrival time of these fiducial points, such as P, Q, R, S, and T peaks. Time intervals between them, including RR, RQ, RS, RP, and RT intervals, are then calculated based on this arrival time, and are used as ECG features to generate random BSes with low latency. According to our analysis on real ECG data, these ECG feature values exhibit the property of randomness and, thus, can be utilized to generate random BSes. Compared with the schemes that solely rely on IPIs to generate BSes, this MFBSG algorithm uses five feature values from one heart beat cycle, and can be up to five times faster than the solely IPI-based methods. So, it achieves a design goal of low latency. According to our analysis, the complexity of the algorithm is comparable to that of fast Fourier transforms. These randomly generated ECG BSes can be used as security keys for encryption or authentication in a WBAN system.
Optimizing the diagnostic power with gastric emptying scintigraphy at multiple time points
Directory of Open Access Journals (Sweden)
Gajewski Byron J
2011-05-01
Full Text Available Abstract Background Gastric Emptying Scintigraphy (GES at intervals over 4 hours after a standardized radio-labeled meal is commonly regarded as the gold standard for diagnosing gastroparesis. The objectives of this study were: 1 to investigate the best time point and the best combination of multiple time points for diagnosing gastroparesis with repeated GES measures, and 2 to contrast and cross-validate Fisher's Linear Discriminant Analysis (LDA, a rank based Distribution Free (DF approach, and the Classification And Regression Tree (CART model. Methods A total of 320 patients with GES measures at 1, 2, 3, and 4 hour (h after a standard meal using a standardized method were retrospectively collected. Area under the Receiver Operating Characteristic (ROC curve and the rate of false classification through jackknife cross-validation were used for model comparison. Results Due to strong correlation and an abnormality in data distribution, no substantial improvement in diagnostic power was found with the best linear combination by LDA approach even with data transformation. With DF method, the linear combination of 4-h and 3-h increased the Area Under the Curve (AUC and decreased the number of false classifications (0.87; 15.0% over individual time points (0.83, 0.82; 15.6%, 25.3%, for 4-h and 3-h, respectively at a higher sensitivity level (sensitivity = 0.9. The CART model using 4 hourly GES measurements along with patient's age was the most accurate diagnostic tool (AUC = 0.88, false classification = 13.8%. Patients having a 4-h gastric retention value >10% were 5 times more likely to have gastroparesis (179/207 = 86.5% than those with ≤10% (18/113 = 15.9%. Conclusions With a mixed group of patients either referred with suspected gastroparesis or investigated for other reasons, the CART model is more robust than the LDA and DF approaches, capable of accommodating covariate effects and can be generalized for cross institutional applications, but
Quantifying natural delta variability using a multiple-point geostatistics prior uncertainty model
Scheidt, Céline; Fernandes, Anjali M.; Paola, Chris; Caers, Jef
2016-10-01
We address the question of quantifying uncertainty associated with autogenic pattern variability in a channelized transport system by means of a modern geostatistical method. This question has considerable relevance for practical subsurface applications as well, particularly those related to uncertainty quantification relying on Bayesian approaches. Specifically, we show how the autogenic variability in a laboratory experiment can be represented and reproduced by a multiple-point geostatistical prior uncertainty model. The latter geostatistical method requires selection of a limited set of training images from which a possibly infinite set of geostatistical model realizations, mimicking the training image patterns, can be generated. To that end, we investigate two methods to determine how many training images and what training images should be provided to reproduce natural autogenic variability. The first method relies on distance-based clustering of overhead snapshots of the experiment; the second method relies on a rate of change quantification by means of a computer vision algorithm termed the demon algorithm. We show quantitatively that with either training image selection method, we can statistically reproduce the natural variability of the delta formed in the experiment. In addition, we study the nature of the patterns represented in the set of training images as a representation of the "eigenpatterns" of the natural system. The eigenpattern in the training image sets display patterns consistent with previous physical interpretations of the fundamental modes of this type of delta system: a highly channelized, incisional mode; a poorly channelized, depositional mode; and an intermediate mode between the two.
Accelerating simulation for the multiple-point statistics algorithm using vector quantization
Zuo, Chen; Pan, Zhibin; Liang, Hao
2018-03-01
Multiple-point statistics (MPS) is a prominent algorithm to simulate categorical variables based on a sequential simulation procedure. Assuming training images (TIs) as prior conceptual models, MPS extracts patterns from TIs using a template and records their occurrences in a database. However, complex patterns increase the size of the database and require considerable time to retrieve the desired elements. In order to speed up simulation and improve simulation quality over state-of-the-art MPS methods, we propose an accelerating simulation for MPS using vector quantization (VQ), called VQ-MPS. First, a variable representation is presented to make categorical variables applicable for vector quantization. Second, we adopt a tree-structured VQ to compress the database so that stationary simulations are realized. Finally, a transformed template and classified VQ are used to address nonstationarity. A two-dimensional (2D) stationary channelized reservoir image is used to validate the proposed VQ-MPS. In comparison with several existing MPS programs, our method exhibits significantly better performance in terms of computational time, pattern reproductions, and spatial uncertainty. Further demonstrations consist of a 2D four facies simulation, two 2D nonstationary channel simulations, and a three-dimensional (3D) rock simulation. The results reveal that our proposed method is also capable of solving multifacies, nonstationarity, and 3D simulations based on 2D TIs.
Paul, Amit K; Hase, William L
2016-01-28
A zero-point energy (ZPE) constraint model is proposed for classical trajectory simulations of unimolecular decomposition and applied to CH4* → H + CH3 decomposition. With this model trajectories are not allowed to dissociate unless they have ZPE in the CH3 product. If not, they are returned to the CH4* region of phase space and, if necessary, given additional opportunities to dissociate with ZPE. The lifetime for dissociation of an individual trajectory is the time it takes to dissociate with ZPE in CH3, including multiple possible returns to CH4*. With this ZPE constraint the dissociation of CH4* is exponential in time as expected for intrinsic RRKM dynamics and the resulting rate constant is in good agreement with the harmonic quantum value of RRKM theory. In contrast, a model that discards trajectories without ZPE in the reaction products gives a CH4* → H + CH3 rate constant that agrees with the classical and not quantum RRKM value. The rate constant for the purely classical simulation indicates that anharmonicity may be important and the rate constant from the ZPE constrained classical trajectory simulation may not represent the complete anharmonicity of the RRKM quantum dynamics. The ZPE constraint model proposed here is compared with previous models for restricting ZPE flow in intramolecular dynamics, and connecting product and reactant/product quantum energy levels in chemical dynamics simulations.
LSHSIM: A Locality Sensitive Hashing based method for multiple-point geostatistics
Moura, Pedro; Laber, Eduardo; Lopes, Hélio; Mesejo, Daniel; Pavanelli, Lucas; Jardim, João; Thiesen, Francisco; Pujol, Gabriel
2017-10-01
Reservoir modeling is a very important task that permits the representation of a geological region of interest, so as to generate a considerable number of possible scenarios. Since its inception, many methodologies have been proposed and, in the last two decades, multiple-point geostatistics (MPS) has been the dominant one. This methodology is strongly based on the concept of training image (TI) and the use of its characteristics, which are called patterns. In this paper, we propose a new MPS method that combines the application of a technique called Locality Sensitive Hashing (LSH), which permits to accelerate the search for patterns similar to a target one, with a Run-Length Encoding (RLE) compression technique that speeds up the calculation of the Hamming similarity. Experiments with both categorical and continuous images show that LSHSIM is computationally efficient and produce good quality realizations. In particular, for categorical data, the results suggest that LSHSIM is faster than MS-CCSIM, one of the state-of-the-art methods.
Directory of Open Access Journals (Sweden)
Yin Yanshu
2017-12-01
Full Text Available In this paper, a location-based multiple point statistics method is developed to model a non-stationary reservoir. The proposed method characterizes the relationship between the sedimentary pattern and the deposit location using the relative central position distance function, which alleviates the requirement that the training image and the simulated grids have the same dimension. The weights in every direction of the distance function can be changed to characterize the reservoir heterogeneity in various directions. The local integral replacements of data events, structured random path, distance tolerance and multi-grid strategy are applied to reproduce the sedimentary patterns and obtain a more realistic result. This method is compared with the traditional Snesim method using a synthesized 3-D training image of Poyang Lake and a reservoir model of Shengli Oilfield in China. The results indicate that the new method can reproduce the non-stationary characteristics better than the traditional method and is more suitable for simulation of delta-front deposits. These results show that the new method is a powerful tool for modelling a reservoir with non-stationary characteristics.
International Nuclear Information System (INIS)
Sarwar, M.; Gillibrand, D.; Bradbury, S.R.
1991-01-01
Advanced surface engineering technologies (physical and chemical vapour deposition) have been successfully applied to high speed steel and carbide cutting tools, and the potential benefits in terms of both performance and longer tool life, are now well established. Although major achievements have been reported by many manufacturers and users, there are a number of applications where surface engineering has been unsuccessful. Considerable attention has been given to the film characteristics and the variables associated with its properties; however, very little attention has been directed towards the benefits to the tool user. In order to apply surface engineering technology effectively to cutting tools, the coater needs to have accurate information relating to cutting conditions, i.e. cutting forces, stress and temperature etc. The present paper describes results obtained with single- and multiple-point cutting tools with examples of failures, which should help the surface coater to appreciate the significance of the cutting conditions, and in particular the magnitude of the forces and stresses present during cutting processes. These results will assist the development of a systems approach to cutting tool technology and surface engineering with a view to developing an improved product. (orig.)
Directory of Open Access Journals (Sweden)
Wenbo Duan
2017-12-01
Full Text Available Ultrasonic guided waves are widely used to inspect and monitor the structural integrity of plates and plate-like structures, such as ship hulls and large storage-tank floors. Recently, ultrasonic guided waves have also been used to remove ice and fouling from ship hulls, wind-turbine blades and aeroplane wings. In these applications, the strength of the sound source must be high for scanning a large area, or to break the bond between ice, fouling and plate substrate. More than one transducer may be used to achieve maximum sound power output. However, multiple sources can interact with each other, and form a sound field in the structure with local constructive and destructive regions. Destructive regions are weak regions and shall be avoided. When multiple transducers are used it is important that they are arranged in a particular way so that the desired wave modes can be excited to cover the whole structure. The objective of this paper is to provide a theoretical basis for generating particular wave mode patterns in finite-width rectangular plates whose length is assumed to be infinitely long with respect to its width and thickness. The wave modes have displacements in both width and thickness directions, and are thus different from the classical Lamb-type wave modes. A two-dimensional semi-analytical finite element (SAFE method was used to study dispersion characteristics and mode shapes in the plate up to ultrasonic frequencies. The modal analysis provided information on the generation of modes suitable for a particular application. The number of point sources and direction of loading for the excitation of a few representative modes was investigated. Based on the SAFE analysis, a standard finite element modelling package, Abaqus, was used to excite the designed modes in a three-dimensional plate. The generated wave patterns in Abaqus were then compared with mode shapes predicted in the SAFE model. Good agreement was observed between the
Hanyu, Ryosuke; Tsuji, Toshiaki
This paper proposes a whole-body haptic sensing system that has multiple supporting points between the body frame and the end-effector. The system consists of an end-effector and multiple force sensors. Using this mechanism, the position of a contact force on the surface can be calculated without any sensor array. A haptic sensing system with a single supporting point structure has previously been developed by the present authors. However, the system has drawbacks such as low stiffness and low strength. Therefore, in this study, a mechanism with multiple supporting points was proposed and its performance was verified. In this paper, the basic concept of the mechanism is first introduced. Next, an evaluation of the proposed method, performed by conducting some experiments, is presented.
Blettner, D.P.; He, Z.; Hu, S.; Bettis, R.
Organizations learn and adapt their aspiration levels based on reference points (prior aspiration, prior performance, and prior performance of reference groups). The relative attention that organizations allocate to these reference points impacts organizational search and strategic decisions.
DEFF Research Database (Denmark)
Kessler, Timo Christian; Nilsson, Bertel; Klint, Knud Erik
2010-01-01
(TPROGS) of alternating geological facies. The second method, multiple-point statistics, uses training images to estimate the conditional probability of sand-lenses at a certain location. Both methods respect field observations such as local stratigraphy, however, only the multiple-point statistics can...... of sand-lenses in clay till. Sand-lenses mainly account for horizontal transport and are prioritised in this study. Based on field observations, the distribution has been modeled using two different geostatistical approaches. One method uses a Markov chain model calculating the transition probabilities...
Langley, Tessa E; McNeill, Ann; Lewis, Sarah; Szatkowski, Lisa; Quinn, Casey
2012-11-01
To evaluate the effect of tobacco control media campaigns and pharmaceutical company-funded advertising for nicotine replacement therapy (NRT) on smoking cessation activity. Multiple time series analysis using structural vector autoregression, January 2002-May 2010. England and Wales. Tobacco control campaign data from the Central Office of Information; commercial NRT campaign data; data on calls to the National Health Service (NHS) stop smoking helpline from the Department of Health; point-of-sale data on over-the-counter (OTC) sales of NRT; and prescribing data from The Health Improvement Network (THIN), a database of UK primary care records. Monthly calls to the NHS stop smoking helpline and monthly rates of OTC sales and prescribing of NRT. A 1% increase in tobacco control television ratings (TVRs), a standard measure of advertising exposure, was associated with a statistically significant 0.085% increase in calls in the same month (P = 0.007), and no statistically significant effect in subsequent months. Tobacco control TVRs were not associated with OTC NRT sales or prescribed NRT. NRT advertising TVRs had a significant effect on NRT sales which became non-significant in the seasonally adjusted model, and no significant effect on prescribing or calls. Tobacco control campaigns appear to be more effective at triggering quitting behaviour than pharmaceutical company NRT campaigns. Any effect of such campaigns on quitting behaviour seems to be restricted to the month of the campaign, suggesting that such campaigns need to be sustained over time. © 2012 The Authors, Addiction © 2012 Society for the Study of Addiction.
Channel capacity of TDD-OFDM-MIMO for multiple access points in a wireless single-frequency-network
DEFF Research Database (Denmark)
Takatori, Y.; Fitzek, Frank; Tsunekawa, K.
2005-01-01
MIMO data transmission scheme, which combines Single-Frequency-Network (SFN) with TDD-OFDM-MIMO applied for wireless LAN networks. In our proposal, we advocate to use SFN for multiple access points (MAP) MIMO data transmission. The goal of this approach is to achieve very high channel capacity in both......The multiple-input-multiple-output (MIMO) technique is the most attractive candidate to improve the spectrum efficiency in the next generation wireless communication systems. However, the efficiency of MIMO techniques reduces in the line of sight (LOS) environments. In this paper, we propose a new...
Incorporating measurement error in n = 1 psychological autoregressive modeling
Schuurman, Noémi K.; Houtveen, Jan H.; Hamaker, Ellen L.
2015-01-01
Measurement error is omnipresent in psychological data. However, the vast majority of applications of autoregressive time series analyses in psychology do not take measurement error into account. Disregarding measurement error when it is present in the data results in a bias of the autoregressive parameters. We discuss two models that take measurement error into account: An autoregressive model with a white noise term (AR+WN), and an autoregressive moving average (ARMA) model. In a simulation study we compare the parameter recovery performance of these models, and compare this performance for both a Bayesian and frequentist approach. We find that overall, the AR+WN model performs better. Furthermore, we find that for realistic (i.e., small) sample sizes, psychological research would benefit from a Bayesian approach in fitting these models. Finally, we illustrate the effect of disregarding measurement error in an AR(1) model by means of an empirical application on mood data in women. We find that, depending on the person, approximately 30–50% of the total variance was due to measurement error, and that disregarding this measurement error results in a substantial underestimation of the autoregressive parameters. PMID:26283988
Høyer, Anne-Sophie; Vignoli, Giulio; Mejer Hansen, Thomas; Thanh Vu, Le; Keefer, Donald A.; Jørgensen, Flemming
2017-12-01
Most studies on the application of geostatistical simulations based on multiple-point statistics (MPS) to hydrogeological modelling focus on relatively fine-scale models and concentrate on the estimation of facies-level structural uncertainty. Much less attention is paid to the use of input data and optimal construction of training images. For instance, even though the training image should capture a set of spatial geological characteristics to guide the simulations, the majority of the research still relies on 2-D or quasi-3-D training images. In the present study, we demonstrate a novel strategy for 3-D MPS modelling characterized by (i) realistic 3-D training images and (ii) an effective workflow for incorporating a diverse group of geological and geophysical data sets. The study covers an area of 2810 km2 in the southern part of Denmark. MPS simulations are performed on a subset of the geological succession (the lower to middle Miocene sediments) which is characterized by relatively uniform structures and dominated by sand and clay. The simulated domain is large and each of the geostatistical realizations contains approximately 45 million voxels with size 100 m × 100 m × 5 m. Data used for the modelling include water well logs, high-resolution seismic data, and a previously published 3-D geological model. We apply a series of different strategies for the simulations based on data quality, and develop a novel method to effectively create observed spatial trends. The training image is constructed as a relatively small 3-D voxel model covering an area of 90 km2. We use an iterative training image development strategy and find that even slight modifications in the training image create significant changes in simulations. Thus, this study shows how to include both the geological environment and the type and quality of input information in order to achieve optimal results from MPS modelling. We present a practical workflow to build the training image and
Tedgren, Åsa Carlsson; Plamondon, Mathieu; Beaulieu, Luc
2015-07-07
The aim of this work was to investigate how dose distributions calculated with the collapsed cone (CC) algorithm depend on the size of the water phantom used in deriving the point kernel for multiple scatter. A research version of the CC algorithm equipped with a set of selectable point kernels for multiple-scatter dose that had initially been derived in water phantoms of various dimensions was used. The new point kernels were generated using EGSnrc in spherical water phantoms of radii 5 cm, 7.5 cm, 10 cm, 15 cm, 20 cm, 30 cm and 50 cm. Dose distributions derived with CC in water phantoms of different dimensions and in a CT-based clinical breast geometry were compared to Monte Carlo (MC) simulations using the Geant4-based brachytherapy specific MC code Algebra. Agreement with MC within 1% was obtained when the dimensions of the phantom used to derive the multiple-scatter kernel were similar to those of the calculation phantom. Doses are overestimated at phantom edges when kernels are derived in larger phantoms and underestimated when derived in smaller phantoms (by around 2% to 7% depending on distance from source and phantom dimensions). CC agrees well with MC in the high dose region of a breast implant and is superior to TG43 in determining skin doses for all multiple-scatter point kernel sizes. Increased agreement between CC and MC is achieved when the point kernel is comparable to breast dimensions. The investigated approximation in multiple scatter dose depends on the choice of point kernel in relation to phantom size and yields a significant fraction of the total dose only at distances of several centimeters from a source/implant which correspond to volumes of low doses. The current implementation of the CC algorithm utilizes a point kernel derived in a comparatively large (radius 20 cm) water phantom. A fixed point kernel leads to predictable behaviour of the algorithm with the worst case being a source/implant located well within a patient
Yi, Faliu; Lee, Jieun; Moon, Inkyu
2014-05-01
The reconstruction of multiple depth images with a ray back-propagation algorithm in three-dimensional (3D) computational integral imaging is computationally burdensome. Further, a reconstructed depth image consists of a focus and an off-focus area. Focus areas are 3D points on the surface of an object that are located at the reconstructed depth, while off-focus areas include 3D points in free-space that do not belong to any object surface in 3D space. Generally, without being removed, the presence of an off-focus area would adversely affect the high-level analysis of a 3D object, including its classification, recognition, and tracking. Here, we use a graphics processing unit (GPU) that supports parallel processing with multiple processors to simultaneously reconstruct multiple depth images using a lookup table containing the shifted values along the x and y directions for each elemental image in a given depth range. Moreover, each 3D point on a depth image can be measured by analyzing its statistical variance with its corresponding samples, which are captured by the two-dimensional (2D) elemental images. These statistical variances can be used to classify depth image pixels as either focus or off-focus points. At this stage, the measurement of focus and off-focus points in multiple depth images is also implemented in parallel on a GPU. Our proposed method is conducted based on the assumption that there is no occlusion of the 3D object during the capture stage of the integral imaging process. Experimental results have demonstrated that this method is capable of removing off-focus points in the reconstructed depth image. The results also showed that using a GPU to remove the off-focus points could greatly improve the overall computational speed compared with using a CPU.
Shih, Ching-Hsiang; Shih, Ching-Tien; Peng, Chin-Ling
2011-01-01
This study evaluated whether two people with multiple disabilities would be able to improve their pointing performance through an Automatic Target Acquisition Program (ATAP) and a newly developed mouse driver (i.e. a new mouse driver replaces standard mouse driver, and is able to monitor mouse movement and intercept click action). Initially, both…
Nurhaida, Subanar, Abdurakhman, Abadi, Agus Maman
2017-08-01
Seismic data is usually modelled using autoregressive processes. The aim of this paper is to find the arrival times of the seismic waves of Mt. Rinjani in Indonesia. Kitagawa algorithm's is used to detect the seismic P and S-wave. Householder transformation used in the algorithm made it effectively finding the number of change points and parameters of the autoregressive models. The results show that the use of Box-Cox transformation on the variable selection level makes the algorithm works well in detecting the change points. Furthermore, when the basic span of the subinterval is set 200 seconds and the maximum AR order is 20, there are 8 change points which occur at 1601, 2001, 7401, 7601,7801, 8001, 8201 and 9601. Finally, The P and S-wave arrival times are detected at time 1671 and 2045 respectively using a precise detection algorithm.
A complex autoregressive model and application to monthly temperature forecasts
Directory of Open Access Journals (Sweden)
X. Gu
2005-11-01
Full Text Available A complex autoregressive model was established based on the mathematic derivation of the least squares for the complex number domain which is referred to as the complex least squares. The model is different from the conventional way that the real number and the imaginary number are separately calculated. An application of this new model shows a better forecast than forecasts from other conventional statistical models, in predicting monthly temperature anomalies in July at 160 meteorological stations in mainland China. The conventional statistical models include an autoregressive model, where the real number and the imaginary number are separately disposed, an autoregressive model in the real number domain, and a persistence-forecast model.
Testing and modelling autoregressive conditional heteroskedasticity of streamflow processes
Directory of Open Access Journals (Sweden)
W. Wang
2005-01-01
Full Text Available Conventional streamflow models operate under the assumption of constant variance or season-dependent variances (e.g. ARMA (AutoRegressive Moving Average models for deseasonalized streamflow series and PARMA (Periodic AutoRegressive Moving Average models for seasonal streamflow series. However, with McLeod-Li test and Engle's Lagrange Multiplier test, clear evidences are found for the existence of autoregressive conditional heteroskedasticity (i.e. the ARCH (AutoRegressive Conditional Heteroskedasticity effect, a nonlinear phenomenon of the variance behaviour, in the residual series from linear models fitted to daily and monthly streamflow processes of the upper Yellow River, China. It is shown that the major cause of the ARCH effect is the seasonal variation in variance of the residual series. However, while the seasonal variation in variance can fully explain the ARCH effect for monthly streamflow, it is only a partial explanation for daily flow. It is also shown that while the periodic autoregressive moving average model is adequate in modelling monthly flows, no model is adequate in modelling daily streamflow processes because none of the conventional time series models takes the seasonal variation in variance, as well as the ARCH effect in the residuals, into account. Therefore, an ARMA-GARCH (Generalized AutoRegressive Conditional Heteroskedasticity error model is proposed to capture the ARCH effect present in daily streamflow series, as well as to preserve seasonal variation in variance in the residuals. The ARMA-GARCH error model combines an ARMA model for modelling the mean behaviour and a GARCH model for modelling the variance behaviour of the residuals from the ARMA model. Since the GARCH model is not followed widely in statistical hydrology, the work can be a useful addition in terms of statistical modelling of daily streamflow processes for the hydrological community.
Visser, Renée M; Lau-Zhu, Alex; Henson, Richard N; Holmes, Emily A
2018-03-19
Memories that have strong emotions associated with them are particularly resilient to forgetting. This is not necessarily problematic, however some aspects of memory can be. In particular, the involuntary expression of those memories, e.g. intrusive memories after trauma, are core to certain psychological disorders. Since the beginning of this century, research using animal models shows that it is possible to change the underlying memory, for example by interfering with its consolidation or reconsolidation. While the idea of targeting maladaptive memories is promising for the treatment of stress and anxiety disorders, a direct application of the procedures used in non-human animals to humans in clinical settings is not straightforward. In translational research, more attention needs to be paid to specifying what aspect of memory (i) can be modified and (ii) should be modified. This requires a clear conceptualization of what aspect of memory is being targeted, and how different memory expressions may map onto clinical symptoms. Furthermore, memory processes are dynamic, so procedural details concerning timing are crucial when implementing a treatment and when assessing its effectiveness. To target emotional memory in its full complexity, including its malleability, science cannot rely on a single method, species or paradigm. Rather, a constructive dialogue is needed between multiple levels of research, all the way 'from mice to mental health'.This article is part of a discussion meeting issue 'Of mice and mental health: facilitating dialogue between basic and clinical neuroscientists'. © 2018 The Authors.
Modeling Autoregressive Processes with Moving-Quantiles-Implied Nonlinearity
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Isao Ishida
2015-01-01
Full Text Available We introduce and investigate some properties of a class of nonlinear time series models based on the moving sample quantiles in the autoregressive data generating process. We derive a test fit to detect this type of nonlinearity. Using the daily realized volatility data of Standard & Poor’s 500 (S&P 500 and several other indices, we obtained good performance using these models in an out-of-sample forecasting exercise compared with the forecasts obtained based on the usual linear heterogeneous autoregressive and other models of realized volatility.
Application of autoregressive moving average model in reactor noise analysis
International Nuclear Information System (INIS)
Tran Dinh Tri
1993-01-01
The application of an autoregressive (AR) model to estimating noise measurements has achieved many successes in reactor noise analysis in the last ten years. The physical processes that take place in the nuclear reactor, however, are described by an autoregressive moving average (ARMA) model rather than by an AR model. Consequently more correct results could be obtained by applying the ARMA model instead of the AR model to reactor noise analysis. In this paper the system of the generalised Yule-Walker equations is derived from the equation of an ARMA model, then a method for its solution is given. Numerical results show the applications of the method proposed. (author)
Zhao, Jing; Zong, Haili
2018-01-01
In this paper, we propose parallel and cyclic iterative algorithms for solving the multiple-set split equality common fixed-point problem of firmly quasi-nonexpansive operators. We also combine the process of cyclic and parallel iterative methods and propose two mixed iterative algorithms. Our several algorithms do not need any prior information about the operator norms. Under mild assumptions, we prove weak convergence of the proposed iterative sequences in Hilbert spaces. As applications, we obtain several iterative algorithms to solve the multiple-set split equality problem.
Estimation bias and bias correction in reduced rank autoregressions
DEFF Research Database (Denmark)
Nielsen, Heino Bohn
2017-01-01
This paper characterizes the finite-sample bias of the maximum likelihood estimator (MLE) in a reduced rank vector autoregression and suggests two simulation-based bias corrections. One is a simple bootstrap implementation that approximates the bias at the MLE. The other is an iterative root...
A note on intrinsic conditional autoregressive models for disconnected graphs
Freni-Sterrantino, Anna; Ventrucci, Massimo; Rue, Haavard
2018-01-01
In this note we discuss (Gaussian) intrinsic conditional autoregressive (CAR) models for disconnected graphs, with the aim of providing practical guidelines for how these models should be defined, scaled and implemented. We show how these suggestions can be implemented in two examples, on disease mapping.
Robust bayesian analysis of an autoregressive model with ...
African Journals Online (AJOL)
In this work, robust Bayesian analysis of the Bayesian estimation of an autoregressive model with exponential innovations is performed. Using a Bayesian robustness methodology, we show that, using a suitable generalized quadratic loss, we obtain optimal Bayesian estimators of the parameters corresponding to the ...
A General Representation Theorem for Integrated Vector Autoregressive Processes
DEFF Research Database (Denmark)
Franchi, Massimo
We study the algebraic structure of an I(d) vector autoregressive process, where d is restricted to be an integer. This is useful to characterize its polynomial cointegrating relations and its moving average representation, that is to prove a version of the Granger representation theorem valid...
Modeling corporate defaults: Poisson autoregressions with exogenous covariates (PARX)
DEFF Research Database (Denmark)
Agosto, Arianna; Cavaliere, Guiseppe; Kristensen, Dennis
We develop a class of Poisson autoregressive models with additional covariates (PARX) that can be used to model and forecast time series of counts. We establish the time series properties of the models, including conditions for stationarity and existence of moments. These results are in turn used...
Single-Index Additive Vector Autoregressive Time Series Models
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
Optimal hedging with the cointegrated vector autoregressive model
DEFF Research Database (Denmark)
Gatarek, Lukasz; Johansen, Søren
We derive the optimal hedging ratios for a portfolio of assets driven by a Coin- tegrated Vector Autoregressive model (CVAR) with general cointegration rank. Our hedge is optimal in the sense of minimum variance portfolio. We consider a model that allows for the hedges to be cointegrated with the...
A note on intrinsic conditional autoregressive models for disconnected graphs
Freni-Sterrantino, Anna
2018-05-23
In this note we discuss (Gaussian) intrinsic conditional autoregressive (CAR) models for disconnected graphs, with the aim of providing practical guidelines for how these models should be defined, scaled and implemented. We show how these suggestions can be implemented in two examples, on disease mapping.
An extension of cointegration to fractional autoregressive processes
DEFF Research Database (Denmark)
Johansen, Søren
This paper contains an overview of some recent results on the statistical analysis of cofractional processes, see Johansen and Nielsen (2010). We first give an brief summary of the analysis of cointegration in the vector autoregressive model and then show how this can be extended to fractional pr...
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.
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 ...
Roadside Multiple Objects Extraction from Mobile Laser Scanning Point Cloud Based on DBN
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LUO Haifeng
2018-02-01
Full Text Available This paper proposed an novel algorithm for exploring deep belief network (DBN architectures to extract and recognize roadside facilities (trees,cars and traffic poles from mobile laser scanning (MLS point cloud.The proposed methods firstly partitioned the raw MLS point cloud into blocks and then removed the ground and building points.In order to partition the off-ground objects into individual objects,off-ground points were organized into an Octree structure and clustered into candidate objects based on connected component.To improve segmentation performance on clusters containing overlapped objects,a refining processing using a voxel-based normalized cut was then implemented.In addition,multi-view features descriptor was generated for each independent roadside facilities based on binary images.Finally,a deep belief network (DBN was trained to extract trees,cars and traffic pole objects.Experiments are undertaken to evaluate the validities of the proposed method with two datasets acquired by Lynx Mobile Mapper System.The precision of trees,cars and traffic poles objects extraction results respectively was 97.31%,97.79% and 92.78%.The recall was 98.30%,98.75% and 96.77% respectively.The quality is 95.70%,93.81% and 90.00%.And the F1 measure was 97.80%,96.81% and 94.73%.
International Nuclear Information System (INIS)
Yang Ming; Xiao Gang; Peng Youlin
2010-01-01
Objective: to investigate the therapeutic efficacy of multiple-point injection with absolute alcohol and pinyangmycin under digital fluoroscopic guidance for superficial venous malformations. Methods: By using a disposal venous transfusion needle the superficial venous malformation was punctured and then contrast media lohexol was injected in to visualize the tumor body, which was followed by the injection of ethanol and pinyangmycin when the needle was confirmed in the correct position. The procedure was successfully performed in 31 patients. The clinical results were observed and analyzed. Results: After one treatment complete cure was achieved in 21 cases and marked effect was obtained in 8 cases, with a total effectiveness of 93.5%. Conclusion: Multiple-point injection with ethanol and pinyangmycin under digital fluoroscopic guidance is an effective and safe technique for the treatment of superficial venous malformations, especially for the lesions that are deeply located and ill-defined. (authors)
Stable Parameter Estimation for Autoregressive Equations with Random Coefficients
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V. B. Goryainov
2014-01-01
Full Text Available In recent yearsthere has been a growing interest in non-linear time series models. They are more flexible than traditional linear models and allow more adequate description of real data. Among these models a autoregressive model with random coefficients plays an important role. It is widely used in various fields of science and technology, for example, in physics, biology, economics and finance. The model parameters are the mean values of autoregressive coefficients. Their evaluation is the main task of model identification. The basic method of estimation is still the least squares method, which gives good results for Gaussian time series, but it is quite sensitive to even small disturbancesin the assumption of Gaussian observations. In this paper we propose estimates, which generalize the least squares estimate in the sense that the quadratic objective function is replaced by an arbitrary convex and even function. Reasonable choice of objective function allows you to keep the benefits of the least squares estimate and eliminate its shortcomings. In particular, you can make it so that they will be almost as effective as the least squares estimate in the Gaussian case, but almost never loose in accuracy with small deviations of the probability distribution of the observations from the Gaussian distribution.The main result is the proof of consistency and asymptotic normality of the proposed estimates in the particular case of the one-parameter model describing the stationary process with finite variance. Another important result is the finding of the asymptotic relative efficiency of the proposed estimates in relation to the least squares estimate. This allows you to compare the two estimates, depending on the probability distribution of innovation process and of autoregressive coefficients. The results can be used to identify an autoregressive process, especially with nonGaussian nature, and/or of autoregressive processes observed with gross
Modeling a Single SEP Event from Multiple Vantage Points Using the iPATH Model
Hu, Junxiang; Li, Gang; Fu, Shuai; Zank, Gary; Ao, Xianzhi
2018-02-01
Using the recently extended 2D improved Particle Acceleration and Transport in the Heliosphere (iPATH) model, we model an example gradual solar energetic particle event as observed at multiple locations. Protons and ions that are energized via the diffusive shock acceleration mechanism are followed at a 2D coronal mass ejection-driven shock where the shock geometry varies across the shock front. The subsequent transport of energetic particles, including cross-field diffusion, is modeled by a Monte Carlo code that is based on a stochastic differential equation method. Time intensity profiles and particle spectra at multiple locations and different radial distances, separated in longitudes, are presented. The results shown here are relevant to the upcoming Parker Solar Probe mission.
Jandrisevits, Carmen; Marschallinger, Robert
2014-05-01
Quarternary sediments in overdeepened alpine valleys and basins in the Eastern Alps bear substantial groundwater resources. The associated aquifer systems are generally geometrically complex with highly variable hydraulic properties. 3D geological models provide predictions of both geometry and properties of the subsurface required for subsequent modelling of groundwater flow and transport. In hydrology, geostatistical Kriging and Kriging based conditional simulations are widely used to predict the spatial distribution of hydrofacies. In the course of investigating the shallow aquifer structures in the Zell basin in the Upper Salzach valley (Salzburg, Austria), a benchmark of available geostatistical modelling and simulation methods was performed: traditional variogram based geostatistical methods, i.e. Indicator Kriging, Sequential Indicator Simulation and Sequential Indicator Co - Simulation were used as well as Multiple Point Statistics. The ~ 6 km2 investigation area is sampled by 56 drillings with depths of 5 to 50 m; in addition, there are 2 geophysical sections with lengths of 2 km and depths of 50 m. Due to clustered drilling sites, indicator Kriging models failed to consistently model the spatial variability of hydrofacies. Using classical variogram based geostatistical simulation (SIS), equally probable realizations were generated with differences among the realizations providing an uncertainty measure. The yielded models are unstructured from a geological point - they do not portray the shapes and lateral extensions of associated sedimentary units. Since variograms consider only two - point spatial correlations, they are unable to capture the spatial variability of complex geological structures. The Multiple Point Statistics approach overcomes these limitations of two point statistics as it uses a Training image instead of variograms. The 3D Training Image can be seen as a reference facies model where geological knowledge about depositional
Meta-Analysis of Effect Sizes Reported at Multiple Time Points Using General Linear Mixed Model
Musekiwa, Alfred; Manda, Samuel O. M.; Mwambi, Henry G.; Chen, Ding-Geng
2016-01-01
Meta-analysis of longitudinal studies combines effect sizes measured at pre-determined time points. The most common approach involves performing separate univariate meta-analyses at individual time points. This simplistic approach ignores dependence between longitudinal effect sizes, which might result in less precise parameter estimates. In this paper, we show how to conduct a meta-analysis of longitudinal effect sizes where we contrast different covariance structures for dependence between effect sizes, both within and between studies. We propose new combinations of covariance structures for the dependence between effect size and utilize a practical example involving meta-analysis of 17 trials comparing postoperative treatments for a type of cancer, where survival is measured at 6, 12, 18 and 24 months post randomization. Although the results from this particular data set show the benefit of accounting for within-study serial correlation between effect sizes, simulations are required to confirm these results. PMID:27798661
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Sarkar Amal
2013-11-01
Full Text Available In this paper we report the measurements of the various moments mean (M, standard deviation (σ skewness (S and kurtosis (κ of the net-Kaon multiplicity distribution at midrapidity from Au+Au collisions at √sNN = 7.7 to 200 GeV in the STAR experiment at RHIC in an effort to locate the critical point in the QCD phase diagram. These moments and their products are related to the thermodynamic susceptibilities of conserved quantities such as net baryon number, net charge, and net strangeness as also to the correlation length of the system. A non-monotonic behavior of these variable indicate the presence of the critical point. In this work we also present the moments products Sσ, κσ2 of net-Kaon multiplicity distribution as a function of collision centrality and energies. The energy and the centrality dependence of higher moments of net-Kaons and their products have been compared with it0s Poisson expectation and with simulations from AMPT which does not include the critical point. From the measurement at all seven available beam energies, we find no evidence for a critical point in the QCD phase diagram for √sNN below 200 GeV.
Autoregressive-model-based missing value estimation for DNA microarray time series data.
Choong, Miew Keen; Charbit, Maurice; Yan, Hong
2009-01-01
Missing value estimation is important in DNA microarray data analysis. A number of algorithms have been developed to solve this problem, but they have several limitations. Most existing algorithms are not able to deal with the situation where a particular time point (column) of the data is missing entirely. In this paper, we present an autoregressive-model-based missing value estimation method (ARLSimpute) that takes into account the dynamic property of microarray temporal data and the local similarity structures in the data. ARLSimpute is especially effective for the situation where a particular time point contains many missing values or where the entire time point is missing. Experiment results suggest that our proposed algorithm is an accurate missing value estimator in comparison with other imputation methods on simulated as well as real microarray time series datasets.
A Two-Factor Autoregressive Moving Average Model Based on Fuzzy Fluctuation Logical Relationships
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Shuang Guan
2017-10-01
Full Text Available Many of the existing autoregressive moving average (ARMA forecast models are based on one main factor. In this paper, we proposed a new two-factor first-order ARMA forecast model based on fuzzy fluctuation logical relationships of both a main factor and a secondary factor of a historical training time series. Firstly, we generated a fluctuation time series (FTS for two factors by calculating the difference of each data point with its previous day, then finding the absolute means of the two FTSs. We then constructed a fuzzy fluctuation time series (FFTS according to the defined linguistic sets. The next step was establishing fuzzy fluctuation logical relation groups (FFLRGs for a two-factor first-order autoregressive (AR(1 model and forecasting the training data with the AR(1 model. Then we built FFLRGs for a two-factor first-order autoregressive moving average (ARMA(1,m model. Lastly, we forecasted test data with the ARMA(1,m model. To illustrate the performance of our model, we used real Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX and Dow Jones datasets as a secondary factor to forecast TAIEX. The experiment results indicate that the proposed two-factor fluctuation ARMA method outperformed the one-factor method based on real historic data. The secondary factor may have some effects on the main factor and thereby impact the forecasting results. Using fuzzified fluctuations rather than fuzzified real data could avoid the influence of extreme values in historic data, which performs negatively while forecasting. To verify the accuracy and effectiveness of the model, we also employed our method to forecast the Shanghai Stock Exchange Composite Index (SHSECI from 2001 to 2015 and the international gold price from 2000 to 2010.
Clutter-free Visualization of Large Point Symbols at Multiple Scales by Offset Quadtrees
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ZHANG Xiang
2016-08-01
Full Text Available To address the cartographic problems in map mash-up applications in the Web 2.0 context, this paper studies a clutter-free technique for visualizing large symbols on Web maps. Basically, a quadtree is used to select one symbol in each grid cell at each zoom level. To resolve the symbol overlaps between neighboring quad-grids, multiple offsets are applied to the quadtree and a voting strategy is used to compute the significant level of symbols for their selection at multiple scales. The method is able to resolve spatial conflicts without explicit conflict detection, thus enabling a highly efficient processing. Also the resulting map forms a visual hierarchy of semantic importance. We discuss issues such as the relative importance, symbol-to-grid size ratio, and effective offset schemes, and propose two extensions to make better use of the free space available on the map. Experiments were carried out to validate the technique,which demonstrates its robustness and efficiency (a non-optimal implementation leads to a sub-second processing for datasets of a 105 magnitude.
Tracking an oil slick from multiple natural sources, Coal Oil Point, California
International Nuclear Information System (INIS)
Leifer, Ira; Luyendyk, Bruce; Broderick, Kris
2006-01-01
Oil slicks on the ocean surface emitted from natural marine hydrocarbon seeps offshore from Coal Oil Point in the Santa Barbara Channel, California were tracked and sampled over a 2-h period. The objectives were to characterize the seep oil and to track its composition over time using a new sampling device, a catamaran drum sampler (CATDRUMS). The sampler was designed and developed at UCSB. Chromatograms showed that oil originating from an informally named, very active seep area, Shane Seep, primarily evolved during the first hour due to mixing with oil originating from a convergence zone slick surrounding Shane Seep. (author)
Tracking an oil slick from multiple natural sources, Coal Oil Point, California
Energy Technology Data Exchange (ETDEWEB)
Leifer, Ira [Marine Sciences Institute, University of California, Santa Barbara, CA 93106 (United States); Luyendyk, Bruce [Department of Geological Sciences, University of California, Santa Barbara, CA 93106 (United States); Broderick, Kris [Exxon/Mobil Exploration Company, 13401 N. Freeway, Houston, TX 77060 (United States)
2006-06-15
Oil slicks on the ocean surface emitted from natural marine hydrocarbon seeps offshore from Coal Oil Point in the Santa Barbara Channel, California were tracked and sampled over a 2-h period. The objectives were to characterize the seep oil and to track its composition over time using a new sampling device, a catamaran drum sampler (CATDRUMS). The sampler was designed and developed at UCSB. Chromatograms showed that oil originating from an informally named, very active seep area, Shane Seep, primarily evolved during the first hour due to mixing with oil originating from a convergence zone slick surrounding Shane Seep. (author)
Xie, Dexuan; Volkmer, Hans W.; Ying, Jinyong
2016-04-01
The nonlocal dielectric approach has led to new models and solvers for predicting electrostatics of proteins (or other biomolecules), but how to validate and compare them remains a challenge. To promote such a study, in this paper, two typical nonlocal dielectric models are revisited. Their analytical solutions are then found in the expressions of simple series for a dielectric sphere containing any number of point charges. As a special case, the analytical solution of the corresponding Poisson dielectric model is also derived in simple series, which significantly improves the well known Kirkwood's double series expansion. Furthermore, a convolution of one nonlocal dielectric solution with a commonly used nonlocal kernel function is obtained, along with the reaction parts of these local and nonlocal solutions. To turn these new series solutions into a valuable research tool, they are programed as a free fortran software package, which can input point charge data directly from a protein data bank file. Consequently, different validation tests can be quickly done on different proteins. Finally, a test example for a protein with 488 atomic charges is reported to demonstrate the differences between the local and nonlocal models as well as the importance of using the reaction parts to develop local and nonlocal dielectric solvers.
LDPC decoder with a limited-precision FPGA-based floating-point multiplication coprocessor
Moberly, Raymond; O'Sullivan, Michael; Waheed, Khurram
2007-09-01
Implementing the sum-product algorithm, in an FPGA with an embedded processor, invites us to consider a tradeoff between computational precision and computational speed. The algorithm, known outside of the signal processing community as Pearl's belief propagation, is used for iterative soft-decision decoding of LDPC codes. We determined the feasibility of a coprocessor that will perform product computations. Our FPGA-based coprocessor (design) performs computer algebra with significantly less precision than the standard (e.g. integer, floating-point) operations of general purpose processors. Using synthesis, targeting a 3,168 LUT Xilinx FPGA, we show that key components of a decoder are feasible and that the full single-precision decoder could be constructed using a larger part. Soft-decision decoding by the iterative belief propagation algorithm is impacted both positively and negatively by a reduction in the precision of the computation. Reducing precision reduces the coding gain, but the limited-precision computation can operate faster. A proposed solution offers custom logic to perform computations with less precision, yet uses the floating-point format to interface with the software. Simulation results show the achievable coding gain. Synthesis results help theorize the the full capacity and performance of an FPGA-based coprocessor.
Multiple organ failure in the newborn: the point of view of the pathologist
Directory of Open Access Journals (Sweden)
Clara Gerosa
2014-06-01
Full Text Available One of the most severe events occurring in critically ill patients admitted to a neonatal intensive care unit (NICU center is represented by the multiple organ failure (MOF, a systemic inflammatory response leading to a progressive organ dysfunction and mortality in newborns. MOF may occur in newborns primarily affected by multiple single organ diseases, including respiratory distress syndrome neonatal sepsis with acute kidney injury, post-asphyxial hypoxic-ischemic encephalopathy and pandemic influenza A (H1N1 infection. In a previous article from our group, based on the histological examination of all organs at autopsy of newborns affected by MOF, all organs studied did not escape to be damaged, including thymus and pancreas normally not mentioned in the literature of MOF. The aim of this article is to review the most important pathological changes pathologists should look for in every case of MOF occurring in the perinatal period, with particular attention to systemic endothelial changes occurring in blood vessels in all organs and sytems. On the basis of our experience, matching data during the last phases of the clinicopathological diagnosis represents a useful method, much more productive as compared to the method based on giving pathological answers to the clinical questions prospected before autopsy. As for the pathological features observed in neonatal MOF, one of them deserves a particular attention: the vascular lesions, and in particular the multiple changes occurring during MOF development in endothelial cells, ending with the loss of the endothelial barrier, probably the most relevant histological lesion followed by the insurgence of interstitial edema and disseminated intravascular coagulation. Small vessels should be observed at high power, with particular attention to the size and shape of endothelial nuclei, in order to evidence endothelial swelling, probably the initial modification of the endothelial cells leading to their
A Microsoft Kinect-Based Point-of-Care Gait Assessment Framework for Multiple Sclerosis Patients.
Gholami, Farnood; Trojan, Daria A; Kovecses, Jozsef; Haddad, Wassim M; Gholami, Behnood
2017-09-01
Gait impairment is a prevalent and important difficulty for patients with multiple sclerosis (MS), a common neurological disorder. An easy to use tool to objectively evaluate gait in MS patients in a clinical setting can assist clinicians to perform an objective assessment. The overall objective of this study is to develop a framework to quantify gait abnormalities in MS patients using the Microsoft Kinect for the Windows sensor; an inexpensive, easy to use, portable camera. Specifically, we aim to evaluate its feasibility for utilization in a clinical setting, assess its reliability, evaluate the validity of gait indices obtained, and evaluate a novel set of gait indices based on the concept of dynamic time warping. In this study, ten ambulatory MS patients, and ten age and sex-matched normal controls were studied at one session in a clinical setting with gait assessment using a Kinect camera. The expanded disability status scale (EDSS) clinical ambulation score was calculated for the MS subjects, and patients completed the Multiple Sclerosis walking scale (MSWS). Based on this study, we established the potential feasibility of using a Microsoft Kinect camera in a clinical setting. Seven out of the eight gait indices obtained using the proposed method were reliable with intraclass correlation coefficients ranging from 0.61 to 0.99. All eight MS gait indices were significantly different from those of the controls (p-values less than 0.05). Finally, seven out of the eight MS gait indices were correlated with the objective and subjective gait measures (Pearson's correlation coefficients greater than 0.40). This study shows that the Kinect camera is an easy to use tool to assess gait in MS patients in a clinical setting.
Caulkins, Jonathan P.; Feichtinger, Gustav; Grass, Dieter; Hartl, Richard F.; Kort, Peter M.; Novak, Andreas J.; Seidl, Andrea
2013-01-01
We present a novel model of corruption dynamics in the form of a nonlinear optimal dynamic control problem. It has a tipping point, but one whose origins and character are distinct from that in the classic Schelling (1978) model. The decision maker choosing a level of corruption is the chief or some other kind of authority figure who presides over a bureaucracy whose state of corruption is influenced by the authority figure’s actions, and whose state in turn influences the pay-off for the authority figure. The policy interpretation is somewhat more optimistic than in other tipping models, and there are some surprising implications, notably that reforming the bureaucracy may be of limited value if the bureaucracy takes its cues from a corrupt leader. PMID:23565027
Caulkins, Jonathan P; Feichtinger, Gustav; Grass, Dieter; Hartl, Richard F; Kort, Peter M; Novak, Andreas J; Seidl, Andrea
2013-03-16
We present a novel model of corruption dynamics in the form of a nonlinear optimal dynamic control problem. It has a tipping point, but one whose origins and character are distinct from that in the classic Schelling (1978) model. The decision maker choosing a level of corruption is the chief or some other kind of authority figure who presides over a bureaucracy whose state of corruption is influenced by the authority figure's actions, and whose state in turn influences the pay-off for the authority figure. The policy interpretation is somewhat more optimistic than in other tipping models, and there are some surprising implications, notably that reforming the bureaucracy may be of limited value if the bureaucracy takes its cues from a corrupt leader.
A method for untriggered time-dependent searches for multiple flares from neutrino point sources
International Nuclear Information System (INIS)
Gora, D.; Bernardini, E.; Cruz Silva, A.H.
2011-04-01
A method for a time-dependent search for flaring astrophysical sources which can be potentially detected by large neutrino experiments is presented. The method uses a time-clustering algorithm combined with an unbinned likelihood procedure. By including in the likelihood function a signal term which describes the contribution of many small clusters of signal-like events, this method provides an effective way for looking for weak neutrino flares over different time-scales. The method is sensitive to an overall excess of events distributed over several flares which are not individually detectable. For standard cases (one flare) the discovery potential of the method is worse than a standard time-dependent point source analysis with unknown duration of the flare by a factor depending on the signal-to-background level. However, for flares sufficiently shorter than the total observation period, the method is more sensitive than a time-integrated analysis. (orig.)
Multiple Information Fusion Face Ｒecognition Using Key Feature Points
Directory of Open Access Journals (Sweden)
LIN Kezheng
2017-06-01
Full Text Available After years of face recognition research，due to the effect of illumination，noise and other conditions have led to the recognition rate is relatively low，2 d face recognition technology has couldn’t keep up with the pace of The Times the forefront，Although 3 d face recognition technology is developing step by step，but it has a higher complexity. In order to solve this problem，based on the traditional depth information positioning method and local characteristic analysis methods LFA，puts forward an improved 3 d face key feature points localization algorithm， and on the basis of the trained sample which obtained by complete cluster，further put forward the global and local feature extraction algorithm of weighted fusion. Through FＲGC and BU-3DFE experiment data comparison and analysis of the two face library，the method in terms of 3 d face recognition effect has a higher robustness.
A method for untriggered time-dependent searches for multiple flares from neutrino point sources
Energy Technology Data Exchange (ETDEWEB)
Gora, D. [Deutsches Elektronen-Synchrotron (DESY), Zeuthen (Germany); Institute of Nuclear Physics PAN, Cracow (Poland); Bernardini, E.; Cruz Silva, A.H. [Institute of Nuclear Physics PAN, Cracow (Poland)
2011-04-15
A method for a time-dependent search for flaring astrophysical sources which can be potentially detected by large neutrino experiments is presented. The method uses a time-clustering algorithm combined with an unbinned likelihood procedure. By including in the likelihood function a signal term which describes the contribution of many small clusters of signal-like events, this method provides an effective way for looking for weak neutrino flares over different time-scales. The method is sensitive to an overall excess of events distributed over several flares which are not individually detectable. For standard cases (one flare) the discovery potential of the method is worse than a standard time-dependent point source analysis with unknown duration of the flare by a factor depending on the signal-to-background level. However, for flares sufficiently shorter than the total observation period, the method is more sensitive than a time-integrated analysis. (orig.)
Liu, Mei-bing; Chen, Xing-wei; Chen, Ying
2015-07-01
Identification of the critical source areas of non-point source pollution is an important means to control the non-point source pollution within the watershed. In order to further reveal the impact of multiple time scales on the spatial differentiation characteristics of non-point source nitrogen loss, a SWAT model of Shanmei Reservoir watershed was developed. Based on the simulation of total nitrogen (TN) loss intensity of all 38 subbasins, spatial distribution characteristics of nitrogen loss and critical source areas were analyzed at three time scales of yearly average, monthly average and rainstorms flood process, respectively. Furthermore, multiple linear correlation analysis was conducted to analyze the contribution of natural environment and anthropogenic disturbance on nitrogen loss. The results showed that there were significant spatial differences of TN loss in Shanmei Reservoir watershed at different time scales, and the spatial differentiation degree of nitrogen loss was in the order of monthly average > yearly average > rainstorms flood process. TN loss load mainly came from upland Taoxi subbasin, which was identified as the critical source area. At different time scales, land use types (such as farmland and forest) were always the dominant factor affecting the spatial distribution of nitrogen loss, while the effect of precipitation and runoff on the nitrogen loss was only taken in no fertilization month and several processes of storm flood at no fertilization date. This was mainly due to the significant spatial variation of land use and fertilization, as well as the low spatial variability of precipitation and runoff.
Shi, Jinfei; Zhu, Songqing; Chen, Ruwen
2017-12-01
An order selection method based on multiple stepwise regressions is proposed for General Expression of Nonlinear Autoregressive model which converts the model order problem into the variable selection of multiple linear regression equation. The partial autocorrelation function is adopted to define the linear term in GNAR model. The result is set as the initial model, and then the nonlinear terms are introduced gradually. Statistics are chosen to study the improvements of both the new introduced and originally existed variables for the model characteristics, which are adopted to determine the model variables to retain or eliminate. So the optimal model is obtained through data fitting effect measurement or significance test. The simulation and classic time-series data experiment results show that the method proposed is simple, reliable and can be applied to practical engineering.
Single-Index Additive Vector Autoregressive Time Series Models
LI, YEHUA
2009-09-01
We study a new class of nonlinear autoregressive models for vector time series, where the current vector depends on single-indexes defined on the past lags and the effects of different lags have an additive form. A sufficient condition is provided for stationarity of such models. We also study estimation of the proposed model using P-splines, hypothesis testing, asymptotics, selection of the order of the autoregression and of the smoothing parameters and nonlinear forecasting. We perform simulation experiments to evaluate our model in various settings. We illustrate our methodology on a climate data set and show that our model provides more accurate yearly forecasts of the El Niño phenomenon, the unusual warming of water in the Pacific Ocean. © 2009 Board of the Foundation of the Scandinavian Journal of Statistics.
Kumaraswamy autoregressive moving average models for double bounded environmental data
Bayer, Fábio Mariano; Bayer, Débora Missio; Pumi, Guilherme
2017-12-01
In this paper we introduce the Kumaraswamy autoregressive moving average models (KARMA), which is a dynamic class of models for time series taking values in the double bounded interval (a,b) following the Kumaraswamy distribution. The Kumaraswamy family of distribution is widely applied in many areas, especially hydrology and related fields. Classical examples are time series representing rates and proportions observed over time. In the proposed KARMA model, the median is modeled by a dynamic structure containing autoregressive and moving average terms, time-varying regressors, unknown parameters and a link function. We introduce the new class of models and discuss conditional maximum likelihood estimation, hypothesis testing inference, diagnostic analysis and forecasting. In particular, we provide closed-form expressions for the conditional score vector and conditional Fisher information matrix. An application to environmental real data is presented and discussed.
Likelihood inference for a nonstationary fractional autoregressive model
DEFF Research Database (Denmark)
Johansen, Søren; Ørregård Nielsen, Morten
2010-01-01
This paper discusses model-based inference in an autoregressive model for fractional processes which allows the process to be fractional of order d or d-b. Fractional differencing involves infinitely many past values and because we are interested in nonstationary processes we model the data X1......,...,X_{T} given the initial values X_{-n}, n=0,1,..., as is usually done. The initial values are not modeled but assumed to be bounded. This represents a considerable generalization relative to all previous work where it is assumed that initial values are zero. For the statistical analysis we assume...... the conditional Gaussian likelihood and for the probability analysis we also condition on initial values but assume that the errors in the autoregressive model are i.i.d. with suitable moment conditions. We analyze the conditional likelihood and its derivatives as stochastic processes in the parameters, including...
Mathematical model with autoregressive process for electrocardiogram signals
Evaristo, Ronaldo M.; Batista, Antonio M.; Viana, Ricardo L.; Iarosz, Kelly C.; Szezech, José D., Jr.; Godoy, Moacir F. de
2018-04-01
The cardiovascular system is composed of the heart, blood and blood vessels. Regarding the heart, cardiac conditions are determined by the electrocardiogram, that is a noninvasive medical procedure. In this work, we propose autoregressive process in a mathematical model based on coupled differential equations in order to obtain the tachograms and the electrocardiogram signals of young adults with normal heartbeats. Our results are compared with experimental tachogram by means of Poincaré plot and dentrended fluctuation analysis. We verify that the results from the model with autoregressive process show good agreement with experimental measures from tachogram generated by electrical activity of the heartbeat. With the tachogram we build the electrocardiogram by means of coupled differential equations.
Does the nervous system use equilibrium-point control to guide single and multiple joint movements?
Bizzi, E; Hogan, N; Mussa-Ivaldi, F A; Giszter, S
1992-12-01
The hypothesis that the central nervous system (CNS) generates movement as a shift of the limb's equilibrium posture has been corroborated experimentally in studies involving single- and multijoint motions. Posture may be controlled through the choice of muscle length-tension curve that set agonist-antagonist torque-angle curves determining an equilibrium position for the limb and the stiffness about the joints. Arm trajectories seem to be generated through a control signal defining a series of equilibrium postures. The equilibrium-point hypothesis drastically simplifies the requisite computations for multijoint movements and mechanical interactions with complex dynamic objects in the environment. Because the neuromuscular system is springlike, the instantaneous difference between the arm's actual position and the equilibrium position specified by the neural activity can generate the requisite torques, avoiding the complex "inverse dynamic" problem of computing the torques at the joints. The hypothesis provides a simple, unified description of posture and movement as well as contact control task performance, in which the limb must exert force stably and do work on objects in the environment. The latter is a surprisingly difficult problem, as robotic experience has shown. The prior evidence for the hypothesis came mainly from psychophysical and behavioral experiments. Our recent work has shown that microstimulation of the frog spinal cord's premotoneural network produces leg movements to various positions in the frog's motor space. The hypothesis can now be investigated in the neurophysiological machinery of the spinal cord.
CICAAR - Convolutive ICA with an Auto-Regressive Inverse Model
DEFF Research Database (Denmark)
Dyrholm, Mads; Hansen, Lars Kai
2004-01-01
We invoke an auto-regressive IIR inverse model for convolutive ICA and derive expressions for the likelihood and its gradient. We argue that optimization will give a stable inverse. When there are more sensors than sources the mixing model parameters are estimated in a second step by least square...... estimation. We demonstrate the method on synthetic data and finally separate speech and music in a real room recording....
Testing exact rational expectations in cointegrated vector autoregressive models
DEFF Research Database (Denmark)
Johansen, Søren; Swensen, Anders Rygh
1999-01-01
This paper considers the testing of restrictions implied by rational expectations hypotheses in a cointegrated vector autoregressive model for I(1) variables. If the rational expectations involve one-step-ahead observations only and the coefficients are known, an explicit parameterization...... of the restrictions is found, and the maximum-likelihood estimator is derived by regression and reduced rank regression. An application is given to a present value model....
DeKock, Brandon; Sanders, Devon; Vanzwieten, Tannen; Capo-Lugo, Pedro
2011-01-01
The FASTSAT-HSV01 spacecraft is a microsatellite with magnetic torque rods as it sole attitude control actuator. FASTSAT s multiple payloads and mission functions require the Attitude Control System (ACS) to maintain Local Vertical Local Horizontal (LVLH)-referenced attitudes without spin-stabilization, while the pointing errors for some attitudes be significantly smaller than the previous best-demonstrated for this type of control system. The mission requires the ACS to hold multiple stable, unstable, and non-equilibrium attitudes, as well as eject a 3U CubeSat from an onboard P-POD and recover from the ensuing tumble. This paper describes the Attitude Control System, the reasons for design choices, how the ACS integrates with the rest of the spacecraft, and gives recommendations for potential future applications of the work.
Identification of BWR feedwater control system using autoregressive integrated model
International Nuclear Information System (INIS)
Kanemoto, Shigeru; Andoh, Yasumasa; Yamamoto, Fumiaki; Idesawa, Masato; Itoh, Kazuo.
1983-01-01
With the view of contributing toward more reliable interpretation of noise behavior under normal operating conditions, which is essential for correct detection and/or diagnosis of incipient anomalies in nuclear power plants by noise analysis technique, studies has been undertaken of the noise behavior in a BWR feedwater control system, with use made of a multivariate autoregressive modeling technique. Noise propagation mechanisms as well as open- and closed-loop responses in the system are identified from noise data by a method in which an autoregressive integrated model is introduced. The closed-loop responses obtained with this method are compared with transient data from an actual test, and confirmed to be reliable in estimating semi-quantitative features. Other analyses performed with this model also yield results that appear most reasonable in their physical characteristics. These results have demonstrated the effectiveness of the noise analyses technique based on the autoregressive integrated model for evaluating and diagnosing the performance of feedwater control systems. (author)
DEFF Research Database (Denmark)
Cordua, Knud Skou; Hansen, Thomas Mejer; Mosegaard, Klaus
2012-01-01
We present a general Monte Carlo full-waveform inversion strategy that integrates a priori information described by geostatistical algorithms with Bayesian inverse problem theory. The extended Metropolis algorithm can be used to sample the a posteriori probability density of highly nonlinear...... inverse problems, such as full-waveform inversion. Sequential Gibbs sampling is a method that allows efficient sampling of a priori probability densities described by geostatistical algorithms based on either two-point (e.g., Gaussian) or multiple-point statistics. We outline the theoretical framework......) Based on a posteriori realizations, complicated statistical questions can be answered, such as the probability of connectivity across a layer. (3) Complex a priori information can be included through geostatistical algorithms. These benefits, however, require more computing resources than traditional...
Directory of Open Access Journals (Sweden)
Gironi Maira
2013-01-01
Full Text Available Abstract Background Multiple Sclerosis (MS is a multi-factorial disease, where a single biomarker unlikely can provide comprehensive information. Moreover, due to the non-linearity of biomarkers, traditional statistic is both unsuitable and underpowered to dissect their relationship. Patients affected with primary (PP=14, secondary (SP=33, benign (BB=26, relapsing-remitting (RR=30 MS, and 42 sex and age matched healthy controls were studied. We performed a depth immune-phenotypic and functional analysis of peripheral blood mononuclear cell (PBMCs by flow-cytometry. Semantic connectivity maps (AutoCM were applied to find the natural associations among immunological markers. AutoCM is a special kind of Artificial Neural Network able to find consistent trends and associations among variables. The matrix of connections, visualized through minimum spanning tree, keeps non linear associations among variables and captures connection schemes among clusters. Results Complex immunological relationships were shown to be related to different disease courses. Low CD4IL25+ cells level was strongly related (link strength, ls=0.81 to SP MS. This phenotype was also associated to high CD4ROR+ cells levels (ls=0.56. BB MS was related to high CD4+IL13 cell levels (ls=0.90, as well as to high CD14+IL6 cells percentage (ls=0.80. RR MS was strongly (ls=0.87 related to CD4+IL25 high cell levels, as well indirectly to high percentages of CD4+IL13 cells. In this latter strong (ls=0.92 association could be confirmed the induction activity of the former cells (CD4+IL25 on the latter (CD4+IL13. Another interesting topographic data was the isolation of Th9 cells (CD4IL9 from the main part of the immunological network related to MS, suggesting a possible secondary role of this new described cell phenotype in MS disease. Conclusions This novel application of non-linear mathematical techniques suggests peculiar immunological signatures for different MS phenotypes. Notably, the
Vasileios Psychas, Dimitrios; Delikaraoglou, Demitris
2016-04-01
The future Global Navigation Satellite Systems (GNSS), including modernized GPS, GLONASS, Galileo and BeiDou, offer three or more signal carriers for civilian use and much more redundant observables. The additional frequencies can significantly improve the capabilities of the traditional geodetic techniques based on GPS signals at two frequencies, especially with regard to the availability, accuracy, interoperability and integrity of high-precision GNSS applications. Furthermore, highly redundant measurements can allow for robust simultaneous estimation of static or mobile user states including more parameters such as real-time tropospheric biases and more reliable ambiguity resolution estimates. This paper presents an investigation and analysis of accuracy improvement techniques in the Precise Point Positioning (PPP) method using signals from the fully operational (GPS and GLONASS), as well as the emerging (Galileo and BeiDou) GNSS systems. The main aim was to determine the improvement in both the positioning accuracy achieved and the time convergence it takes to achieve geodetic-level (10 cm or less) accuracy. To this end, freely available observation data from the recent Multi-GNSS Experiment (MGEX) of the International GNSS Service, as well as the open source program RTKLIB were used. Following a brief background of the PPP technique and the scope of MGEX, the paper outlines the various observational scenarios that were used in order to test various data processing aspects of PPP solutions with multi-frequency, multi-constellation GNSS systems. Results from the processing of multi-GNSS observation data from selected permanent MGEX stations are presented and useful conclusions and recommendations for further research are drawn. As shown, data fusion from GPS, GLONASS, Galileo and BeiDou systems is becoming increasingly significant nowadays resulting in a position accuracy increase (mostly in the less favorable East direction) and a large reduction of convergence
Singh, Sarvesh Kumar; Rani, Raj
2015-10-01
The study addresses the identification of multiple point sources, emitting the same tracer, from their limited set of merged concentration measurements. The identification, here, refers to the estimation of locations and strengths of a known number of simultaneous point releases. The source-receptor relationship is described in the framework of adjoint modelling by using an analytical Gaussian dispersion model. A least-squares minimization framework, free from an initialization of the release parameters (locations and strengths), is presented to estimate the release parameters. This utilizes the distributed source information observable from the given monitoring design and number of measurements. The technique leads to an exact retrieval of the true release parameters when measurements are noise free and exactly described by the dispersion model. The inversion algorithm is evaluated using the real data from multiple (two, three and four) releases conducted during Fusion Field Trials in September 2007 at Dugway Proving Ground, Utah. The release locations are retrieved, on average, within 25-45 m of the true sources with the distance from retrieved to true source ranging from 0 to 130 m. The release strengths are also estimated within a factor of three to the true release rates. The average deviations in retrieval of source locations are observed relatively large in two release trials in comparison to three and four release trials.
Dynamic analysis of multiple nuclear-coupled boiling channels based on a multi-point reactor model
International Nuclear Information System (INIS)
Lee, J.D.; Pan Chin
2005-01-01
This work investigates the non-linear dynamics and stabilities of a multiple nuclear-coupled boiling channel system based on a multi-point reactor model using the Galerkin nodal approximation method. The nodal approximation method for the multiple boiling channels developed by Lee and Pan [Lee, J.D., Pan, C., 1999. Dynamics of multiple parallel boiling channel systems with forced flows. Nucl. Eng. Des. 192, 31-44] is extended to address the two-phase flow dynamics in the present study. The multi-point reactor model, modified from Uehiro et al. [Uehiro, M., Rao, Y.F., Fukuda, K., 1996. Linear stability analysis on instabilities of in-phase and out-of-phase modes in boiling water reactors. J. Nucl. Sci. Technol. 33, 628-635], is employed to study a multiple-channel system with unequal steady-state neutron density distribution. Stability maps, non-linear dynamics and effects of major parameters on the multiple nuclear-coupled boiling channel system subject to a constant total flow rate are examined. This study finds that the void-reactivity feedback and neutron interactions among subcores are coupled and their competing effects may influence the system stability under different operating conditions. For those cases with strong neutron interaction conditions, by strengthening the void-reactivity feedback, the nuclear-coupled effect on the non-linear dynamics may induce two unstable oscillation modes, the supercritical Hopf bifurcation and the subcritical Hopf bifurcation. Moreover, for those cases with weak neutron interactions, by quadrupling the void-reactivity feedback coefficient, period-doubling and complex chaotic oscillations may appear in a three-channel system under some specific operating conditions. A unique type of complex chaotic attractor may evolve from the Rossler attractor because of the coupled channel-to-channel thermal-hydraulic and subcore-to-subcore neutron interactions. Such a complex chaotic attractor has the imbedding dimension of 5 and the
Sin, Kuek Jia; Cheong, Chin Wen; Hooi, Tan Siow
2017-04-01
This study aims to investigate the crude oil volatility using a two components autoregressive conditional heteroscedasticity (ARCH) model with the inclusion of abrupt jump feature. The model is able to capture abrupt jumps, news impact, clustering volatility, long persistence volatility and heavy-tailed distributed error which are commonly observed in the crude oil time series. For the empirical study, we have selected the WTI crude oil index from year 2000 to 2016. The results found that by including the multiple-abrupt jumps in ARCH model, there are significant improvements of estimation evaluations as compared with the standard ARCH models. The outcomes of this study can provide useful information for risk management and portfolio analysis in the crude oil markets.
Methodology for the AutoRegressive Planet Search (ARPS) Project
Feigelson, Eric; Caceres, Gabriel; ARPS Collaboration
2018-01-01
The detection of periodic signals of transiting exoplanets is often impeded by the presence of aperiodic photometric variations. This variability is intrinsic to the host star in space-based observations (typically arising from magnetic activity) and from observational conditions in ground-based observations. The most common statistical procedures to remove stellar variations are nonparametric, such as wavelet decomposition or Gaussian Processes regression. However, many stars display variability with autoregressive properties, wherein later flux values are correlated with previous ones. Providing the time series is evenly spaced, parametric autoregressive models can prove very effective. Here we present the methodology of the Autoregessive Planet Search (ARPS) project which uses Autoregressive Integrated Moving Average (ARIMA) models to treat a wide variety of stochastic short-memory processes, as well as nonstationarity. Additionally, we introduce a planet-search algorithm to detect periodic transits in the time-series residuals after application of ARIMA models. Our matched-filter algorithm, the Transit Comb Filter (TCF), replaces the traditional box-fitting step. We construct a periodogram based on the TCF to concentrate the signal of these periodic spikes. Various features of the original light curves, the ARIMA fits, the TCF periodograms, and folded light curves at peaks of the TCF periodogram can then be collected to provide constraints for planet detection. These features provide input into a multivariate classifier when a training set is available. The ARPS procedure has been applied NASA's Kepler mission observations of ~200,000 stars (Caceres, Dissertation Talk, this meeting) and will be applied in the future to other datasets.
Autoregressive Processes in Homogenization of GNSS Tropospheric Data
Klos, A.; Bogusz, J.; Teferle, F. N.; Bock, O.; Pottiaux, E.; Van Malderen, R.
2016-12-01
Offsets due to changes in hardware equipment or any other artificial event are all a subject of a task of homogenization of tropospheric data estimated within a processing of Global Navigation Satellite System (GNSS) observables. This task is aimed at identifying exact epochs of offsets and estimate their magnitudes since they may artificially under- or over-estimate trend and its uncertainty delivered from tropospheric data and used in climate studies. In this research, we analysed a common data set of differences of Integrated Water Vapour (IWV) from GPS and ERA-Interim (1995-2010) provided for a homogenization group working within ES1206 COST Action GNSS4SWEC. We analysed daily IWV records of GPS and ERA-Interim in terms of trend, seasonal terms and noise model with Maximum Likelihood Estimation in Hector software. We found that this data has a character of autoregressive process (AR). Basing on this analysis, we performed Monte Carlo simulations of 25 years long data with two different noise types: white as well as combination of white and autoregressive and also added few strictly defined offsets. This synthetic data set of exactly the same character as IWV from GPS and ERA-Interim was then subjected to a task of manual and automatic/statistical homogenization. We made blind tests and detected possible epochs of offsets manually. We found that simulated offsets were easily detected in series with white noise, no influence of seasonal signal was noticed. The autoregressive series were much more problematic when offsets had to be determined. We found few epochs, for which no offset was simulated. This was mainly due to strong autocorrelation of data, which brings an artificial trend within. Due to regime-like behaviour of AR it is difficult for statistical methods to properly detect epochs of offsets, which was previously reported by climatologists.
DEFF Research Database (Denmark)
He, Xiulan; Sonnenborg, Torben; Jørgensen, Flemming
2017-01-01
-stationary geological system characterized by a network of connected buried valleys that incise deeply into layered Miocene sediments (case study in Denmark). The results show that, based on fragmented information of the formation boundaries, the MPS partition method is able to simulate a non-stationary system......Stationarity has traditionally been a requirement of geostatistical simulations. A common way to deal with non-stationarity is to divide the system into stationary sub-regions and subsequently merge the realizations for each region. Recently, the so-called partition approach that has...... the flexibility to model non-stationary systems directly was developed for multiple-point statistics simulation (MPS). The objective of this study is to apply the MPS partition method with conventional borehole logs and high-resolution airborne electromagnetic (AEM) data, for simulation of a real-world non...
DEFF Research Database (Denmark)
Barfod, Adrian
The PhD thesis presents a new method for analyzing the relationship between resistivity and lithology, as well as a method for quantifying the hydrostratigraphic modeling uncertainty related to Multiple-Point Statistical (MPS) methods. Three-dimensional (3D) geological models are im...... is to improve analysis and research of the resistivity-lithology relationship and ensemble geological/hydrostratigraphic modeling. The groundwater mapping campaign in Denmark, beginning in the 1990’s, has resulted in the collection of large amounts of borehole and geophysical data. The data has been compiled...... in two publicly available databases, the JUPITER and GERDA databases, which contain borehole and geophysical data, respectively. The large amounts of available data provided a unique opportunity for studying the resistivity-lithology relationship. The method for analyzing the resistivity...
DEFF Research Database (Denmark)
Pinson, Pierre; Madsen, Henrik
2012-01-01
optimized is based on penalized maximum likelihood, with exponential forgetting of past observations. MSAR models are then employed for one-step-ahead point forecasting of 10 min resolution time series of wind power at two large offshore wind farms. They are favourably compared against persistence......Wind power production data at temporal resolutions of a few minutes exhibit successive periods with fluctuations of various dynamic nature and magnitude, which cannot be explained (so far) by the evolution of some explanatory variable. Our proposal is to capture this regime-switching behaviour...... and autoregressive models. It is finally shown that the main interest of MSAR models lies in their ability to generate interval/density forecasts of significantly higher skill....
Temporal aggregation in first order cointegrated vector autoregressive models
DEFF Research Database (Denmark)
La Cour, Lisbeth Funding; Milhøj, Anders
We study aggregation - or sample frequencies - of time series, e.g. aggregation from weekly to monthly or quarterly time series. Aggregation usually gives shorter time series but spurious phenomena, in e.g. daily observations, can on the other hand be avoided. An important issue is the effect of ...... of aggregation on the adjustment coefficient in cointegrated systems. We study only first order vector autoregressive processes for n dimensional time series Xt, and we illustrate the theory by a two dimensional and a four dimensional model for prices of various grades of gasoline...
Temporal aggregation in first order cointegrated vector autoregressive
DEFF Research Database (Denmark)
la Cour, Lisbeth Funding; Milhøj, Anders
2006-01-01
We study aggregation - or sample frequencies - of time series, e.g. aggregation from weekly to monthly or quarterly time series. Aggregation usually gives shorter time series but spurious phenomena, in e.g. daily observations, can on the other hand be avoided. An important issue is the effect of ...... of aggregation on the adjustment coefficient in cointegrated systems. We study only first order vector autoregressive processes for n dimensional time series Xt, and we illustrate the theory by a two dimensional and a four dimensional model for prices of various grades of gasoline....
Bias-corrected estimation in potentially mildly explosive autoregressive models
DEFF Research Database (Denmark)
Haufmann, Hendrik; Kruse, Robinson
This paper provides a comprehensive Monte Carlo comparison of different finite-sample bias-correction methods for autoregressive processes. We consider classic situations where the process is either stationary or exhibits a unit root. Importantly, the case of mildly explosive behaviour is studied...... that the indirect inference approach oers a valuable alternative to other existing techniques. Its performance (measured by its bias and root mean squared error) is balanced and highly competitive across many different settings. A clear advantage is its applicability for mildly explosive processes. In an empirical...
Analysis of nonlinear systems using ARMA [autoregressive moving average] models
International Nuclear Information System (INIS)
Hunter, N.F. Jr.
1990-01-01
While many vibration systems exhibit primarily linear behavior, a significant percentage of the systems encountered in vibration and model testing are mildly to severely nonlinear. Analysis methods for such nonlinear systems are not yet well developed and the response of such systems is not accurately predicted by linear models. Nonlinear ARMA (autoregressive moving average) models are one method for the analysis and response prediction of nonlinear vibratory systems. In this paper we review the background of linear and nonlinear ARMA models, and illustrate the application of these models to nonlinear vibration systems. We conclude by summarizing the advantages and disadvantages of ARMA models and emphasizing prospects for future development. 14 refs., 11 figs
Testing the Conditional Mean Function of Autoregressive Conditional Duration Models
DEFF Research Database (Denmark)
Hautsch, Nikolaus
be subject to censoring structures. In an empirical study based on financial transaction data we present an application of the model to estimate conditional asset price change probabilities. Evaluating the forecasting properties of the model, it is shown that the proposed approach is a promising competitor......This paper proposes a dynamic proportional hazard (PH) model with non-specified baseline hazard for the modelling of autoregressive duration processes. A categorization of the durations allows us to reformulate the PH model as an ordered response model based on extreme value distributed errors...
Monthly streamflow forecasting with auto-regressive integrated moving average
Nasir, Najah; Samsudin, Ruhaidah; Shabri, Ani
2017-09-01
Forecasting of streamflow is one of the many ways that can contribute to better decision making for water resource management. The auto-regressive integrated moving average (ARIMA) model was selected in this research for monthly streamflow forecasting with enhancement made by pre-processing the data using singular spectrum analysis (SSA). This study also proposed an extension of the SSA technique to include a step where clustering was performed on the eigenvector pairs before reconstruction of the time series. The monthly streamflow data of Sungai Muda at Jeniang, Sungai Muda at Jambatan Syed Omar and Sungai Ketil at Kuala Pegang was gathered from the Department of Irrigation and Drainage Malaysia. A ratio of 9:1 was used to divide the data into training and testing sets. The ARIMA, SSA-ARIMA and Clustered SSA-ARIMA models were all developed in R software. Results from the proposed model are then compared to a conventional auto-regressive integrated moving average model using the root-mean-square error and mean absolute error values. It was found that the proposed model can outperform the conventional model.
Liu, Siwei; Molenaar, Peter C M
2014-12-01
This article introduces iVAR, an R program for imputing missing data in multivariate time series on the basis of vector autoregressive (VAR) models. We conducted a simulation study to compare iVAR with three methods for handling missing data: listwise deletion, imputation with sample means and variances, and multiple imputation ignoring time dependency. The results showed that iVAR produces better estimates for the cross-lagged coefficients than do the other three methods. We demonstrate the use of iVAR with an empirical example of time series electrodermal activity data and discuss the advantages and limitations of the program.
DEFF Research Database (Denmark)
He, Changli; Kang, Jian; Terasvirta, Timo
In this paper we introduce an autoregressive model with seasonal dummy variables in which coefficients of seasonal dummies vary smoothly and deterministically over time. The error variance of the model is seasonally heteroskedastic and multiplicatively decomposed, the decomposition being similar ...... temperature series. More specifically, the idea is to find out in which way and by how much the monthly temperatures are varying over time during the period of more than 240 years, if they do. Misspecification tests are applied to the estimated model and the findings discussed....
Directory of Open Access Journals (Sweden)
Shihuang Hong
2009-01-01
Full Text Available We present sufficient conditions for the existence of at least twin or triple positive solutions of a nonlinear four-point singular boundary value problem with a p-Laplacian dynamic equation on a time scale. Our results are obtained via some new multiple fixed point theorems.
Dang, Shilpa; Chaudhury, Santanu; Lall, Brejesh; Roy, Prasun Kumar
2017-02-15
Effective connectivity (EC) analysis of neuronal groups using fMRI delivers insights about functional-integration. However, fMRI signal has low-temporal resolution due to down-sampling and indirectly measures underlying neuronal activity. The aim is to address above issues for more reliable EC estimates. This paper proposes use of autoregressive hidden Markov model with missing data (AR-HMM-md) in dynamically multi-linked (DML) framework for learning EC using multiple fMRI time series. In our recent work (Dang et al., 2016), we have shown how AR-HMM-md for modelling single fMRI time series outperforms the existing methods. AR-HMM-md models unobserved neuronal activity and lost data over time as variables and estimates their values by joint optimization given fMRI observation sequence. The effectiveness in learning EC is shown using simulated experiments. Also the effects of sampling and noise are studied on EC. Moreover, classification-experiments are performed for Attention-Deficit/Hyperactivity Disorder subjects and age-matched controls for performance evaluation of real data. Using Bayesian model selection, we see that the proposed model converged to higher log-likelihood and demonstrated that group-classification can be performed with higher cross-validation accuracy of above 94% using distinctive network EC which characterizes patients vs. The full data EC obtained from DML-AR-HMM-md is more consistent with previous literature than the classical multivariate Granger causality method. The proposed architecture leads to reliable estimates of EC than the existing latent models. This framework overcomes the disadvantage of low-temporal resolution and improves cross-validation accuracy significantly due to presence of missing data variables and autoregressive process. Copyright © 2016 Elsevier B.V. All rights reserved.
Yin, Gaohong
2016-05-01
Since the failure of the Scan Line Corrector (SLC) instrument on Landsat 7, observable gaps occur in the acquired Landsat 7 imagery, impacting the spatial continuity of observed imagery. Due to the highly geometric and radiometric accuracy provided by Landsat 7, a number of approaches have been proposed to fill the gaps. However, all proposed approaches have evident constraints for universal application. The main issues in gap-filling are an inability to describe the continuity features such as meandering streams or roads, or maintaining the shape of small objects when filling gaps in heterogeneous areas. The aim of the study is to validate the feasibility of using the Direct Sampling multiple-point geostatistical method, which has been shown to reconstruct complicated geological structures satisfactorily, to fill Landsat 7 gaps. The Direct Sampling method uses a conditional stochastic resampling of known locations within a target image to fill gaps and can generate multiple reconstructions for one simulation case. The Direct Sampling method was examined across a range of land cover types including deserts, sparse rural areas, dense farmlands, urban areas, braided rivers and coastal areas to demonstrate its capacity to recover gaps accurately for various land cover types. The prediction accuracy of the Direct Sampling method was also compared with other gap-filling approaches, which have been previously demonstrated to offer satisfactory results, under both homogeneous area and heterogeneous area situations. Studies have shown that the Direct Sampling method provides sufficiently accurate prediction results for a variety of land cover types from homogeneous areas to heterogeneous land cover types. Likewise, it exhibits superior performances when used to fill gaps in heterogeneous land cover types without input image or with an input image that is temporally far from the target image in comparison with other gap-filling approaches.
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Guangli Yan
2013-01-01
Full Text Available Acupuncture has a history of over 3000 years and is a traditional Chinese medical therapy that uses hair-thin metal needles to puncture the skin at specific points on the body to promote wellbeing, while its molecular mechanism and ideal biological pathways are still not clear. High-throughput metabolomics is the global assessment of endogenous metabolites within a biologic system and can potentially provide a more accurate snap shot of the actual physiological state. We hypothesize that acupuncture-treated human would produce unique characterization of metabolic phenotypes. In this study, UPLC/ESI-HDMS coupled with pattern recognition methods and system analysis were carried out to investigate the mechanism and metabolite biomarkers for acupuncture treatment at “Zusanli” acupoint (ST-36 as a case study. The top 5 canonical pathways including alpha-linolenic acid metabolism, d-glutamine and d-glutamate metabolism, citrate cycle, alanine, aspartate, and glutamate metabolism, and vitamin B6 metabolism pathways were acutely perturbed, and 53 differential metabolites were identified by chemical profiling and may be useful to clarify the physiological basis and mechanism of ST-36. More importantly, network construction has led to the integration of metabolites associated with the multiple perturbation pathways. Urine metabolic profiling might be a promising method to investigate the molecular mechanism of acupuncture.
Yan, Guangli; Zhang, Aihua; Sun, Hui; Cheng, Weiping; Meng, Xiangcai; Liu, Li; Zhang, Yingzhi; Xie, Ning; Wang, Xijun
2013-01-01
Acupuncture has a history of over 3000 years and is a traditional Chinese medical therapy that uses hair-thin metal needles to puncture the skin at specific points on the body to promote wellbeing, while its molecular mechanism and ideal biological pathways are still not clear. High-throughput metabolomics is the global assessment of endogenous metabolites within a biologic system and can potentially provide a more accurate snap shot of the actual physiological state. We hypothesize that acupuncture-treated human would produce unique characterization of metabolic phenotypes. In this study, UPLC/ESI-HDMS coupled with pattern recognition methods and system analysis were carried out to investigate the mechanism and metabolite biomarkers for acupuncture treatment at "Zusanli" acupoint (ST-36) as a case study. The top 5 canonical pathways including alpha-linolenic acid metabolism, d-glutamine and d-glutamate metabolism, citrate cycle, alanine, aspartate, and glutamate metabolism, and vitamin B6 metabolism pathways were acutely perturbed, and 53 differential metabolites were identified by chemical profiling and may be useful to clarify the physiological basis and mechanism of ST-36. More importantly, network construction has led to the integration of metabolites associated with the multiple perturbation pathways. Urine metabolic profiling might be a promising method to investigate the molecular mechanism of acupuncture.
Implementing Modifed Burg Algorithms in Multivariate Subset Autoregressive Modeling
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A. Alexandre Trindade
2003-02-01
Full Text Available The large number of parameters in subset vector autoregressive models often leads one to procure fast, simple, and efficient alternatives or precursors to maximum likelihood estimation. We present the solution of the multivariate subset Yule-Walker equations as one such alternative. In recent work, Brockwell, Dahlhaus, and Trindade (2002, show that the Yule-Walker estimators can actually be obtained as a special case of a general recursive Burg-type algorithm. We illustrate the structure of this Algorithm, and discuss its implementation in a high-level programming language. Applications of the Algorithm in univariate and bivariate modeling are showcased in examples. Univariate and bivariate versions of the Algorithm written in Fortran 90 are included in the appendix, and their use illustrated.
Bias-correction in vector autoregressive models: A simulation study
DEFF Research Database (Denmark)
Engsted, Tom; Pedersen, Thomas Quistgaard
We analyze and compare the properties of various methods for bias-correcting parameter estimates in vector autoregressions. First, we show that two analytical bias formulas from the existing literature are in fact identical. Next, based on a detailed simulation study, we show that this simple...... and easy-to-use analytical bias formula compares very favorably to the more standard but also more computer intensive bootstrap bias-correction method, both in terms of bias and mean squared error. Both methods yield a notable improvement over both OLS and a recently proposed WLS estimator. We also...... of pushing an otherwise stationary model into the non-stationary region of the parameter space during the process of correcting for bias....
Least squares estimation in a simple random coefficient autoregressive model
DEFF Research Database (Denmark)
Johansen, S; Lange, T
2013-01-01
The question we discuss is whether a simple random coefficient autoregressive model with infinite variance can create the long swings, or persistence, which are observed in many macroeconomic variables. The model is defined by yt=stρyt−1+εt,t=1,…,n, where st is an i.i.d. binary variable with p...... we prove the curious result that View the MathML source. The proof applies the notion of a tail index of sums of positive random variables with infinite variance to find the order of magnitude of View the MathML source and View the MathML source and hence the limit of View the MathML source...
The cointegrated vector autoregressive model with general deterministic terms
DEFF Research Database (Denmark)
Johansen, Søren; Nielsen, Morten Ørregaard
2017-01-01
In the cointegrated vector autoregression (CVAR) literature, deterministic terms have until now been analyzed on a case-by-case, or as-needed basis. We give a comprehensive unified treatment of deterministic terms in the additive model X(t)=Z(t) Y(t), where Z(t) belongs to a large class...... of deterministic regressors and Y(t) is a zero-mean CVAR. We suggest an extended model that can be estimated by reduced rank regression and give a condition for when the additive and extended models are asymptotically equivalent, as well as an algorithm for deriving the additive model parameters from the extended...... model parameters. We derive asymptotic properties of the maximum likelihood estimators and discuss tests for rank and tests on the deterministic terms. In particular, we give conditions under which the estimators are asymptotically (mixed) Gaussian, such that associated tests are X 2 -distributed....
The cointegrated vector autoregressive model with general deterministic terms
DEFF Research Database (Denmark)
Johansen, Søren; Nielsen, Morten Ørregaard
In the cointegrated vector autoregression (CVAR) literature, deterministic terms have until now been analyzed on a case-by-case, or as-needed basis. We give a comprehensive unified treatment of deterministic terms in the additive model X(t)= Z(t) + Y(t), where Z(t) belongs to a large class...... of deterministic regressors and Y(t) is a zero-mean CVAR. We suggest an extended model that can be estimated by reduced rank regression and give a condition for when the additive and extended models are asymptotically equivalent, as well as an algorithm for deriving the additive model parameters from the extended...... model parameters. We derive asymptotic properties of the maximum likelihood estimators and discuss tests for rank and tests on the deterministic terms. In particular, we give conditions under which the estimators are asymptotically (mixed) Gaussian, such that associated tests are khi squared distributed....
4K Video Traffic Prediction using Seasonal Autoregressive Modeling
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D. R. Marković
2017-06-01
Full Text Available From the perspective of average viewer, high definition video streams such as HD (High Definition and UHD (Ultra HD are increasing their internet presence year over year. This is not surprising, having in mind expansion of HD streaming services, such as YouTube, Netflix etc. Therefore, high definition video streams are starting to challenge network resource allocation with their bandwidth requirements and statistical characteristics. Need for analysis and modeling of this demanding video traffic has essential importance for better quality of service and experience support. In this paper we use an easy-to-apply statistical model for prediction of 4K video traffic. Namely, seasonal autoregressive modeling is applied in prediction of 4K video traffic, encoded with HEVC (High Efficiency Video Coding. Analysis and modeling were performed within R programming environment using over 17.000 high definition video frames. It is shown that the proposed methodology provides good accuracy in high definition video traffic modeling.
Asymptotically stable phase synchronization revealed by autoregressive circle maps
Drepper, F. R.
2000-11-01
A specially designed of nonlinear time series analysis is introduced based on phases, which are defined as polar angles in spaces spanned by a finite number of delayed coordinates. A canonical choice of the polar axis and a related implicit estimation scheme for the potentially underlying autoregressive circle map (next phase map) guarantee the invertibility of reconstructed phase space trajectories to the original coordinates. The resulting Fourier approximated, invertibility enforcing phase space map allows us to detect conditional asymptotic stability of coupled phases. This comparatively general synchronization criterion unites two existing generalizations of the old concept and can successfully be applied, e.g., to phases obtained from electrocardiogram and airflow recordings characterizing cardiorespiratory interaction.
Bias-Correction in Vector Autoregressive Models: A Simulation Study
Directory of Open Access Journals (Sweden)
Tom Engsted
2014-03-01
Full Text Available We analyze the properties of various methods for bias-correcting parameter estimates in both stationary and non-stationary vector autoregressive models. First, we show that two analytical bias formulas from the existing literature are in fact identical. Next, based on a detailed simulation study, we show that when the model is stationary this simple bias formula compares very favorably to bootstrap bias-correction, both in terms of bias and mean squared error. In non-stationary models, the analytical bias formula performs noticeably worse than bootstrapping. Both methods yield a notable improvement over ordinary least squares. We pay special attention to the risk of pushing an otherwise stationary model into the non-stationary region of the parameter space when correcting for bias. Finally, we consider a recently proposed reduced-bias weighted least squares estimator, and we find that it compares very favorably in non-stationary models.
Drought Patterns Forecasting using an Auto-Regressive Logistic Model
del Jesus, M.; Sheffield, J.; Méndez Incera, F. J.; Losada, I. J.; Espejo, A.
2014-12-01
Drought is characterized by a water deficit that may manifest across a large range of spatial and temporal scales. Drought may create important socio-economic consequences, many times of catastrophic dimensions. A quantifiable definition of drought is elusive because depending on its impacts, consequences and generation mechanism, different water deficit periods may be identified as a drought by virtue of some definitions but not by others. Droughts are linked to the water cycle and, although a climate change signal may not have emerged yet, they are also intimately linked to climate.In this work we develop an auto-regressive logistic model for drought prediction at different temporal scales that makes use of a spatially explicit framework. Our model allows to include covariates, continuous or categorical, to improve the performance of the auto-regressive component.Our approach makes use of dimensionality reduction (principal component analysis) and classification techniques (K-Means and maximum dissimilarity) to simplify the representation of complex climatic patterns, such as sea surface temperature (SST) and sea level pressure (SLP), while including information on their spatial structure, i.e. considering their spatial patterns. This procedure allows us to include in the analysis multivariate representation of complex climatic phenomena, as the El Niño-Southern Oscillation. We also explore the impact of other climate-related variables such as sun spots. The model allows to quantify the uncertainty of the forecasts and can be easily adapted to make predictions under future climatic scenarios. The framework herein presented may be extended to other applications such as flash flood analysis, or risk assessment of natural hazards.
Inflation, Exchange Rates and Interest Rates in Ghana: an Autoregressive Distributed Lag Model
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Dennis Nchor
2015-01-01
Full Text Available This paper investigates the impact of exchange rate movement and the nominal interest rate on inflation in Ghana. It also looks at the presence of the Fisher Effect and the International Fisher Effect scenarios. It makes use of an autoregressive distributed lag model and an unrestricted error correction model. Ordinary Least Squares regression methods were also employed to determine the presence of the Fischer Effect and the International Fisher Effect. The results from the study show that in the short run a percentage point increase in the level of depreciation of the Ghana cedi leads to an increase in the rate of inflation by 0.20%. A percentage point increase in the level of nominal interest rates however results in a decrease in inflation by 0.98%. Inflation increases by 1.33% for every percentage point increase in the nominal interest rate in the long run. An increase in inflation on the other hand increases the nominal interest rate by 0.51% which demonstrates the partial Fisher effect. A 1% increase in the interest rate differential leads to a depreciation of the Ghana cedi by approximately 1% which indicates the full International Fisher effect.
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Barash Danny
2008-04-01
Full Text Available Abstract Background RNAmute is an interactive Java application which, given an RNA sequence, calculates the secondary structure of all single point mutations and organizes them into categories according to their similarity to the predicted structure of the wild type. The secondary structure predictions are performed using the Vienna RNA package. A more efficient implementation of RNAmute is needed, however, to extend from the case of single point mutations to the general case of multiple point mutations, which may often be desired for computational predictions alongside mutagenesis experiments. But analyzing multiple point mutations, a process that requires traversing all possible mutations, becomes highly expensive since the running time is O(nm for a sequence of length n with m-point mutations. Using Vienna's RNAsubopt, we present a method that selects only those mutations, based on stability considerations, which are likely to be conformational rearranging. The approach is best examined using the dot plot representation for RNA secondary structure. Results Using RNAsubopt, the suboptimal solutions for a given wild-type sequence are calculated once. Then, specific mutations are selected that are most likely to cause a conformational rearrangement. For an RNA sequence of about 100 nts and 3-point mutations (n = 100, m = 3, for example, the proposed method reduces the running time from several hours or even days to several minutes, thus enabling the practical application of RNAmute to the analysis of multiple-point mutations. Conclusion A highly efficient addition to RNAmute that is as user friendly as the original application but that facilitates the practical analysis of multiple-point mutations is presented. Such an extension can now be exploited prior to site-directed mutagenesis experiments by virologists, for example, who investigate the change of function in an RNA virus via mutations that disrupt important motifs in its secondary
Characteristics of the transmission of autoregressive sub-patterns in financial time series
Gao, Xiangyun; An, Haizhong; Fang, Wei; Huang, Xuan; Li, Huajiao; Zhong, Weiqiong
2014-09-01
There are many types of autoregressive patterns in financial time series, and they form a transmission process. Here, we define autoregressive patterns quantitatively through an econometrical regression model. We present a computational algorithm that sets the autoregressive patterns as nodes and transmissions between patterns as edges, and then converts the transmission process of autoregressive patterns in a time series into a network. We utilised daily Shanghai (securities) composite index time series to study the transmission characteristics of autoregressive patterns. We found statistically significant evidence that the financial market is not random and that there are similar characteristics between parts and whole time series. A few types of autoregressive sub-patterns and transmission patterns drive the oscillations of the financial market. A clustering effect on fluctuations appears in the transmission process, and certain non-major autoregressive sub-patterns have high media capabilities in the financial time series. Different stock indexes exhibit similar characteristics in the transmission of fluctuation information. This work not only proposes a distinctive perspective for analysing financial time series but also provides important information for investors.
A method simulating random magnetic field in interplanetary space by an autoregressive method
International Nuclear Information System (INIS)
Kato, Masahito; Sakai, Takasuke
1985-01-01
With an autoregressive method, we tried to generate the random noise fitting in with the power spectrum which can be analytically Fouriertransformed into an autocorrelation function. Although we can not directly compare our method with FFT by Owens (1978), we can only point out the following; FFT method should determine at first the number of data points N, or the total length to be generated and we cannot generate random data more than N. Because, beyond the NΔy, the generated data repeats the same pattern as below NΔy, where Δy = minimum interval for random noise. So if you want to change or increase N after generating the random noise, you should start the generation from the first step. The characteristic of the generated random number may depend upon the number of N, judging from the generating method. Once the prediction error filters are determined, our method can produce successively the random numbers, that is, we can possibly extend N to infinite without any effort. (author)
International Nuclear Information System (INIS)
Bell, Nathaniel; Repáraz, Laura; Fry, William R.; Smith, R. Stephen; Luis, Alejandro
2016-01-01
Research has shown that uninsured patients receive fewer radiographic studies during trauma care, but less is known as to whether differences in care are present among other insurance groups or across different time points during hospitalization. Our objective was to examine the number of radiographic studies administered to a cohort of trauma patients over the entire hospital stay as well as during the first 24-hours of care. Patient data were obtained from an American College of Surgeons (ACS) verified Level I Trauma Center between January 1, 2011 and December 31, 2012. We used negative binomial regression to construct relative risk (RR) ratios for type and frequency of radiographic imaging received among persons with Medicare, Medicaid, no insurance, or government insurance plans in reference to those with commercial indemnity plans. The analysis was adjusted for patient age, sex, race/ethnicity, injury severity score, injury mechanism, comorbidities, complications, hospital length of stay, and Intensive Care Unit (ICU) admission. A total of 3621 records from surviving patients age > =18 years were assessed. After adjustment for potential confounders, the expected number of radiographic studies decreased by 15 % among Medicare recipients (RR 0.85, 95 % CI 0.78–0.93), 11 % among Medicaid recipients (0.89, 0.81–0.99), 10 % among the uninsured (0.90, 0.85–0.96) and 19 % among government insurance groups (0.81, 0.72–0.90), compared with the reference group. This disparity was observed during the first 24-hours of care among patients with Medicare (0.78, 0.71–0.86) and government insurance plans (0.83, 0.74–0.94). Overall, there were no differences in the number of radiographic studies among the uninsured or among Medicaid patients during the first 24-hours of care compared with the reference group, but differences were observed among the uninsured in a sub-analysis of severely injured patients (ISS > 15). Both uninsured and insured patients treated at a
Directory of Open Access Journals (Sweden)
A.-S. Høyer
2017-12-01
Full Text Available Most studies on the application of geostatistical simulations based on multiple-point statistics (MPS to hydrogeological modelling focus on relatively fine-scale models and concentrate on the estimation of facies-level structural uncertainty. Much less attention is paid to the use of input data and optimal construction of training images. For instance, even though the training image should capture a set of spatial geological characteristics to guide the simulations, the majority of the research still relies on 2-D or quasi-3-D training images. In the present study, we demonstrate a novel strategy for 3-D MPS modelling characterized by (i realistic 3-D training images and (ii an effective workflow for incorporating a diverse group of geological and geophysical data sets. The study covers an area of 2810 km2 in the southern part of Denmark. MPS simulations are performed on a subset of the geological succession (the lower to middle Miocene sediments which is characterized by relatively uniform structures and dominated by sand and clay. The simulated domain is large and each of the geostatistical realizations contains approximately 45 million voxels with size 100 m × 100 m × 5 m. Data used for the modelling include water well logs, high-resolution seismic data, and a previously published 3-D geological model. We apply a series of different strategies for the simulations based on data quality, and develop a novel method to effectively create observed spatial trends. The training image is constructed as a relatively small 3-D voxel model covering an area of 90 km2. We use an iterative training image development strategy and find that even slight modifications in the training image create significant changes in simulations. Thus, this study shows how to include both the geological environment and the type and quality of input information in order to achieve optimal results from MPS modelling. We present a practical
The comparison study among several data transformations in autoregressive modeling
Setiyowati, Susi; Waluyo, Ramdhani Try
2015-12-01
In finance, the adjusted close of stocks are used to observe the performance of a company. The extreme prices, which may increase or decrease drastically, are often become particular concerned since it can impact to bankruptcy. As preventing action, the investors have to observe the future (forecasting) stock prices comprehensively. For that purpose, time series analysis could be one of statistical methods that can be implemented, for both stationary and non-stationary processes. Since the variability process of stocks prices tend to large and also most of time the extreme values are always exist, then it is necessary to do data transformation so that the time series models, i.e. autoregressive model, could be applied appropriately. One of popular data transformation in finance is return model, in addition to ratio of logarithm and some others Tukey ladder transformation. In this paper these transformations are applied to AR stationary models and non-stationary ARCH and GARCH models through some simulations with varying parameters. As results, this work present the suggestion table that shows transformations behavior for some condition of parameters and models. It is confirmed that the better transformation is obtained, depends on type of data distributions. In other hands, the parameter conditions term give significant influence either.
An algebraic method for constructing stable and consistent autoregressive filters
International Nuclear Information System (INIS)
Harlim, John; Hong, Hoon; Robbins, Jacob L.
2015-01-01
In this paper, we introduce an algebraic method to construct stable and consistent univariate autoregressive (AR) models of low order for filtering and predicting nonlinear turbulent signals with memory depth. By stable, we refer to the classical stability condition for the AR model. By consistent, we refer to the classical consistency constraints of Adams–Bashforth methods of order-two. One attractive feature of this algebraic method is that the model parameters can be obtained without directly knowing any training data set as opposed to many standard, regression-based parameterization methods. It takes only long-time average statistics as inputs. The proposed method provides a discretization time step interval which guarantees the existence of stable and consistent AR model and simultaneously produces the parameters for the AR models. In our numerical examples with two chaotic time series with different characteristics of decaying time scales, we find that the proposed AR models produce significantly more accurate short-term predictive skill and comparable filtering skill relative to the linear regression-based AR models. These encouraging results are robust across wide ranges of discretization times, observation times, and observation noise variances. Finally, we also find that the proposed model produces an improved short-time prediction relative to the linear regression-based AR-models in forecasting a data set that characterizes the variability of the Madden–Julian Oscillation, a dominant tropical atmospheric wave pattern
Prediction of municipal solid waste generation using nonlinear autoregressive network.
Younes, Mohammad K; Nopiah, Z M; Basri, N E Ahmad; Basri, H; Abushammala, Mohammed F M; Maulud, K N A
2015-12-01
Most of the developing countries have solid waste management problems. Solid waste strategic planning requires accurate prediction of the quality and quantity of the generated waste. In developing countries, such as Malaysia, the solid waste generation rate is increasing rapidly, due to population growth and new consumption trends that characterize society. This paper proposes an artificial neural network (ANN) approach using feedforward nonlinear autoregressive network with exogenous inputs (NARX) to predict annual solid waste generation in relation to demographic and economic variables like population number, gross domestic product, electricity demand per capita and employment and unemployment numbers. In addition, variable selection procedures are also developed to select a significant explanatory variable. The model evaluation was performed using coefficient of determination (R(2)) and mean square error (MSE). The optimum model that produced the lowest testing MSE (2.46) and the highest R(2) (0.97) had three inputs (gross domestic product, population and employment), eight neurons and one lag in the hidden layer, and used Fletcher-Powell's conjugate gradient as the training algorithm.
Likelihood inference for a fractionally cointegrated vector autoregressive model
DEFF Research Database (Denmark)
Johansen, Søren; Ørregård Nielsen, Morten
2012-01-01
such that the process X_{t} is fractional of order d and cofractional of order d-b; that is, there exist vectors ß for which ß'X_{t} is fractional of order d-b, and no other fractionality order is possible. We define the statistical model by 0inference when the true values satisfy b0¿1/2 and d0-b0......We consider model based inference in a fractionally cointegrated (or cofractional) vector autoregressive model with a restricted constant term, ¿, based on the Gaussian likelihood conditional on initial values. The model nests the I(d) VAR model. We give conditions on the parameters...... process in the parameters when errors are i.i.d. with suitable moment conditions and initial values are bounded. When the limit is deterministic this implies uniform convergence in probability of the conditional likelihood function. If the true value b0>1/2, we prove that the limit distribution of (ß...
On the maximum-entropy/autoregressive modeling of time series
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.
Sparse representation based image interpolation with nonlocal autoregressive modeling.
Dong, Weisheng; Zhang, Lei; Lukac, Rastislav; Shi, Guangming
2013-04-01
Sparse representation is proven to be a promising approach to image super-resolution, where the low-resolution (LR) image is usually modeled as the down-sampled version of its high-resolution (HR) counterpart after blurring. When the blurring kernel is the Dirac delta function, i.e., the LR image is directly down-sampled from its HR counterpart without blurring, the super-resolution problem becomes an image interpolation problem. In such cases, however, the conventional sparse representation models (SRM) become less effective, because the data fidelity term fails to constrain the image local structures. In natural images, fortunately, many nonlocal similar patches to a given patch could provide nonlocal constraint to the local structure. In this paper, we incorporate the image nonlocal self-similarity into SRM for image interpolation. More specifically, a nonlocal autoregressive model (NARM) is proposed and taken as the data fidelity term in SRM. We show that the NARM-induced sampling matrix is less coherent with the representation dictionary, and consequently makes SRM more effective for image interpolation. Our extensive experimental results demonstrate that the proposed NARM-based image interpolation method can effectively reconstruct the edge structures and suppress the jaggy/ringing artifacts, achieving the best image interpolation results so far in terms of PSNR as well as perceptual quality metrics such as SSIM and FSIM.
Adaptive Autoregressive Model for Reduction of Noise in SPECT
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Reijo Takalo
2015-01-01
Full Text Available This paper presents improved autoregressive modelling (AR to reduce noise in SPECT images. An AR filter was applied to prefilter projection images and postfilter ordered subset expectation maximisation (OSEM reconstruction images (AR-OSEM-AR method. The performance of this method was compared with filtered back projection (FBP preceded by Butterworth filtering (BW-FBP method and the OSEM reconstruction method followed by Butterworth filtering (OSEM-BW method. A mathematical cylinder phantom was used for the study. It consisted of hot and cold objects. The tests were performed using three simulated SPECT datasets. Image quality was assessed by means of the percentage contrast resolution (CR% and the full width at half maximum (FWHM of the line spread functions of the cylinders. The BW-FBP method showed the highest CR% values and the AR-OSEM-AR method gave the lowest CR% values for cold stacks. In the analysis of hot stacks, the BW-FBP method had higher CR% values than the OSEM-BW method. The BW-FBP method exhibited the lowest FWHM values for cold stacks and the AR-OSEM-AR method for hot stacks. In conclusion, the AR-OSEM-AR method is a feasible way to remove noise from SPECT images. It has good spatial resolution for hot objects.
Biometeorological and autoregressive indices for predicting olive pollen intensity.
Oteros, J; García-Mozo, H; Hervás, C; Galán, C
2013-03-01
This paper reports on modelling to predict airborne olive pollen season severity, expressed as a pollen index (PI), in Córdoba province (southern Spain) several weeks prior to the pollen season start. Using a 29-year database (1982-2010), a multivariate regression model based on five indices-the index-based model-was built to enhance the efficacy of prediction models. Four of the indices used were biometeorological indices: thermal index, pre-flowering hydric index, dormancy hydric index and summer index; the fifth was an autoregressive cyclicity index based on pollen data from previous years. The extreme weather events characteristic of the Mediterranean climate were also taken into account by applying different adjustment criteria. The results obtained with this model were compared with those yielded by a traditional meteorological-based model built using multivariate regression analysis of simple meteorological-related variables. The performance of the models (confidence intervals, significance levels and standard errors) was compared, and they were also validated using the bootstrap method. The index-based model built on biometeorological and cyclicity indices was found to perform better for olive pollen forecasting purposes than the traditional meteorological-based model.
DEFF Research Database (Denmark)
Kock, Anders Bredahl
2016-01-01
We show that the adaptive Lasso is oracle efficient in stationary and nonstationary autoregressions. This means that it estimates parameters consistently, selects the correct sparsity pattern, and estimates the coefficients belonging to the relevant variables at the same asymptotic efficiency...
Directory of Open Access Journals (Sweden)
C. Serio
1997-06-01
Full Text Available The time dynamics of geoelectrical precursory time series has been investigated and a method to discriminate chaotic behaviour in geoelectrical precursory time series is proposed. It allows us to detect low-dimensional chaos when the only information about the time series comes from the time series themselves. The short-term predictability of these time series is evaluated using two possible forecasting approaches: global autoregressive approximation and local autoregressive approximation. The first views the data as a realization of a linear stochastic process, whereas the second considers the data points as a realization of a deterministic process, supposedly non-linear. The comparison of the predictive skill of the two techniques is a test to discriminate between low-dimensional chaos and random dynamics. The analyzed time series are geoelectrical measurements recorded by an automatic station located in Tito (Southern Italy in one of the most seismic areas of the Mediterranean region. Our findings are that the global (linear approach is superior to the local one and the physical system governing the phenomena of electrical nature is characterized by a large number of degrees of freedom. Power spectra of the filtered time series follow a P(f = F-a scaling law: they exhibit the typical behaviour of a broad class of fractal stochastic processes and they are a signature of the self-organized systems.
Autoregressive Prediction with Rolling Mechanism for Time Series Forecasting with Small Sample Size
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Zhihua Wang
2014-01-01
Full Text Available Reasonable prediction makes significant practical sense to stochastic and unstable time series analysis with small or limited sample size. Motivated by the rolling idea in grey theory and the practical relevance of very short-term forecasting or 1-step-ahead prediction, a novel autoregressive (AR prediction approach with rolling mechanism is proposed. In the modeling procedure, a new developed AR equation, which can be used to model nonstationary time series, is constructed in each prediction step. Meanwhile, the data window, for the next step ahead forecasting, rolls on by adding the most recent derived prediction result while deleting the first value of the former used sample data set. This rolling mechanism is an efficient technique for its advantages of improved forecasting accuracy, applicability in the case of limited and unstable data situations, and requirement of little computational effort. The general performance, influence of sample size, nonlinearity dynamic mechanism, and significance of the observed trends, as well as innovation variance, are illustrated and verified with Monte Carlo simulations. The proposed methodology is then applied to several practical data sets, including multiple building settlement sequences and two economic series.
Fong Chao, B.
1983-12-01
The homogeneous set of 80-year-long (1900-1979) International Latitude Service (ILS) polar motion data is analyzed using the autoregressive method (Chao and Gilbert, 1980) which resolves and produces estimates for the complex frequency (or frequency and Q) and complex amplitude (or amplitude and phase) of each harmonic component in the data. Principal conclusion of this analysis are that (1) the ILS data support the multiple-component hypothesis of the Chandler wobble (it is found that the Chandler wobble can be adequately modeled as a linear combination of four (coherent) harmonic components, each of which represents a steady, nearly circular, prograte motion, a behavior that is inconsistent with the hypothesis of a single Chandler period excited in a temporally and/or spatially random fashion). (2) the four-component Chandler wobble model ``explains'' the apparent phase reversal during 1920-1940 and the pre-1950 empirical period-amplitude relation, (3) the annual wobble is shown to be rather stationary over the years both in amplitude and in phase and no evidence is found to support the large variations reported by earlier investigations. (4) the Markowitz wobble is found to support the large variations reported by earlier investigations. (4) the Markowitz wobble is found to be marginally retrograde and appears to have a complicated behavior which cannot be resolved because of the shortness of the data set.
Multivariate Autoregressive Model Based Heart Motion Prediction Approach for Beating Heart Surgery
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Fan Liang
2013-02-01
Full Text Available A robotic tool can enable a surgeon to conduct off-pump coronary artery graft bypass surgery on a beating heart. The robotic tool actively alleviates the relative motion between the point of interest (POI on the heart surface and the surgical tool and allows the surgeon to operate as if the heart were stationary. Since the beating heart's motion is relatively high-band, with nonlinear and nonstationary characteristics, it is difficult to follow. Thus, precise beating heart motion prediction is necessary for the tracking control procedure during the surgery. In the research presented here, we first observe that Electrocardiography (ECG signal contains the causal phase information on heart motion and non-stationary heart rate dynamic variations. Then, we investigate the relationship between ECG signal and beating heart motion using Granger Causality Analysis, which describes the feasibility of the improved prediction of heart motion. Next, we propose a nonlinear time-varying multivariate vector autoregressive (MVAR model based adaptive prediction method. In this model, the significant correlation between ECG and heart motion enables the improvement of the prediction of sharp changes in heart motion and the approximation of the motion with sufficient detail. Dual Kalman Filters (DKF estimate the states and parameters of the model, respectively. Last, we evaluate the proposed algorithm through comparative experiments using the two sets of collected vivo data.
International Nuclear Information System (INIS)
Klosek, M.M.
2004-01-01
We study effects of noisy and deterministic perturbations on oscillatory solutions to delay differential equations. We develop the multiscale technique and derive amplitude equations for noisy oscillations near a critical delay. We investigate effects of additive and multiplicative noise. We show that if the magnitudes of noise and deterministic perturbations are balanced, then the oscillatory behavior persists for long times being sustained by the noise. We illustrate the technique and its results on linear and logistic delay equations. (author)
Bandyopadhyay, Pradipta
2008-04-07
The efficiency of the two-surface monte carlo (TSMC) method depends on the closeness of the actual potential and the biasing potential used to propagate the system of interest. In this work, it is shown that by combining the basin hopping method with TSMC, the efficiency of the method can be increased by several folds. TSMC with basin hopping is used to generate quantum mechanical trajectory and large number of stationary points of water clusters.
Duffy, Sandra; Sykes, Melissa L; Jones, Amy J; Shelper, Todd B; Simpson, Moana; Lang, Rebecca; Poulsen, Sally-Ann; Sleebs, Brad E; Avery, Vicky M
2017-09-01
Open-access drug discovery provides a substantial resource for diseases primarily affecting the poor and disadvantaged. The open-access Pathogen Box collection is comprised of compounds with demonstrated biological activity against specific pathogenic organisms. The supply of this resource by the Medicines for Malaria Venture has the potential to provide new chemical starting points for a number of tropical and neglected diseases, through repurposing of these compounds for use in drug discovery campaigns for these additional pathogens. We tested the Pathogen Box against kinetoplastid parasites and malaria life cycle stages in vitro Consequently, chemical starting points for malaria, human African trypanosomiasis, Chagas disease, and leishmaniasis drug discovery efforts have been identified. Inclusive of this in vitro biological evaluation, outcomes from extensive literature reviews and database searches are provided. This information encompasses commercial availability, literature reference citations, other aliases and ChEMBL number with associated biological activity, where available. The release of this new data for the Pathogen Box collection into the public domain will aid the open-source model of drug discovery. Importantly, this will provide novel chemical starting points for drug discovery and target identification in tropical disease research. Copyright © 2017 Duffy et al.
Zhou, Tony; Dickson, Jennifer L; Geoffrey Chase, J
2018-01-01
Continuous glucose monitoring (CGM) devices have been effective in managing diabetes and offer potential benefits for use in the intensive care unit (ICU). Use of CGM devices in the ICU has been limited, primarily due to the higher point accuracy errors over currently used traditional intermittent blood glucose (BG) measures. General models of CGM errors, including drift and random errors, are lacking, but would enable better design of protocols to utilize these devices. This article presents an autoregressive (AR) based modeling method that separately characterizes the drift and random noise of the GlySure CGM sensor (GlySure Limited, Oxfordshire, UK). Clinical sensor data (n = 33) and reference measurements were used to generate 2 AR models to describe sensor drift and noise. These models were used to generate 100 Monte Carlo simulations based on reference blood glucose measurements. These were then compared to the original CGM clinical data using mean absolute relative difference (MARD) and a Trend Compass. The point accuracy MARD was very similar between simulated and clinical data (9.6% vs 9.9%). A Trend Compass was used to assess trend accuracy, and found simulated and clinical sensor profiles were similar (simulated trend index 11.4° vs clinical trend index 10.9°). The model and method accurately represents cohort sensor behavior over patients, providing a general modeling approach to any such sensor by separately characterizing each type of error that can arise in the data. Overall, it enables better protocol design based on accurate expected CGM sensor behavior, as well as enabling the analysis of what level of each type of sensor error would be necessary to obtain desired glycemic control safety and performance with a given protocol.
International Nuclear Information System (INIS)
Hanada, R.; Sugawara, H.; Aoki, Y.; Sato, H.; Shigeto, K.; Shinjo, T.; Ono, T.; Miyajima, H.
2002-01-01
We have simultaneously measured the field dependences of voltages at multiple pairs of resistance and transverse voltage probes in ferromagnetic wires (with either magnetic or non-magnetic voltage probes). Both the resistive (through the giant magnetoresistance and anisotropic magnetoresistance) and transverse voltages (through the planar Hall effect) exhibit abrupt jumps, reflecting discrete motion of domain walls or rotations of magnetization. Voltage probes, even if non-magnetic, are found to affect the jump fields depending on the sample conditions. We demonstrate that the specific information on the domain (wall) motion along a thin ferromagnetic wire could be obtained from the jump fields. (author)
To center or not to center? Investigating inertia with a multilevel autoregressive model
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Ellen L. Hamaker
2015-01-01
Full Text Available Whether level 1 predictors should be centered per cluster has received considerable attention in the multilevel literature. While most agree that there is no one preferred approach, it has also been argued that cluster mean centering is desirable when the within-cluster slope and the between-cluster slope are expected to deviate, and the main interest is in the within-cluster slope. However, we show in a series of simulations that if one has a multilevel autoregressive model in which the level 1 predictor is the lagged outcome variable (i.e., the outcome variable at the previous occasion, cluster mean centering will in general lead to a downward bias in the parameter estimate of the within-cluster slope (i.e., the autoregressive relationship. This is particularly relevant if the main question is whether there is on average an autoregressive effect. Nonetheless, we show that if the main interest is in estimating the effect of a level 2 predictor on the autoregressive parameter (i.e., a cross-level interaction, cluster mean centering should be preferred over other forms of centering. Hence, researchers should be clear on what is considered the main goal of their study, and base their choice of centering method on this when using a multilevel autoregressive model.
Multiple critical points and liquid-liquid equilibria from the van der Waals like equations of state
International Nuclear Information System (INIS)
Artemenko, Sergey; Lozovsky, Taras; Mazur, Victor
2008-01-01
The principal aim of this work is a comprehensive analysis of the phase diagram of water via the van der Waals like equations of state (EoSs) which are considered as superpositions of repulsive and attractive forces. We test more extensively the modified van der Waals EoS (MVDW) proposed by Skibinski et al (2004 Phys. Rev. E 69 061206) and refine this model by introducing instead of the classical van der Waals repulsive term a very accurate hard sphere EoS over the entire stable and metastable regions (Liu 2006 Preprint cond-mat/0605392). It was detected that the simplest form of MVDW EoS displays a complex phase behavior, including three critical points, and identifies four fluid phases (gas, low density liquid (LDL), high density liquid (HDL), and very high density liquid (VHDL)). Moreover the experimentally observed (Mallamace et al 2007 Proc. Natl Acad. Sci. USA 104 18387) anomalous behavior of the density of water in the deeply supercooled region (a density minimum) is reproduced by the MWDW EoS. An improvement of the repulsive part does not change the topological picture of the phase behavior of water in the wide range of thermodynamic variables. The new parameters set for second and third critical points are recognized by thorough analysis of experimental data for the loci of thermodynamic response function extrema
Lombardo, Luigi
2018-02-13
We develop a stochastic modeling approach based on spatial point processes of log-Gaussian Cox type for a collection of around 5000 landslide events provoked by a precipitation trigger in Sicily, Italy. Through the embedding into a hierarchical Bayesian estimation framework, we can use the integrated nested Laplace approximation methodology to make inference and obtain the posterior estimates of spatially distributed covariate and random effects. Several mapping units are useful to partition a given study area in landslide prediction studies. These units hierarchically subdivide the geographic space from the highest grid-based resolution to the stronger morphodynamic-oriented slope units. Here we integrate both mapping units into a single hierarchical model, by treating the landslide triggering locations as a random point pattern. This approach diverges fundamentally from the unanimously used presence–absence structure for areal units since we focus on modeling the expected landslide count jointly within the two mapping units. Predicting this landslide intensity provides more detailed and complete information as compared to the classically used susceptibility mapping approach based on relative probabilities. To illustrate the model’s versatility, we compute absolute probability maps of landslide occurrences and check their predictive power over space. While the landslide community typically produces spatial predictive models for landslides only in the sense that covariates are spatially distributed, no actual spatial dependence has been explicitly integrated so far. Our novel approach features a spatial latent effect defined at the slope unit level, allowing us to assess the spatial influence that remains unexplained by the covariates in the model. For rainfall-induced landslides in regions where the raingauge network is not sufficient to capture the spatial distribution of the triggering precipitation event, this latent effect provides valuable imaging support
Directory of Open Access Journals (Sweden)
Mohammad Ghahremanzadeh
2014-06-01
Full Text Available Agriculture as one of the major economic sectors of Iran, has an important role in Gross Domestic Production by providing about 14% of GDP. This study attempts to forecast the value of the agriculture GDP using Periodic Autoregressive model (PAR, as the new seasonal time series techniques. To address this aim, the quarterly data were collected from March 1988 to July 1989. The collected data was firstly analyzed using periodic unit root test Franses & Paap (2004. The analysis found non-periodic unit root in the seasonal data. Second, periodic seasonal behavior (Boswijk & Franses, 1996 was examined. The results showed that periodic autoregressive model fits agriculture GDP well. This makes an accurate forecast of agriculture GDP possible. Using the estimated model, the future value of quarter agricultural GDP from March 2011 to July 2012was forecasted. With consideration to the fair fit of this model with agricultural GDP, It is recommended to use periodic autoregressive model for the future studies.
Directory of Open Access Journals (Sweden)
Usman M. Umer
2018-06-01
Full Text Available Travel and leisure recorded a consecutive robust growth and become among the fastest economic sectors in the world. Various forecasting models are proposed by researchers that serve as an early recommendation for investors and policy makers. Numerous studies proposed distinct forecasting models to predict the dynamics of this sector and provide early recommendation for investors and policy makers. In this paper, we compare the performance of smooth transition autoregressive (STAR and linear autoregressive (AR models using monthly returns of Turkey and FTSE travel and leisure index from April 1997 to August 2016. MSCI world index used as a proxy of the overall market. The result shows that nonlinear LSTAR model cannot improve the out-of-sample forecast of linear AR model. This finding demonstrates little to be gained from using LSTAR model in the prediction of travel and leisure stock index. Keywords: Nonlinear time-series, Out-of-sample forecasting, Smooth transition autoregressive, Travel and leisure
International Nuclear Information System (INIS)
Hashemi, Seyyedhossein; Javaherian, Abdolrahim; Ataee-pour, Majid; Khoshdel, Hossein
2014-01-01
Facies models try to explain facies architectures which have a primary control on the subsurface heterogeneities and the fluid flow characteristics of a given reservoir. In the process of facies modeling, geostatistical methods are implemented to integrate different sources of data into a consistent model. The facies models should describe facies interactions; the shape and geometry of the geobodies as they occur in reality. Two distinct categories of geostatistical techniques are two-point and multiple-point (geo) statistics (MPS). In this study, both of the aforementioned categories were applied to generate facies models. A sequential indicator simulation (SIS) and a truncated Gaussian simulation (TGS) represented two-point geostatistical methods, and a single normal equation simulation (SNESIM) selected as an MPS simulation representative. The dataset from an extremely channelized carbonate reservoir located in southwest Iran was applied to these algorithms to analyze their performance in reproducing complex curvilinear geobodies. The SNESIM algorithm needs consistent training images (TI) in which all possible facies architectures that are present in the area are included. The TI model was founded on the data acquired from modern occurrences. These analogies delivered vital information about the possible channel geometries and facies classes that are typically present in those similar environments. The MPS results were conditioned to both soft and hard data. Soft facies probabilities were acquired from a neural network workflow. In this workflow, seismic-derived attributes were implemented as the input data. Furthermore, MPS realizations were conditioned to hard data to guarantee the exact positioning and continuity of the channel bodies. A geobody extraction workflow was implemented to extract the most certain parts of the channel bodies from the seismic data. These extracted parts of the channel bodies were applied to the simulation workflow as hard data
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Fei Jin
2013-05-01
Full Text Available This paper studies the generalized spatial two stage least squares (GS2SLS estimation of spatial autoregressive models with autoregressive disturbances when there are endogenous regressors with many valid instruments. Using many instruments may improve the efficiency of estimators asymptotically, but the bias might be large in finite samples, making the inference inaccurate. We consider the case that the number of instruments K increases with, but at a rate slower than, the sample size, and derive the approximate mean square errors (MSE that account for the trade-offs between the bias and variance, for both the GS2SLS estimator and a bias-corrected GS2SLS estimator. A criterion function for the optimal K selection can be based on the approximate MSEs. Monte Carlo experiments are provided to show the performance of our procedure of choosing K.
Thompson, Matthew; Weigl, Bernhard; Fitzpatrick, Annette; Ide, Nicole
2016-01-01
Current frameworks for evaluating diagnostic tests are constrained by a focus on diagnostic accuracy, and assume that all aspects of the testing process and test attributes are discrete and equally important. Determining the balance between the benefits and harms associated with new or existing tests has been overlooked. Yet, this is critically important information for stakeholders involved in developing, testing, and implementing tests. This is particularly important for point of care tests (POCTs) where tradeoffs exist between numerous aspects of the testing process and test attributes. We developed a new model that multiple stakeholders (e.g., clinicians, patients, researchers, test developers, industry, regulators, and health care funders) can use to visualize the multiple attributes of tests, the interactions that occur between these attributes, and their impacts on health outcomes. We use multiple examples to illustrate interactions between test attributes (test availability, test experience, and test results) and outcomes, including several POCTs. The model could be used to prioritize research and development efforts, and inform regulatory submissions for new diagnostics. It could potentially provide a way to incorporate the relative weights that various subgroups or clinical settings might place on different test attributes. Our model provides a novel way that multiple stakeholders can use to visualize test attributes, their interactions, and impacts on individual and population outcomes. We anticipate that this will facilitate more informed decision making around diagnostic tests.
Stock Market Autoregressive Dynamics: A Multinational Comparative Study with Quantile Regression
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Lili Li
2016-01-01
Full Text Available We study the nonlinear autoregressive dynamics of stock index returns in seven major advanced economies (G7 and China. The quantile autoregression model (QAR enables us to investigate the autocorrelation across the whole spectrum of return distribution, which provides more insightful conditional information on multinational stock market dynamics than conventional time series models. The relation between index return and contemporaneous trading volume is also investigated. While prior studies have mixed results on stock market autocorrelations, we find that the dynamics is usually state dependent. The results for G7 stock markets exhibit conspicuous similarities, but they are in manifest contrast to the findings on Chinese stock markets.
International Nuclear Information System (INIS)
McCall, K C; Jeraj, R
2007-01-01
A new approach to the problem of modelling and predicting respiration motion has been implemented. This is a dual-component model, which describes the respiration motion as a non-periodic time series superimposed onto a periodic waveform. A periodic autoregressive moving average algorithm has been used to define a mathematical model of the periodic and non-periodic components of the respiration motion. The periodic components of the motion were found by projecting multiple inhale-exhale cycles onto a common subspace. The component of the respiration signal that is left after removing this periodicity is a partially autocorrelated time series and was modelled as an autoregressive moving average (ARMA) process. The accuracy of the periodic ARMA model with respect to fluctuation in amplitude and variation in length of cycles has been assessed. A respiration phantom was developed to simulate the inter-cycle variations seen in free-breathing and coached respiration patterns. At ±14% variability in cycle length and maximum amplitude of motion, the prediction errors were 4.8% of the total motion extent for a 0.5 s ahead prediction, and 9.4% at 1.0 s lag. The prediction errors increased to 11.6% at 0.5 s and 21.6% at 1.0 s when the respiration pattern had ±34% variations in both these parameters. Our results have shown that the accuracy of the periodic ARMA model is more strongly dependent on the variations in cycle length than the amplitude of the respiration cycles
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Hong-Yun Zhang
2012-09-01
Full Text Available Quantum-behaved particle swarm optimization (QPSO is an efficient and powerful population-based optimization technique, which is inspired by the conventional particle swarm optimization (PSO and quantum mechanics theories. In this paper, an improved QPSO named SQPSO is proposed, which combines QPSO with a selective probability operator to solve the economic dispatch (ED problems with valve-point effects and multiple fuel options. To show the performance of the proposed SQPSO, it is tested on five standard benchmark functions and two ED benchmark problems, including a 40-unit ED problem with valve-point effects and a 10-unit ED problem with multiple fuel options. The results are compared with differential evolution (DE, particle swarm optimization (PSO and basic QPSO, as well as a number of other methods reported in the literature in terms of solution quality, convergence speed and robustness. The simulation results confirm that the proposed SQPSO is effective and reliable for both function optimization and ED problems.
International Nuclear Information System (INIS)
Chuang, Kuo-Chih; Ma, Chien-Ching; Liao, Heng-Tseng
2012-01-01
In this work, active vibration suppression of a smart cantilever beam subjected to disturbances from multiple impact loadings is investigated with a point-wise fiber Bragg grating (FBG) displacement sensing system. An FBG demodulator is employed in the proposed fiber sensing system to dynamically demodulate the responses obtained by the FBG displacement sensor with high sensitivity. To investigate the ability of the proposed FBG displacement sensor as a feedback sensor, velocity feedback control and delay control are employed to suppress the vibrations of the first three bending modes of the smart cantilever beam. To improve the control performance for the first bending mode when the cantilever beam is subjected to an impact loading, we improve the conventional velocity feedback controller by tuning the control gain online with the aid of information from a higher vibration mode. Finally, active control of vibrations induced by multiple impact loadings due to a plastic ball is performed with the improved velocity feedback control. The experimental results show that active vibration control of smart structures subjected to disturbances such as impact loadings can be achieved by employing the proposed FBG sensing system to feed back out-of-plane point-wise displacement responses with high sensitivity. (paper)
Estimation and Forecasting in Vector Autoregressive Moving Average Models for Rich Datasets
DEFF Research Database (Denmark)
Dias, Gustavo Fruet; Kapetanios, George
We address the issue of modelling and forecasting macroeconomic variables using rich datasets, by adopting the class of Vector Autoregressive Moving Average (VARMA) models. We overcome the estimation issue that arises with this class of models by implementing an iterative ordinary least squares (...
de Vries, S O; Fidler, Vaclav; Kuipers, Wietze D; Hunink, Maria G M
1998-01-01
The purpose of this study was to develop a model that predicts the outcome of supervised exercise for intermittent claudication. The authors present an example of the use of autoregressive logistic regression for modeling observed longitudinal data. Data were collected from 329 participants in a
Multivariate Self-Exciting Threshold Autoregressive Models with eXogenous Input
Addo, Peter Martey
2014-01-01
This study defines a multivariate Self--Exciting Threshold Autoregressive with eXogenous input (MSETARX) models and present an estimation procedure for the parameters. The conditions for stationarity of the nonlinear MSETARX models is provided. In particular, the efficiency of an adaptive parameter estimation algorithm and LSE (least squares estimate) algorithm for this class of models is then provided via simulations.
Adaptive interpolation of discrete-time signals that can be modeled as autoregressive processes
Janssen, A.J.E.M.; Veldhuis, R.N.J.; Vries, L.B.
1986-01-01
The authors present an adaptive algorithm for the restoration of lost sample values in discrete-time signals that can locally be described by means of autoregressive processes. The only restrictions are that the positions of the unknown samples should be known and that they should be embedded in a
Adaptive interpolation of discrete-time signals that can be modeled as autoregressive processes
Janssen, A.J.E.M.; Veldhuis, Raymond N.J.; Vries, Lodewijk B.
1986-01-01
This paper presents an adaptive algorithm for the restoration of lost sample values in discrete-time signals that can locally be described by means of autoregressive processes. The only restrictions are that the positions of the unknown samples should be known and that they should be embedded in a
A Vector AutoRegressive (VAR) Approach to the Credit Channel for ...
African Journals Online (AJOL)
This paper is an attempt to determine the presence and empirical significance of monetary policy and the bank lending view of the credit channel for Mauritius, which is particularly relevant at these times. A vector autoregressive (VAR) model of order three is used to examine the monetary transmission mechanism using ...
On the Oracle Property of the Adaptive LASSO in Stationary and Nonstationary Autoregressions
DEFF Research Database (Denmark)
Kock, Anders Bredahl
We show that the Adaptive LASSO is oracle efficient in stationary and non-stationary autoregressions. This means that it estimates parameters consistently, selects the correct sparsity pattern, and estimates the coefficients belonging to the relevant variables at the same asymptotic efficiency...
Fernández-de-las-Peñas, César; Caminero, Ana B; Madeleine, Pascal; Guillem-Mesado, Amparo; Ge, Hong-You; Arendt-Nielsen, Lars; Pareja, Juan A
2009-01-01
To describe the common locations of active trigger points (TrPs) in the temporalis muscle and their referred pain patterns in chronic tension type headache (CTTH), and to determine if pressure sensitivity maps of this muscle can be used to describe the spatial distribution of active TrPs. Forty women with CTTH were included. An electronic pressure algometer was used to assess pressure pain thresholds (PPT) from 9 points over each temporalis muscle: 3 points in the anterior, medial and posterior part, respectively. Both muscles were examined for the presence of active TrPs over each of the 9 points. The referred pain pattern of each active TrP was assessed. Two-way analysis of variance detected significant differences in mean PPT levels between the measurement points (F=30.3; P<0.001), but not between sides (F=2.1; P=0.2). PPT scores decreased from the posterior to the anterior column (P<0.001). No differences were found in the number of active TrPs (F=0.3; P=0.9) between the dominant side the nondominant side. Significant differences were found in the distribution of the active TrPs (chi2=12.2; P<0.001): active TrPs were mostly found in the anterior column and in the middle of the muscle belly. The analysis of variance did not detect significant differences in the referred pain pattern between active TrPs (F=1.1, P=0.4). The topographical pressure pain sensitivity maps showed the distinct distribution of the TrPs indicated by locations with low PPTs. Multiple active TrPs in the temporalis muscle were found, particularly in the anterior column and in the middle of the muscle belly. Bilateral posterior to anterior decreased distribution of PPTs in the temporalis muscle in women with CTTH was found. The locations of active TrPs in the temporalis muscle corresponded well to the muscle areas with lower PPT, supporting the relationship between multiple active muscle TrPs and topographical pressure sensitivity maps in the temporalis muscle in women with CTTH.
Linear and non-linear autoregressive models for short-term wind speed forecasting
International Nuclear Information System (INIS)
Lydia, M.; Suresh Kumar, S.; Immanuel Selvakumar, A.; Edwin Prem Kumar, G.
2016-01-01
Highlights: • Models for wind speed prediction at 10-min intervals up to 1 h built on time-series wind speed data. • Four different multivariate models for wind speed built based on exogenous variables. • Non-linear models built using three data mining algorithms outperform the linear models. • Autoregressive models based on wind direction perform better than other models. - Abstract: Wind speed forecasting aids in estimating the energy produced from wind farms. The soaring energy demands of the world and minimal availability of conventional energy sources have significantly increased the role of non-conventional sources of energy like solar, wind, etc. Development of models for wind speed forecasting with higher reliability and greater accuracy is the need of the hour. In this paper, models for predicting wind speed at 10-min intervals up to 1 h have been built based on linear and non-linear autoregressive moving average models with and without external variables. The autoregressive moving average models based on wind direction and annual trends have been built using data obtained from Sotavento Galicia Plc. and autoregressive moving average models based on wind direction, wind shear and temperature have been built on data obtained from Centre for Wind Energy Technology, Chennai, India. While the parameters of the linear models are obtained using the Gauss–Newton algorithm, the non-linear autoregressive models are developed using three different data mining algorithms. The accuracy of the models has been measured using three performance metrics namely, the Mean Absolute Error, Root Mean Squared Error and Mean Absolute Percentage Error.
Kerry, Ruth; Goovaerts, Pierre; Smit, Izak P J; Ingram, Ben R
Kruger National Park (KNP), South Africa, provides protected habitats for the unique animals of the African savannah. For the past 40 years, annual aerial surveys of herbivores have been conducted to aid management decisions based on (1) the spatial distribution of species throughout the park and (2) total species populations in a year. The surveys are extremely time consuming and costly. For many years, the whole park was surveyed, but in 1998 a transect survey approach was adopted. This is cheaper and less time consuming but leaves gaps in the data spatially. Also the distance method currently employed by the park only gives estimates of total species populations but not their spatial distribution. We compare the ability of multiple indicator kriging and area-to-point Poisson kriging to accurately map species distribution in the park. A leave-one-out cross-validation approach indicates that multiple indicator kriging makes poor estimates of the number of animals, particularly the few large counts, as the indicator variograms for such high thresholds are pure nugget. Poisson kriging was applied to the prediction of two types of abundance data: spatial density and proportion of a given species. Both Poisson approaches had standardized mean absolute errors (St. MAEs) of animal counts at least an order of magnitude lower than multiple indicator kriging. The spatial density, Poisson approach (1), gave the lowest St. MAEs for the most abundant species and the proportion, Poisson approach (2), did for the least abundant species. Incorporating environmental data into Poisson approach (2) further reduced St. MAEs.
Karacan, C. Özgen; Olea, Ricardo A.
2013-01-01
Coal seam degasification and its success are important for controlling methane, and thus for the health and safety of coal miners. During the course of degasification, properties of coal seams change. Thus, the changes in coal reservoir conditions and in-place gas content as well as methane emission potential into mines should be evaluated by examining time-dependent changes and the presence of major heterogeneities and geological discontinuities in the field. In this work, time-lapsed reservoir and fluid storage properties of the New Castle coal seam, Mary Lee/Blue Creek seam, and Jagger seam of Black Warrior Basin, Alabama, were determined from gas and water production history matching and production forecasting of vertical degasification wellbores. These properties were combined with isotherm and other important data to compute gas-in-place (GIP) and its change with time at borehole locations. Time-lapsed training images (TIs) of GIP and GIP difference corresponding to each coal and date were generated by using these point-wise data and Voronoi decomposition on the TI grid, which included faults as discontinuities for expansion of Voronoi regions. Filter-based multiple-point geostatistical simulations, which were preferred in this study due to anisotropies and discontinuities in the area, were used to predict time-lapsed GIP distributions within the study area. Performed simulations were used for mapping spatial time-lapsed methane quantities as well as their uncertainties within the study area.
Schmuck, Sebastian; Mamach, Martin; Wilke, Florian; von Klot, Christoph A; Henkenberens, Christoph; Thackeray, James T; Sohns, Jan M; Geworski, Lilli; Ross, Tobias L; Wester, Hans-Juergen; Christiansen, Hans; Bengel, Frank M; Derlin, Thorsten
2017-06-01
The aims of this study were to gain mechanistic insights into prostate cancer biology using dynamic imaging and to evaluate the usefulness of multiple time-point Ga-prostate-specific membrane antigen (PSMA) I&T PET/CT for the assessment of primary prostate cancer before prostatectomy. Twenty patients with prostate cancer underwent Ga-PSMA I&T PET/CT before prostatectomy. The PET protocol consisted of early dynamic pelvic imaging, followed by static scans at 60 and 180 minutes postinjection (p.i.). SUVs, time-activity curves, quantitative analysis based on a 2-tissue compartment model, Patlak analysis, histopathology, and Gleason grading were compared between prostate cancer and benign prostate gland. Primary tumors were identified on both early dynamic and delayed imaging in 95% of patients. Tracer uptake was significantly higher in prostate cancer compared with benign prostate tissue at any time point (P ≤ 0.0003) and increased over time. Consequently, the tumor-to-nontumor ratio within the prostate gland improved over time (2.8 at 10 minutes vs 17.1 at 180 minutes p.i.). Tracer uptake at both 60 and 180 minutes p.i. was significantly higher in patients with higher Gleason scores (P dynamic and static delayed Ga-PSMA ligand PET images. The tumor-to-nontumor ratio in the prostate gland improves over time, supporting a role of delayed imaging for optimal visualization of prostate cancer.
Vetrivel, Anand; Gerke, Markus; Kerle, Norman; Nex, Francesco; Vosselman, George
2018-06-01
Oblique aerial images offer views of both building roofs and façades, and thus have been recognized as a potential source to detect severe building damages caused by destructive disaster events such as earthquakes. Therefore, they represent an important source of information for first responders or other stakeholders involved in the post-disaster response process. Several automated methods based on supervised learning have already been demonstrated for damage detection using oblique airborne images. However, they often do not generalize well when data from new unseen sites need to be processed, hampering their practical use. Reasons for this limitation include image and scene characteristics, though the most prominent one relates to the image features being used for training the classifier. Recently features based on deep learning approaches, such as convolutional neural networks (CNNs), have been shown to be more effective than conventional hand-crafted features, and have become the state-of-the-art in many domains, including remote sensing. Moreover, often oblique images are captured with high block overlap, facilitating the generation of dense 3D point clouds - an ideal source to derive geometric characteristics. We hypothesized that the use of CNN features, either independently or in combination with 3D point cloud features, would yield improved performance in damage detection. To this end we used CNN and 3D features, both independently and in combination, using images from manned and unmanned aerial platforms over several geographic locations that vary significantly in terms of image and scene characteristics. A multiple-kernel-learning framework, an effective way for integrating features from different modalities, was used for combining the two sets of features for classification. The results are encouraging: while CNN features produced an average classification accuracy of about 91%, the integration of 3D point cloud features led to an additional
Nan, Zhufen; Chi, Xuefen
2016-12-20
The IEEE 802.15.7 protocol suggests that it could coordinate the channel access process based on the competitive method of carrier sensing. However, the directionality of light and randomness of diffuse reflection would give rise to a serious imperfect carrier sense (ICS) problem [e.g., hidden node (HN) problem and exposed node (EN) problem], which brings great challenges in realizing the optical carrier sense multiple access (CSMA) mechanism. In this paper, the carrier sense process implemented by diffuse reflection light is modeled as the choice of independent sets. We establish an ICS model with the presence of ENs and HNs for the multi-point to multi-point visible light communication (VLC) uplink communications system. Considering the severe optical ICS problem, an optical hard core point process (OHCPP) is developed, which characterizes the optical CSMA for the indoor VLC uplink communications system. Due to the limited coverage of the transmitted optical signal, in our OHCPP, the ENs within the transmitters' carrier sense region could be retained provided that they could not corrupt the ongoing communications. Moreover, because of the directionality of both light emitting diode (LED) transmitters and receivers, theoretical analysis of the HN problem becomes difficult. In this paper, we derive the closed-form expression for approximating the outage probability and transmission capacity of VLC networks with the presence of HNs and ENs. Simulation results validate the analysis and also show the existence of an optimal physical carrier-sensing threshold that maximizes the transmission capacity for a given emission angle of LED.
Krishnan, M.; Bhowmik, B.; Hazra, B.; Pakrashi, V.
2018-02-01
In this paper, a novel baseline free approach for continuous online damage detection of multi degree of freedom vibrating structures using Recursive Principal Component Analysis (RPCA) in conjunction with Time Varying Auto-Regressive Modeling (TVAR) is proposed. In this method, the acceleration data is used to obtain recursive proper orthogonal components online using rank-one perturbation method, followed by TVAR modeling of the first transformed response, to detect the change in the dynamic behavior of the vibrating system from its pristine state to contiguous linear/non-linear-states that indicate damage. Most of the works available in the literature deal with algorithms that require windowing of the gathered data owing to their data-driven nature which renders them ineffective for online implementation. Algorithms focussed on mathematically consistent recursive techniques in a rigorous theoretical framework of structural damage detection is missing, which motivates the development of the present framework that is amenable for online implementation which could be utilized along with suite experimental and numerical investigations. The RPCA algorithm iterates the eigenvector and eigenvalue estimates for sample covariance matrices and new data point at each successive time instants, using the rank-one perturbation method. TVAR modeling on the principal component explaining maximum variance is utilized and the damage is identified by tracking the TVAR coefficients. This eliminates the need for offline post processing and facilitates online damage detection especially when applied to streaming data without requiring any baseline data. Numerical simulations performed on a 5-dof nonlinear system under white noise excitation and El Centro (also known as 1940 Imperial Valley earthquake) excitation, for different damage scenarios, demonstrate the robustness of the proposed algorithm. The method is further validated on results obtained from case studies involving
International Nuclear Information System (INIS)
Aruquipa Coloma, Wilmer
2017-01-01
Nuclear reactors are susceptible to instability, causing oscillations in reactor power in specific working regions characterized by determined values of power and coolant mass flow. During reactor startup, there is a greater probability that these regions of instability will be present; another reason may be due to transient processes in some reactor parameters. The analysis of the temporal evolution of the power reveals a stable or unstable process after the disturbance in a light water reactor of type BWR (Boiling Water Reactor). In this work, the instability problem was approached in two ways. The first form is based on the ARMA (Autoregressive Moving Average models) model. This model was used to calculate the Decay Ratio (DR) and natural frequency (NF) of the oscillations, parameters that indicate if the one power signal is stable or not. In this sense, the DRARMA code was developed. In the second form, the problems of instability were analyzed using the classical concepts of non-linear systems, such as Lyapunov exponents, phase space and attractors. The Lyapunov exponents quantify the exponential divergence of the trajectories initially close to the phase space and estimate the amount of chaos in a system; the phase space and the attractors describe the dynamic behavior of the system. The main aim of the instability phenomena studies in nuclear reactors is to try to identify points or regions of operation that can lead to power oscillations conditions. The two approaches were applied to two sets of signals. The first set comes from signals of instability events of the commercial Forsmark reactors 1 and 2 and were used to validate the DRARMA code. The second set was obtained from the simulation of transient events of the Peach Bottom reactor; for the simulation, the PARCS and RELAP5 codes were used for the neutronic/thermal hydraulic coupling calculation. For all analyzes made in this work, the Matlab software was used due to its ease of programming and
Directory of Open Access Journals (Sweden)
Luis Gonzaga Baca Ruiz
2016-08-01
Full Text Available This paper addresses the problem of energy consumption prediction using neural networks over a set of public buildings. Since energy consumption in the public sector comprises a substantial share of overall consumption, the prediction of such consumption represents a decisive issue in the achievement of energy savings. In our experiments, we use the data provided by an energy consumption monitoring system in a compound of faculties and research centers at the University of Granada, and provide a methodology to predict future energy consumption using nonlinear autoregressive (NAR and the nonlinear autoregressive neural network with exogenous inputs (NARX, respectively. Results reveal that NAR and NARX neural networks are both suitable for performing energy consumption prediction, but also that exogenous data may help to improve the accuracy of predictions.
International Nuclear Information System (INIS)
Yao, Ruigen; Pakzad, Shamim N
2014-01-01
In the past few decades many types of structural damage indices based on structural health monitoring signals have been proposed, requiring performance evaluation and comparison studies on these indices in a quantitative manner. One tool to help accomplish this objective is analytical sensitivity analysis, which has been successfully used to evaluate the influences of system operational parameters on observable characteristics in many fields of study. In this paper, the sensitivity expressions of two damage features, namely the Mahalanobis distance of autoregressive coefficients and the Cosh distance of autoregressive spectra, will be derived with respect to both structural damage and measurement noise level. The effectiveness of the proposed methods is illustrated in a numerical case study on a 10-DOF system, where their results are compared with those from direct simulation and theoretical calculation. (paper)
Medium- and Long-term Prediction of LOD Change by the Leap-step Autoregressive Model
Wang, Qijie
2015-08-01
The accuracy of medium- and long-term prediction of length of day (LOD) change base on combined least-square and autoregressive (LS+AR) deteriorates gradually. Leap-step autoregressive (LSAR) model can significantly reduce the edge effect of the observation sequence. Especially, LSAR model greatly improves the resolution of signals’ low-frequency components. Therefore, it can improve the efficiency of prediction. In this work, LSAR is used to forecast the LOD change. The LOD series from EOP 08 C04 provided by IERS is modeled by both the LSAR and AR models. The results of the two models are analyzed and compared. When the prediction length is between 10-30 days, the accuracy improvement is less than 10%. When the prediction length amounts to above 30 day, the accuracy improved obviously, with the maximum being around 19%. The results show that the LSAR model has higher prediction accuracy and stability in medium- and long-term prediction.
Medium- and Long-term Prediction of LOD Change with the Leap-step Autoregressive Model
Liu, Q. B.; Wang, Q. J.; Lei, M. F.
2015-09-01
It is known that the accuracies of medium- and long-term prediction of changes of length of day (LOD) based on the combined least-square and autoregressive (LS+AR) decrease gradually. The leap-step autoregressive (LSAR) model is more accurate and stable in medium- and long-term prediction, therefore it is used to forecast the LOD changes in this work. Then the LOD series from EOP 08 C04 provided by IERS (International Earth Rotation and Reference Systems Service) is used to compare the effectiveness of the LSAR and traditional AR methods. The predicted series resulted from the two models show that the prediction accuracy with the LSAR model is better than that from AR model in medium- and long-term prediction.
Brillouin Scattering Spectrum Analysis Based on Auto-Regressive Spectral Estimation
Huang, Mengyun; Li, Wei; Liu, Zhangyun; Cheng, Linghao; Guan, Bai-Ou
2018-06-01
Auto-regressive (AR) spectral estimation technology is proposed to analyze the Brillouin scattering spectrum in Brillouin optical time-domain refelectometry. It shows that AR based method can reliably estimate the Brillouin frequency shift with an accuracy much better than fast Fourier transform (FFT) based methods provided the data length is not too short. It enables about 3 times improvement over FFT at a moderate spatial resolution.
Bušs, Ginters
2009-01-01
Bayesian inference requires an analyst to set priors. Setting the right prior is crucial for precise forecasts. This paper analyzes how optimal prior changes when an economy is hit by a recession. For this task, an autoregressive distributed lag (ADL) model is chosen. The results show that a sharp economic slowdown changes the optimal prior in two directions. First, it changes the structure of the optimal weight prior, setting smaller weight on the lagged dependent variable compared to varia...
Use of (Time-Domain) Vector Autoregressions to Test Uncovered Interest Parity
Takatoshi Ito
1984-01-01
In this paper, a vector autoregression model (VAR) is proposed in order to test uncovered interest parity (UIP) in the foreign exchange market. Consider a VAR system of the spot exchange rate (yen/dollar), the domestic (US) interest rate and the foreign (Japanese) interest rate, describing the interdependence of the domestic and international financia lmarkets. Uncovered interest parity is stated as a null hypothesis that the current difference between the two interest rates is equal to the d...
Directory of Open Access Journals (Sweden)
Sun Zhangzhen
2012-08-01
Full Text Available In this paper, an improved weighted least squares (WLS, together with autoregressive (AR model, is proposed to improve prediction accuracy of earth rotation parameters(ERP. Four weighting schemes are developed and the optimal power e for determination of the weight elements is studied. The results show that the improved WLS-AR model can improve the ERP prediction accuracy effectively, and for different prediction intervals of ERP, different weight scheme should be chosen.
A Dynamic Model of U.S. Sugar-Related Markets: A Cointegrated Vector Autoregression Approach
Babula, Ronald A.; Newman, Douglas; Rogowsky, Robert A.
2006-01-01
The methods of the cointegrated vector autoregression (VAR) model are applied to monthly U.S. markets for sugar and for sugar-using markets for confectionary, soft drink, and bakery products. Primarily a methods paper, we apply Johansen and Juselius' advanced procedures to these markets for perhaps the first time, with focus on achievement of a statistically adequate model through analysis of a battery of advanced statistical diagnostic tests and on exploitation of the system's cointegration ...
Brillouin Scattering Spectrum Analysis Based on Auto-Regressive Spectral Estimation
Huang, Mengyun; Li, Wei; Liu, Zhangyun; Cheng, Linghao; Guan, Bai-Ou
2018-03-01
Auto-regressive (AR) spectral estimation technology is proposed to analyze the Brillouin scattering spectrum in Brillouin optical time-domain refelectometry. It shows that AR based method can reliably estimate the Brillouin frequency shift with an accuracy much better than fast Fourier transform (FFT) based methods provided the data length is not too short. It enables about 3 times improvement over FFT at a moderate spatial resolution.
Exchange rate pass-through in Switzerland: Evidence from vector autoregressions
Jonas Stulz
2007-01-01
This study investigates the pass-through of exchange rate and import price shocks to different aggregated prices in Switzerland. The baseline analysis is carried out with recursively identified vector autoregressive (VAR) models. The data set comprises monthly observations, and pass-through effects are quantified by means of impulse response functions. Evidence shows that the exchange rate pass-through to import prices is substantial (although incomplete), but only moderate to total consumer ...
Estimation of the order of an autoregressive time series: a Bayesian approach
International Nuclear Information System (INIS)
Robb, L.J.
1980-01-01
Finite-order autoregressive models for time series are often used for prediction and other inferences. Given the order of the model, the parameters of the models can be estimated by least-squares, maximum-likelihood, or Yule-Walker method. The basic problem is estimating the order of the model. The problem of autoregressive order estimation is placed in a Bayesian framework. This approach illustrates how the Bayesian method brings the numerous aspects of the problem together into a coherent structure. A joint prior probability density is proposed for the order, the partial autocorrelation coefficients, and the variance; and the marginal posterior probability distribution for the order, given the data, is obtained. It is noted that the value with maximum posterior probability is the Bayes estimate of the order with respect to a particular loss function. The asymptotic posterior distribution of the order is also given. In conclusion, Wolfer's sunspot data as well as simulated data corresponding to several autoregressive models are analyzed according to Akaike's method and the Bayesian method. Both methods are observed to perform quite well, although the Bayesian method was clearly superior, in most cases
Lee, Duncan; Rushworth, Alastair; Sahu, Sujit K
2014-06-01
Estimation of the long-term health effects of air pollution is a challenging task, especially when modeling spatial small-area disease incidence data in an ecological study design. The challenge comes from the unobserved underlying spatial autocorrelation structure in these data, which is accounted for using random effects modeled by a globally smooth conditional autoregressive model. These smooth random effects confound the effects of air pollution, which are also globally smooth. To avoid this collinearity a Bayesian localized conditional autoregressive model is developed for the random effects. This localized model is flexible spatially, in the sense that it is not only able to model areas of spatial smoothness, but also it is able to capture step changes in the random effects surface. This methodological development allows us to improve the estimation performance of the covariate effects, compared to using traditional conditional auto-regressive models. These results are established using a simulation study, and are then illustrated with our motivating study on air pollution and respiratory ill health in Greater Glasgow, Scotland in 2011. The model shows substantial health effects of particulate matter air pollution and nitrogen dioxide, whose effects have been consistently attenuated by the currently available globally smooth models. © 2014, The Authors Biometrics published by Wiley Periodicals, Inc. on behalf of International Biometric Society.
Experimental designs for autoregressive models applied to industrial maintenance
International Nuclear Information System (INIS)
Amo-Salas, M.; López-Fidalgo, J.; Pedregal, D.J.
2015-01-01
Some time series applications require data which are either expensive or technically difficult to obtain. In such cases scheduling the points in time at which the information should be collected is of paramount importance in order to optimize the resources available. In this paper time series models are studied from a new perspective, consisting in the use of Optimal Experimental Design setup to obtain the best times to take measurements, with the principal aim of saving costs or discarding useless information. The model and the covariance function are expressed in an explicit form to apply the usual techniques of Optimal Experimental Design. Optimal designs for various approaches are computed and their efficiencies are compared. The methods working in an application of industrial maintenance of a critical piece of equipment at a petrochemical plant are shown. This simple model allows explicit calculations in order to show openly the procedure to find the correlation structure, needed for computing the optimal experimental design. In this sense the techniques used in this paper to compute optimal designs may be transferred to other situations following the ideas of the paper, but taking into account the increasing difficulty of the procedure for more complex models. - Highlights: • Optimal experimental design theory is applied to AR models to reduce costs. • The first observation has an important impact on any optimal design. • Either the lack of precision or small starting observations claim for large times. • Reasonable optimal times were obtained relaxing slightly the efficiency. • Optimal designs were computed in a predictive maintenance context
Directory of Open Access Journals (Sweden)
J. Soete
2017-01-01
Full Text Available Microcomputed tomography (μCT and Lattice Boltzmann Method (LBM simulations were applied to continental carbonates to quantify fluid flow. Fluid flow characteristics in these complex carbonates with multiscale pore networks are unique and the applied method allows studying their heterogeneity and anisotropy. 3D pore network models were introduced to single-phase flow simulations in Palabos, a software tool for particle-based modelling of classic computational fluid dynamics. In addition, permeability simulations were also performed on rock models generated with multiple-point geostatistics (MPS. This allowed assessing the applicability of MPS in upscaling high-resolution porosity patterns into large rock models that exceed the volume limitations of the μCT. Porosity and tortuosity control fluid flow in these porous media. Micro- and mesopores influence flow properties at larger scales in continental carbonates. Upscaling with MPS is therefore necessary to overcome volume-resolution problems of CT scanning equipment. The presented LBM-MPS workflow is applicable to other lithologies, comprising different pore types, shapes, and pore networks altogether. The lack of straightforward porosity-permeability relationships in complex carbonates highlights the necessity for a 3D approach. 3D fluid flow studies provide the best understanding of flow through porous media, which is of crucial importance in reservoir modelling.
Possemiers, Mathias; Huysmans, Marijke; Batelaan, Okke
2015-08-01
Adequate aquifer characterization and simulation using heat transport models are indispensible for determining the optimal design for aquifer thermal energy storage (ATES) systems and wells. Recent model studies indicate that meter-scale heterogeneities in the hydraulic conductivity field introduce a considerable uncertainty in the distribution of thermal energy around an ATES system and can lead to a reduction in the thermal recoverability. In a study site in Bierbeek, Belgium, the influence of centimeter-scale clay drapes on the efficiency of a doublet ATES system and the distribution of the thermal energy around the ATES wells are quantified. Multiple-point geostatistical simulation of edge properties is used to incorporate the clay drapes in the models. The results show that clay drapes have an influence both on the distribution of thermal energy in the subsurface and on the efficiency of the ATES system. The distribution of the thermal energy is determined by the strike of the clay drapes, with the major axis of anisotropy parallel to the clay drape strike. The clay drapes have a negative impact (3.3-3.6 %) on the energy output in the models without a hydraulic gradient. In the models with a hydraulic gradient, however, the presence of clay drapes has a positive influence (1.6-10.2 %) on the energy output of the ATES system. It is concluded that it is important to incorporate small-scale heterogeneities in heat transport models to get a better estimate on ATES efficiency and distribution of thermal energy.
Possemiers, Mathias; Huysmans, Marijke; Batelaan, Okke
2015-04-01
Adequate aquifer characterization and simulation using heat transport models are indispensible for determining the optimal design for Aquifer Thermal Energy Storage (ATES) systems and wells. Recent model studies indicate that meter scale heterogeneities in the hydraulic conductivity field introduce a considerable uncertainty in the distribution of thermal energy around an ATES system and can lead to a reduction in the thermal recoverability. In this paper, the influence of centimeter scale clay drapes on the efficiency of a doublet ATES system and the distribution of the thermal energy around the ATES wells are quantified. Multiple-point geostatistical simulation of edge properties is used to incorporate the clay drapes in the models. The results show that clay drapes have an influence both on the distribution of thermal energy in the subsurface and on the efficiency of the ATES system. The distribution of the thermal energy is determined by the strike of the clay drapes, with the major axis of anisotropy parallel to the clay drape strike. The clay drapes have a negative impact (3.3 - 3.6%) on the energy output in the models without a hydraulic gradient. In the models with a hydraulic gradient, however, the presence of clay drapes has a positive influence (1.6 - 10.2%) on the energy output of the ATES system. It is concluded that it is important to incorporate small scale heterogeneities in heat transport models to get a better estimate on ATES efficiency and distribution of thermal energy.
Directory of Open Access Journals (Sweden)
A Francina Webster
Full Text Available Many regulatory agencies are exploring ways to integrate toxicogenomic data into their chemical risk assessments. The major challenge lies in determining how to distill the complex data produced by high-content, multi-dose gene expression studies into quantitative information. It has been proposed that benchmark dose (BMD values derived from toxicogenomics data be used as point of departure (PoD values in chemical risk assessments. However, there is limited information regarding which genomics platforms are most suitable and how to select appropriate PoD values. In this study, we compared BMD values modeled from RNA sequencing-, microarray-, and qPCR-derived gene expression data from a single study, and explored multiple approaches for selecting a single PoD from these data. The strategies evaluated include several that do not require prior mechanistic knowledge of the compound for selection of the PoD, thus providing approaches for assessing data-poor chemicals. We used RNA extracted from the livers of female mice exposed to non-carcinogenic (0, 2 mg/kg/day, mkd and carcinogenic (4, 8 mkd doses of furan for 21 days. We show that transcriptional BMD values were consistent across technologies and highly predictive of the two-year cancer bioassay-based PoD. We also demonstrate that filtering data based on statistically significant changes in gene expression prior to BMD modeling creates more conservative BMD values. Taken together, this case study on mice exposed to furan demonstrates that high-content toxicogenomics studies produce robust data for BMD modelling that are minimally affected by inter-technology variability and highly predictive of cancer-based PoD doses.
Lee, Cameron C; Sheridan, Scott C
2018-07-01
Temperature-mortality relationships are nonlinear, time-lagged, and can vary depending on the time of year and geographic location, all of which limits the applicability of simple regression models in describing these associations. This research demonstrates the utility of an alternative method for modeling such complex relationships that has gained recent traction in other environmental fields: nonlinear autoregressive models with exogenous input (NARX models). All-cause mortality data and multiple temperature-based data sets were gathered from 41 different US cities, for the period 1975-2010, and subjected to ensemble NARX modeling. Models generally performed better in larger cities and during the winter season. Across the US, median absolute percentage errors were 10% (ranging from 4% to 15% in various cities), the average improvement in the r-squared over that of a simple persistence model was 17% (6-24%), and the hit rate for modeling spike days in mortality (>80th percentile) was 54% (34-71%). Mortality responded acutely to hot summer days, peaking at 0-2 days of lag before dropping precipitously, and there was an extended mortality response to cold winter days, peaking at 2-4 days of lag and dropping slowly and continuing for multiple weeks. Spring and autumn showed both of the aforementioned temperature-mortality relationships, but generally to a lesser magnitude than what was seen in summer or winter. When compared to distributed lag nonlinear models, NARX model output was nearly identical. These results highlight the applicability of NARX models for use in modeling complex and time-dependent relationships for various applications in epidemiology and environmental sciences. Copyright © 2018 Elsevier Inc. All rights reserved.
African Journals Online (AJOL)
2017-09-10
Sep 10, 2017 ... The NAR identification process is done in two steps namely model structure selection and parameter .... with MATLAB 2014a as the development platform. ..... on Modeling, Simulation and Applied Optimization, 2011, pp. 1-5.
Karacan, C Özgen; Olea, Ricardo A
2013-08-01
Coal seam degasification and its success are important for controlling methane, and thus for the health and safety of coal miners. During the course of degasification, properties of coal seams change. Thus, the changes in coal reservoir conditions and in-place gas content as well as methane emission potential into mines should be evaluated by examining time-dependent changes and the presence of major heterogeneities and geological discontinuities in the field. In this work, time-lapsed reservoir and fluid storage properties of the New Castle coal seam, Mary Lee/Blue Creek seam, and Jagger seam of Black Warrior Basin, Alabama, were determined from gas and water production history matching and production forecasting of vertical degasification wellbores. These properties were combined with isotherm and other important data to compute gas-in-place (GIP) and its change with time at borehole locations. Time-lapsed training images (TIs) of GIP and GIP difference corresponding to each coal and date were generated by using these point-wise data and Voronoi decomposition on the TI grid, which included faults as discontinuities for expansion of Voronoi regions. Filter-based multiple-point geostatistical simulations, which were preferred in this study due to anisotropies and discontinuities in the area, were used to predict time-lapsed GIP distributions within the study area. Performed simulations were used for mapping spatial time-lapsed methane quantities as well as their uncertainties within the study area. The systematic approach presented in this paper is the first time in literature that history matching, TIs of GIPs and filter simulations are used for degasification performance evaluation and for assessing GIP for mining safety. Results from this study showed that using production history matching of coalbed methane wells to determine time-lapsed reservoir data could be used to compute spatial GIP and representative GIP TIs generated through Voronoi decomposition
Recognition of NEMP and LEMP signals based on auto-regression model and artificial neutral network
International Nuclear Information System (INIS)
Li Peng; Song Lijun; Han Chao; Zheng Yi; Cao Baofeng; Li Xiaoqiang; Zhang Xueqin; Liang Rui
2010-01-01
Auto-regression (AR) model, one power spectrum estimation method of stationary random signals, and artificial neutral network were adopted to recognize nuclear and lightning electromagnetic pulses. Self-correlation function and Burg algorithms were used to acquire the AR model coefficients as eigenvalues, and BP artificial neural network was introduced as the classifier with different numbers of hidden layers and hidden layer nodes. The results show that AR model is effective in those signals, feature extraction, and the Burg algorithm is more effective than the self-correlation function algorithm. (authors)
Insurance-growth nexus in Ghana: An autoregressive distributed lag bounds cointegration approach
Directory of Open Access Journals (Sweden)
Abdul Latif Alhassan
2014-12-01
Full Text Available This paper examines the long-run causal relationship between insurance penetration and economic growth in Ghana from 1990 to 2010. Using the autoregressive distributed lag (ARDL bounds approach to cointegration by Pesaran et al. (1996, 2001, the study finds a long-run positive relationship between insurance penetration and economic growth which implies that funds mobilized from insurance business have a long run impact on economic growth. A unidirectional causality was found to run from aggregate insurance penetration, life and non-life insurance penetration to economic growth to support the ‘supply-leading’ hypothesis. The findings have implications for insurance market development in Ghana.
Non-Gaussian Autoregressive Processes with Tukey g-and-h Transformations
Yan, Yuan
2017-11-20
When performing a time series analysis of continuous data, for example from climate or environmental problems, the assumption that the process is Gaussian is often violated. Therefore, we introduce two non-Gaussian autoregressive time series models that are able to fit skewed and heavy-tailed time series data. Our two models are based on the Tukey g-and-h transformation. We discuss parameter estimation, order selection, and forecasting procedures for our models and examine their performances in a simulation study. We demonstrate the usefulness of our models by applying them to two sets of wind speed data.
I PUTU YUDI PRABHADIKA; NI KETUT TARI TASTRAWATI; LUH PUTU IDA HARINI
2018-01-01
Infusion supplies are an important thing that must be considered by the hospital in meeting the needs of patients. This study aims to predict the need for infusion of 0.9% 500 ml of NaCl and 5% 500 ml glucose infusion at Sanglah General Hospital (RSUP) Sanglah so that the hospital can estimate the many infusions needed for the next six months. The forecasting method used in this research is the autoregressive integrated moving average (ARIMA) time series method. The results of this study indi...
Autoregressive Model with Partial Forgetting within Rao-Blackwellized Particle Filter
Czech Academy of Sciences Publication Activity Database
Dedecius, Kamil; Hofman, Radek
2012-01-01
Roč. 41, č. 5 (2012), s. 582-589 ISSN 0361-0918 R&D Projects: GA MV VG20102013018; GA ČR GA102/08/0567 Grant - others:ČVUT(CZ) SGS 10/099/OHK3/1T/16 Institutional research plan: CEZ:AV0Z10750506 Keywords : Bayesian methods * Particle filters * Recursive estimation Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 0.295, year: 2012 http://library.utia.cas.cz/separaty/2012/AS/dedecius-autoregressive model with partial forgetting within rao-blackwellized particle filter.pdf
Autcha Araveeporn
2013-01-01
This paper compares a Least-Squared Random Coefficient Autoregressive (RCA) model with a Least-Squared RCA model based on Autocorrelated Errors (RCA-AR). We looked at only the first order models, denoted RCA(1) and RCA(1)-AR(1). The efficiency of the Least-Squared method was checked by applying the models to Brownian motion and Wiener process, and the efficiency followed closely the asymptotic properties of a normal distribution. In a simulation study, we compared the performance of RCA(1) an...
1987-02-04
U5tr,)! P(U 5-t Since U - F with F RS, we get (3.1). Case b: 0 S 5 k -a Now P([U~t]riM) = P(UZk-a) and P([ Ugt ]rM) = P(US-k-a) S P(US-(k-a)) which again...robustness for autoregressive processes." The Annals of Statistics, 12, 843-863. Mallows, C.L. (1980). "Some theory of nonlinear smoothen." The Annals of
Robust estimation of autoregressive processes using a mixture-based filter-bank
Czech Academy of Sciences Publication Activity Database
Šmídl, V.; Anthony, Q.; Kárný, Miroslav; Guy, Tatiana Valentine
2005-01-01
Roč. 54, č. 4 (2005), s. 315-323 ISSN 0167-6911 R&D Projects: GA AV ČR IBS1075351; GA ČR GA102/03/0049; GA ČR GP102/03/P010; GA MŠk 1M0572 Institutional research plan: CEZ:AV0Z10750506 Keywords : Bayesian estimation * probabilistic mixtures * recursive estimation Subject RIV: BC - Control Systems Theory Impact factor: 1.239, year: 2005 http://library.utia.cas.cz/separaty/historie/karny-robust estimation of autoregressive processes using a mixture-based filter- bank .pdf
Business cycles and fertility dynamics in the United States: a vector autoregressive model.
Mocan, N H
1990-01-01
"Using vector-autoregressions...this paper shows that fertility moves countercyclically over the business cycle....[It] shows that the United States fertility is not governed by a deterministic trend as was assumed by previous studies. Rather, fertility evolves around a stochastic trend. It is shown that a bivariate analysis between fertility and unemployment yields a procyclical picture of fertility. However, when one considers the effects on fertility of early marriages and the divorce behavior as well as economic activity, fertility moves countercyclically." excerpt
Ağaç, Kübra; Koçak, Kasım; Deniz, Ali
2015-04-01
A time series approach using autoregressive model (AR), moving average model (MA) and seasonal autoregressive integrated moving average model (SARIMA) were used in this study to simulate and forecast daily PM10 concentrations in Kagithane Creek Valley, Istanbul. Hourly PM10 concentrations have been measured in Kagithane Creek Valley between 2010 and 2014 periods. Bosphorus divides the city in two parts as European and Asian parts. The historical part of the city takes place in Golden Horn. Our study area Kagithane Creek Valley is connected with this historical part. The study area is highly polluted because of its topographical structure and industrial activities. Also population density is extremely high in this site. The dispersion conditions are highly poor in this creek valley so it is necessary to calculate PM10 levels for air quality and human health. For given period there were some missing PM10 concentration values so to make an accurate calculations and to obtain exact results gap filling method was applied by Singular Spectrum Analysis (SSA). SSA is a new and efficient method for gap filling and it is an state-of-art modeling. SSA-MTM Toolkit was used for our study. SSA is considered as a noise reduction algorithm because it decomposes an original time series to trend (if exists), oscillatory and noise components by way of a singular value decomposition. The basic SSA algorithm has stages of decomposition and reconstruction. For given period daily and monthly PM10 concentrations were calculated and episodic periods are determined. Long term and short term PM10 concentrations were analyzed according to European Union (EU) standards. For simulation and forecasting of high level PM10 concentrations, meteorological data (wind speed, pressure and temperature) were used to see the relationship between daily PM10 concentrations. Fast Fourier Transformation (FFT) was also applied to the data to see the periodicity and according to these periods models were built
A Mixture Innovation Heterogeneous Autoregressive Model for Structural Breaks and Long Memory
DEFF Research Database (Denmark)
Nonejad, Nima
We propose a flexible model to describe nonlinearities and long-range dependence in time series dynamics. Our model is an extension of the heterogeneous autoregressive model. Structural breaks occur through mixture distributions in state innovations of linear Gaussian state space models. Monte...... Carlo simulations evaluate the properties of the estimation procedures. Results show that the proposed model is viable and flexible for purposes of forecasting volatility. Model uncertainty is accounted for by employing Bayesian model averaging. Bayesian model averaging provides very competitive...... forecasts compared to any single model specification. It provides further improvements when we average over nonlinear specifications....
Non-Gaussian Autoregressive Processes with Tukey g-and-h Transformations
Yan, Yuan; Genton, Marc G.
2017-01-01
When performing a time series analysis of continuous data, for example from climate or environmental problems, the assumption that the process is Gaussian is often violated. Therefore, we introduce two non-Gaussian autoregressive time series models that are able to fit skewed and heavy-tailed time series data. Our two models are based on the Tukey g-and-h transformation. We discuss parameter estimation, order selection, and forecasting procedures for our models and examine their performances in a simulation study. We demonstrate the usefulness of our models by applying them to two sets of wind speed data.
A representation theory for a class of vector autoregressive models for fractional processes
DEFF Research Database (Denmark)
Johansen, Søren
2008-01-01
Based on an idea of Granger (1986), we analyze a new vector autoregressive model defined from the fractional lag operator 1-(1-L)^{d}. We first derive conditions in terms of the coefficients for the model to generate processes which are fractional of order zero. We then show that if there is a un...... root, the model generates a fractional process X(t) of order d, d>0, for which there are vectors ß so that ß'X(t) is fractional of order d-b, 0...
DEFF Research Database (Denmark)
Chon, K H; Cohen, R J; Holstein-Rathlou, N H
1997-01-01
A linear and nonlinear autoregressive moving average (ARMA) identification algorithm is developed for modeling time series data. The algorithm uses Laguerre expansion of kernals (LEK) to estimate Volterra-Wiener kernals. However, instead of estimating linear and nonlinear system dynamics via moving...... average models, as is the case for the Volterra-Wiener analysis, we propose an ARMA model-based approach. The proposed algorithm is essentially the same as LEK, but this algorithm is extended to include past values of the output as well. Thus, all of the advantages associated with using the Laguerre...
Recursive wind speed forecasting based on Hammerstein Auto-Regressive model
International Nuclear Information System (INIS)
Ait Maatallah, Othman; Achuthan, Ajit; Janoyan, Kerop; Marzocca, Pier
2015-01-01
Highlights: • Developed a new recursive WSF model for 1–24 h horizon based on Hammerstein model. • Nonlinear HAR model successfully captured chaotic dynamics of wind speed time series. • Recursive WSF intrinsic error accumulation corrected by applying rotation. • Model verified for real wind speed data from two sites with different characteristics. • HAR model outperformed both ARIMA and ANN models in terms of accuracy of prediction. - Abstract: A new Wind Speed Forecasting (WSF) model, suitable for a short term 1–24 h forecast horizon, is developed by adapting Hammerstein model to an Autoregressive approach. The model is applied to real data collected for a period of three years (2004–2006) from two different sites. The performance of HAR model is evaluated by comparing its prediction with the classical Autoregressive Integrated Moving Average (ARIMA) model and a multi-layer perceptron Artificial Neural Network (ANN). Results show that the HAR model outperforms both the ARIMA model and ANN model in terms of root mean square error (RMSE), mean absolute error (MAE), and Mean Absolute Percentage Error (MAPE). When compared to the conventional models, the new HAR model can better capture various wind speed characteristics, including asymmetric (non-gaussian) wind speed distribution, non-stationary time series profile, and the chaotic dynamics. The new model is beneficial for various applications in the renewable energy area, particularly for power scheduling
Lohani, A. K.; Kumar, Rakesh; Singh, R. D.
2012-06-01
SummaryTime series modeling is necessary for the planning and management of reservoirs. More recently, the soft computing techniques have been used in hydrological modeling and forecasting. In this study, the potential of artificial neural networks and neuro-fuzzy system in monthly reservoir inflow forecasting are examined by developing and comparing monthly reservoir inflow prediction models, based on autoregressive (AR), artificial neural networks (ANNs) and adaptive neural-based fuzzy inference system (ANFIS). To take care the effect of monthly periodicity in the flow data, cyclic terms are also included in the ANN and ANFIS models. Working with time series flow data of the Sutlej River at Bhakra Dam, India, several ANN and adaptive neuro-fuzzy models are trained with different input vectors. To evaluate the performance of the selected ANN and adaptive neural fuzzy inference system (ANFIS) models, comparison is made with the autoregressive (AR) models. The ANFIS model trained with the input data vector including previous inflows and cyclic terms of monthly periodicity has shown a significant improvement in the forecast accuracy in comparison with the ANFIS models trained with the input vectors considering only previous inflows. In all cases ANFIS gives more accurate forecast than the AR and ANN models. The proposed ANFIS model coupled with the cyclic terms is shown to provide better representation of the monthly inflow forecasting for planning and operation of reservoir.
Galka, Andreas; Siniatchkin, Michael; Stephani, Ulrich; Groening, Kristina; Wolff, Stephan; Bosch-Bayard, Jorge; Ozaki, Tohru
2010-12-01
The analysis of time series obtained by functional magnetic resonance imaging (fMRI) may be approached by fitting predictive parametric models, such as nearest-neighbor autoregressive models with exogeneous input (NNARX). As a part of the modeling procedure, it is possible to apply instantaneous linear transformations to the data. Spatial smoothing, a common preprocessing step, may be interpreted as such a transformation. The autoregressive parameters may be constrained, such that they provide a response behavior that corresponds to the canonical haemodynamic response function (HRF). We present an algorithm for estimating the parameters of the linear transformations and of the HRF within a rigorous maximum-likelihood framework. Using this approach, an optimal amount of both the spatial smoothing and the HRF can be estimated simultaneously for a given fMRI data set. An example from a motor-task experiment is discussed. It is found that, for this data set, weak, but non-zero, spatial smoothing is optimal. Furthermore, it is demonstrated that activated regions can be estimated within the maximum-likelihood framework.
Directory of Open Access Journals (Sweden)
Rahul Tripathi
2014-01-01
Full Text Available Forecasting of rice area, production, and productivity of Odisha was made from the historical data of 1950-51 to 2008-09 by using univariate autoregressive integrated moving average (ARIMA models and was compared with the forecasted all Indian data. The autoregressive (p and moving average (q parameters were identified based on the significant spikes in the plots of partial autocorrelation function (PACF and autocorrelation function (ACF of the different time series. ARIMA (2, 1, 0 model was found suitable for all Indian rice productivity and production, whereas ARIMA (1, 1, 1 was best fitted for forecasting of rice productivity and production in Odisha. Prediction was made for the immediate next three years, that is, 2007-08, 2008-09, and 2009-10, using the best fitted ARIMA models based on minimum value of the selection criterion, that is, Akaike information criteria (AIC and Schwarz-Bayesian information criteria (SBC. The performances of models were validated by comparing with percentage deviation from the actual values and mean absolute percent error (MAPE, which was found to be 0.61 and 2.99% for the area under rice in Odisha and India, respectively. Similarly for prediction of rice production and productivity in Odisha and India, the MAPE was found to be less than 6%.
International Nuclear Information System (INIS)
Frank, T D; Mongkolsakulvong, S
2015-01-01
In a previous study strongly nonlinear autoregressive (SNAR) models have been introduced as a generalization of the widely-used time-discrete autoregressive models that are known to apply both to Markov and non-Markovian systems. In contrast to conventional autoregressive models, SNAR models depend on process mean values. So far, only linear dependences have been studied. We consider the case in which process mean values can have a nonlinear impact on the processes under consideration. It is shown that such models describe Markov and non-Markovian many-body systems with mean field forces that exhibit a nonlinear impact on single subsystems. We exemplify that such nonlinear dependences can describe order-disorder phase transitions of time-discrete Markovian and non-Markovian many-body systems. The relevant order parameter equations are derived and issues of stability and stationarity are studied. (paper)
... with multiple mononeuropathy are prone to new nerve injuries at pressure points such as the knees and elbows. They should avoid putting pressure on these areas, for example, by not leaning on the elbows, crossing the knees, ...
Wang, L.; Toshioka, T.; Nakajima, T.; Narita, A.; Xue, Z.
2017-12-01
In recent years, more and more Carbon Capture and Storage (CCS) studies focus on seismicity monitoring. For the safety management of geological CO2 storage at Tomakomai, Hokkaido, Japan, an Advanced Traffic Light System (ATLS) combined different seismic messages (magnitudes, phases, distributions et al.) is proposed for injection controlling. The primary task for ATLS is the seismic events detection in a long-term sustained time series record. Considering the time-varying characteristics of Signal to Noise Ratio (SNR) of a long-term record and the uneven energy distributions of seismic event waveforms will increase the difficulty in automatic seismic detecting, in this work, an improved probability autoregressive (AR) method for automatic seismic event detecting is applied. This algorithm, called sequentially discounting AR learning (SDAR), can identify the effective seismic event in the time series through the Change Point detection (CPD) of the seismic record. In this method, an anomaly signal (seismic event) can be designed as a change point on the time series (seismic record). The statistical model of the signal in the neighborhood of event point will change, because of the seismic event occurrence. This means the SDAR aims to find the statistical irregularities of the record thought CPD. There are 3 advantages of SDAR. 1. Anti-noise ability. The SDAR does not use waveform messages (such as amplitude, energy, polarization) for signal detecting. Therefore, it is an appropriate technique for low SNR data. 2. Real-time estimation. When new data appears in the record, the probability distribution models can be automatic updated by SDAR for on-line processing. 3. Discounting property. the SDAR introduces a discounting parameter to decrease the influence of present statistic value on future data. It makes SDAR as a robust algorithm for non-stationary signal processing. Within these 3 advantages, the SDAR method can handle the non-stationary time-varying long
Medium term municipal solid waste generation prediction by autoregressive integrated moving average
International Nuclear Information System (INIS)
Younes, Mohammad K.; Nopiah, Z. M.; Basri, Noor Ezlin A.; Basri, Hassan
2014-01-01
Generally, solid waste handling and management are performed by municipality or local authority. In most of developing countries, local authorities suffer from serious solid waste management (SWM) problems and insufficient data and strategic planning. Thus it is important to develop robust solid waste generation forecasting model. It helps to proper manage the generated solid waste and to develop future plan based on relatively accurate figures. In Malaysia, solid waste generation rate increases rapidly due to the population growth and new consumption trends that characterize the modern life style. This paper aims to develop monthly solid waste forecasting model using Autoregressive Integrated Moving Average (ARIMA), such model is applicable even though there is lack of data and will help the municipality properly establish the annual service plan. The results show that ARIMA (6,1,0) model predicts monthly municipal solid waste generation with root mean square error equals to 0.0952 and the model forecast residuals are within accepted 95% confident interval
Bekti, Rokhana Dwi; Nurhadiyanti, Gita; Irwansyah, Edy
2014-10-01
The diarrhea case pattern information, especially for toddler, is very important. It is used to show the distribution of diarrhea in every region, relationship among that locations, and regional economic characteristic or environmental behavior. So, this research uses spatial pattern to perform them. This method includes: Moran's I, Spatial Autoregressive Models (SAR), and Local Indicator of Spatial Autocorrelation (LISA). It uses sample from 23 sub districts of Bekasi Regency, West Java, Indonesia. Diarrhea case, regional economic, and environmental behavior of households have a spatial relationship among sub district. SAR shows that the percentage of Regional Gross Domestic Product is significantly effect on diarrhea at α = 10%. Therefore illiteracy and health center facilities are significant at α = 5%. With LISA test, sub districts in southern Bekasi have high dependencies with Cikarang Selatan, Serang Baru, and Setu. This research also builds development application that is based on java and R to support data analysis.
Packet loss replacement in voip using a recursive low-order autoregressive modelbased speech
International Nuclear Information System (INIS)
Miralavi, Seyed Reza; Ghorshi, Seyed; Mortazavi, Mohammad; Choupan, Jeiran
2011-01-01
In real-time packet-based communication systems one major problem is misrouted or delayed packets which results in degraded perceived voice quality. When some speech packets are not available on time, the packet is known as lost packet in real-time communication systems. The easiest task of a network terminal receiver is to replace silence for the duration of lost speech segments. In a high quality communication system in order to avoid quality reduction due to packet loss a suitable method and/or algorithm is needed to replace the missing segments of speech. In this paper, we introduce a recursive low order autoregressive (AR) model for replacement of lost speech segment. The evaluation results show that this method has a lower mean square error (MSE) and low complexity compared to the other efficient methods like high-order AR model without any substantial degradation in perceived voice quality.
A revival of the autoregressive distributed lag model in estimating energy demand relationships
Energy Technology Data Exchange (ETDEWEB)
Bentzen, J.; Engsted, T.
1999-07-01
The findings in the recent energy economics literature that energy economic variables are non-stationary, have led to an implicit or explicit dismissal of the standard autoregressive distribution lag (ARDL) model in estimating energy demand relationships. However, Pesaran and Shin (1997) show that the ARDL model remains valid when the underlying variables are non-stationary, provided the variables are co-integrated. In this paper we use the ARDL approach to estimate a demand relationship for Danish residential energy consumption, and the ARDL estimates are compared to the estimates obtained using co-integration techniques and error-correction models (ECM's). It turns out that both quantitatively and qualitatively, the ARDL approach and the co-integration/ECM approach give very similar results. (au)
Remaining Useful Life Prediction of Gas Turbine Engine using Autoregressive Model
Directory of Open Access Journals (Sweden)
Ahsan Shazaib
2017-01-01
Full Text Available Gas turbine (GT engines are known for their high availability and reliability and are extensively used for power generation, marine and aero-applications. Maintenance of such complex machines should be done proactively to reduce cost and sustain high availability of the GT. The aim of this paper is to explore the use of autoregressive (AR models to predict remaining useful life (RUL of a GT engine. The Turbofan Engine data from NASA benchmark data repository is used as case study. The parametric investigation is performed to check on any effect of changing model parameter on modelling accuracy. Results shows that a single sensory data cannot accurately predict RUL of GT and further research need to be carried out by incorporating multi-sensory data. Furthermore, the predictions made using AR model seems to give highly pessimistic values for RUL of GT.
Autoregressive techniques for acoustic detection of in-sodium water leaks
International Nuclear Information System (INIS)
Hayashi, K.
1997-01-01
We have been applied a background signal whitening filter built by univariate autoregressive model to the estimation problem of the leak start time and duration. In the 1995 present benchmark stage, we evaluated the method using acoustic signals from real hydrogen or water/steam injection experiments. The results show that the signal processing technique using this filter can detect reliability the leak signals with a sufficient signal-to-noise ratio. Even if the sensor signal contains non-boiling or non-leak high-amplitude pulses, they can be classified by spectral information. Especially, the feature signal made from the time-frequency spectrum of the filtered signal is very sensitive and useful. (author). 8 refs, 14 figs, 6 tabs
Palm oil price forecasting model: An autoregressive distributed lag (ARDL) approach
Hamid, Mohd Fahmi Abdul; Shabri, Ani
2017-05-01
Palm oil price fluctuated without any clear trend or cyclical pattern in the last few decades. The instability of food commodities price causes it to change rapidly over time. This paper attempts to develop Autoregressive Distributed Lag (ARDL) model in modeling and forecasting the price of palm oil. In order to use ARDL as a forecasting model, this paper modifies the data structure where we only consider lagged explanatory variables to explain the variation in palm oil price. We then compare the performance of this ARDL model with a benchmark model namely ARIMA in term of their comparative forecasting accuracy. This paper also utilize ARDL bound testing approach to co-integration in examining the short run and long run relationship between palm oil price and its determinant; production, stock, and price of soybean as the substitute of palm oil and price of crude oil. The comparative forecasting accuracy suggests that ARDL model has a better forecasting accuracy compared to ARIMA.
International Nuclear Information System (INIS)
Morishima, N.
1996-01-01
The multivariate autoregressive (MAR) modeling of a vector noise process is discussed in terms of the estimation of dominant noise sources in BWRs. The discussion is based on a physical approach: a transfer function model on BWR core dynamics is utilized in developing a noise model; a set of input-output relations between three system variables and twelve different noise sources is obtained. By the least-square fitting of a theoretical PSD on neutron noise to an experimental one, four kinds of dominant noise sources are selected. It is shown that some of dominant noise sources consist of two or more different noise sources and have the spectral properties of being coloured and correlated with each other. By diagonalizing the PSD matrix for dominant noise sources, we may obtain an MAR expression for a vector noise process as a response to the diagonal elements(i.e. residual noises) being white and mutually-independent. (Author)
Autoregressive Integrated Adaptive Neural Networks Classifier for EEG-P300 Classification
Directory of Open Access Journals (Sweden)
Demi Soetraprawata
2013-06-01
Full Text Available Brain Computer Interface has a potency to be applied in mechatronics apparatus and vehicles in the future. Compared to the other techniques, EEG is the most preferred for BCI designs. In this paper, a new adaptive neural network classifier of different mental activities from EEG-based P300 signals is proposed. To overcome the over-training that is caused by noisy and non-stationary data, the EEG signals are filtered and extracted using autoregressive models before passed to the adaptive neural networks classifier. To test the improvement in the EEG classification performance with the proposed method, comparative experiments were conducted using Bayesian Linear Discriminant Analysis. The experiment results show that the all subjects achieve a classification accuracy of 100%.
Integer valued autoregressive processes with generalized discrete Mittag-Leffler marginals
Directory of Open Access Journals (Sweden)
Kanichukattu K. Jose
2013-05-01
Full Text Available In this paper we consider a generalization of discrete Mittag-Leffler distributions. We introduce and study the properties of a new distribution called geometric generalized discrete Mittag-Leffler distribution. Autoregressive processes with geometric generalized discrete Mittag-Leffler distributions are developed and studied. The distributions are further extended to develop a more general class of geometric generalized discrete semi-Mittag-Leffler distributions. The processes are extended to higher orders also. An application with respect to an empirical data on customer arrivals in a bank counter is also given. Various areas of potential applications like human resource development, insect growth, epidemic modeling, industrial risk modeling, insurance and actuaries, town planning etc are also discussed.
International Nuclear Information System (INIS)
Benmouiza, Khalil; Cheknane, Ali
2013-01-01
Highlights: • An unsupervised clustering algorithm with a neural network model was explored. • The forecasting results of solar radiation time series and the comparison of their performance was simulated. • A new method was proposed combining k-means algorithm and NAR network to provide better prediction results. - Abstract: In this paper, we review our work for forecasting hourly global horizontal solar radiation based on the combination of unsupervised k-means clustering algorithm and artificial neural networks (ANN). k-Means algorithm focused on extracting useful information from the data with the aim of modeling the time series behavior and find patterns of the input space by clustering the data. On the other hand, nonlinear autoregressive (NAR) neural networks are powerful computational models for modeling and forecasting nonlinear time series. Taking the advantage of both methods, a new method was proposed combining k-means algorithm and NAR network to provide better forecasting results
Comparison of Classical and Robust Estimates of Threshold Auto-regression Parameters
Directory of Open Access Journals (Sweden)
V. B. Goryainov
2017-01-01
Full Text Available The study object is the first-order threshold auto-regression model with a single zero-located threshold. The model describes a stochastic temporal series with discrete time by means of a piecewise linear equation consisting of two linear classical first-order autoregressive equations. One of these equations is used to calculate a running value of the temporal series. A control variable that determines the choice between these two equations is the sign of the previous value of the same series.The first-order threshold autoregressive model with a single threshold depends on two real parameters that coincide with the coefficients of the piecewise linear threshold equation. These parameters are assumed to be unknown. The paper studies an estimate of the least squares, an estimate the least modules, and the M-estimates of these parameters. The aim of the paper is a comparative study of the accuracy of these estimates for the main probabilistic distributions of the updating process of the threshold autoregressive equation. These probability distributions were normal, contaminated normal, logistic, double-exponential distributions, a Student's distribution with different number of degrees of freedom, and a Cauchy distribution.As a measure of the accuracy of each estimate, was chosen its variance to measure the scattering of the estimate around the estimated parameter. An estimate with smaller variance made from the two estimates was considered to be the best. The variance was estimated by computer simulation. To estimate the smallest modules an iterative weighted least-squares method was used and the M-estimates were done by the method of a deformable polyhedron (the Nelder-Mead method. To calculate the least squares estimate, an explicit analytic expression was used.It turned out that the estimation of least squares is best only with the normal distribution of the updating process. For the logistic distribution and the Student's distribution with the
DEFF Research Database (Denmark)
Thomsen, C E; Rosenfalck, A; Nørregaard Christensen, K
1991-01-01
The brain activity electroencephalogram (EEG) was recorded from 30 healthy women scheduled for hysterectomy. The patients were anaesthetized with isoflurane, halothane or etomidate/fentanyl. A multiparametric method was used for extraction of amplitude and frequency information from the EEG....... The method applied autoregressive modelling of the signal, segmented in 2 s fixed intervals. The features from the EEG segments were used for learning and for classification. The learning process was unsupervised and hierarchical clustering analysis was used to construct a learning set of EEG amplitude......-frequency patterns for each of the three anaesthetic drugs. These EEG patterns were assigned to a colour code corresponding to similar clinical states. A common learning set could be used for all patients anaesthetized with the same drug. The classification process could be performed on-line and the results were...
Forecasting and simulating wind speed in Corsica by using an autoregressive model
International Nuclear Information System (INIS)
Poggi, P.; Muselli, M.; Notton, G.; Cristofari, C.; Louche, A.
2003-01-01
Alternative approaches for generating wind speed time series are discussed. The method utilized involves the use of an autoregressive process model. The model has been applied to three Mediterranean sites in Corsica and has been used to generate 3-hourly synthetic time series for these considered sites. The synthetic time series have been examined to determine their ability to preserve the statistical properties of the Corsican wind speed time series. In this context, using the main statistical characteristics of the wind speed (mean, variance, probability distribution, autocorrelation function), the data simulated are compared to experimental ones in order to check whether the wind speed behavior was correctly reproduced over the studied periods. The purpose is to create a data generator in order to construct a reference year for wind systems simulation in Corsica
METODE VECTOR AUTOREGRESSIVE (VAR DALAM PERAMALAN JUMLAH WISATAWAN MANCANEGARA KE BALI
Directory of Open Access Journals (Sweden)
TJOK GDE SAHITYAHUTTI RANANGGA
2018-05-01
Full Text Available The purposes of this research were to model and to forecast the number of foreign tourists (Australia, China, and Japan arrival to Bali using vector autoregressive (VAR method. The estimated of VAR model obtained to forecast the number of foreign tourists to Bali is the sixth order VAR (VAR(6.We used multivariate least square method to estimate the VAR(6’s parameters.The mean absolute percentage error (MAPE in this model were as follows 6.8% in predicting the number of Australian tourists, 15.9% in predicting the number of Chinese tourists, and 9% in predicting the number of Japanese tourists. The prediction of Australian, Chinese, and Japanese tourists arrival to Bali for July 2017 to December 2017 tended to experience up and downs that were not too high compared to the previous months.
International Nuclear Information System (INIS)
Oguma, Ritsuo
1980-01-01
In the HBWR (Halden Boiling Water Reactor), there exists a resonant power oscillation with period about 0.04 Hz at power levels higher than about 9.5 MWt. While the resonant oscillation in not so large as to affect the normal reactor operation, it is significant, from the viewpoint of reactor diagnosis, to grasp its characteristics and find the cause. Noise analysis based on the autoregressive (AR) modeling technique has been made to reveal the driving source for this oscillation which led to the suggestion that it is attributed to the dynamic interference of heat exchange process between two parallel-connected steam transformers against the reactor. The present study demonstrates that the method used here is highly effective for tracing back to a noise source inducing the variation of quantities in a system, and also applicable to problems of reactor noise analysis and diagnosis. (author)
The impact of oil-price shocks on Hawaii's economy: A case study using vector autoregression
International Nuclear Information System (INIS)
Gopalakrishnan, C.; Tian, X.; Tran, D.
1991-01-01
The effects of oil-price shocks on the macroeconomic performance of a non-oil-producing, oil-importing state are studied in terms of Hawaii's experience (1974-1986) using Vector Autoregression (VAR). The VAR model contains three macrovariables-real oil price, interest rate, and real GNP, and three regional variable-total civilian labor force, Honolulu consumer price index, and real personal income. The results suggested that oil-price shock had a positive effect on interest rate as well as local price (i.e., higher interest and higher local price), but a negative influence on real GNP. The negative income effect, however, was offset by the positive employment effect. The price of oil was found to be exogenous to all other variables in the system. The macrovariables exerted a pronounced impact on Hawaii's economy, most notably on consumer price
A revival of the autoregressive distributed lag model in estimating energy demand relationships
Energy Technology Data Exchange (ETDEWEB)
Bentzen, J; Engsted, T
1999-07-01
The findings in the recent energy economics literature that energy economic variables are non-stationary, have led to an implicit or explicit dismissal of the standard autoregressive distribution lag (ARDL) model in estimating energy demand relationships. However, Pesaran and Shin (1997) show that the ARDL model remains valid when the underlying variables are non-stationary, provided the variables are co-integrated. In this paper we use the ARDL approach to estimate a demand relationship for Danish residential energy consumption, and the ARDL estimates are compared to the estimates obtained using co-integration techniques and error-correction models (ECM's). It turns out that both quantitatively and qualitatively, the ARDL approach and the co-integration/ECM approach give very similar results. (au)
Medium term municipal solid waste generation prediction by autoregressive integrated moving average
Younes, Mohammad K.; Nopiah, Z. M.; Basri, Noor Ezlin A.; Basri, Hassan
2014-09-01
Generally, solid waste handling and management are performed by municipality or local authority. In most of developing countries, local authorities suffer from serious solid waste management (SWM) problems and insufficient data and strategic planning. Thus it is important to develop robust solid waste generation forecasting model. It helps to proper manage the generated solid waste and to develop future plan based on relatively accurate figures. In Malaysia, solid waste generation rate increases rapidly due to the population growth and new consumption trends that characterize the modern life style. This paper aims to develop monthly solid waste forecasting model using Autoregressive Integrated Moving Average (ARIMA), such model is applicable even though there is lack of data and will help the municipality properly establish the annual service plan. The results show that ARIMA (6,1,0) model predicts monthly municipal solid waste generation with root mean square error equals to 0.0952 and the model forecast residuals are within accepted 95% confident interval.
DEFF Research Database (Denmark)
Lassen Kaspersen, Line; Føyn, Tullik Helene Ystanes
This paper investigates price transmission for agricultural commodities between world markets and the Ugandan market in an attempt to determine the impact of world market prices on the Ugandan market. Based on the realization that price formation is not a static concept, a dynamic vector...... price relations, i.e. the price variations between geographically separated markets in Uganda and the world markets. Our analysis indicates that food markets in Uganda, based on our study of sorghum price transmission, are not integrated into world markets, and that oil prices are a very determining...... autoregressive (VAR) model is presented. The prices of Robusta coffee and sorghum are examined, as both of these crops are important for the domestic economy of Uganda – Robusta as a cash crop, mainly traded internationally, and sorghum for consumption at household level. The analysis focuses on the spatial...
Debt Contagion in Europe: A Panel-Vector Autoregressive (VAR Analysis
Directory of Open Access Journals (Sweden)
Florence Bouvet
2013-12-01
Full Text Available The European sovereign-debt crisis began in Greece when the government announced in December, 2009, that its debt reached 121% of GDP (or 300 billion euros and its 2009 budget deficit was 12.7% of GDP, four times the level allowed by the Maastricht Treaty. The Greek crisis soon spread to other Economic and Monetary Union (EMU countries, notably Ireland, Portugal, Spain and Italy. Using quarterly data for the 2000–2011 period, we implement a panel-vector autoregressive (PVAR model for 11 EMU countries to examine the extent to which a rise in a country’s bond-yield spread or debt-to-GDP ratio affects another EMU countries’ fiscal and macroeconomic outcomes. To distinguish between interdependence and contagion among EMU countries, we compare results obtained for the pre-crisis period (2000–2007 with the crisis period (2008–2011 and control for global risk aversion.
Medium term municipal solid waste generation prediction by autoregressive integrated moving average
Energy Technology Data Exchange (ETDEWEB)
Younes, Mohammad K.; Nopiah, Z. M.; Basri, Noor Ezlin A.; Basri, Hassan [Department of Civil and Structural Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor (Malaysia)
2014-09-12
Generally, solid waste handling and management are performed by municipality or local authority. In most of developing countries, local authorities suffer from serious solid waste management (SWM) problems and insufficient data and strategic planning. Thus it is important to develop robust solid waste generation forecasting model. It helps to proper manage the generated solid waste and to develop future plan based on relatively accurate figures. In Malaysia, solid waste generation rate increases rapidly due to the population growth and new consumption trends that characterize the modern life style. This paper aims to develop monthly solid waste forecasting model using Autoregressive Integrated Moving Average (ARIMA), such model is applicable even though there is lack of data and will help the municipality properly establish the annual service plan. The results show that ARIMA (6,1,0) model predicts monthly municipal solid waste generation with root mean square error equals to 0.0952 and the model forecast residuals are within accepted 95% confident interval.
DEFF Research Database (Denmark)
Chon, K H; Hoyer, D; Armoundas, A A
1999-01-01
In this study, we introduce a new approach for estimating linear and nonlinear stochastic autoregressive moving average (ARMA) model parameters, given a corrupt signal, using artificial recurrent neural networks. This new approach is a two-step approach in which the parameters of the deterministic...... part of the stochastic ARMA model are first estimated via a three-layer artificial neural network (deterministic estimation step) and then reestimated using the prediction error as one of the inputs to the artificial neural networks in an iterative algorithm (stochastic estimation step). The prediction...... error is obtained by subtracting the corrupt signal of the estimated ARMA model obtained via the deterministic estimation step from the system output response. We present computer simulation examples to show the efficacy of the proposed stochastic recurrent neural network approach in obtaining accurate...
Statistical aspects of autoregressive-moving average models in the assessment of radon mitigation
International Nuclear Information System (INIS)
Dunn, J.E.; Henschel, D.B.
1989-01-01
Radon values, as reflected by hourly scintillation counts, seem dominated by major, pseudo-periodic, random fluctuations. This methodological paper reports a moderate degree of success in modeling these data using relatively simple autoregressive-moving average models to assess the effectiveness of radon mitigation techniques in existing housing. While accounting for the natural correlation of successive observations, familiar summary statistics such as steady state estimates, standard errors, confidence limits, and tests of hypothesis are produced. The Box-Jenkins approach is used throughout. In particular, intervention analysis provides an objective means of assessing the effectiveness of an active mitigation measure, such as a fan off/on cycle. Occasionally, failure to declare a significant intervention has suggested a means of remedial action in the data collection procedure
Schliep, E. M.; Gelfand, A. E.; Holland, D. M.
2015-12-01
There is considerable demand for accurate air quality information in human health analyses. The sparsity of ground monitoring stations across the United States motivates the need for advanced statistical models to predict air quality metrics, such as PM2.5, at unobserved sites. Remote sensing technologies have the potential to expand our knowledge of PM2.5 spatial patterns beyond what we can predict from current PM2.5 monitoring networks. Data from satellites have an additional advantage in not requiring extensive emission inventories necessary for most atmospheric models that have been used in earlier data fusion models for air pollution. Statistical models combining monitoring station data with satellite-obtained aerosol optical thickness (AOT), also referred to as aerosol optical depth (AOD), have been proposed in the literature with varying levels of success in predicting PM2.5. The benefit of using AOT is that satellites provide complete gridded spatial coverage. However, the challenges involved with using it in fusion models are (1) the correlation between the two data sources varies both in time and in space, (2) the data sources are temporally and spatially misaligned, and (3) there is extensive missingness in the monitoring data and also in the satellite data due to cloud cover. We propose a hierarchical autoregressive spatially varying coefficients model to jointly model the two data sources, which addresses the foregoing challenges. Additionally, we offer formal model comparison for competing models in terms of model fit and out of sample prediction of PM2.5. The models are applied to daily observations of PM2.5 and AOT in the summer months of 2013 across the conterminous United States. Most notably, during this time period, we find small in-sample improvement incorporating AOT into our autoregressive model but little out-of-sample predictive improvement.
Benbenishty, Rami; Astor, Ron Avi; Roziner, Ilan; Wrabel, Stephani L.
2016-01-01
The present study explores the causal link between school climate, school violence, and a school's general academic performance over time using a school-level, cross-lagged panel autoregressive modeling design. We hypothesized that reductions in school violence and climate improvement would lead to schools' overall improved academic performance.…
DEFF Research Database (Denmark)
Kock, Anders Bredahl; Callot, Laurent
We show that the adaptive Lasso (aLasso) and the adaptive group Lasso (agLasso) are oracle efficient in stationary vector autoregressions where the number of parameters per equation is smaller than the number of observations. In particular, this means that the parameters are estimated consistently...
International Nuclear Information System (INIS)
Cahier, Jean-Pierre; Desfriches, Orelie; Zacklad, Manuel
2009-01-01
The authors present a digital space (a web site - 'ExploRe') which would allows a community to share a set of pluri-disciplinary information items concerning reversibility, and in which the community members describe the items by using attributes and themes belonging to different points of view
Olson, Catherine Applefeld
2009-01-01
They say desperate times call for desperate measures. But in this time of economic uncertainty, the desperate cutting of budgets for arts funding and, by extension, all types of arts education, including music, is not prudent. That is the consensus of several national and local studies, which converge on a single point--that the arts actually can…
Chen, Yongqin David; Jiang, Jianmin; Zhu, Yuxiang; Huang, Changxing; Zhang, Qiang
2018-05-01
This article, as part II, illustrates applications of other two algorithms, i.e., the scanning F test of change points in trend and the scanning t test of change points in mean, to both series of the normalized streamflow index (NSI) at Makou section in the Xijiang River and the normalized precipitation index (NPI) over the watershed of Xijiang River. The results from these two tests show mainly positive coherency of changes between the NSI and NPI. However, some minor negative coherency patches may expose somewhat impacts of human activities, but they were often associated with nearly normal climate periods. These suggest that the runoff still depends upon well the precipitation in the Xijiang catchment. The anthropogenic disturbances have not yet reached up to violating natural relationship on the whole in this river.
Möstl, C.; Farrugia, C. J.; Kilpua, E. K. J.; Jian, L. K.; Liu, Y.; Eastwood, J. P.; Harrison, R. A.; Webb, D. F.; Temmer, M.; Odstrcil, D.; Davies, J. A.; Rollett, T.; Luhmann, J. G.; Nitta, N.; Mulligan, T.; Jensen, E. A.; Forsyth, R.; Lavraud, B.; de Koning, C. A.; Veronig, A. M.; Galvin, A. B.; Zhang, T. L.; Anderson, B. J.
2012-10-01
We present multi-point in situ observations of a complex sequence of coronal mass ejections (CMEs) which may serve as a benchmark event for numerical and empirical space weather prediction models. On 2010 August 1, instruments on various space missions, Solar Dynamics Observatory/Solar and Heliospheric Observatory/Solar-TErrestrial-RElations-Observatory (SDO/SOHO/STEREO), monitored several CMEs originating within tens of degrees from the solar disk center. We compare their imprints on four widely separated locations, spanning 120° in heliospheric longitude, with radial distances from the Sun ranging from MESSENGER (0.38 AU) to Venus Express (VEX, at 0.72 AU) to Wind, ACE, and ARTEMIS near Earth and STEREO-B close to 1 AU. Calculating shock and flux rope parameters at each location points to a non-spherical shape of the shock, and shows the global configuration of the interplanetary coronal mass ejections (ICMEs), which have interacted, but do not seem to have merged. VEX and STEREO-B observed similar magnetic flux ropes (MFRs), in contrast to structures at Wind. The geomagnetic storm was intense, reaching two minima in the Dst index (≈ - 100 nT), and was caused by the sheath region behind the shock and one of two observed MFRs. MESSENGER received a glancing blow of the ICMEs, and the events missed STEREO-A entirely. The observations demonstrate how sympathetic solar eruptions may immerse at least 1/3 of the heliosphere in the ecliptic with their distinct plasma and magnetic field signatures. We also emphasize the difficulties in linking the local views derived from single-spacecraft observations to a consistent global picture, pointing to possible alterations from the classical picture of ICMEs.
Energy Technology Data Exchange (ETDEWEB)
Moestl, C.; Liu, Y.; Luhmann, J. G. [Space Science Laboratory, University of California, Berkeley, CA (United States); Farrugia, C. J. [Space Science Center and Department of Physics, University of New Hampshire, Durham, NH (United States); Kilpua, E. K. J. [Department of Physics, University of Helsinki, FI-00560 Helsinki (Finland); Jian, L. K. [Department of Astronomy, University of Maryland, College Park, MD (United States); Eastwood, J. P.; Forsyth, R. [The Blackett Laboratory, Imperial College, London (United Kingdom); Harrison, R. A.; Davies, J. A. [RAL Space, Harwell Oxford, Didcot (United Kingdom); Webb, D. F. [Institute for Scientific Research, Boston College, Newton, MA (United States); Temmer, M.; Rollett, T.; Veronig, A. M. [Kanzelhoehe Observatory-IGAM, Institute of Physics, University of Graz, A-8010 Graz (Austria); Odstrcil, D. [NASA Goddard Space Flight Center, Greenbelt, MD (United States); Nitta, N. [Solar and Astrophysics Laboratory, Lockheed Martin Advanced Technology Center, Palo Alto, CA (United States); Mulligan, T. [Space Science Applications Laboratory, The Aerospace Corporation, El Segundo, CA (United States); Jensen, E. A. [ACS Consulting, Houston, TX (United States); Lavraud, B. [Institut de Recherche en Astrophysique et Planetologie, Universite de Toulouse (UPS), F-31400 Toulouse (France); De Koning, C. A., E-mail: christian.moestl@uni-graz.at [NOAA/SWPC, Boulder, Colorado (United States); and others
2012-10-10
We present multi-point in situ observations of a complex sequence of coronal mass ejections (CMEs) which may serve as a benchmark event for numerical and empirical space weather prediction models. On 2010 August 1, instruments on various space missions, Solar Dynamics Observatory/Solar and Heliospheric Observatory/Solar-TErrestrial-RElations-Observatory (SDO/SOHO/STEREO), monitored several CMEs originating within tens of degrees from the solar disk center. We compare their imprints on four widely separated locations, spanning 120 Degree-Sign in heliospheric longitude, with radial distances from the Sun ranging from MESSENGER (0.38 AU) to Venus Express (VEX, at 0.72 AU) to Wind, ACE, and ARTEMIS near Earth and STEREO-B close to 1 AU. Calculating shock and flux rope parameters at each location points to a non-spherical shape of the shock, and shows the global configuration of the interplanetary coronal mass ejections (ICMEs), which have interacted, but do not seem to have merged. VEX and STEREO-B observed similar magnetic flux ropes (MFRs), in contrast to structures at Wind. The geomagnetic storm was intense, reaching two minima in the Dst index ( Almost-Equal-To - 100 nT), and was caused by the sheath region behind the shock and one of two observed MFRs. MESSENGER received a glancing blow of the ICMEs, and the events missed STEREO-A entirely. The observations demonstrate how sympathetic solar eruptions may immerse at least 1/3 of the heliosphere in the ecliptic with their distinct plasma and magnetic field signatures. We also emphasize the difficulties in linking the local views derived from single-spacecraft observations to a consistent global picture, pointing to possible alterations from the classical picture of ICMEs.
Hoffer, Roger M.; Hussin, Yousif Ali
1989-01-01
Multipolarized aircraft L-band radar data are classified using two different image classification algorithms: (1) a per-point classifier, and (2) a contextual, or per-field, classifier. Due to the distinct variations in radar backscatter as a function of incidence angle, the data are stratified into three incidence-angle groupings, and training and test data are defined for each stratum. A low-pass digital mean filter with varied window size (i.e., 3x3, 5x5, and 7x7 pixels) is applied to the data prior to the classification. A predominately forested area in northern Florida was the study site. The results obtained by using these image classifiers are then presented and discussed.
Directory of Open Access Journals (Sweden)
Fuyi Xu
2010-04-01
\\end{array}\\right.$$ where $1\\leq k\\leq s\\leq m-2, a_i, b_i\\in(0,+\\infty$ with $0<\\sum_{i=1}^{k}b_{i}-\\sum_{i=k+1}^{s}b_{i}<1, 0<\\sum_{i=1}^{m-2}a_{i}<1, 0<\\xi_1<\\xi_2<\\cdots<\\xi_{m-2}<\\rho(T$, $f\\in C( [0,+\\infty,[0,+\\infty$, $a(t$ may be singular at $t=0$. We show that there exist two positive solutions by using two different fixed point theorems respectively. As an application, some examples are included to illustrate the main results. In particular, our criteria extend and improve some known results.
Novakovic, A M; Thorsted, A; Schindler, E; Jönsson, S; Munafo, A; Karlsson, M O
2018-05-10
The aim of this work was to assess the relationship between the absolute lymphocyte count (ALC), and disability (as measured by the Expanded Disability Status Scale [EDSS]) and occurrence of relapses, 2 efficacy endpoints, respectively, in patients with remitting-relasping multiple sclerosis. Data for ALC, EDSS, and relapse rate were available from 1319 patients receiving placebo and/or cladribine tablets. Pharmacodynamic models were developed to characterize the time course of the endpoints. ALC-related measures were then evaluated as predictors of the efficacy endpoints. EDSS data were best fitted by a model where the logit-linear disease progression is affected by the dynamics of ALC change from baseline. Relapse rate data were best described by the Weibull hazard function, and the ALC change from baseline was also found to be a significant predictor of time to relapse. Presented models have shown that once cladribine exposure driven ALC-derived measures are included in the model, the need for drug effect components is of less importance (EDSS) or disappears (relapse rate). This simplifies the models and theoretically makes them mechanism specific rather than drug specific. Having a reliable mechanism-specific model would allow leveraging historical data across compounds, to support decision making in drug development and possibly shorten the time to market. © 2018, The American College of Clinical Pharmacology.
Energy Technology Data Exchange (ETDEWEB)
Ciftcioglu, O.; Hoogenboom, J.E.; Dam, H. van
1988-01-01
Studies on the multivariate autoregressive (MAR) analysis are carried out for the choice of the parameters for modelling the data obtained from various sensors optimally. Accordingly, the roles of the parameters on the analysis results are identified and the related ambiguities are reduced. Experimental investigations are carried out by means of synthesized reactor noise-like data obtained from a digital simulator providing simulated stochastic signals of an operating nuclear reactor so that the simulator constitutes a favourable tool for the present studies aimed. As the system is well defined with its known structure, precise comparison of the MAR analysis results with the true values is performed. With the help of the information gained through the studies carried out, conditions to be taken care of for optimal signal processing in MAR modelling are determined. Although the parameters involved are related among themselves and they have to be given different values suitable for the particular application in hand, some criteria, namely memory-time and sample length-time play an essential role in AR modelling and they are found to be applicable to each individual case commonly, for the establishment of the optimality.
International Nuclear Information System (INIS)
Ciftcioglu, O.
1988-01-01
Studies on the multivariate autoregressive (MAR) analysis are carried out for the choice of the parameters for modelling the data obtained from various sensors optimally. Accordingly, the roles of the parameters on the analysis results are identified and the related ambiguities are reduced. Experimental investigations are carried out by means of synthesized reactor noise-like data obtained from a digital simulator providing simulated stochastic signals of an operating nuclear reactor so that the simulator constitutes a favourable tool for the present studies aimed. As the system is well defined with its known structure, precise comparison of the MAR analysis results with the true values is performed. With the help of the information gained through the studies carried out, conditions to be taken care of for optimal signal processing in MAR modelling are determined. Although the parameters involved are related among themselves and they have to be given different values suitable for the particular application in hand, some criteria, namely memory-time and sample length-time play an essential role in AR modelling and they are found to be applicable to each individual case commonly, for the establishment of the optimality. (author)
International Nuclear Information System (INIS)
Yi Guo-Sheng; Wang Jiang; Deng Bin; Wei Xi-Le; Han Chun-Xiao
2013-01-01
To investigate whether and how manual acupuncture (MA) modulates brain activities, we design an experiment where acupuncture at acupoint ST36 of the right leg is used to obtain electroencephalograph (EEG) signals in healthy subjects. We adopt the autoregressive (AR) Burg method to estimate the power spectrum of EEG signals and analyze the relative powers in delta (0 Hz–4 Hz), theta (4 Hz–8 Hz), alpha (8 Hz–13 Hz), and beta (13 Hz–30 Hz) bands. Our results show that MA at ST36 can significantly increase the EEG slow wave relative power (delta band) and reduce the fast wave relative powers (alpha and beta bands), while there are no statistical differences in theta band relative power between different acupuncture states. In order to quantify the ratio of slow to fast wave EEG activity, we compute the power ratio index. It is found that the MA can significantly increase the power ratio index, especially in frontal and central lobes. All the results highlight the modulation of brain activities with MA and may provide potential help for the clinical use of acupuncture. The proposed quantitative method of acupuncture signals may be further used to make MA more standardized. (interdisciplinary physics and related areas of science and technology)
Assessment and prediction of air quality using fuzzy logic and autoregressive models
Carbajal-Hernández, José Juan; Sánchez-Fernández, Luis P.; Carrasco-Ochoa, Jesús A.; Martínez-Trinidad, José Fco.
2012-12-01
In recent years, artificial intelligence methods have been used for the treatment of environmental problems. This work, presents two models for assessment and prediction of air quality. First, we develop a new computational model for air quality assessment in order to evaluate toxic compounds that can harm sensitive people in urban areas, affecting their normal activities. In this model we propose to use a Sigma operator to statistically asses air quality parameters using their historical data information and determining their negative impact in air quality based on toxicity limits, frequency average and deviations of toxicological tests. We also introduce a fuzzy inference system to perform parameter classification using a reasoning process and integrating them in an air quality index describing the pollution levels in five stages: excellent, good, regular, bad and danger, respectively. The second model proposed in this work predicts air quality concentrations using an autoregressive model, providing a predicted air quality index based on the fuzzy inference system previously developed. Using data from Mexico City Atmospheric Monitoring System, we perform a comparison among air quality indices developed for environmental agencies and similar models. Our results show that our models are an appropriate tool for assessing site pollution and for providing guidance to improve contingency actions in urban areas.
International Nuclear Information System (INIS)
Ueki, Taro
2010-01-01
The noise propagation of tallies in the Monte Carlo power method can be represented by the autoregressive moving average process of orders p and p-1 (ARMA(p,p-1)], where p is an integer larger than or equal to two. The formula of the autocorrelation of ARMA(p,q), p≥q+1, indicates that ARMA(3,2) fitting is equivalent to lumping the eigenmodes of fluctuation propagation in three modes such as the slow, intermediate and fast attenuation modes. Therefore, ARMA(3,2) fitting was applied to the real standard deviation estimation of fuel assemblies at particular heights. The numerical results show that straightforward ARMA(3,2) fitting is promising but a stability issue must be resolved toward the incorporation in the distributed version of production Monte Carlo codes. The same numerical results reveal that the average performance of ARMA(3,2) fitting is equivalent to that of the batch method in MCNP with a batch size larger than one hundred and smaller than two hundred cycles for a 1100 MWe pressurized water reactor. The bias correction of low lag autocovariances in MVP/GMVP is demonstrated to have the potential of improving the average performance of ARMA(3,2) fitting. (author)
Genetic risk prediction using a spatial autoregressive model with adaptive lasso.
Wen, Yalu; Shen, Xiaoxi; Lu, Qing
2018-05-31
With rapidly evolving high-throughput technologies, studies are being initiated to accelerate the process toward precision medicine. The collection of the vast amounts of sequencing data provides us with great opportunities to systematically study the role of a deep catalog of sequencing variants in risk prediction. Nevertheless, the massive amount of noise signals and low frequencies of rare variants in sequencing data pose great analytical challenges on risk prediction modeling. Motivated by the development in spatial statistics, we propose a spatial autoregressive model with adaptive lasso (SARAL) for risk prediction modeling using high-dimensional sequencing data. The SARAL is a set-based approach, and thus, it reduces the data dimension and accumulates genetic effects within a single-nucleotide variant (SNV) set. Moreover, it allows different SNV sets having various magnitudes and directions of effect sizes, which reflects the nature of complex diseases. With the adaptive lasso implemented, SARAL can shrink the effects of noise SNV sets to be zero and, thus, further improve prediction accuracy. Through simulation studies, we demonstrate that, overall, SARAL is comparable to, if not better than, the genomic best linear unbiased prediction method. The method is further illustrated by an application to the sequencing data from the Alzheimer's Disease Neuroimaging Initiative. Copyright © 2018 John Wiley & Sons, Ltd.
A Ramp Cosine Cepstrum Model for the Parameter Estimation of Autoregressive Systems at Low SNR
Directory of Open Access Journals (Sweden)
Zhu Wei-Ping
2010-01-01
Full Text Available A new cosine cepstrum model-based scheme is presented for the parameter estimation of a minimum-phase autoregressive (AR system under low levels of signal-to-noise ratio (SNR. A ramp cosine cepstrum (RCC model for the one-sided autocorrelation function (OSACF of an AR signal is first proposed by considering both white noise and periodic impulse-train excitations. Using the RCC model, a residue-based least-squares optimization technique that guarantees the stability of the system is then presented in order to estimate the AR parameters from noisy output observations. For the purpose of implementation, the discrete cosine transform, which can efficiently handle the phase unwrapping problem and offer computational advantages as compared to the discrete Fourier transform, is employed. From extensive experimentations on AR systems of different orders, it is shown that the proposed method is capable of estimating parameters accurately and consistently in comparison to some of the existing methods for the SNR levels as low as −5 dB. As a practical application of the proposed technique, simulation results are also provided for the identification of a human vocal tract system using noise-corrupted natural speech signals demonstrating a superior estimation performance in terms of the power spectral density of the synthesized speech signals.
Dynamics analysis of a boiling water reactor based on multivariable autoregressive modeling
International Nuclear Information System (INIS)
Oguma, Ritsuo; Matsubara, Kunihiko
1980-01-01
The establishment of the highly reliable mathematical model for the dynamic characteristics of a reactor is indispensable for the achievement of safe operation in reactor plants. The authors have tried to model the dynamic characteristics of a reactor based on the identification technique, taking the JPDR (Japan Power Demonstration Reactor) as the object, as one of the technical studies for diagnosing BWR anomaly, and employed the multivariable autoregressive modeling (MAR method) as one of the useful methods for forwarding the analysis. In this paper, the outline of the system analysis by MAR modeling is explained, and the identification experiments and their analysis results performed in the phase 4 of the power increase test of the JPDR are described. The authors evaluated the results of identification based on only reactor noises, making reference to the results of identification in the case of exciting the system by applying artificial irregular disturbance, in order to clarify the extent in which the modeling is possible by reactor noises only. However, some difficulties were encountered. The largest problem is the one concerning the separation and identification of the noise sources exciting the variables from the dynamic characteristics among the variables. If the effective technique can be obtained to this problem, the approach by the identification technique based on the probability model might be a powerful tool in the field of reactor noise analysis and the development of diagnosis technics. (Wakatsuki, Y.)
Noise source analysis of nuclear ship Mutsu plant using multivariate autoregressive model
International Nuclear Information System (INIS)
Hayashi, K.; Shimazaki, J.; Shinohara, Y.
1996-01-01
The present study is concerned with the noise sources in N.S. Mutsu reactor plant. The noise experiments on the Mutsu plant were performed in order to investigate the plant dynamics and the effect of sea condition and and ship motion on the plant. The reactor noise signals as well as the ship motion signals were analyzed by a multivariable autoregressive (MAR) modeling method to clarify the noise sources in the reactor plant. It was confirmed from the analysis results that most of the plant variables were affected mainly by a horizontal component of the ship motion, that is the sway, through vibrations of the plant structures. Furthermore, the effect of ship motion on the reactor power was evaluated through the analysis of wave components extracted by a geometrical transform method. It was concluded that the amplitude of the reactor power oscillation was about 0.15% in normal sea condition, which was small enough for safe operation of the reactor plant. (authors)
Nabelek, Daniel P.; Ho, K. C.
2013-06-01
The detection of shallow buried low-metal content objects using ground penetrating radar (GPR) is a challenging task. This is because these targets are right underneath the ground and the ground bounce reflection interferes with their detections. They do not create distinctive hyperbolic signatures as required by most existing GPR detection algorithms due to their special geometric shapes and low metal content. This paper proposes the use of the Autoregressive (AR) modeling method for the detection of these targets. We fit an A-scan of the GPR data to an AR model. It is found that the fitting error will be small when such a target is present and large when it is absent. The ratio of the energy in an Ascan before and after AR model fitting is used as the confidence value for detection. We also apply AR model fitting over scans and utilize the fitting residual energies over several scans to form a feature vector for improving the detections. Using the data collected from a government test site, the proposed method can improve the detection of this kind of targets by 30% compared to the pre-screener, at a false alarm rate of 0.002/m2.
Non-linear auto-regressive models for cross-frequency coupling in neural time series
Tallot, Lucille; Grabot, Laetitia; Doyère, Valérie; Grenier, Yves; Gramfort, Alexandre
2017-01-01
We address the issue of reliably detecting and quantifying cross-frequency coupling (CFC) in neural time series. Based on non-linear auto-regressive models, the proposed method provides a generative and parametric model of the time-varying spectral content of the signals. As this method models the entire spectrum simultaneously, it avoids the pitfalls related to incorrect filtering or the use of the Hilbert transform on wide-band signals. As the model is probabilistic, it also provides a score of the model “goodness of fit” via the likelihood, enabling easy and legitimate model selection and parameter comparison; this data-driven feature is unique to our model-based approach. Using three datasets obtained with invasive neurophysiological recordings in humans and rodents, we demonstrate that these models are able to replicate previous results obtained with other metrics, but also reveal new insights such as the influence of the amplitude of the slow oscillation. Using simulations, we demonstrate that our parametric method can reveal neural couplings with shorter signals than non-parametric methods. We also show how the likelihood can be used to find optimal filtering parameters, suggesting new properties on the spectrum of the driving signal, but also to estimate the optimal delay between the coupled signals, enabling a directionality estimation in the coupling. PMID:29227989
Men, Zhongxian; Yee, Eugene; Lien, Fue-Sang; Yang, Zhiling; Liu, Yongqian
2014-01-01
Short-term wind speed and wind power forecasts (for a 72 h period) are obtained using a nonlinear autoregressive exogenous artificial neural network (ANN) methodology which incorporates either numerical weather prediction or high-resolution computational fluid dynamics wind field information as an exogenous input. An ensemble approach is used to combine the predictions from many candidate ANNs in order to provide improved forecasts for wind speed and power, along with the associated uncertainties in these forecasts. More specifically, the ensemble ANN is used to quantify the uncertainties arising from the network weight initialization and from the unknown structure of the ANN. All members forming the ensemble of neural networks were trained using an efficient particle swarm optimization algorithm. The results of the proposed methodology are validated using wind speed and wind power data obtained from an operational wind farm located in Northern China. The assessment demonstrates that this methodology for wind speed and power forecasting generally provides an improvement in predictive skills when compared to the practice of using an "optimal" weight vector from a single ANN while providing additional information in the form of prediction uncertainty bounds.
Electricity demand loads modeling using AutoRegressive Moving Average (ARMA) models
Energy Technology Data Exchange (ETDEWEB)
Pappas, S.S. [Department of Information and Communication Systems Engineering, University of the Aegean, Karlovassi, 83 200 Samos (Greece); Ekonomou, L.; Chatzarakis, G.E. [Department of Electrical Engineering Educators, ASPETE - School of Pedagogical and Technological Education, N. Heraklion, 141 21 Athens (Greece); Karamousantas, D.C. [Technological Educational Institute of Kalamata, Antikalamos, 24100 Kalamata (Greece); Katsikas, S.K. [Department of Technology Education and Digital Systems, University of Piraeus, 150 Androutsou Srt., 18 532 Piraeus (Greece); Liatsis, P. [Division of Electrical Electronic and Information Engineering, School of Engineering and Mathematical Sciences, Information and Biomedical Engineering Centre, City University, Northampton Square, London EC1V 0HB (United Kingdom)
2008-09-15
This study addresses the problem of modeling the electricity demand loads in Greece. The provided actual load data is deseasonilized and an AutoRegressive Moving Average (ARMA) model is fitted on the data off-line, using the Akaike Corrected Information Criterion (AICC). The developed model fits the data in a successful manner. Difficulties occur when the provided data includes noise or errors and also when an on-line/adaptive modeling is required. In both cases and under the assumption that the provided data can be represented by an ARMA model, simultaneous order and parameter estimation of ARMA models under the presence of noise are performed. The produced results indicate that the proposed method, which is based on the multi-model partitioning theory, tackles successfully the studied problem. For validation purposes the produced results are compared with three other established order selection criteria, namely AICC, Akaike's Information Criterion (AIC) and Schwarz's Bayesian Information Criterion (BIC). The developed model could be useful in the studies that concern electricity consumption and electricity prices forecasts. (author)
Vector autoregressive model approach for forecasting outflow cash in Central Java
hoyyi, Abdul; Tarno; Maruddani, Di Asih I.; Rahmawati, Rita
2018-05-01
Multivariate time series model is more applied in economic and business problems as well as in other fields. Applications in economic problems one of them is the forecasting of outflow cash. This problem can be viewed globally in the sense that there is no spatial effect between regions, so the model used is the Vector Autoregressive (VAR) model. The data used in this research is data on the money supply in Bank Indonesia Semarang, Solo, Purwokerto and Tegal. The model used in this research is VAR (1), VAR (2) and VAR (3) models. Ordinary Least Square (OLS) is used to estimate parameters. The best model selection criteria use the smallest Akaike Information Criterion (AIC). The result of data analysis shows that the AIC value of VAR (1) model is equal to 42.72292, VAR (2) equals 42.69119 and VAR (3) equals 42.87662. The difference in AIC values is not significant. Based on the smallest AIC value criteria, the best model is the VAR (2) model. This model has satisfied the white noise assumption.
An Autoregressive and Distributed Lag Model Approach to Inflation in Nigeria
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Chimere Okechukwu Iheonu
2017-03-01
Full Text Available This study scrutinized the precursors of Inflation in Nigeria between the periods 1980 to 2014. The Augmented Dickey-Fuller test was engaged to test for stationarity of the variables while the Autoregressive and Distributed lag (ARDL Model was applied to capture the affiliation between inflation and selected macroeconomic variables. Our findings revealed that there exists a long run relationship between Inflation, money supply, interest rate, GDP per capita and exchange rate in Nigeria while in the short run, money supply has a significant positive one period lag effect on Inflation and Interest Rate also has a significant negative one period lag influence on Inflation in Nigeria. Recommendations are that in the short run, monetary policies should be geared towards the control of money supply and interest rate in Nigeria in other to regulate Inflation and also, the Nigerian economy can afford to vary any of human capital development or technological advancement to boost productivity without causing inflation as GDP per capita proved insignificant in the short run.
A time series model: First-order integer-valued autoregressive (INAR(1))
Simarmata, D. M.; Novkaniza, F.; Widyaningsih, Y.
2017-07-01
Nonnegative integer-valued time series arises in many applications. A time series model: first-order Integer-valued AutoRegressive (INAR(1)) is constructed by binomial thinning operator to model nonnegative integer-valued time series. INAR (1) depends on one period from the process before. The parameter of the model can be estimated by Conditional Least Squares (CLS). Specification of INAR(1) is following the specification of (AR(1)). Forecasting in INAR(1) uses median or Bayesian forecasting methodology. Median forecasting methodology obtains integer s, which is cumulative density function (CDF) until s, is more than or equal to 0.5. Bayesian forecasting methodology forecasts h-step-ahead of generating the parameter of the model and parameter of innovation term using Adaptive Rejection Metropolis Sampling within Gibbs sampling (ARMS), then finding the least integer s, where CDF until s is more than or equal to u . u is a value taken from the Uniform(0,1) distribution. INAR(1) is applied on pneumonia case in Penjaringan, Jakarta Utara, January 2008 until April 2016 monthly.
Self-organising mixture autoregressive model for non-stationary time series modelling.
Ni, He; Yin, Hujun
2008-12-01
Modelling non-stationary time series has been a difficult task for both parametric and nonparametric methods. One promising solution is to combine the flexibility of nonparametric models with the simplicity of parametric models. In this paper, the self-organising mixture autoregressive (SOMAR) network is adopted as a such mixture model. It breaks time series into underlying segments and at the same time fits local linear regressive models to the clusters of segments. In such a way, a global non-stationary time series is represented by a dynamic set of local linear regressive models. Neural gas is used for a more flexible structure of the mixture model. Furthermore, a new similarity measure has been introduced in the self-organising network to better quantify the similarity of time series segments. The network can be used naturally in modelling and forecasting non-stationary time series. Experiments on artificial, benchmark time series (e.g. Mackey-Glass) and real-world data (e.g. numbers of sunspots and Forex rates) are presented and the results show that the proposed SOMAR network is effective and superior to other similar approaches.
Modeling Nonstationary Emotion Dynamics in Dyads using a Time-Varying Vector-Autoregressive Model.
Bringmann, Laura F; Ferrer, Emilio; Hamaker, Ellen L; Borsboom, Denny; Tuerlinckx, Francis
2018-01-01
Emotion dynamics are likely to arise in an interpersonal context. Standard methods to study emotions in interpersonal interaction are limited because stationarity is assumed. This means that the dynamics, for example, time-lagged relations, are invariant across time periods. However, this is generally an unrealistic assumption. Whether caused by an external (e.g., divorce) or an internal (e.g., rumination) event, emotion dynamics are prone to change. The semi-parametric time-varying vector-autoregressive (TV-VAR) model is based on well-studied generalized additive models, implemented in the software R. The TV-VAR can explicitly model changes in temporal dependency without pre-existing knowledge about the nature of change. A simulation study is presented, showing that the TV-VAR model is superior to the standard time-invariant VAR model when the dynamics change over time. The TV-VAR model is applied to empirical data on daily feelings of positive affect (PA) from a single couple. Our analyses indicate reliable changes in the male's emotion dynamics over time, but not in the female's-which were not predicted by her own affect or that of her partner. This application illustrates the usefulness of using a TV-VAR model to detect changes in the dynamics in a system.
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)
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Ernest Kissi
2018-03-01
Full Text Available Prices of construction resources keep on fluctuating due to unstable economic situations that have been experienced over the years. Clients knowledge of their financial commitments toward their intended project remains the basis for their final decision. The use of construction tender price index provides a realistic estimate at the early stage of the project. Tender price index (TPI is influenced by various economic factors, hence there are several statistical techniques that have been employed in forecasting. Some of these include regression, time series, vector error correction among others. However, in recent times the integrated modelling approach is gaining popularity due to its ability to give powerful predictive accuracy. Thus, in line with this assumption, the aim of this study is to apply autoregressive integrated moving average with exogenous variables (ARIMAX in modelling TPI. The results showed that ARIMAX model has a better predictive ability than the use of the single approach. The study further confirms the earlier position of previous research of the need to use the integrated model technique in forecasting TPI. This model will assist practitioners to forecast the future values of tender price index. Although the study focuses on the Ghanaian economy, the findings can be broadly applicable to other developing countries which share similar economic characteristics.
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Carlos Quispe
2013-04-01
Full Text Available El Niño connects globally climate, ecosystems and socio-economic activities. Since 1980 this event has been tried to be predicted, but until now the statistical and dynamical models are insuffi cient. Thus, the objective of the present work was to explore using an autoregressive moving average model the effect of El Niño over the sea surface temperature (TSM off the Peruvian coast. The work involved 5 stages: identifi cation, estimation, diagnostic checking, forecasting and validation. Simple and partial autocorrelation functions (FAC and FACP were used to identify and reformulate the orders of the model parameters, as well as Akaike information criterium (AIC and Schwarz criterium (SC for the selection of the best models during the diagnostic checking. Among the main results the models ARIMA(12,0,11 were proposed, which simulated monthly conditions in agreement with the observed conditions off the Peruvian coast: cold conditions at the end of 2004, and neutral conditions at the beginning of 2005.
Forecasting Nord Pool day-ahead prices with an autoregressive model
International Nuclear Information System (INIS)
Kristiansen, Tarjei
2012-01-01
This paper presents a model to forecast Nord Pool hourly day-ahead prices. The model is based on but reduced in terms of estimation parameters (from 24 sets to 1) and modified to include Nordic demand and Danish wind power as exogenous variables. We model prices across all hours in the analysis period rather than across each single hour of 24 hours. By applying three model variants on Nord Pool data, we achieve a weekly mean absolute percentage error (WMAE) of around 6–7% and an hourly mean absolute percentage error (MAPE) ranging from 8% to 11%. Out of sample results yields a WMAE and an hourly MAPE of around 5%. The models enable analysts and traders to forecast hourly day-ahead prices accurately. Moreover, the models are relatively straightforward and user-friendly to implement. They can be set up in any trading organization. - Highlights: ► Forecasting Nord Pool day-ahead prices with an autoregressive model. ► The model is based on but with the set of parameters reduced from 24 to 1. ► The model includes Nordic demand and Danish wind power as exogenous variables. ► Hourly mean absolute percentage error ranges from 8% to 11%. ► Out of sample results yields a WMAE and an hourly MAPE of around 5%.
Liu, Zhuofu; Wang, Lin; Luo, Zhongming; Heusch, Andrew I; Cascioli, Vincenzo; McCarthy, Peter W
2015-11-01
There is a need to develop a greater understanding of temperature at the skin-seat interface during prolonged seating from the perspectives of both industrial design (comfort/discomfort) and medical care (skin ulcer formation). Here we test the concept of predicting temperature at the seat surface and skin interface during prolonged sitting (such as required from wheelchair users). As caregivers are usually busy, such a method would give them warning ahead of a problem. This paper describes a data-driven model capable of predicting thermal changes and thus having the potential to provide an early warning (15- to 25-min ahead prediction) of an impending temperature that may increase the risk for potential skin damages for those subject to enforced sitting and who have little or no sensory feedback from this area. Initially, the oscillations of the original signal are suppressed using the reconstruction strategy of empirical mode decomposition (EMD). Consequentially, the autoregressive data-driven model can be used to predict future thermal trends based on a shorter period of acquisition, which reduces the possibility of introducing human errors and artefacts associated with longer duration "enforced" sitting by volunteers. In this study, the method had a maximum predictive error of body insensitivity and disability requiring them to be immobile in seats for prolonged periods. Copyright © 2015 Tissue Viability Society. Published by Elsevier Ltd. All rights reserved.
Sensor network based solar forecasting using a local vector autoregressive ridge framework
Energy Technology Data Exchange (ETDEWEB)
Xu, J. [Stony Brook Univ., NY (United States); Yoo, S. [Brookhaven National Lab. (BNL), Upton, NY (United States); Heiser, J. [Brookhaven National Lab. (BNL), Upton, NY (United States); Kalb, P. [Brookhaven National Lab. (BNL), Upton, NY (United States)
2016-04-04
The significant improvements and falling costs of photovoltaic (PV) technology make solar energy a promising resource, yet the cloud induced variability of surface solar irradiance inhibits its effective use in grid-tied PV generation. Short-term irradiance forecasting, especially on the minute scale, is critically important for grid system stability and auxiliary power source management. Compared to the trending sky imaging devices, irradiance sensors are inexpensive and easy to deploy but related forecasting methods have not been well researched. The prominent challenge of applying classic time series models on a network of irradiance sensors is to address their varying spatio-temporal correlations due to local changes in cloud conditions. We propose a local vector autoregressive framework with ridge regularization to forecast irradiance without explicitly determining the wind field or cloud movement. By using local training data, our learned forecast model is adaptive to local cloud conditions and by using regularization, we overcome the risk of overfitting from the limited training data. Our systematic experimental results showed an average of 19.7% RMSE and 20.2% MAE improvement over the benchmark Persistent Model for 1-5 minute forecasts on a comprehensive 25-day dataset.
Investigating Spatial Interdependence in E-Bike Choice Using Spatially Autoregressive Model
Directory of Open Access Journals (Sweden)
Chengcheng Xu
2017-08-01
Full Text Available Increased attention has been given to promoting e-bike usage in recent years. However, the research gap still exists in understanding the effects of spatial interdependence on e-bike choice. This study investigated how spatial interdependence affected the e-bike choice. The Moran’s I statistic test showed that spatial interdependence exists in e-bike choice at aggregated level. Bayesian spatial autoregressive logistic analyses were then used to investigate the spatial interdependence at individual level. Separate models were developed for commuting and non-commuting trips. The factors affecting e-bike choice are different between commuting and non-commuting trips. Spatial interdependence exists at both origin and destination sides of commuting and non-commuting trips. Travellers are more likely to choose e-bikes if their neighbours at the trip origin and destination also travel by e-bikes. And the magnitude of this spatial interdependence is different across various traffic analysis zones. The results suggest that, without considering spatial interdependence, the traditional methods may have biased estimation results and make systematic forecasting errors.
Chin, Wen Cheong; Lee, Min Cherng; Yap, Grace Lee Ching
2016-01-01
High frequency financial data modelling has become one of the important research areas in the field of financial econometrics. However, the possible structural break in volatile financial time series often trigger inconsistency issue in volatility estimation. In this study, we propose a structural break heavy-tailed heterogeneous autoregressive (HAR) volatility econometric model with the enhancement of jump-robust estimators. The breakpoints in the volatility are captured by dummy variables after the detection by Bai-Perron sequential multi breakpoints procedure. In order to further deal with possible abrupt jump in the volatility, the jump-robust volatility estimators are composed by using the nearest neighbor truncation approach, namely the minimum and median realized volatility. Under the structural break improvements in both the models and volatility estimators, the empirical findings show that the modified HAR model provides the best performing in-sample and out-of-sample forecast evaluations as compared with the standard HAR models. Accurate volatility forecasts have direct influential to the application of risk management and investment portfolio analysis.
Kühn, Simone; Strelow, Enrique; Gallinat, Jürgen
2016-08-01
We set out to forecast consumer behaviour in a supermarket based on functional magnetic resonance imaging (fMRI). Data was collected while participants viewed six chocolate bar communications and product pictures before and after each communication. Then self-reports liking judgement were collected. fMRI data was extracted from a priori selected brain regions: nucleus accumbens, medial orbitofrontal cortex, amygdala, hippocampus, inferior frontal gyrus, dorsomedial prefrontal cortex assumed to contribute positively and dorsolateral prefrontal cortex and insula were hypothesized to contribute negatively to sales. The resulting values were rank ordered. After our fMRI-based forecast an instore test was conducted in a supermarket on n=63.617 shoppers. Changes in sales were best forecasted by fMRI signal during communication viewing, second best by a comparison of brain signal during product viewing before and after communication and least by explicit liking judgements. The results demonstrate the feasibility of applying neuroimaging methods in a relatively small sample to correctly forecast sales changes at point-of-sale. Copyright © 2016. Published by Elsevier Inc.
Directory of Open Access Journals (Sweden)
Beaufort B. Longest
2015-11-01
Full Text Available The authors of “Management matters: a leverage point for health systems strengthening in global health,” raise a crucial issue. Because more effective management can contribute to better performing health systems, attempts to strengthen health systems require attention to management. As a guide toward management capacity building, the authors outline a comprehensive set of core management competencies needed for managing global health efforts. Although, I agree with the authors’ central premise about the important role of management in improving global health and concur that focusing on competencies can guide management capacity building, I think it is important to recognize that a set of relevant competencies is not the only way to conceptualize and organize efforts to teach, learn, practice, or conduct research on management. I argue the added utility of also viewing management as a set of functions or activities as an alternative paradigm and suggest that the greatest utility could lie in some hybrid that combines various ways of conceptualizing management for study, practice, and research.
Kammerdiner, Alla; Xanthopoulos, Petros; Pardalos, Panos M.
2007-11-01
In this chapter a potential problem with application of the Granger-causality based on the simple vector autoregressive (VAR) modeling to EEG data is investigated. Although some initial studies tested whether the data support the stationarity assumption of VAR, the stability of the estimated model is rarely (if ever) been verified. In fact, in cases when the stability condition is violated the process may exhibit a random walk like behavior or even be explosive. The problem is illustrated by an example.
Wo-Chiang Lee; Joe-Ming Lee
2014-01-01
This article applies the threshold autoregressive model to investigate the relationship between bond funds’ net flow and investment risk in Taiwan. Our empirical findings show that bond funds’ investors are concerned about the investment return and neglect the investment risk. In particular, when expanding the size of the bond funds, fund investors believe that the fund cannot lose any money on investment products. In order to satisfy investors, bond fund managers only target short-term retur...
Lana, X.; Burgueño, A.; Serra, C.; Martínez, M. D.
2017-01-01
Dry spell lengths, DSL, defined as the number of consecutive days with daily rain amounts below a given threshold, may provide relevant information about drought regimes. Taking advantage of a daily pluviometric database covering a great extension of Europe, a detailed analysis of the multifractality of the dry spell regimes is achieved. At the same time, an autoregressive process is applied with the aim of predicting DSL. A set of parameters, namely Hurst exponent, H, estimated from multifractal spectrum, f( α), critical Hölder exponent, α 0, for which f( α) reaches its maximum value, spectral width, W, and spectral asymmetry, B, permits a first clustering of European rain gauges in terms of the complexity of their DSL series. This set of parameters also allows distinguishing between time series describing fine- or smooth-structure of the DSL regime by using the complexity index, CI. Results of previous monofractal analyses also permits establishing comparisons between smooth-structures, relatively low correlation dimensions, notable predictive instability and anti-persistence of DSL for European areas, sometimes submitted to long droughts. Relationships are also found between the CI and the mean absolute deviation, MAD, and the optimum autoregressive order, OAO, of an ARIMA( p, d,0) autoregressive process applied to the DSL series. The detailed analysis of the discrepancies between empiric and predicted DSL underlines the uncertainty over predictability of long DSL, particularly for the Mediterranean region.
International Nuclear Information System (INIS)
Benth, Fred Espen; Taib, Che Mohd Imran Che
2013-01-01
We extend the concept of half life of an Ornstein–Uhlenbeck process to Lévy-driven continuous-time autoregressive moving average processes with stochastic volatility. The half life becomes state dependent, and we analyze its properties in terms of the characteristics of the process. An empirical example based on daily temperatures observed in Petaling Jaya, Malaysia, is presented, where the proposed model is estimated and the distribution of the half life is simulated. The stationarity of the dynamics yield futures prices which asymptotically tend to constant at an exponential rate when time to maturity goes to infinity. The rate is characterized by the eigenvalues of the dynamics. An alternative description of this convergence can be given in terms of our concept of half life. - Highlights: • The concept of half life is extended to Levy-driven continuous time autoregressive moving average processes • The dynamics of Malaysian temperatures are modeled using a continuous time autoregressive model with stochastic volatility • Forward prices on temperature become constant when time to maturity tends to infinity • Convergence in time to maturity is at an exponential rate given by the eigenvalues of the model temperature model
Kargoll, Boris; Omidalizarandi, Mohammad; Loth, Ina; Paffenholz, Jens-André; Alkhatib, Hamza
2018-03-01
In this paper, we investigate a linear regression time series model of possibly outlier-afflicted observations and autocorrelated random deviations. This colored noise is represented by a covariance-stationary autoregressive (AR) process, in which the independent error components follow a scaled (Student's) t-distribution. This error model allows for the stochastic modeling of multiple outliers and for an adaptive robust maximum likelihood (ML) estimation of the unknown regression and AR coefficients, the scale parameter, and the degree of freedom of the t-distribution. This approach is meant to be an extension of known estimators, which tend to focus only on the regression model, or on the AR error model, or on normally distributed errors. For the purpose of ML estimation, we derive an expectation conditional maximization either algorithm, which leads to an easy-to-implement version of iteratively reweighted least squares. The estimation performance of the algorithm is evaluated via Monte Carlo simulations for a Fourier as well as a spline model in connection with AR colored noise models of different orders and with three different sampling distributions generating the white noise components. We apply the algorithm to a vibration dataset recorded by a high-accuracy, single-axis accelerometer, focusing on the evaluation of the estimated AR colored noise model.
Full Text Available ... en español Blog About OnSafety CPSC Stands for Safety The Tipping Point Home > 60 Seconds of Safety (Videos) > The Tipping Point The Tipping Point by ... danger death electrical fall furniture head injury product safety television tipover tv Watch the video in Adobe ...
Point specificity in acupuncture
Directory of Open Access Journals (Sweden)
Choi Emma M
2012-02-01
Full Text Available Abstract The existence of point specificity in acupuncture is controversial, because many acupuncture studies using this principle to select control points have found that sham acupoints have similar effects to those of verum acupoints. Furthermore, the results of pain-related studies based on visual analogue scales have not supported the concept of point specificity. In contrast, hemodynamic, functional magnetic resonance imaging and neurophysiological studies evaluating the responses to stimulation of multiple points on the body surface have shown that point-specific actions are present. This review article focuses on clinical and laboratory studies supporting the existence of point specificity in acupuncture and also addresses studies that do not support this concept. Further research is needed to elucidate the point-specific actions of acupuncture.
Critical point predication device
International Nuclear Information System (INIS)
Matsumura, Kazuhiko; Kariyama, Koji.
1996-01-01
An operation for predicting a critical point by using a existent reverse multiplication method has been complicated, and an effective multiplication factor could not be plotted directly to degrade the accuracy for the prediction. The present invention comprises a detector counting memory section for memorizing the counting sent from a power detector which monitors the reactor power, a reverse multiplication factor calculation section for calculating the reverse multiplication factor based on initial countings and current countings of the power detector, and a critical point prediction section for predicting the criticality by the reverse multiplication method relative to effective multiplication factors corresponding to the state of the reactor core previously determined depending on the cases. In addition, a reactor core characteristic calculation section is added for analyzing an effective multiplication factor depending on the state of the reactor core. Then, if the margin up to the criticality is reduced to lower than a predetermined value during critical operation, an alarm is generated to stop the critical operation when generation of a period of more than a predetermined value predicted by succeeding critical operation. With such procedures, forecasting for the critical point can be easily predicted upon critical operation to greatly mitigate an operator's burden and improve handling for the operation. (N.H.)
Directory of Open Access Journals (Sweden)
Helen Higgs
2014-03-01
Full Text Available This paper models the price and income elasticity of retail finance in Australia using aggregate quarterly data and an autoregressive distributed lag (ARDL approach. We particularly focus on the impact of the global financial crisis (GFC from 2007 onwards on retail finance demand and analyse four submarkets (period analysed in brackets: owneroccupied housing loans (Sep 1985–June 2010, term loans (for motor vehicles, household goods and debt consolidation, etc. (Dec 1988–Jun 2010, credit card loans (Mar 1990–Jun 2010, and margin loans (Sep 2000–Jun 2010. Other than the indicator lending rates and annual full-time earnings respectively used as proxies for the price and income effects, we specify a large number of other variables as demand factors, particularly reflecting the value of the asset for which retail finance demand is derived. These variously include the yield on indexed bonds as a proxy for inflation expectations, median housing prices, consumer sentiment indices as measures of consumer confidence, motor vehicle and retail trade sales, housing debt-to-housing assets as a measure of leverage, the proportion of protected margin lending, the available credit limit on credit cards, and the All Ordinaries Index. In the long run, we find significant price elasticities only for term loans and margin loans, and significant income elasticities of demand for housing loans, term loans and margin loans. We also find that the GFC only significantly affected the longrun demand for term loans and margin loans. In the short run, we find that the GFC has had a significant effect on the price elasticity of demand for term loans and margin loans. Expected inflation is also a key factor affecting retail finance demand. Overall, most of the submarkets in the analysis indicate that retail finance demand is certainly price inelastic but more income elastic than conventionally thought.
Prediction of high airway pressure using a non-linear autoregressive model of pulmonary mechanics.
Langdon, Ruby; Docherty, Paul D; Schranz, Christoph; Chase, J Geoffrey
2017-11-02
For mechanically ventilated patients with acute respiratory distress syndrome (ARDS), suboptimal PEEP levels can cause ventilator induced lung injury (VILI). In particular, high PEEP and high peak inspiratory pressures (PIP) can cause over distension of alveoli that is associated with VILI. However, PEEP must also be sufficient to maintain recruitment in ARDS lungs. A lung model that accurately and precisely predicts the outcome of an increase in PEEP may allow dangerous high PIP to be avoided, and reduce the incidence of VILI. Sixteen pressure-flow data sets were collected from nine mechanically ventilated ARDs patients that underwent one or more recruitment manoeuvres. A nonlinear autoregressive (NARX) model was identified on one or more adjacent PEEP steps, and extrapolated to predict PIP at 2, 4, and 6 cmH 2 O PEEP horizons. The analysis considered whether the predicted and measured PIP exceeded a threshold of 40 cmH 2 O. A direct comparison of the method was made using the first order model of pulmonary mechanics (FOM(I)). Additionally, a further, more clinically appropriate method for the FOM was tested, in which the FOM was trained on a single PEEP prior to prediction (FOM(II)). The NARX model exhibited very high sensitivity (> 0.96) in all cases, and a high specificity (> 0.88). While both FOM methods had a high specificity (> 0.96), the sensitivity was much lower, with a mean of 0.68 for FOM(I), and 0.82 for FOM(II). Clinically, false negatives are more harmful than false positives, as a high PIP may result in distension and VILI. Thus, the NARX model may be more effective than the FOM in allowing clinicians to reduce the risk of applying a PEEP that results in dangerously high airway pressures.
Indian Academy of Sciences (India)
Home; Journals; Resonance – Journal of Science Education; Volume 5; Issue 5. Fixed Points - From Russia with Love - A Primer of Fixed Point Theory. A K Vijaykumar. Book Review Volume 5 Issue 5 May 2000 pp 101-102. Fulltext. Click here to view fulltext PDF. Permanent link:
Full Text Available ... OnSafety CPSC Stands for Safety The Tipping Point Home > 60 Seconds of Safety (Videos) > The Tipping Point ... 24 hours a day. For young children whose home is a playground, it’s the best way to ...
Full Text Available ... 60 Seconds of Safety (Videos) > The Tipping Point The Tipping Point by CPSC Blogger September 22, 2009 appliance child Childproofing CPSC danger death electrical fall furniture head injury product safety television tipover tv Watch the video in Adobe Flash ...
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Ryan Louise
2007-11-01
Full Text Available Abstract Background The Conditional Autoregressive (CAR model is widely used in many small-area ecological studies to analyse outcomes measured at an areal level. There has been little evaluation of the influence of different neighbourhood weight matrix structures on the amount of smoothing performed by the CAR model. We examined this issue in detail. Methods We created several neighbourhood weight matrices and applied them to a large dataset of births and birth defects in New South Wales (NSW, Australia within 198 Statistical Local Areas. Between the years 1995–2003, there were 17,595 geocoded birth defects and 770,638 geocoded birth records with available data. Spatio-temporal models were developed with data from 1995–2000 and their fit evaluated within the following time period: 2001–2003. Results We were able to create four adjacency-based weight matrices, seven distance-based weight matrices and one matrix based on similarity in terms of a key covariate (i.e. maternal age. In terms of agreement between observed and predicted relative risks, categorised in epidemiologically relevant groups, generally the distance-based matrices performed better than the adjacency-based neighbourhoods. In terms of recovering the underlying risk structure, the weight-7 model (smoothing by maternal-age 'Covariate model' was able to correctly classify 35/47 high-risk areas (sensitivity 74% with a specificity of 47%, and the 'Gravity' model had sensitivity and specificity values of 74% and 39% respectively. Conclusion We found considerable differences in the smoothing properties of the CAR model, depending on the type of neighbours specified. This in turn had an effect on the models' ability to recover the observed risk in an area. Prior to risk mapping or ecological modelling, an exploratory analysis of the neighbourhood weight matrix to guide the choice of a suitable weight matrix is recommended. Alternatively, the weight matrix can be chosen a priori
International Nuclear Information System (INIS)
Iwama, N.; Inoue, A.; Tsukishima, T.; Sato, M.; Kawahata, K.
1981-07-01
A new procedure for the maximum entropy spectral estimation is studied for the purpose of data processing in Fourier transform spectroscopy. The autoregressive model fitting is examined under a least squares criterion based on the Yule-Walker equations. An AIC-like criterion is suggested for selecting the model order. The principal advantage of the new procedure lies in the enhanced frequency resolution particularly for small values of the maximum optical path-difference of the interferogram. The usefulness of the procedure is ascertained by some numerical simulations and further by experiments with respect to a highly coherent submillimeter wave and the electron cyclotron emission from a stellarator plasma. (author)
DEFF Research Database (Denmark)
Li, Chunjian; Andersen, Søren Vang
2007-01-01
We propose two blind system identification methods that exploit the underlying dynamics of non-Gaussian signals. The two signal models to be identified are: an Auto-Regressive (AR) model driven by a discrete-state Hidden Markov process, and the same model whose output is perturbed by white Gaussi...... outputs. The signal models are general and suitable to numerous important signals, such as speech signals and base-band communication signals. Applications to speech analysis and blind channel equalization are given to exemplify the efficiency of the new methods....
Goldsmith, Shelly
1999-01-01
Dew Point was a solo exhibition originating at PriceWaterhouseCoopers Headquarters Gallery, London, UK and toured to the Centre de Documentacio i Museu Textil, Terrassa, Spain and Gallery Aoyama, Tokyo, Japan.
Full Text Available ... Point by CPSC Blogger September 22, 2009 appliance child Childproofing CPSC danger death electrical fall furniture head injury product safety television tipover tv Watch the video in Adobe Flash ...
... Point by CPSC Blogger September 22, 2009 appliance child Childproofing CPSC danger death electrical fall furniture head injury product safety television tipover tv Watch the video in Adobe Flash ...
Full Text Available ... Point by CPSC Blogger September 22, 2009 appliance child Childproofing CPSC danger death electrical fall furniture head ... see news reports about horrible accidents involving young children and furniture, appliance and tv tip-overs. The ...
Full Text Available ... Point by CPSC Blogger September 22, 2009 appliance child Childproofing CPSC danger death electrical fall furniture head ... TV falls with about the same force as child falling from the third story of a building. ...
Full Text Available ... Tipping Point by CPSC Blogger September 22, 2009 appliance child Childproofing CPSC danger death electrical fall furniture ... about horrible accidents involving young children and furniture, appliance and tv tip-overs. The force of a ...
Modeling Polio Data Using the First Order Non-Negative Integer-Valued Autoregressive, INAR(1), Model
Vazifedan, Turaj; Shitan, Mahendran
Time series data may consists of counts, such as the number of road accidents, the number of patients in a certain hospital, the number of customers waiting for service at a certain time and etc. When the value of the observations are large it is usual to use Gaussian Autoregressive Moving Average (ARMA) process to model the time series. However if the observed counts are small, it is not appropriate to use ARMA process to model the observed phenomenon. In such cases we need to model the time series data by using Non-Negative Integer valued Autoregressive (INAR) process. The modeling of counts data is based on the binomial thinning operator. In this paper we illustrate the modeling of counts data using the monthly number of Poliomyelitis data in United States between January 1970 until December 1983. We applied the AR(1), Poisson regression model and INAR(1) model and the suitability of these models were assessed by using the Index of Agreement(I.A.). We found that INAR(1) model is more appropriate in the sense it had a better I.A. and it is natural since the data are counts.
Energy Technology Data Exchange (ETDEWEB)
Lu, Fengbin, E-mail: fblu@amss.ac.cn [Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190 (China); Qiao, Han, E-mail: qiaohan@ucas.ac.cn [School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190 (China); Wang, Shouyang, E-mail: sywang@amss.ac.cn [School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190 (China); Lai, Kin Keung, E-mail: mskklai@cityu.edu.hk [Department of Management Sciences, City University of Hong Kong (Hong Kong); Li, Yuze, E-mail: richardyz.li@mail.utoronto.ca [Department of Industrial Engineering, University of Toronto (Canada)
2017-01-15
This paper proposes a new time-varying coefficient vector autoregressions (VAR) model, in which the coefficient is a linear function of dynamic lagged correlation. The proposed model allows for flexibility in choices of dynamic correlation models (e.g. dynamic conditional correlation generalized autoregressive conditional heteroskedasticity (GARCH) models, Markov-switching GARCH models and multivariate stochastic volatility models), which indicates that it can describe many types of time-varying causal effects. Time-varying causal relations between West Texas Intermediate (WTI) crude oil and the US Standard and Poor’s 500 (S&P 500) stock markets are examined by the proposed model. The empirical results show that their causal relations evolve with time and display complex characters. Both positive and negative causal effects of the WTI on the S&P 500 in the subperiods have been found and confirmed by the traditional VAR models. Similar results have been obtained in the causal effects of S&P 500 on WTI. In addition, the proposed model outperforms the traditional VAR model.
Ou, Lu; Chow, Sy-Miin; Ji, Linying; Molenaar, Peter C M
2017-01-01
The autoregressive latent trajectory (ALT) model synthesizes the autoregressive model and the latent growth curve model. The ALT model is flexible enough to produce a variety of discrepant model-implied change trajectories. While some researchers consider this a virtue, others have cautioned that this may confound interpretations of the model's parameters. In this article, we show that some-but not all-of these interpretational difficulties may be clarified mathematically and tested explicitly via likelihood ratio tests (LRTs) imposed on the initial conditions of the model. We show analytically the nested relations among three variants of the ALT model and the constraints needed to establish equivalences. A Monte Carlo simulation study indicated that LRTs, particularly when used in combination with information criterion measures, can allow researchers to test targeted hypotheses about the functional forms of the change process under study. We further demonstrate when and how such tests may justifiably be used to facilitate our understanding of the underlying process of change using a subsample (N = 3,995) of longitudinal family income data from the National Longitudinal Survey of Youth.
International Nuclear Information System (INIS)
Lu, Fengbin; Qiao, Han; Wang, Shouyang; Lai, Kin Keung; Li, Yuze
2017-01-01
This paper proposes a new time-varying coefficient vector autoregressions (VAR) model, in which the coefficient is a linear function of dynamic lagged correlation. The proposed model allows for flexibility in choices of dynamic correlation models (e.g. dynamic conditional correlation generalized autoregressive conditional heteroskedasticity (GARCH) models, Markov-switching GARCH models and multivariate stochastic volatility models), which indicates that it can describe many types of time-varying causal effects. Time-varying causal relations between West Texas Intermediate (WTI) crude oil and the US Standard and Poor’s 500 (S&P 500) stock markets are examined by the proposed model. The empirical results show that their causal relations evolve with time and display complex characters. Both positive and negative causal effects of the WTI on the S&P 500 in the subperiods have been found and confirmed by the traditional VAR models. Similar results have been obtained in the causal effects of S&P 500 on WTI. In addition, the proposed model outperforms the traditional VAR model.
Directory of Open Access Journals (Sweden)
MEHDI AMIAN
2013-10-01
Full Text Available Functional near infrared spectroscopy (fNIRS is a technique that is used for noninvasive measurement of the oxyhemoglobin (HbO2 and deoxyhemoglobin (HHb concentrations in the brain tissue. Since the ratio of the concentration of these two agents is correlated with the neuronal activity, fNIRS can be used for the monitoring and quantifying the cortical activity. The portability of fNIRS makes it a good candidate for studies involving subject's movement. The fNIRS measurements, however, are sensitive to artifacts generated by subject's head motion. This makes fNIRS signals less effective in such applications. In this paper, the autoregressive moving average (ARMA modeling of the fNIRS signal is proposed for state-space representation of the signal which is then fed to the Kalman filter for estimating the motionless signal from motion corrupted signal. Results are compared to the autoregressive model (AR based approach, which has been done previously, and show that the ARMA models outperform AR models. We attribute it to the richer structure, containing more terms indeed, of ARMA than AR. We show that the signal to noise ratio (SNR is about 2 dB higher for ARMA based method.
Miftahurrohmah, Brina; Iriawan, Nur; Fithriasari, Kartika
2017-06-01
Stocks are known as the financial instruments traded in the capital market which have a high level of risk. Their risks are indicated by their uncertainty of their return which have to be accepted by investors in the future. The higher the risk to be faced, the higher the return would be gained. Therefore, the measurements need to be made against the risk. Value at Risk (VaR) as the most popular risk measurement method, is frequently ignore when the pattern of return is not uni-modal Normal. The calculation of the risks using VaR method with the Normal Mixture Autoregressive (MNAR) approach has been considered. This paper proposes VaR method couple with the Mixture Laplace Autoregressive (MLAR) that would be implemented for analysing the first three biggest capitalization Islamic stock return in JII, namely PT. Astra International Tbk (ASII), PT. Telekomunikasi Indonesia Tbk (TLMK), and PT. Unilever Indonesia Tbk (UNVR). Parameter estimation is performed by employing Bayesian Markov Chain Monte Carlo (MCMC) approaches.
Zhao, Yu Xi; Xie, Ping; Sang, Yan Fang; Wu, Zi Yi
2018-04-01
Hydrological process evaluation is temporal dependent. Hydrological time series including dependence components do not meet the data consistency assumption for hydrological computation. Both of those factors cause great difficulty for water researches. Given the existence of hydrological dependence variability, we proposed a correlationcoefficient-based method for significance evaluation of hydrological dependence based on auto-regression model. By calculating the correlation coefficient between the original series and its dependence component and selecting reasonable thresholds of correlation coefficient, this method divided significance degree of dependence into no variability, weak variability, mid variability, strong variability, and drastic variability. By deducing the relationship between correlation coefficient and auto-correlation coefficient in each order of series, we found that the correlation coefficient was mainly determined by the magnitude of auto-correlation coefficient from the 1 order to p order, which clarified the theoretical basis of this method. With the first-order and second-order auto-regression models as examples, the reasonability of the deduced formula was verified through Monte-Carlo experiments to classify the relationship between correlation coefficient and auto-correlation coefficient. This method was used to analyze three observed hydrological time series. The results indicated the coexistence of stochastic and dependence characteristics in hydrological process.
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Mei-Yu LEE
2014-11-01
Full Text Available This paper investigates the effect of the nonzero autocorrelation coefficients on the sampling distributions of the Durbin-Watson test estimator in three time-series models that have different variance-covariance matrix assumption, separately. We show that the expected values and variances of the Durbin-Watson test estimator are slightly different, but the skewed and kurtosis coefficients are considerably different among three models. The shapes of four coefficients are similar between the Durbin-Watson model and our benchmark model, but are not the same with the autoregressive model cut by one-lagged period. Second, the large sample case shows that the three models have the same expected values, however, the autoregressive model cut by one-lagged period explores different shapes of variance, skewed and kurtosis coefficients from the other two models. This implies that the large samples lead to the same expected values, 2(1 – ρ0, whatever the variance-covariance matrix of the errors is assumed. Finally, comparing with the two sample cases, the shape of each coefficient is almost the same, moreover, the autocorrelation coefficients are negatively related with expected values, are inverted-U related with variances, are cubic related with skewed coefficients, and are U related with kurtosis coefficients.
Lu, Fengbin; Qiao, Han; Wang, Shouyang; Lai, Kin Keung; Li, Yuze
2017-01-01
This paper proposes a new time-varying coefficient vector autoregressions (VAR) model, in which the coefficient is a linear function of dynamic lagged correlation. The proposed model allows for flexibility in choices of dynamic correlation models (e.g. dynamic conditional correlation generalized autoregressive conditional heteroskedasticity (GARCH) models, Markov-switching GARCH models and multivariate stochastic volatility models), which indicates that it can describe many types of time-varying causal effects. Time-varying causal relations between West Texas Intermediate (WTI) crude oil and the US Standard and Poor's 500 (S&P 500) stock markets are examined by the proposed model. The empirical results show that their causal relations evolve with time and display complex characters. Both positive and negative causal effects of the WTI on the S&P 500 in the subperiods have been found and confirmed by the traditional VAR models. Similar results have been obtained in the causal effects of S&P 500 on WTI. In addition, the proposed model outperforms the traditional VAR model. Copyright Â© 2016 Elsevier Ltd. All rights reserved.
Multiple Perspectives / Multiple Readings
Directory of Open Access Journals (Sweden)
Simon Biggs
2005-01-01
Full Text Available People experience things from their own physical point of view. What they see is usually a function of where they are and what physical attitude they adopt relative to the subject. With augmented vision (periscopes, mirrors, remote cameras, etc we are able to see things from places where we are not present. With time-shifting technologies, such as the video recorder, we can also see things from the past; a time and a place we may never have visited.In recent artistic work I have been exploring the implications of digital technology, interactivity and internet connectivity that allow people to not so much space/time-shift their visual experience of things but rather see what happens when everybody is simultaneously able to see what everybody else can see. This is extrapolated through the remote networking of sites that are actual installation spaces; where the physical movements of viewers in the space generate multiple perspectives, linked to other similar sites at remote locations or to other viewers entering the shared data-space through a web based version of the work.This text explores the processes involved in such a practice and reflects on related questions regarding the non-singularity of being and the sense of self as linked to time and place.
Dean, Roger T; Dunsmuir, William T M
2016-06-01
Many articles on perception, performance, psychophysiology, and neuroscience seek to relate pairs of time series through assessments of their cross-correlations. Most such series are individually autocorrelated: they do not comprise independent values. Given this situation, an unfounded reliance is often placed on cross-correlation as an indicator of relationships (e.g., referent vs. response, leading vs. following). Such cross-correlations can indicate spurious relationships, because of autocorrelation. Given these dangers, we here simulated how and why such spurious conclusions can arise, to provide an approach to resolving them. We show that when multiple pairs of series are aggregated in several different ways for a cross-correlation analysis, problems remain. Finally, even a genuine cross-correlation function does not answer key motivating questions, such as whether there are likely causal relationships between the series. Thus, we illustrate how to obtain a transfer function describing such relationships, informed by any genuine cross-correlations. We illustrate the confounds and the meaningful transfer functions by two concrete examples, one each in perception and performance, together with key elements of the R software code needed. The approach involves autocorrelation functions, the establishment of stationarity, prewhitening, the determination of cross-correlation functions, the assessment of Granger causality, and autoregressive model development. Autocorrelation also limits the interpretability of other measures of possible relationships between pairs of time series, such as mutual information. We emphasize that further complexity may be required as the appropriate analysis is pursued fully, and that causal intervention experiments will likely also be needed.
Simms, Laura; Engebretson, Mark; Clilverd, Mark; Rodger, Craig; Lessard, Marc; Gjerloev, Jesper; Reeves, Geoffrey
2018-05-01
Relativistic electron flux at geosynchronous orbit depends on enhancement and loss processes driven by ultralow frequency (ULF) Pc5, chorus, and electromagnetic ion cyclotron (EMIC) waves, seed electron flux, magnetosphere compression, the "Dst effect," and substorms, while solar wind inputs such as velocity, number density, and interplanetary magnetic field Bz drive these factors and thus correlate with flux. Distributed lag regression models show the time delay of highest influence of these factors on log10 high-energy electron flux (0.7-7.8 MeV, Los Alamos National Laboratory satellites). Multiple regression with an autoregressive term (flux persistence) allows direct comparison of the magnitude of each effect while controlling other correlated parameters. Flux enhancements due to ULF Pc5 and chorus waves are of equal importance. The direct effect of substorms on high-energy electron flux is strong, possibly due to injection of high-energy electrons by the substorms themselves. Loss due to electromagnetic ion cyclotron waves is less influential. Southward Bz shows only moderate influence when correlated processes are accounted for. Adding covariate compression effects (pressure and interplanetary magnetic field magnitude) allows wave-driven enhancements to be more clearly seen. Seed electrons (270 keV) are most influential at lower relativistic energies, showing that such a population must be available for acceleration. However, they are not accelerated directly to the highest energies. Source electrons (31.7 keV) show no direct influence when other factors are controlled. Their action appears to be indirect via the chorus waves they generate. Determination of specific effects of each parameter when studied in combination will be more helpful in furthering modeling work than studying them individually.
Klos, Anna; Pottiaux, Eric; Van Malderen, Roeland; Bock, Olivier; Bogusz, Janusz
2017-04-01
A synthetic benchmark dataset of Integrated Water Vapour (IWV) was created within the activity of "Data homogenisation" of sub-working group WG3 of COST ES1206 Action. The benchmark dataset was created basing on the analysis of IWV differences retrieved by Global Positioning System (GPS) International GNSS Service (IGS) stations using European Centre for Medium-Range Weather Forecats (ECMWF) reanalysis data (ERA-Interim). Having analysed a set of 120 series of IWV differences (ERAI-GPS) derived for IGS stations, we delivered parameters of a number of gaps and breaks for every certain station. Moreover, we estimated values of trends, significant seasonalities and character of residuals when deterministic model was removed. We tested five different noise models and found that a combination of white and autoregressive processes of first order describes the stochastic part with a good accuracy. Basing on this analysis, we performed Monte Carlo simulations of 25 years long data with two different types of noise: white as well as combination of white and autoregressive processes. We also added few strictly defined offsets, creating three variants of synthetic dataset: easy, less-complicated and fully-complicated. The 'Easy' dataset included seasonal signals (annual, semi-annual, 3 and 4 months if present for a particular station), offsets and white noise. The 'Less-complicated' dataset included above-mentioned, as well as the combination of white and first order autoregressive processes (AR(1)+WH). The 'Fully-complicated' dataset included, beyond above, a trend and gaps. In this research, we show the impact of manual homogenisation on the estimates of trend and its error. We also cross-compare the results for three above-mentioned datasets, as the synthetized noise type might have a significant influence on manual homogenisation. Therefore, it might mostly affect the values of trend and their uncertainties when inappropriately handled. In a future, the synthetic dataset
Directory of Open Access Journals (Sweden)
Cristhian Moreno-Chaparro
2011-12-01
Full Text Available This paper proposes a monthly electricity forecast method for the National Interconnected System (SIN of Colombia. The method preprocesses the time series using a Multiresolution Analysis (MRA with Discrete Wavelet Transform (DWT; a study for the selection of the mother wavelet and her order, as well as the level decomposition was carried out. Given that original series follows a non-linear behaviour, a neural nonlinear autoregressive (NAR model was used. The prediction was obtained by adding the forecast trend with the estimated obtained by the residual series combined with further components extracted from preprocessing. A bibliographic review of studies conducted internationally and in Colombia is included, in addition to references to investigations made with wavelet transform applied to electric energy prediction and studies reporting the use of NAR in prediction.
DEFF Research Database (Denmark)
Pinson, Pierre; Madsen, Henrik
2008-01-01
Better modelling and forecasting of very short-term power fluctuations at large offshore wind farms may significantly enhance control and management strategies of their power output. The paper introduces a new methodology for modelling and forecasting such very short-term fluctuations. The proposed...... consists in 1-step ahead forecasting exercise on time-series of wind generation with a time resolution of 10 minute. The quality of the introduced forecasting methodology and its interest for better understanding power fluctuations are finally discussed....... methodology is based on a Markov-switching autoregressive model with time-varying coefficients. An advantage of the method is that one can easily derive full predictive densities. The quality of this methodology is demonstrated from the test case of 2 large offshore wind farms in Denmark. The exercise...
Levine, Matthew E; Albers, David J; Hripcsak, George
2016-01-01
Time series analysis methods have been shown to reveal clinical and biological associations in data collected in the electronic health record. We wish to develop reliable high-throughput methods for identifying adverse drug effects that are easy to implement and produce readily interpretable results. To move toward this goal, we used univariate and multivariate lagged regression models to investigate associations between twenty pairs of drug orders and laboratory measurements. Multivariate lagged regression models exhibited higher sensitivity and specificity than univariate lagged regression in the 20 examples, and incorporating autoregressive terms for labs and drugs produced more robust signals in cases of known associations among the 20 example pairings. Moreover, including inpatient admission terms in the model attenuated the signals for some cases of unlikely associations, demonstrating how multivariate lagged regression models' explicit handling of context-based variables can provide a simple way to probe for health-care processes that confound analyses of EHR data.
Directory of Open Access Journals (Sweden)
Syarif Hidayatullah
2017-04-01
Full Text Available Penelitian ini membahas analisis risiko data runtun waktu dengan model Value at Risk- Asymmetric Power Autoregressive Conditional Heteroscedasticity (VaR-APARCHdalam pasar modal syariah. Metode yang digunakan dalam penelitian ini adalah penerapan kasus.Data yang digunakan adalah harga penutupan harian saham dalam Jakarta Islamic Index (JIIperiode 4 Maret 2013 sampai 8 April 2015.Model APARCH yang dipilih berdasarkan nilai Schwarz Criterion (SC.Langkah-langkah dalam penelitian ini adalah menguji kestasioneran data, mengidentifikasi model ARIMA,mengestimasi parameter model ARIMA, menguji diagnostik model ARIMA, mendeteksi ada tidaknya unsur ARCH atau unsur heteroskedastisitas, uji asimetris data saham, mengestimasi model APARCH, menguji diagnostik model APARCH, dan menghitung risiko dengan VaR-APARCH.Model terbaik yang dipilih adalah ARIMA ((3,0,0 dan APARCH (1,1. Model ini valid untuk menganalisis besar risiko investasi dalam jangka waktu 10 hari ke depan.
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Youngjae Chang
2016-11-01
Full Text Available We reviewed the data pertaining to the illegal dumping of municipal solid waste in the Republic of Korea for the year 2011 to check for the presence of spatial autoregression of illegal dumping among 224 basic autonomous units with reference to the “Broken Windows Theory.” We found that a pure neighborhood effect exists even after controlling for conventional variables that explain illegal dumping behavior. Interestingly, however, the neighborhood effect is largely offset by so-called relative price effect such that the number of illegal dumping reported in one region is in fact decreased as the price of vinyl bag for MSW in neighboring regions increases, which is seemingly against the implication of the “Broken Windows Theory.”
Directory of Open Access Journals (Sweden)
Zina Boussaada
2018-03-01
Full Text Available The solar photovoltaic (PV energy has an important place among the renewable energy sources. Therefore, several researchers have been interested by its modelling and its prediction, in order to improve the management of the electrical systems which include PV arrays. Among the existing techniques, artificial neural networks have proved their performance in the prediction of the solar radiation. However, the existing neural network models don’t satisfy the requirements of certain specific situations such as the one analyzed in this paper. The aim of this research work is to supply, with electricity, a race sailboat using exclusively renewable sources. The developed solution predicts the direct solar radiation on a horizontal surface. For that, a Nonlinear Autoregressive Exogenous (NARX neural network is used. All the specific conditions of the sailboat operation are taken into account. The results show that the best prediction performance is obtained when the training phase of the neural network is performed periodically.
Yu, Bing; Shu, Wenjun; Cao, Can
2018-05-01
A novel modeling method for aircraft engine using nonlinear autoregressive exogenous (NARX) models based on wavelet neural networks is proposed. The identification principle and process based on wavelet neural networks are studied, and the modeling scheme based on NARX is proposed. Then, the time series data sets from three types of aircraft engines are utilized to build the corresponding NARX models, and these NARX models are validated by the simulation. The results show that all the best NARX models can capture the original aircraft engine's dynamic characteristic well with the high accuracy. For every type of engine, the relative identification errors of its best NARX model and the component level model are no more than 3.5 % and most of them are within 1 %.
International Nuclear Information System (INIS)
Marseguerra, M.; Minoggio, S.; Rossi, A.; Zio, E.
1992-01-01
The correlated noise affecting many industrial plants under stationary or cyclo-stationary conditions - nuclear reactors included -has been successfully modeled by autoregressive moving average (ARMA) due to the versatility of this technique. The relatively recent neural network methods have similar features and much effort is being devoted to exploring their usefulness in forecasting and control. Identifying a signal by means of an ARMA model gives rise to the problem of selecting its correct order. Similar difficulties must be faced when applying neural network methods and, specifically, particular care must be given to the setting up of the appropriate network topology, the data normalization procedure and the learning code. In the present paper the capability of some neural networks of learning ARMA and seasonal ARMA processes is investigated. The results of the tested cases look promising since they indicate that the neural networks learn the underlying process with relative ease so that their forecasting capability may represent a convenient fault diagnosis tool. (Author)
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Teheni El Ghak
2017-05-01
Full Text Available In recent years, the changing economic and political environment in Tunisia led to a renewed interest on the drivers of foreign direct investment, given its potential important gains. In this study, we investigated the impact of various factors over the period 1980-2012. In doing this, three categories of determinants were considered: economic, political and sociocultural variables. Empirical findings drawn from the autoregressive distributed lag bounds testing approach show that variation in foreign direct investment inflow in the short-run and long-run is affected by the majority of variables considered, except exchange rate, urban population and gross domestic savings. As a matter of policy, it is essential that government should continue its efforts to create a macroeconomic environment which is attractive to foreign direct investment.
Porto, Markus; Roman, H Eduardo
2002-04-01
We consider autoregressive conditional heteroskedasticity (ARCH) processes in which the variance sigma(2)(y) depends linearly on the absolute value of the random variable y as sigma(2)(y) = a+b absolute value of y. While for the standard model, where sigma(2)(y) = a + b y(2), the corresponding probability distribution function (PDF) P(y) decays as a power law for absolute value of y-->infinity, in the linear case it decays exponentially as P(y) approximately exp(-alpha absolute value of y), with alpha = 2/b. We extend these results to the more general case sigma(2)(y) = a+b absolute value of y(q), with 0 history of the ARCH process is taken into account, the resulting PDF becomes a stretched exponential even for q = 1, with a stretched exponent beta = 2/3, in a much better agreement with the empirical data.
Jones, A. L.; Smart, P. L.
2005-08-01
Autoregressive modelling is used to investigate the internal structure of long-term (1935-1999) records of nitrate concentration for five karst springs in the Mendip Hills. There is a significant short term (1-2 months) positive autocorrelation at three of the five springs due to the availability of sufficient nitrate within the soil store to maintain concentrations in winter recharge for several months. The absence of short term (1-2 months) positive autocorrelation in the other two springs is due to the marked contrast in land use between the limestone and swallet parts of the catchment, rapid concentrated recharge from the latter causing short term switching in the dominant water source at the spring and thus fluctuating nitrate concentrations. Significant negative autocorrelation is evident at lags varying from 4 to 7 months through to 14-22 months for individual springs, with positive autocorrelation at 19-20 months at one site. This variable timing is explained by moderation of the exhaustion effect in the soil by groundwater storage, which gives longer residence times in large catchments and those with a dominance of diffuse flow. The lags derived from autoregressive modelling may therefore provide an indication of average groundwater residence times. Significant differences in the structure of the autocorrelation function for successive 10-year periods are evident at Cheddar Spring, and are explained by the effect the ploughing up of grasslands during the Second World War and increased fertiliser usage on available nitrogen in the soil store. This effect is moderated by the influence of summer temperatures on rates of mineralization, and of both summer and winter rainfall on the timing and magnitude of nitrate leaching. The pattern of nitrate leaching also appears to have been perturbed by the 1976 drought.
Cleaning Massive Sonar Point Clouds
DEFF Research Database (Denmark)
Arge, Lars Allan; Larsen, Kasper Green; Mølhave, Thomas
2010-01-01
We consider the problem of automatically cleaning massive sonar data point clouds, that is, the problem of automatically removing noisy points that for example appear as a result of scans of (shoals of) fish, multiple reflections, scanner self-reflections, refraction in gas bubbles, and so on. We...
DEFF Research Database (Denmark)
Pinson, Pierre; Madsen, Henrik
optimized is based on penalized maximum-likelihood, with exponential forgetting of past observations. MSAR models are then employed for 1-step-ahead point forecasting of 10-minute resolution time-series of wind power at two large offshore wind farms. They are favourably compared against persistence and Auto......Wind power production data at temporal resolutions of a few minutes exhibits successive periods with fluctuations of various dynamic nature and magnitude, which cannot be explained (so far) by the evolution of some explanatory variable. Our proposal is to capture this regime-switching behaviour......Regressive (AR) models. It is finally shown that the main interest of MSAR models lies in their ability to generate interval/density forecasts of significantly higher skill....
International Nuclear Information System (INIS)
Che Jinxing; Wang Jianzhou
2010-01-01
In this paper, we present the use of different mathematical models to forecast electricity price under deregulated power. A successful prediction tool of electricity price can help both power producers and consumers plan their bidding strategies. Inspired by that the support vector regression (SVR) model, with the ε-insensitive loss function, admits of the residual within the boundary values of ε-tube, we propose a hybrid model that combines both SVR and Auto-regressive integrated moving average (ARIMA) models to take advantage of the unique strength of SVR and ARIMA models in nonlinear and linear modeling, which is called SVRARIMA. A nonlinear analysis of the time-series indicates the convenience of nonlinear modeling, the SVR is applied to capture the nonlinear patterns. ARIMA models have been successfully applied in solving the residuals regression estimation problems. The experimental results demonstrate that the model proposed outperforms the existing neural-network approaches, the traditional ARIMA models and other hybrid models based on the root mean square error and mean absolute percentage error.
Taghvaei, Sajjad; Jahanandish, Mohammad Hasan; Kosuge, Kazuhiro
2017-01-01
Population aging of the societies requires providing the elderly with safe and dependable assistive technologies in daily life activities. Improving the fall detection algorithms can play a major role in achieving this goal. This article proposes a real-time fall prediction algorithm based on the acquired visual data of a user with walking assistive system from a depth sensor. In the lack of a coupled dynamic model of the human and the assistive walker a hybrid "system identification-machine learning" approach is used. An autoregressive-moving-average (ARMA) model is fitted on the time-series walking data to forecast the upcoming states, and a hidden Markov model (HMM) based classifier is built on the top of the ARMA model to predict falling in the upcoming time frames. The performance of the algorithm is evaluated through experiments with four subjects including an experienced physiotherapist while using a walker robot in five different falling scenarios; namely, fall forward, fall down, fall back, fall left, and fall right. The algorithm successfully predicts the fall with a rate of 84.72%.
Directory of Open Access Journals (Sweden)
Chieh-Fan Chen
2011-01-01
Full Text Available This study analyzed meteorological, clinical and economic factors in terms of their effects on monthly ED revenue and visitor volume. Monthly data from January 1, 2005 to September 30, 2009 were analyzed. Spearman correlation and cross-correlation analyses were performed to identify the correlation between each independent variable, ED revenue, and visitor volume. Autoregressive integrated moving average (ARIMA model was used to quantify the relationship between each independent variable, ED revenue, and visitor volume. The accuracies were evaluated by comparing model forecasts to actual values with mean absolute percentage of error. Sensitivity of prediction errors to model training time was also evaluated. The ARIMA models indicated that mean maximum temperature, relative humidity, rainfall, non-trauma, and trauma visits may correlate positively with ED revenue, but mean minimum temperature may correlate negatively with ED revenue. Moreover, mean minimum temperature and stock market index fluctuation may correlate positively with trauma visitor volume. Mean maximum temperature, relative humidity and stock market index fluctuation may correlate positively with non-trauma visitor volume. Mean maximum temperature and relative humidity may correlate positively with pediatric visitor volume, but mean minimum temperature may correlate negatively with pediatric visitor volume. The model also performed well in forecasting revenue and visitor volume.
Fisher, Aaron J; Reeves, Jonathan W; Chi, Cyrus
2016-07-01
Expanding on recently published methods, the current study presents an approach to estimating the dynamic, regulatory effect of the parasympathetic nervous system on heart period on a moment-to-moment basis. We estimated second-to-second variation in respiratory sinus arrhythmia (RSA) in order to estimate the contemporaneous and time-lagged relationships among RSA, interbeat interval (IBI), and respiration rate via vector autoregression. Moreover, we modeled these relationships at lags of 1 s to 10 s, in order to evaluate the optimal latency for estimating dynamic RSA effects. The IBI (t) on RSA (t-n) regression parameter was extracted from individual models as an operationalization of the regulatory effect of RSA on IBI-referred to as dynamic RSA (dRSA). Dynamic RSA positively correlated with standard averages of heart rate and negatively correlated with standard averages of RSA. We propose that dRSA reflects the active downregulation of heart period by the parasympathetic nervous system and thus represents a novel metric that provides incremental validity in the measurement of autonomic cardiac control-specifically, a method by which parasympathetic regulatory effects can be measured in process. © 2016 Society for Psychophysiological Research.
Directory of Open Access Journals (Sweden)
Jaime Buitrago
2017-01-01
Full Text Available Short-term load forecasting is crucial for the operations planning of an electrical grid. Forecasting the next 24 h of electrical load in a grid allows operators to plan and optimize their resources. The purpose of this study is to develop a more accurate short-term load forecasting method utilizing non-linear autoregressive artificial neural networks (ANN with exogenous multi-variable input (NARX. The proposed implementation of the network is new: the neural network is trained in open-loop using actual load and weather data, and then, the network is placed in closed-loop to generate a forecast using the predicted load as the feedback input. Unlike the existing short-term load forecasting methods using ANNs, the proposed method uses its own output as the input in order to improve the accuracy, thus effectively implementing a feedback loop for the load, making it less dependent on external data. Using the proposed framework, mean absolute percent errors in the forecast in the order of 1% have been achieved, which is a 30% improvement on the average error using feedforward ANNs, ARMAX and state space methods, which can result in large savings by avoiding commissioning of unnecessary power plants. The New England electrical load data are used to train and validate the forecast prediction.
International Nuclear Information System (INIS)
Geraldo, Issa Cherif; Bose, Tanmoy; Pekpe, Komi Midzodzi; Cassar, Jean-Philippe; Mohanty, A.R.; Paumel, Kévin
2014-01-01
Highlights: • The work deals with sodium boiling detection in a liquid metal fast breeder reactor. • The authors choose to use acoustic data instead of thermal data. • The method is designed to not to be disturbed by the environment noises. • A real time boiling detection methods are proposed in the paper. - Abstract: This paper deals with acoustic monitoring of sodium boiling in a liquid metal fast breeder reactor (LMFBR) based on auto regressive (AR) models which have low computational complexities. Some authors have used AR models for sodium boiling or sodium–water reaction detection. These works are based on the characterization of the difference between fault free condition and current functioning of the system. However, even in absence of faults, it is possible to observe a change in the AR models due to the change of operating mode of the LMFBR. This sets up the delicate problem of how to distinguish a change in operating mode in absence of faults and a change due to presence of faults. In this paper we propose a new approach for boiling detection based on the estimation of AR models on sliding windows. Afterwards, classification of the models into boiling or non-boiling models is made by comparing their coefficients by two statistical methods, multiple linear regression (LR) and support vectors machines (SVM). The proposed approach takes into account operating mode information in order to avoid false alarms. Experimental data include non-boiling background noise data collected from Phenix power plant (France) and provided by the CEA (Commissariat à l’Energie Atomique et aux énergies alternatives, France) and boiling condition data generated in laboratory. High boiling detection rates as well as low false alarms rates obtained on these experimental data show that the proposed method is efficient for boiling detection. Most importantly, it shows that the boiling phenomenon introduces a disturbance into the AR models that can be clearly detected
Bildirici, Melike; Ersin, Özgür Ömer
2018-01-01
The study aims to combine the autoregressive distributed lag (ARDL) cointegration framework with smooth transition autoregressive (STAR)-type nonlinear econometric models for causal inference. Further, the proposed STAR distributed lag (STARDL) models offer new insights in terms of modeling nonlinearity in the long- and short-run relations between analyzed variables. The STARDL method allows modeling and testing nonlinearity in the short-run and long-run parameters or both in the short- and long-run relations. To this aim, the relation between CO 2 emissions and economic growth rates in the USA is investigated for the 1800-2014 period, which is one of the largest data sets available. The proposed hybrid models are the logistic, exponential, and second-order logistic smooth transition autoregressive distributed lag (LSTARDL, ESTARDL, and LSTAR2DL) models combine the STAR framework with nonlinear ARDL-type cointegration to augment the linear ARDL approach with smooth transitional nonlinearity. The proposed models provide a new approach to the relevant econometrics and environmental economics literature. Our results indicated the presence of asymmetric long-run and short-run relations between the analyzed variables that are from the GDP towards CO 2 emissions. By the use of newly proposed STARDL models, the results are in favor of important differences in terms of the response of CO 2 emissions in regimes 1 and 2 for the estimated LSTAR2DL and LSTARDL models.
International Nuclear Information System (INIS)
Murphy, P.J.
1987-01-01
The Theory of Inflation, namely, that at some point the entropy content of the universe was greatly increased, has much promise. It may solve the puzzles of homogeneity and the creation of structure. However, no particle physics model has yet been found that can successfully drive inflation. The difficulty in satisfying the constraint that the isotropy of the microwave background places on the effective potential of prospective models is immense. In this work we have codified the requirements of such models in a most general form. We have carefully calculated the amounts of inflation the various problems of the Standard Model need for their solution. We have derived a completely model independent upper bond on the inflationary Hubble parameter. We have developed a general notation with which to probe the possibilities of Multiple Inflation. We have shown that only in very unlikely circumstances will any evidence of an earlier inflation, survive the de Sitter period of its successor. In particular, it is demonstrated that it is most unlikely that two bouts of inflation will yield high amplitudes of density perturbations on small scales and low amplitudes on large. We conclude that, while multiple inflation will be of great theoretical interest, it is unlikely to have any observational impact
Assessing CO2 emissions in China’s iron and steel industry: A dynamic vector autoregression model
International Nuclear Information System (INIS)
Xu, Bin; Lin, Boqiang
2016-01-01
Highlights: • We explore the driving forces of the iron and steel industry’s CO 2 emissions in China. • Energy efficiency plays a dominant role in reducing carbon dioxide emissions. • Urbanization has significant effect on CO 2 emissions due to mass real estate construction. • The role of economic growth in reducing emissions is more important than industrialization. - Abstract: Energy saving and carbon dioxide emission reduction in China is attracting increasing attention worldwide. At present, China is in the phase of rapid urbanization and industrialization, which is characterized by rapid growth of energy consumption and carbon dioxide (CO 2 ) emissions. China’s steel industry is highly energy-consuming and pollution-intensive. Between 1980 and 2013, the carbon dioxide emissions in China’s steel industry increased approximately 11 times, with an average annual growth rate of 8%. Identifying the drivers of carbon dioxide emissions in the iron and steel industry is vital for developing effective environmental policies. This study uses Vector Autoregressive model to analyze the influencing factors of the changes in carbon dioxide emissions in the industry. The results show that energy efficiency plays a dominant role in reducing carbon dioxide emissions. Urbanization also has significant effect on CO 2 emissions because of mass urban infrastructure and real estate construction. Economic growth has more impact on emission reduction than industrialization due to the massive fixed asset investment and industrial energy optimization. These findings are important for the relevant authorities in China in developing appropriate energy policy and planning for the iron and steel industry.
International Nuclear Information System (INIS)
Xu, Bin; Lin, Boqiang
2015-01-01
Energy saving and carbon dioxide emission reduction in China is attracting increasing attention worldwide. At present, China is in the phase of rapid urbanization and industrialization, which is characterized by rapid growth of energy consumption. China's transport sector is highly energy-consuming and pollution-intensive. Between 1980 and 2012, the carbon dioxide emissions in China's transport sector increased approximately 9.7 times, with an average annual growth rate of 7.4%. Identifying the driving forces of the increase in carbon dioxide emissions in the transport sector is vital to developing effective environmental policies. This study uses Vector Autoregressive model to analyze the influencing factors of the changes in carbon dioxide emissions in the sector. The results show that energy efficiency plays a dominant role in reducing carbon dioxide emissions. Private vehicles have more impact on emission reduction than cargo turnover due to the surge in private car population and its low energy efficiency. Urbanization also has significant effect on carbon dioxide emissions because of large-scale population movements and the transformation of the industrial structure. These findings are important for the relevant authorities in China in developing appropriate energy policy and planning for the transport sector. - Highlights: • The driving forces of CO 2 emissions in China's transport sector were investigated. • Energy efficiency plays a dominant role in reducing carbon dioxide emissions. • Urbanization has significant effect on CO 2 emissions due to large-scale migration. • The role of private cars in reducing emissions is more important than cargo turnover
Non-contact video-based vital sign monitoring using ambient light and auto-regressive models
International Nuclear Information System (INIS)
Tarassenko, L; Villarroel, M; Guazzi, A; Jorge, J; Clifton, D A; Pugh, C
2014-01-01
Remote sensing of the reflectance photoplethysmogram using a video camera typically positioned 1 m away from the patient’s face is a promising method for monitoring the vital signs of patients without attaching any electrodes or sensors to them. Most of the papers in the literature on non-contact vital sign monitoring report results on human volunteers in controlled environments. We have been able to obtain estimates of heart rate and respiratory rate and preliminary results on changes in oxygen saturation from double-monitored patients undergoing haemodialysis in the Oxford Kidney Unit. To achieve this, we have devised a novel method of cancelling out aliased frequency components caused by artificial light flicker, using auto-regressive (AR) modelling and pole cancellation. Secondly, we have been able to construct accurate maps of the spatial distribution of heart rate and respiratory rate information from the coefficients of the AR model. In stable sections with minimal patient motion, the mean absolute error between the camera-derived estimate of heart rate and the reference value from a pulse oximeter is similar to the mean absolute error between two pulse oximeter measurements at different sites (finger and earlobe). The activities of daily living affect the respiratory rate, but the camera-derived estimates of this parameter are at least as accurate as those derived from a thoracic expansion sensor (chest belt). During a period of obstructive sleep apnoea, we tracked changes in oxygen saturation using the ratio of normalized reflectance changes in two colour channels (red and blue), but this required calibration against the reference data from a pulse oximeter. (paper)
Directory of Open Access Journals (Sweden)
Jinbao Zhao
2018-02-01
Full Text Available Transportation is generally perceived as a catalyst for economic development. This has been highlighted in previous studies. However, less attention has been paid to examine the relationship between economy and transport demand by exploring spatially cross-sectional data, especially for countries with significant regional economic imbalance, like China. In this article, we assess the economic influence of intercity multimodal transport demand at the prefecture level in China. Spatial autoregressive regression models are used to examine the impact of transport demand on economy by deep analysis of transport modes (land, air, and water and regions (eastern, central, and western. Through contrasting results from spatial lag model and spatial error model with those from the ordinary least square, this study finds that the estimation results can become more accurate by controlling for spatial autocorrelation, especially at the national level. Through rigorous analysis it is identified that except for water passenger traffic, all other intercity transport demand significantly contribute to a city’s economic development level in gross domestic product. In particular, air transport demands distribute more evenly and are estimated with the highest beta coefficients at both national and regional levels. In addition, the beta coefficients for land, air and water transportation are estimated with different magnitudes and significances at the national and regional levels. This study contributes to the ongoing discussion on the relationship between intercity multimodal transport demand and economic development level. Findings from this paper provide planning makers with valid and efficient strategies to better develop the economy by leveraging the special “⊣” cluster pattern of economic development and the benefits of air transportation.
Directory of Open Access Journals (Sweden)
Shuntaro Okazaki
Full Text Available People's behaviors synchronize. It is difficult, however, to determine whether synchronized behaviors occur in a mutual direction--two individuals influencing one another--or in one direction--one individual leading the other, and what the underlying mechanism for synchronization is. To answer these questions, we hypothesized a non-leader-follower postural sway synchronization, caused by a reciprocal visuo-postural feedback system operating on pairs of individuals, and tested that hypothesis both experimentally and via simulation. In the behavioral experiment, 22 participant pairs stood face to face either 20 or 70 cm away from each other wearing glasses with or without vision blocking lenses. The existence and direction of visual information exchanged between pairs of participants were systematically manipulated. The time series data for the postural sway of these pairs were recorded and analyzed with cross correlation and causality. Results of cross correlation showed that postural sway of paired participants was synchronized, with a shorter time lag when participant pairs could see one another's head motion than when one of the participants was blindfolded. In addition, there was less of a time lag in the observed synchronization when the distance between participant pairs was smaller. As for the causality analysis, noise contribution ratio (NCR, the measure of influence using a multivariate autoregressive model, was also computed to identify the degree to which one's postural sway is explained by that of the other's and how visual information (sighted vs. blindfolded interacts with paired participants' postural sway. It was found that for synchronization to take place, it is crucial that paired participants be sighted and exert equal influence on one another by simultaneously exchanging visual information. Furthermore, a simulation for the proposed system with a wider range of visual input showed a pattern of results similar to the
Directory of Open Access Journals (Sweden)
Ali Akbar Akbari
2014-08-01
Full Text Available Introduction In order to improve the quality of life of amputees, biomechatronic researchers and biomedical engineers have been trying to use a combination of various techniques to provide suitable rehabilitation systems. Diverse biomedical signals, acquired from a specialized organ or cell system, e.g., the nervous system, are the driving force for the whole system. Electromyography(EMG, as an experimental technique,is concerned with the development, recording, and analysis of myoelectric signals. EMG-based research is making progress in the development of simple, robust, user-friendly, and efficient interface devices for the amputees. Materials and Methods Prediction of muscular activity and motion patterns is a common, practical problem in prosthetic organs. Recurrent neural network (RNN models are not only applicable for the prediction of time series, but are also commonly used for the control of dynamical systems. The prediction can be assimilated to identification of a dynamic process. An architectural approach of RNN with embedded memory is Nonlinear Autoregressive Exogenous (NARX model, which seems to be suitable for dynamic system applications. Results Performance of NARX model is verified for several chaotic time series, which are applied as input for the neural network. The results showed that NARX has the potential to capture the model of nonlinear dynamic systems. The R-value and MSE are and , respectively. Conclusion EMG signals of deltoid and pectoralis major muscles are the inputs of the NARX network. It is possible to obtain EMG signals of muscles in other arm motions to predict the lost functions of the absent arm in above-elbow amputees, using NARX model.
Wang, Yiyi; Kockelman, Kara M
2013-11-01
This work examines the relationship between 3-year pedestrian crash counts across Census tracts in Austin, Texas, and various land use, network, and demographic attributes, such as land use balance, residents' access to commercial land uses, sidewalk density, lane-mile densities (by roadway class), and population and employment densities (by type). The model specification allows for region-specific heterogeneity, correlation across response types, and spatial autocorrelation via a Poisson-based multivariate conditional auto-regressive (CAR) framework and is estimated using Bayesian Markov chain Monte Carlo methods. Least-squares regression estimates of walk-miles traveled per zone serve as the exposure measure. Here, the Poisson-lognormal multivariate CAR model outperforms an aspatial Poisson-lognormal multivariate model and a spatial model (without cross-severity correlation), both in terms of fit and inference. Positive spatial autocorrelation emerges across neighborhoods, as expected (due to latent heterogeneity or missing variables that trend in space, resulting in spatial clustering of crash counts). In comparison, the positive aspatial, bivariate cross correlation of severe (fatal or incapacitating) and non-severe crash rates reflects latent covariates that have impacts across severity levels but are more local in nature (such as lighting conditions and local sight obstructions), along with spatially lagged cross correlation. Results also suggest greater mixing of residences and commercial land uses is associated with higher pedestrian crash risk across different severity levels, ceteris paribus, presumably since such access produces more potential conflicts between pedestrian and vehicle movements. Interestingly, network densities show variable effects, and sidewalk provision is associated with lower severe-crash rates. Copyright © 2013 Elsevier Ltd. All rights reserved.
Pachhai, S.; Masters, G.; Laske, G.
2017-12-01
Earth's normal-mode spectra are crucial to studying the long wavelength structure of the Earth. Such observations have been used extensively to estimate "splitting coefficients" which, in turn, can be used to determine the three-dimensional velocity and density structure. Most past studies apply a non-linear iterative inversion to estimate the splitting coefficients which requires that the earthquake source is known. However, it is challenging to know the source details, particularly for big events as used in normal-mode analyses. Additionally, the final solution of the non-linear inversion can depend on the choice of damping parameter and starting model. To circumvent the need to know the source, a two-step linear inversion has been developed and successfully applied to many mantle and core sensitive modes. The first step takes combinations of the data from a single event to produce spectra known as "receiver strips". The autoregressive nature of the receiver strips can then be used to estimate the structure coefficients without the need to know the source. Based on this approach, we recently employed a neighborhood algorithm to measure the splitting coefficients for an isolated inner-core sensitive mode (13S2). This approach explores the parameter space efficiently without any need of regularization and finds the structure coefficients which best fit the observed strips. Here, we implement a Bayesian approach to data collected for earthquakes from early 2000 and more recent. This approach combines the data (through likelihood) and prior information to provide rigorous parameter values and their uncertainties for both isolated and coupled modes. The likelihood function is derived from the inferred errors of the receiver strips which allows us to retrieve proper uncertainties. Finally, we apply model selection criteria that balance the trade-offs between fit (likelihood) and model complexity to investigate the degree and type of structure (elastic and anelastic
Sood, Mehak; Besson, Pierre; Muthalib, Makii; Jindal, Utkarsh; Perrey, Stephane; Dutta, Anirban; Hayashibe, Mitsuhiro
2016-12-01
Transcranial direct current stimulation (tDCS) has been shown to perturb both cortical neural activity and hemodynamics during (online) and after the stimulation, however mechanisms of these tDCS-induced online and after-effects are not known. Here, online resting-state spontaneous brain activation may be relevant to monitor tDCS neuromodulatory effects that can be measured using electroencephalography (EEG) in conjunction with near-infrared spectroscopy (NIRS). We present a Kalman Filter based online parameter estimation of an autoregressive (ARX) model to track the transient coupling relation between the changes in EEG power spectrum and NIRS signals during anodal tDCS (2mA, 10min) using a 4×1 ring high-definition montage. Our online ARX parameter estimation technique using the cross-correlation between log (base-10) transformed EEG band-power (0.5-11.25Hz) and NIRS oxy-hemoglobin signal in the low frequency (≤0.1Hz) range was shown in 5 healthy subjects to be sensitive to detect transient EEG-NIRS coupling changes in resting-state spontaneous brain activation during anodal tDCS. Conventional sliding window cross-correlation calculations suffer a fundamental problem in computing the phase relationship as the signal in the window is considered time-invariant and the choice of the window length and step size are subjective. Here, Kalman Filter based method allowed online ARX parameter estimation using time-varying signals that could capture transients in the coupling relationship between EEG and NIRS signals. Our new online ARX model based tracking method allows continuous assessment of the transient coupling between the electrophysiological (EEG) and the hemodynamic (NIRS) signals representing resting-state spontaneous brain activation during anodal tDCS. Published by Elsevier B.V.
Energy Technology Data Exchange (ETDEWEB)
Barnthouse, L. W.; Cannon, J. B.; Christensen, S. G.
1977-07-01
Because of the location of the Bowline, Roseton, and Indian Point power generating facilities in the low-salinity zone of the Hudson estuary, operation of these plants with the present once-through cooling systems will adversely influence the fish populations that use the area for spawning and initial periods of growth and development. Recruitment rates and standing crops of several fish species may be lowered in response to the increased mortality caused by entrainment of nonscreenable eggs and larvae and by impingement of screenable young of the year. Entrainment and impingement data are particularly relevant for assessing which fish species have the greatest potential for being adversely affected by operation of Bowline, Roseton, and Indian Point with once-through cooling. These data from each of these three plants suggest that the six species that merit the greatest consideration are striped bass, white perch, tomcod, alewife, blueback herring, and bay anchovy. Two points of view are available for assessing the relative importance of the fish species in the Hudson River. From the fisheries point of view, the only two species of major importance are striped bass and shad. From the fish-community and ecosystem point of view, the dominant species, as determined by seasonal and regional standing crops (in numbers and biomass per hectare), are the six species most commonly entrained and impinged, namely, striped bass, white perch, tomcod, alewife, blueback herring, and anchovy.
Directory of Open Access Journals (Sweden)
Githure John I
2009-09-01
Full Text Available Abstract Background Autoregressive regression coefficients for Anopheles arabiensis aquatic habitat models are usually assessed using global error techniques and are reported as error covariance matrices. A global statistic, however, will summarize error estimates from multiple habitat locations. This makes it difficult to identify where there are clusters of An. arabiensis aquatic habitats of acceptable prediction. It is therefore useful to conduct some form of spatial error analysis to detect clusters of An. arabiensis aquatic habitats based on uncertainty residuals from individual sampled habitats. In this research, a method of error estimation for spatial simulation models was demonstrated using autocorrelation indices and eigenfunction spatial filters to distinguish among the effects of parameter uncertainty on a stochastic simulation of ecological sampled Anopheles aquatic habitat covariates. A test for diagnostic checking error residuals in an An. arabiensis aquatic habitat model may enable intervention efforts targeting productive habitats clusters, based on larval/pupal productivity, by using the asymptotic distribution of parameter estimates from a residual autocovariance matrix. The models considered in this research extends a normal regression analysis previously considered in the literature. Methods Field and remote-sampled data were collected during July 2006 to December 2007 in Karima rice-village complex in Mwea, Kenya. SAS 9.1.4® was used to explore univariate statistics, correlations, distributions, and to generate global autocorrelation statistics from the ecological sampled datasets. A local autocorrelation index was also generated using spatial covariance parameters (i.e., Moran's Indices in a SAS/GIS® database. The Moran's statistic was decomposed into orthogonal and uncorrelated synthetic map pattern components using a Poisson model with a gamma-distributed mean (i.e. negative binomial regression. The eigenfunction
Empirical Vector Autoregressive Modeling
M. Ooms (Marius)
1993-01-01
textabstractChapter 2 introduces the baseline version of the VAR model, with its basic statistical assumptions that we examine in the sequel. We first check whether the variables in the VAR can be transformed to meet these assumptions. We analyze the univariate characteristics of the series.
Halina, Marta; Liebal, Katja; Tomasello, Michael
2018-01-01
Captive great apes regularly use pointing gestures in their interactions with humans. However, the precise function of this gesture is unknown. One possibility is that apes use pointing primarily to direct attention (as in "please look at that"); another is that they point mainly as an action request (such as "can you give that to me?"). We investigated these two possibilities here by examining how the looking behavior of recipients affects pointing in chimpanzees (Pan troglodytes) and bonobos (Pan paniscus). Upon pointing to food, subjects were faced with a recipient who either looked at the indicated object (successful-look) or failed to look at the indicated object (failed-look). We predicted that, if apes point primarily to direct attention, subjects would spend more time pointing in the failed-look condition because the goal of their gesture had not been met. Alternatively, we expected that, if apes point primarily to request an object, subjects would not differ in their pointing behavior between the successful-look and failed-look conditions because these conditions differed only in the looking behavior of the recipient. We found that subjects did differ in their pointing behavior across the successful-look and failed-look conditions, but contrary to our prediction subjects spent more time pointing in the successful-look condition. These results suggest that apes are sensitive to the attentional states of gestural recipients, but their adjustments are aimed at multiple goals. We also found a greater number of individuals with a strong right-hand than left-hand preference for pointing.
Suharsono, Agus; Aziza, Auliya; Pramesti, Wara
2017-12-01
Capital markets can be an indicator of the development of a country's economy. The presence of capital markets also encourages investors to trade; therefore investors need information and knowledge of which shares are better. One way of making decisions for short-term investments is the need for modeling to forecast stock prices in the period to come. Issue of stock market-stock integration ASEAN is very important. The problem is that ASEAN does not have much time to implement one market in the economy, so it would be very interesting if there is evidence whether the capital market in the ASEAN region, especially the countries of Indonesia, Malaysia, Philippines, Singapore and Thailand deserve to be integrated or still segmented. Furthermore, it should also be known and proven What kind of integration is happening: what A capital market affects only the market Other capital, or a capital market only Influenced by other capital markets, or a Capital market as well as affecting as well Influenced by other capital markets in one ASEAN region. In this study, it will compare forecasting of Indonesian share price (IHSG) with neighboring countries (ASEAN) including developed and developing countries such as Malaysia (KLSE), Singapore (SGE), Thailand (SETI), Philippines (PSE) to find out which stock country the most superior and influential. These countries are the founders of ASEAN and share price index owners who have close relations with Indonesia in terms of trade, especially exports and imports. Stock price modeling in this research is using multivariate time series analysis that is VAR (Vector Autoregressive) and VECM (Vector Error Correction Modeling). VAR and VECM models not only predict more than one variable but also can see the interrelations between variables with each other. If the assumption of white noise is not met in the VAR modeling, then the cause can be assumed that there is an outlier. With this modeling will be able to know the pattern of relationship
Common pitfalls in statistical analysis: The perils of multiple testing
Ranganathan, Priya; Pramesh, C. S.; Buyse, Marc
2016-01-01
Multiple testing refers to situations where a dataset is subjected to statistical testing multiple times - either at multiple time-points or through multiple subgroups or for multiple end-points. This amplifies the probability of a false-positive finding. In this article, we look at the consequences of multiple testing and explore various methods to deal with this issue. PMID:27141478
Directory of Open Access Journals (Sweden)
Richard J Chen, MD
2018-04-01
pacing. Since this patient was asymptomatic, he was given 2gm of magnesium sulfate and placed on an amiodarone infusion, after which Tdp terminated with a resulting sinus rhythm. AICD interrogation showed multiple episodes of ventricular fibrillation. The patient was admitted for further management and to determine why his AICD was not functioning properly. Topics: EKG, ECG, cardiology, ventricular tachycardia, arrhythmia, Torsades de Pointes
Poisson point processes imaging, tracking, and sensing
Streit, Roy L
2010-01-01
This overview of non-homogeneous and multidimensional Poisson point processes and their applications features mathematical tools and applications from emission- and transmission-computed tomography to multiple target tracking and distributed sensor detection.
... indwelling catheter Osteoporosis or thinning of the bones Pressure sores Side effects of medicines used to treat the ... Daily bowel care program Multiple sclerosis - discharge Preventing pressure ulcers Swallowing problems Images Multiple sclerosis MRI of the ...
Directory of Open Access Journals (Sweden)
Hualin Xie
2013-12-01
Full Text Available Ecological land is one of the key resources and conditions for the survival of humans because it can provide ecosystem services and is particularly important to public health and safety. It is extremely valuable for effective ecological management to explore the evolution mechanisms of ecological land. Based on spatial statistical analyses, we explored the spatial disparities and primary potential drivers of ecological land change in the Poyang Lake Eco-economic Zone of China. The results demonstrated that the global Moran’s I value is 0.1646 during the 1990 to 2005 time period and indicated signiﬁcant positive spatial correlation (p < 0.05. The results also imply that the clustering trend of ecological land changes weakened in the study area. Some potential driving forces were identified by applying the spatial autoregressive model in this study. The results demonstrated that the higher economic development level and industrialization rate were the main drivers for the faster change of ecological land in the study area. This study also tested the superiority of the spatial autoregressive model to study the mechanisms of ecological land change by comparing it with the traditional linear regressive model.
Xie, Hualin; Liu, Zhifei; Wang, Peng; Liu, Guiying; Lu, Fucai
2013-01-01
Ecological land is one of the key resources and conditions for the survival of humans because it can provide ecosystem services and is particularly important to public health and safety. It is extremely valuable for effective ecological management to explore the evolution mechanisms of ecological land. Based on spatial statistical analyses, we explored the spatial disparities and primary potential drivers of ecological land change in the Poyang Lake Eco-economic Zone of China. The results demonstrated that the global Moran’s I value is 0.1646 during the 1990 to 2005 time period and indicated significant positive spatial correlation (p ecological land changes weakened in the study area. Some potential driving forces were identified by applying the spatial autoregressive model in this study. The results demonstrated that the higher economic development level and industrialization rate were the main drivers for the faster change of ecological land in the study area. This study also tested the superiority of the spatial autoregressive model to study the mechanisms of ecological land change by comparing it with the traditional linear regressive model. PMID:24384778
Xie, Hualin; Liu, Zhifei; Wang, Peng; Liu, Guiying; Lu, Fucai
2013-12-31
Ecological land is one of the key resources and conditions for the survival of humans because it can provide ecosystem services and is particularly important to public health and safety. It is extremely valuable for effective ecological management to explore the evolution mechanisms of ecological land. Based on spatial statistical analyses, we explored the spatial disparities and primary potential drivers of ecological land change in the Poyang Lake Eco-economic Zone of China. The results demonstrated that the global Moran's I value is 0.1646 during the 1990 to 2005 time period and indicated signiﬁcant positive spatial correlation (p ecological land changes weakened in the study area. Some potential driving forces were identified by applying the spatial autoregressive model in this study. The results demonstrated that the higher economic development level and industrialization rate were the main drivers for the faster change of ecological land in the study area. This study also tested the superiority of the spatial autoregressive model to study the mechanisms of ecological land change by comparing it with the traditional linear regressive model.
Directory of Open Access Journals (Sweden)
A. A. Bosov
2015-04-01
Full Text Available Purpose. The development of complicated techniques of production and management processes, information systems, computer science, applied objects of systems theory and others requires improvement of mathematical methods, new approaches for researches of application systems. And the variety and diversity of subject systems makes necessary the development of a model that generalizes the classical sets and their development – sets of sets. Multiple objects unlike sets are constructed by multiple structures and represented by the structure and content. The aim of the work is the analysis of multiple structures, generating multiple objects, the further development of operations on these objects in application systems. Methodology. To achieve the objectives of the researches, the structure of multiple objects represents as constructive trio, consisting of media, signatures and axiomatic. Multiple object is determined by the structure and content, as well as represented by hybrid superposition, composed of sets, multi-sets, ordered sets (lists and heterogeneous sets (sequences, corteges. Findings. In this paper we study the properties and characteristics of the components of hybrid multiple objects of complex systems, proposed assessments of their complexity, shown the rules of internal and external operations on objects of implementation. We introduce the relation of arbitrary order over multiple objects, we define the description of functions and display on objects of multiple structures. Originality.In this paper we consider the development of multiple structures, generating multiple objects.Practical value. The transition from the abstract to the subject of multiple structures requires the transformation of the system and multiple objects. Transformation involves three successive stages: specification (binding to the domain, interpretation (multiple sites and particularization (goals. The proposed describe systems approach based on hybrid sets
A travel time forecasting model based on change-point detection method
LI, Shupeng; GUANG, Xiaoping; QIAN, Yongsheng; ZENG, Junwei
2017-06-01
Travel time parameters obtained from road traffic sensors data play an important role in traffic management practice. A travel time forecasting model is proposed for urban road traffic sensors data based on the method of change-point detection in this paper. The first-order differential operation is used for preprocessing over the actual loop data; a change-point detection algorithm is designed to classify the sequence of large number of travel time data items into several patterns; then a travel time forecasting model is established based on autoregressive integrated moving average (ARIMA) model. By computer simulation, different control parameters are chosen for adaptive change point search for travel time series, which is divided into several sections of similar state.Then linear weight function is used to fit travel time sequence and to forecast travel time. The results show that the model has high accuracy in travel time forecasting.
Visualizing Matrix Multiplication
Daugulis, Peteris; Sondore, Anita
2018-01-01
Efficient visualizations of computational algorithms are important tools for students, educators, and researchers. In this article, we point out an innovative visualization technique for matrix multiplication. This method differs from the standard, formal approach by using block matrices to make computations more visual. We find this method a…
2004-01-01
CERN - Globe of Science and Innovation 20 and 21 October Acrobatics, mime, a cappella singing, projections of images, a magical setting... a host of different tools of a grandeur matching that of the Universe they relate. A camera makes a massive zoom out to reveal the multiple dimensions of Nature. Freeze the frame: half way between the infinitesimally small and the infinitesimally large, a man suspends his everyday life (hence the title "Point de Suspension", which refers to the three dots at the end of an uncompleted sentence) to take a glimpse of the place he occupies in the great history of the Universe. An unusual perspective on what it means to be a human being... This wondrous show in the Globe of Science and Innovation, specially created by the Miméscope* company for the official ceremony marking CERN's fiftieth anniversary, is a gift from the Government of the Republic and Canton of Geneva, which also wishes to share this moment of wonder with the local population. There will be three perfo...
2004-01-01
http://www.cern.ch/cern50/ CERN - Globe of Science and Innovation 20 and 21 October Acrobatics, mime, a cappella singing, projections of images, a magical setting... a host of different tools of a grandeur matching that of the Universe they relate. A camera makes a massive zoom out to reveal the multiple dimensions of Nature. Freeze the frame: half way between the infinitesimally small and the infinitesimally large, a man suspends his everyday life (hence the title "Point de Suspension", which refers to the three dots at the end of an uncompleted sentence) to take a glimpse of the place he occupies in the great history of the Universe. An unusual perspective on what it means to be a human being... This wondrous show in the Globe of Science and Innovation, specially created by the Miméscope* company for the official ceremony marking CERN's fiftieth anniversary, is a gift from the Government of the Republic and Canton of Geneva, which also wishes to share this moment of wonder with the local pop...
2004-01-01
CERN - Globe of Science and Innovation 20 and 21 October Acrobatics, mime, a cappella singing, projections of images, a magical setting... a host of different tools of a grandeur matching that of the Universe they relate. A camera makes a massive zoom out to reveal the multiple dimensions of Nature. Freeze the frame: half way between the infinitesimally small and the infinitesimally large, a man suspends his everyday life (hence the title "Point de Suspension", which refers to the three dots at the end of an uncompleted sentence) to take a glimpse of the place he occupies in the great history of the Universe. An unusual perspective on what it means to be a human being... This spectacle in the Globe of Science and Innovation, specially created by the Miméscope* company for the official ceremony marking CERN's fiftieth anniversary, is a gift from the Government of the Republic and Canton of Geneva, which also wishes to share this moment of wonder with the local population. There will be three performances for...
Directory of Open Access Journals (Sweden)
Antonio Paulo Nassar Junior
2005-03-01
Full Text Available A esclerose múltipla (EM é afecção neurológica que acomete principalmente adultos jovens e evolui, geralmente, para graus variados de incapacidade física dos pacientes. Assim, a abordagem destes pacientes faz com que o médico depare-se com diversas questões éticas. OBJETIVO: Identificar as percepções de médicos e pacientes sobre a doença e, com isso, melhorar o relacionamento médico-paciente. MÉTODO: Foram feitos dois questionários, um respondido por 44 médicos e outro, por 103 pacientes, abordando questões sobre o diagnóstico e a conduta na EM. RESULTADOS: 96,1% dos pacientes sabiam seu diagnóstico, os outros gostariam de sabê-lo. Daqueles, 74,7% achavam que a forma contada foi correta e 90,9% que o médico é que deve contá-lo. Os sintomas que mais os incomodam são a fadiga (29,1% e os déficits motores (28,1%. Por outro lado, 68% dos pacientes afirmaram sofrer com a doença. O motivo mais importante para os médicos contarem o diagnóstico foi para melhorar a adesão ao tratamento (56,8%. A presença de um familiar neste momento é exigida por 54,6% dos médicos. Quando perguntados sobre as orientações de uma gravidez, 50% dos médicos não responderam adequadamente. Finalmente, 50% dos médicos manifestaram-se de forma contrária às terapias complementares. CONCLUSÃO: Os pacientes querem saber seu diagnóstico e o médico deve contá-lo da forma mais adequada e dar mais informações. Um debate sobre cuidados paliativos também faz-se necessário.Multiple sclerosis (MS is a neurologic disorder that mostly affects young adults and can usually evolute to physical disability. Thus, caring patients with MS brings many ethic questions for the physician. OBJECTIVE: To identify physicians and patients' perceptions about the illness and so improve doctor-patient relationship. METHOD: It was made two different questionnaires, one for patients and another for physicians, 103 patients and 44 physicians answered them. RESULTS
International Nuclear Information System (INIS)
Grunwald, I.Q.; Kuehn, A.L.; Backens, M.; Papanagiotou, P.; Shariat, K.; Kostopoulos, P.
2008-01-01
Multiple sclerosis is the most common chronic inflammatory disease of myelin with interspersed lesions in the white matter of the central nervous system. Magnetic resonance imaging (MRI) plays a key role in the diagnosis and monitoring of white matter diseases. This article focuses on key findings in multiple sclerosis as detected by MRI. (orig.) [de
Rehabilitation and multiple sclerosis
DEFF Research Database (Denmark)
Dalgas, Ulrik
2011-01-01
In a chronic and disabling disease like multiple sclerosis, rehabilitation becomes of major importance in the preservation of physical, psychological and social functioning. Approximately 80% of patients have multiple sclerosis for more than 35 years and most will develop disability at some point......, a paradigm shift is taking place and it is now increasingly acknowledged that exercise therapy is both safe and beneficial. Robot-assisted training is also attracting attention in multiple sclerosis rehabilitation. Several sophisticated commercial robots exist, but so far the number of scientific studies...... promising. This drug has been shown to improve walking ability in some patients with multiple sclerosis, associated with a reduction of patients' self-reported ambulatory disability. Rehabilitation strategies involving these different approaches, or combinations of them, may be of great use in improving...
Unbounded critical points for a class of lower semicontinuous functionals
Pellacci, Benedetta; Squassina, Marco
2003-01-01
In this paper we prove existence and multiplicity results of unbounded critical points for a general class of weakly lower semicontinuous functionals. We will apply a suitable nonsmooth critical point theory.
R.W. Strachan (Rodney); H.K. van Dijk (Herman)
2007-01-01
textabstractA Bayesian model averaging procedure is presented within the class of vector autoregressive (VAR) processes and applied to two empirical issues. First, stability of the "Great Ratios" in U.S. macro-economic time series is investigated, together with the presence and e¤ects of permanent
Copeland, A R
1989-09-01
A study of multiple homicides or multiple deaths involving a solitary incident of violence by another individual was performed on the case files of the Office of the Medical Examiner of Metropolitan Dade County in Miami, Florida, during 1983-1987. A total of 107 multiple homicides were studied: 88 double, 17 triple, one quadruple, and one quintuple. The 236 victims were analyzed regarding age, race, sex, cause of death, toxicologic data, perpetrator, locale of the incident, and reason for the incident. This article compares this type of slaying with other types of homicide including those perpetrated by serial killers. Suggestions for future research in this field are offered.
Feldman, Jo
2018-01-01
Have teachers become too dependent on points? This article explores educators' dependency on their points systems, and the ways that points can distract teachers from really analyzing students' capabilities and achievements. Feldman argues that using a more subjective grading system can help illuminate crucial information about students and what…
2006-01-01
In 2012, 21 of the 27 EU Member States had some form of demerit points system. In theory, demerit points systems contribute to road safety through three mechanisms: 1) prevention of unsafe behaviour through the risk of receiving penalty points, 2) selection and suspension of the most frequent
Valenza, Gaetano; Citi, Luca; Barbieri, Riccardo
2013-01-01
We report an exemplary study of instantaneous assessment of cardiovascular dynamics performed using point-process nonlinear models based on Laguerre expansion of the linear and nonlinear Wiener-Volterra kernels. As quantifiers, instantaneous measures such as high order spectral features and Lyapunov exponents can be estimated from a quadratic and cubic autoregressive formulation of the model first order moment, respectively. Here, these measures are evaluated on heartbeat series coming from 16 healthy subjects and 14 patients with Congestive Hearth Failure (CHF). Data were gathered from the on-line repository PhysioBank, which has been taken as landmark for testing nonlinear indices. Results show that the proposed nonlinear Laguerre-Volterra point-process methods are able to track the nonlinear and complex cardiovascular dynamics, distinguishing significantly between CHF and healthy heartbeat series.
Multiple sclerosis (MS) is a nervous system disease that affects your brain and spinal cord. It damages the myelin sheath, the material that surrounds and protects your nerve cells. This damage slows down ...
LENUS (Irish Health Repository)
Collins, Conor D
2012-02-01
Advances in the imaging and treatment of multiple myeloma have occurred over the past decade. This article summarises the current status and highlights how an understanding of both is necessary for optimum management.
Vision-Based Interest Point Extraction Evaluation in Multiple Environments
National Research Council Canada - National Science Library
McKeehan, Zachary D
2008-01-01
Computer-based vision is becoming a primary sensor mechanism in many facets of real world 2-D and 3-D applications, including autonomous robotics, augmented reality, object recognition, motion tracking, and biometrics...
Simultaneous comparisons of treatments at multiple time points
DEFF Research Database (Denmark)
Pallmann, Philip; Pretorius, Mias; Ritz, Christian
2017-01-01
that is measured repeatedly over time and contrast two basic modeling strategies: fitting a joint model across all occasions (with random effects and/or some residual covariance structure to account for heteroscedasticity and serial dependence), and a novel approach combining a set of simple marginal, i...
Simultaneous Tracking of Multiple Points Using a Wiimote
Skeffington, Alex; Scully, Kyle
2012-01-01
This paper reviews the construction of an inexpensive motion tracking and data logging system, which can be used for a wide variety of teaching experiments ranging from entry-level physics courses to advanced courses. The system utilizes an affordable infrared camera found in a Nintendo Wiimote to track IR LEDs mounted to the objects to be…
Terrier, L-M; François, P
2016-06-01
Multiple meningiomas (MMs) or meningiomatosis are defined by the presence of at least 2 lesions that appear simultaneously or not, at different intracranial locations, without the association of neurofibromatosis. They present 1-9 % of meningiomas with a female predominance. The occurrence of multiple meningiomas is not clear. There are 2 main hypotheses for their development, one that supports the independent evolution of these tumors and the other, completely opposite, that suggests the propagation of tumor cells of a unique clone transformation, through cerebrospinal fluid. NF2 gene mutation is an important intrinsic risk factor in the etiology of multiple meningiomas and some exogenous risk factors have been suspected but only ionizing radiation exposure has been proven. These tumors can grow anywhere in the skull but they are more frequently observed in supratentorial locations. Their histologic types are similar to unique meningiomas of psammomatous, fibroblastic, meningothelial or transitional type and in most cases are benign tumors. The prognosis of these tumors is eventually good and does not differ from the unique tumors except for the cases of radiation-induced multiple meningiomas, in the context of NF2 or when diagnosed in children where the outcome is less favorable. Each meningioma lesion should be dealt with individually and their multiple character should not justify their resection at all costs. Copyright © 2016 Elsevier Masson SAS. All rights reserved.
DEFF Research Database (Denmark)
Aanæs, Henrik; Dahl, Anders Lindbjerg; Pedersen, Kim Steenstrup
2012-01-01
on spatial invariance of interest points under changing acquisition parameters by measuring the spatial recall rate. The scope of this paper is to investigate the performance of a number of existing well-established interest point detection methods. Automatic performance evaluation of interest points is hard......Not all interest points are equally interesting. The most valuable interest points lead to optimal performance of the computer vision method in which they are employed. But a measure of this kind will be dependent on the chosen vision application. We propose a more general performance measure based...... position. The LED illumination provides the option for artificially relighting the scene from a range of light directions. This data set has given us the ability to systematically evaluate the performance of a number of interest point detectors. The highlights of the conclusions are that the fixed scale...
DEFF Research Database (Denmark)
Stenager, Egon; Stenager, E N; Knudsen, Lone
1994-01-01
In a cross-sectional study of 117 randomly selected patients (52 men, 65 women) with definite multiple sclerosis, it was found that 76 percent were married or cohabitant, 8 percent divorced. Social contacts remained unchanged for 70 percent, but outgoing social contacts were reduced for 45 percent......, need for structural changes in home and need for pension became greater with increasing physical handicap. No significant differences between gender were found. It is concluded that patients and relatives are under increased social strain, when multiple sclerosis progresses to a moderate handicap...
Interval Mathematics Applied to Critical Point Transitions
Directory of Open Access Journals (Sweden)
Benito A. Stradi
2012-03-01
Full Text Available The determination of critical points of mixtures is important for both practical and theoretical reasons in the modeling of phase behavior, especially at high pressure. The equations that describe the behavior of complex mixtures near critical points are highly nonlinear and with multiplicity of solutions to the critical point equations. Interval arithmetic can be used to reliably locate all the critical points of a given mixture. The method also verifies the nonexistence of a critical point if a mixture of a given composition does not have one. This study uses an interval Newton/Generalized Bisection algorithm that provides a mathematical and computational guarantee that all mixture critical points are located. The technique is illustrated using several example problems. These problems involve cubic equation of state models; however, the technique is general purpose and can be applied in connection with other nonlinear problems.
Generalized internal multiple imaging
Zuberi, M. A. H.
2014-08-05
Internal multiples deteriorate the image when the imaging procedure assumes only single scattering, especially if the velocity model does not have sharp contrasts to reproduce such scattering in the Green’s function through forward modeling. If properly imaged, internal multiples (internally scattered energy) can enhance the seismic image. Conventionally, to image internal multiples, accurate, sharp contrasts in the velocity model are required to construct a Green’s function with all the scattered energy. As an alternative, we have developed a generalized internal multiple imaging procedure that images any order internal scattering using the background Green’s function (from the surface to each image point), constructed from a smooth velocity model, usually used for conventional imaging. For the first-order internal multiples, the approach consisted of three steps, in which we first back propagated the recorded surface seismic data using the background Green’s function, then crosscorrelated the back-propagated data with the recorded data, and finally crosscorrelated the result with the original background Green’s function. This procedure images the contribution of the recorded first-order internal multiples, and it is almost free of the single-scattering recorded energy. The cost includes one additional crosscorrelation over the conventional single-scattering imaging application. We generalized this method to image internal multiples of any order separately. The resulting images can be added to the conventional single-scattering image, obtained, e.g., from Kirchhoff or reverse-time migration, to enhance the image. Application to synthetic data with reflectors illuminated by multiple scattering (double scattering) demonstrated the effectiveness of the approach.
International Nuclear Information System (INIS)
Sohn, Jeong Ick; Ha, Choon Ho; Choi, Karp Shik
1994-01-01
Multiple myeloma is a malignant plasma cell tumor that is thought to originate proliferation of a single clone of abnormal plasma cell resulting production of a whole monoclonal paraprotein. The authors experienced a case of multiple myeloma with severe mandibular osteolytic lesions in 46-year-old female. As a result of careful analysis of clinical, radiological, histopathological features, and laboratory findings, we diagnosed it as multiple myeloma, and the following results were obtained. 1. Main clinical symptoms were intermittent dull pain on the mandibular body area, abnormal sensation of lip and pain due to the fracture on the right clavicle. 2. Laboratory findings revealed M-spike, reversed serum albumin-globulin ratio, markedly elevated ESR and hypercalcemia. 3. Radiographically, multiple osteolytic punched-out radiolucencies were evident on the skull, zygoma, jaw bones, ribs, clavicle and upper extremities. Enlarged liver and increased uptakes on the lesional sites in RN scan were also observed. 4. Histopathologically, markedly hypercellular marrow with sheets of plasmoblasts and megakaryocytes were also observed.
DEFF Research Database (Denmark)
Stenager, E; Jensen, K
1988-01-01
Forty-two (12%) of a total of 366 patients with multiple sclerosis (MS) had psychiatric admissions. Of these, 34 (81%) had their first psychiatric admission in conjunction with or after the onset of MS. Classification by psychiatric diagnosis showed that there was a significant positive correlation...
DEFF Research Database (Denmark)
Stenager, E; Knudsen, L; Jensen, K
1991-01-01
In a cross-sectional investigation of 116 patients with multiple sclerosis, the social and sparetime activities of the patient were assessed by both patient and his/her family. The assessments were correlated to physical disability which showed that particularly those who were moderately disabled...
DEFF Research Database (Denmark)
Stenager, E; Jensen, K
1990-01-01
An investigation on the correlation between ability to read TV subtitles and the duration of visual evoked potential (VEP) latency in 14 patients with definite multiple sclerosis (MS), indicated that VEP latency in patients unable to read the TV subtitles was significantly delayed in comparison...
DEFF Research Database (Denmark)
Stenager, E; Knudsen, L; Jensen, K
1994-01-01
In a cross-sectional study of 94 patients (42 males, 52 females) with definite multiple sclerosis (MS) in the age range 25-55 years, the correlation of neuropsychological tests with the ability to read TV-subtitles and with the use of sedatives is examined. A logistic regression analysis reveals...
Plummer, Nancy; Michael, Nancy, Ed.
This module on multiple sclerosis is intended for use in inservice or continuing education programs for persons who administer medications in long-term care facilities. Instructor information, including teaching suggestions, and a listing of recommended audiovisual materials and their sources appear first. The module goal and objectives are then…
... when your babies do. Though it can be hard to let go of the thousand other things you need to do, remember that your well-being is key to your ability to take care of your babies. What Problems Can Happen? It may be hard to tell multiple babies apart when they first ...
Directory of Open Access Journals (Sweden)
Juan D Velásquez
2008-12-01
Full Text Available Una red neuronal autorregresiva es estimada para el precio mensual brasileño de corto plazo de la electricidad, la cual describe mejor la dinámica de los precios que un modelo lineal autorregresivo y que un perceptrón multicapa clásico que usan las mismas entradas y neuronas en la capa oculta. El modelo propuesto es especificado usando un procedimiento estadístico basado en el contraste del radio de verosimilitud. El modelo pasa una batería de pruebas de diagnóstico. El procedimiento de especificación propuesto permite seleccionar el número de unidades en la capa oculta y las entradas a la red neuronal, usando pruebas estadísticas que tienen en cuenta la cantidad de los datos y el ajuste del modelo a la serie de precios. La especificación del modelo final demuestra que el precio para el próximo mes es una función no lineal del precio actual, de la energía afluente actual y de la energía almacenada en el embalse equivalente en el mes actual y dos meses atrás.An autoregressive neural network model is estimated for the monthly Brazilian electricity spot price, which describes the prices dynamics better than a linear autoregressive model and a classical multilayer perceptron using the same input and neurons in the hidden layer. The proposed model is specified using a statistical procedure based on a likelihood ratio test. The model passes a battery of diagnostic tests. The proposed specification procedure allows us to select the number of units in hidden layer and the inputs to the neural network based on statistical tests, taking into account the number of data and the model fitting to the price time series. The final model specification demonstrates that the price for the next month is a nonlinear function of the current price, the current energy inflow, and the energy saved in the equivalent reservoir in the current month and two months ago.
International Nuclear Information System (INIS)
Anon.
1978-01-01
The principal properties of point defects are studied: thermodynamics, electronic structure, interactions with etended defects, production by irradiation. Some measuring methods are presented: atomic diffusion, spectroscopic methods, diffuse scattering of neutron and X rays, positron annihilation, molecular dynamics. Then points defects in various materials are investigated: ionic crystals, oxides, semiconductor materials, metals, intermetallic compounds, carbides, nitrides [fr
DEFF Research Database (Denmark)
Agarwal, Pankaj K.; Arge, Lars Allan; Erickson, Jeff
2003-01-01
We propose three indexing schemes for storing a set S of N points in the plane, each moving along a linear trajectory, so that any query of the following form can be answered quickly: Given a rectangle R and a real value t, report all K points of S that lie inside R at time t. We first present an...
Generalized zero point anomaly
International Nuclear Information System (INIS)
Nogueira, Jose Alexandre; Maia Junior, Adolfo
1994-01-01
It is defined Zero point Anomaly (ZPA) as the difference between the Effective Potential (EP) and the Zero point Energy (ZPE). It is shown, for a massive and interacting scalar field that, in very general conditions, the renormalized ZPA vanishes and then the renormalized EP and ZPE coincide. (author). 3 refs
Poisson branching point processes
International Nuclear Information System (INIS)
Matsuo, K.; Teich, M.C.; Saleh, B.E.A.
1984-01-01
We investigate the statistical properties of a special branching point process. The initial process is assumed to be a homogeneous Poisson point process (HPP). The initiating events at each branching stage are carried forward to the following stage. In addition, each initiating event independently contributes a nonstationary Poisson point process (whose rate is a specified function) located at that point. The additional contributions from all points of a given stage constitute a doubly stochastic Poisson point process (DSPP) whose rate is a filtered version of the initiating point process at that stage. The process studied is a generalization of a Poisson branching process in which random time delays are permitted in the generation of events. Particular attention is given to the limit in which the number of branching stages is infinite while the average number of added events per event of the previous stage is infinitesimal. In the special case when the branching is instantaneous this limit of continuous branching corresponds to the well-known Yule--Furry process with an initial Poisson population. The Poisson branching point process provides a useful description for many problems in various scientific disciplines, such as the behavior of electron multipliers, neutron chain reactions, and cosmic ray showers
Linton, J. Oliver
2017-01-01
There are five unique points in a star/planet system where a satellite can be placed whose orbital period is equal to that of the planet. Simple methods for calculating the positions of these points, or at least justifying their existence, are developed.
Energy Technology Data Exchange (ETDEWEB)
Aruquipa Coloma, Wilmer
2017-07-01
Nuclear reactors are susceptible to instability, causing oscillations in reactor power in specific working regions characterized by determined values of power and coolant mass flow. During reactor startup, there is a greater probability that these regions of instability will be present; another reason may be due to transient processes in some reactor parameters. The analysis of the temporal evolution of the power reveals a stable or unstable process after the disturbance in a light water reactor of type BWR (Boiling Water Reactor). In this work, the instability problem was approached in two ways. The first form is based on the ARMA (Autoregressive Moving Average models) model. This model was used to calculate the Decay Ratio (DR) and natural frequency (NF) of the oscillations, parameters that indicate if the one power signal is stable or not. In this sense, the DRARMA code was developed. In the second form, the problems of instability were analyzed using the classical concepts of non-linear systems, such as Lyapunov exponents, phase space and attractors. The Lyapunov exponents quantify the exponential divergence of the trajectories initially close to the phase space and estimate the amount of chaos in a system; the phase space and the attractors describe the dynamic behavior of the system. The main aim of the instability phenomena studies in nuclear reactors is to try to identify points or regions of operation that can lead to power oscillations conditions. The two approaches were applied to two sets of signals. The first set comes from signals of instability events of the commercial Forsmark reactors 1 and 2 and were used to validate the DRARMA code. The second set was obtained from the simulation of transient events of the Peach Bottom reactor; for the simulation, the PARCS and RELAP5 codes were used for the neutronic/thermal hydraulic coupling calculation. For all analyzes made in this work, the Matlab software was used due to its ease of programming and
Young, K. R.
2017-12-01
Graphic nonfiction: what is it? Some call these books and articles science comics, but they're no joke: created through research and direct experience by artists invested in creating multiple entry points for new learners, comics can open doors for discovery as introduction, enrichment, or as a vital center point to teaching. Find out what educational pedagogists, scientists, and - yes! - students themselves think about reading, viewing, learning from, and creating science comics in the classroom. Karen Romano Young is the award-winning author of traditional and graphic fiction and nonfiction for children, including Doodlebug, the forthcoming Diving for Deep-Sea Dragons, and the Odyssey/Muse magazine comics feature Humanimal Doodles. In spring 2018 (Antarctic autumn) Young will work as part of a Bigelow Laboratory team studying the production of DMSP by phytoplankton, and the resulting cloud formation. This is invisible stuff, difficult for lay audiences to envision and comprehend. But the audience is already forming around "Antarctic Log," a science comic that tells the story of the science and the experience of doing climate research at Palmer Station as winter draws near. Science comics aren't just for enrichment. They're an invitation, providing multiple entry points for diverse learners. I have received unanticipated support from education groups (including NSTA and IRA), parenting groups, and special educators because these highly visual presentations of middle grade and middle school level material makes the stories and concepts accessible to atypical fiction- and science-reading audiences. As a result, I've learned a great deal about the underlying differences between my material and traditional, text-oriented materials in which visuals may be highly coordinated but are still ancillary. An article that might seem forbidding as text appears open to interpretation in my format, so that readers can pick where to begin reading and how to proceed through the
DEFF Research Database (Denmark)
Elleby, Anita; Ingwersen, Peter
2010-01-01
; the Cumulated Publication Point Indicator (CPPI), which graphically illustrates the cumulated gain of obtained vs. ideal points, both seen as vectors; and the normalized Cumulated Publication Point Index (nCPPI) that represents the cumulated gain of publication success as index values, either graphically......The paper presents comparative analyses of two publication point systems, The Norwegian and the in-house system from the interdisciplinary Danish Institute of International Studies (DIIS), used as case in the study for publications published 2006, and compares central citation-based indicators...... with novel publication point indicators (PPIs) that are formalized and exemplified. Two diachronic citation windows are applied: 2006-07 and 2006-08. Web of Science (WoS) as well as Google Scholar (GS) are applied to observe the cite delay and citedness for the different document types published by DIIS...
Multispectral Image Feature Points
Directory of Open Access Journals (Sweden)
Cristhian Aguilera
2012-09-01
Full Text Available This paper presents a novel feature point descriptor for the multispectral image case: Far-Infrared and Visible Spectrum images. It allows matching interest points on images of the same scene but acquired in different spectral bands. Initially, points of interest are detected on both images through a SIFT-like based scale space representation. Then, these points are characterized using an Edge Oriented Histogram (EOH descriptor. Finally, points of interest from multispectral images are matched by finding nearest couples using the information from the descriptor. The provided experimental results and comparisons with similar methods show both the validity of the proposed approach as well as the improvements it offers with respect to the current state-of-the-art.
International Nuclear Information System (INIS)
Yan Xiaohui; Zhang Xinyi; Liu Chenglin; Dang, Ruishan; Huang Yuying; He Wei; Ding Guanghong
2009-01-01
We used synchrotron x-ray fluorescence analysis to probe the distribution of four chemical elements in and around acupuncture points, two located in the forearm and two in the lower leg. Three of the four acupuncture points showed significantly elevated concentrations of elements Ca, Fe, Cu and Zn in relation to levels in the surrounding tissue, with similar elevation ratios for Cu and Fe. The mapped distribution of these elements implies that each acupuncture point seems to be elliptical with the long axis along the meridian. (note)
Energy Technology Data Exchange (ETDEWEB)
Yan Xiaohui; Zhang Xinyi [Department of Physics, Surface Physics Laboratory (State Key Laboratory), and Synchrotron Radiation Research Center of Fudan University, Shanghai 200433 (China); Liu Chenglin [Physics Department of Yancheng Teachers' College, Yancheng 224002 (China); Dang, Ruishan [Second Military Medical University, Shanghai 200433 (China); Huang Yuying; He Wei [Beijing Synchrotron Radiation Facility, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100039 (China); Ding Guanghong [Shanghai Research Center of Acupuncture and Meridian, Pudong, Shanghai 201203 (China)
2009-05-07
We used synchrotron x-ray fluorescence analysis to probe the distribution of four chemical elements in and around acupuncture points, two located in the forearm and two in the lower leg. Three of the four acupuncture points showed significantly elevated concentrations of elements Ca, Fe, Cu and Zn in relation to levels in the surrounding tissue, with similar elevation ratios for Cu and Fe. The mapped distribution of these elements implies that each acupuncture point seems to be elliptical with the long axis along the meridian. (note)
International Nuclear Information System (INIS)
Sadashima, Hiromichi; Kusaka, Hirofumi; Imai, Terukuni; Takahashi, Ryosuke; Matsumoto, Sadayuki; Yamamoto, Toru; Yamasaki, Masahiro; Maya, Kiyomi
1986-01-01
Eleven patients with a definite diagnosis of multiple sclerosis were examined in terms of correlations between the clinical features and the results of cranial computed tomography (CT), and magnetic resonance imaging (MRI). Results: In 5 of the 11 patients, both CT and MRI demonstrated lesions consistent with a finding of multiple sclerosis. In 3 patients, only MRI demonstrated lesions. In the remaining 3 patients, neither CT nor MRI revealed any lesion in the brain. All 5 patients who showed abnormal findings on both CT and MRI had clinical signs either of cerebral or brainstem - cerebellar lesions. On the other hand, two of the 3 patients with normal CT and MRI findings had optic-nerve and spinal-cord signs. Therefore, our results suggested relatively good correlations between the clinical features, CT, and MRI. MRI revealed cerebral lesions in two of the four patients with clinical signs of only optic-nerve and spinal-cord lesions. MRI demonstrated sclerotic lesions in 3 of the 6 patients whose plaques were not detected by CT. In conclusion, MRI proved to be more helpful in the demonstration of lesions attributable to chronic multiple sclerosis. (author)
Directory of Open Access Journals (Sweden)
Egil Nygaard
2017-06-01
Full Text Available Self-efficacy is assumed to promote posttraumatic adaption, and several cross-sectional studies support this notion. However, there is a lack of prospective longitudinal studies to further illuminate the temporal relationship between self-efficacy and posttraumatic stress symptoms. Thus, an important unresolved research question is whether posttraumatic stress disorder (PTSD symptoms affect the level of self-efficacy or vice versa or whether they mutually influence each other. The present prospective longitudinal study investigated the reciprocal relationship between general self-efficacy (GSE and posttraumatic stress symptoms in 143 physical assault victims. We used an autoregressive cross-lagged model across four assessment waves: within 4 months after the assault (T1 and then 3 months (T2, 12 months (T3 and 8 years (T4 after the first assessment. Stress symptoms at T1 and T2 predicted subsequent self-efficacy, while self-efficacy at T1 and T2 was not related to subsequent stress symptoms. These relationships were reversed after T3; higher levels of self-efficacy at T3 predicted lower levels of posttraumatic stress symptoms at T4, while posttraumatic tress symptoms at T3 did not predict self-efficacy at T4. In conclusion, posttraumatic stress symptoms may have a deteriorating effect on self-efficacy in the early phase after physical assault, whereas self-efficacy may promote recovery from posttraumatic stress symptoms over the long term.
International Nuclear Information System (INIS)
Murase, Kenya; Yamazaki, Youichi; Shinohara, Masaaki
2003-01-01
The purpose of this study was to investigate the feasibility of the autoregressive moving average (ARMA) model for quantification of cerebral blood flow (CBF) with dynamic susceptibility contrast-enhanced magnetic resonance imaging (DSC-MRI) in comparison with deconvolution analysis based on singular value decomposition (DA-SVD). Using computer simulations, we generated a time-dependent concentration of the contrast agent in the volume of interest (VOI) from the arterial input function (AIF) modeled as a gamma-variate function under various CBFs, cerebral blood volumes and signal-to-noise ratios (SNRs) for three different types of residue function (exponential, triangular, and box-shaped). We also considered the effects of delay and dispersion in AIF. The ARMA model and DA-SVD were used to estimate CBF values from the simulated concentration-time curves in the VOI and AIFs, and the estimated values were compared with the assumed values. We found that the CBF value estimated by the ARMA model was more sensitive to the SNR and the delay in AIF than that obtained by DA-SVD. Although the ARMA model considerably overestimated CBF at low SNRs, it estimated the CBF more accurately than did DA-SVD at high SNRs for the exponential or triangular residue function. We believe this study will contribute to an understanding of the usefulness and limitations of the ARMA model when applied to quantification of CBF with DSC-MRI. (author)
Nygaard, Egil; Johansen, Venke A; Siqveland, Johan; Hussain, Ajmal; Heir, Trond
2017-01-01
Self-efficacy is assumed to promote posttraumatic adaption, and several cross-sectional studies support this notion. However, there is a lack of prospective longitudinal studies to further illuminate the temporal relationship between self-efficacy and posttraumatic stress symptoms. Thus, an important unresolved research question is whether posttraumatic stress disorder (PTSD) symptoms affect the level of self-efficacy or vice versa or whether they mutually influence each other. The present prospective longitudinal study investigated the reciprocal relationship between general self-efficacy (GSE) and posttraumatic stress symptoms in 143 physical assault victims. We used an autoregressive cross-lagged model across four assessment waves: within 4 months after the assault (T1) and then 3 months (T2), 12 months (T3) and 8 years (T4) after the first assessment. Stress symptoms at T1 and T2 predicted subsequent self-efficacy, while self-efficacy at T1 and T2 was not related to subsequent stress symptoms. These relationships were reversed after T3; higher levels of self-efficacy at T3 predicted lower levels of posttraumatic stress symptoms at T4, while posttraumatic tress symptoms at T3 did not predict self-efficacy at T4. In conclusion, posttraumatic stress symptoms may have a deteriorating effect on self-efficacy in the early phase after physical assault, whereas self-efficacy may promote recovery from posttraumatic stress symptoms over the long term.
Jia, Song; Xu, Tian-he; Sun, Zhang-zhen; Li, Jia-jing
2017-02-01
UT1-UTC is an important part of the Earth Orientation Parameters (EOP). The high-precision predictions of UT1-UTC play a key role in practical applications of deep space exploration, spacecraft tracking and satellite navigation and positioning. In this paper, a new prediction method with combination of Gray Model (GM(1, 1)) and Autoregressive Integrated Moving Average (ARIMA) is developed. The main idea is as following. Firstly, the UT1-UTC data are preprocessed by removing the leap second and Earth's zonal harmonic tidal to get UT1R-TAI data. Periodic terms are estimated and removed by the least square to get UT2R-TAI. Then the linear terms of UT2R-TAI data are modeled by the GM(1, 1), and the residual terms are modeled by the ARIMA. Finally, the UT2R-TAI prediction can be performed based on the combined model of GM(1, 1) and ARIMA, and the UT1-UTC predictions are obtained by adding the corresponding periodic terms, leap second correction and the Earth's zonal harmonic tidal correction. The results show that the proposed model can be used to predict UT1-UTC effectively with higher middle and long-term (from 32 to 360 days) accuracy than those of LS + AR, LS + MAR and WLS + MAR.
Energy Technology Data Exchange (ETDEWEB)
Biyanto, Totok R. [Department of Engineering Physics, Institute Technology of Sepuluh Nopember Surabaya, Surabaya, Indonesia 60111 (Indonesia)
2016-06-03
Fouling in a heat exchanger in Crude Preheat Train (CPT) refinery is an unsolved problem that reduces the plant efficiency, increases fuel consumption and CO{sub 2} emission. The fouling resistance behavior is very complex. It is difficult to develop a model using first principle equation to predict the fouling resistance due to different operating conditions and different crude blends. In this paper, Artificial Neural Networks (ANN) MultiLayer Perceptron (MLP) with input structure using Nonlinear Auto-Regressive with eXogenous (NARX) is utilized to build the fouling resistance model in shell and tube heat exchanger (STHX). The input data of the model are flow rates and temperatures of the streams of the heat exchanger, physical properties of product and crude blend data. This model serves as a predicting tool to optimize operating conditions and preventive maintenance of STHX. The results show that the model can capture the complexity of fouling characteristics in heat exchanger due to thermodynamic conditions and variations in crude oil properties (blends). It was found that the Root Mean Square Error (RMSE) are suitable to capture the nonlinearity and complexity of the STHX fouling resistance during phases of training and validation.
Foffani, Guglielmo; Bianchi, Anna M.; Priori, Alberto; Baselli, Giuseppe
2004-09-01
We propose a method that combines adaptive autoregressive (AAR) identification and spectral power decomposition for the study of movement-related spectral changes in scalp EEG signals and basal ganglia local field potentials (LFPs). This approach introduces the concept of movement-related poles, allowing one to study not only the classical event-related desynchronizations (ERD) and synchronizations (ERS), which correspond to modulations of power, but also event-related modulations of frequency. We applied the method to analyze movement-related EEG signals and LFPs contemporarily recorded from the sensorimotor cortex, the globus pallidus internus (GPi) and the subthalamic nucleus (STN) in a patient with Parkinson's disease who underwent stereotactic neurosurgery for the implant of deep brain stimulation (DBS) electrodes. In the AAR identification we compared the whale and the exponential forgetting factors, showing that the whale forgetting provides a better disturbance rejection and it is therefore more suitable to investigate movement-related brain activity. Movement-related power modulations were consistent with previous studies. In addition, movement-related frequency modulations were observed from both scalp EEG signals and basal ganglia LFPs. The method therefore represents an effective approach to the study of movement-related brain activity.
Tani, Yuji; Ogasawara, Katsuhiko
2012-01-01
This study aimed to contribute to the management of a healthcare organization by providing management information using time-series analysis of business data accumulated in the hospital information system, which has not been utilized thus far. In this study, we examined the performance of the prediction method using the auto-regressive integrated moving-average (ARIMA) model, using the business data obtained at the Radiology Department. We made the model using the data used for analysis, which was the number of radiological examinations in the past 9 years, and we predicted the number of radiological examinations in the last 1 year. Then, we compared the actual value with the forecast value. We were able to establish that the performance prediction method was simple and cost-effective by using free software. In addition, we were able to build the simple model by pre-processing the removal of trend components using the data. The difference between predicted values and actual values was 10%; however, it was more important to understand the chronological change rather than the individual time-series values. Furthermore, our method was highly versatile and adaptable compared to the general time-series data. Therefore, different healthcare organizations can use our method for the analysis and forecasting of their business data.
Directory of Open Access Journals (Sweden)
Yu-Pin Liao
2017-11-01
Full Text Available In the past few decades, demand forecasting has become relatively difficult due to rapid changes in the global environment. This research illustrates the use of the make-to-stock (MTS production strategy in order to explain how forecasting plays an essential role in business management. The linear mixed-effect (LME model has been extensively developed and is widely applied in various fields. However, no study has used the LME model for business forecasting. We suggest that the LME model be used as a tool for prediction and to overcome environment complexity. The data analysis is based on real data in an international display company, where the company needs accurate demand forecasting before adopting a MTS strategy. The forecasting result from the LME model is compared to the commonly used approaches, including the regression model, autoregressive model, times series model, and exponential smoothing model, with the results revealing that prediction performance provided by the LME model is more stable than using the other methods. Furthermore, product types in the data are regarded as a random effect in the LME model, hence demands of all types can be predicted simultaneously using a single LME model. However, some approaches require splitting the data into different type categories, and then predicting the type demand by establishing a model for each type. This feature also demonstrates the practicability of the LME model in real business operations.
Alwee, Razana; Shamsuddin, Siti Mariyam Hj; Sallehuddin, Roselina
2013-01-01
Crimes forecasting is an important area in the field of criminology. Linear models, such as regression and econometric models, are commonly applied in crime forecasting. However, in real crimes data, it is common that the data consists of both linear and nonlinear components. A single model may not be sufficient to identify all the characteristics of the data. The purpose of this study is to introduce a hybrid model that combines support vector regression (SVR) and autoregressive integrated moving average (ARIMA) to be applied in crime rates forecasting. SVR is very robust with small training data and high-dimensional problem. Meanwhile, ARIMA has the ability to model several types of time series. However, the accuracy of the SVR model depends on values of its parameters, while ARIMA is not robust to be applied to small data sets. Therefore, to overcome this problem, particle swarm optimization is used to estimate the parameters of the SVR and ARIMA models. The proposed hybrid model is used to forecast the property crime rates of the United State based on economic indicators. The experimental results show that the proposed hybrid model is able to produce more accurate forecasting results as compared to the individual models.
Directory of Open Access Journals (Sweden)
Razana Alwee
2013-01-01
Full Text Available Crimes forecasting is an important area in the field of criminology. Linear models, such as regression and econometric models, are commonly applied in crime forecasting. However, in real crimes data, it is common that the data consists of both linear and nonlinear components. A single model may not be sufficient to identify all the characteristics of the data. The purpose of this study is to introduce a hybrid model that combines support vector regression (SVR and autoregressive integrated moving average (ARIMA to be applied in crime rates forecasting. SVR is very robust with small training data and high-dimensional problem. Meanwhile, ARIMA has the ability to model several types of time series. However, the accuracy of the SVR model depends on values of its parameters, while ARIMA is not robust to be applied to small data sets. Therefore, to overcome this problem, particle swarm optimization is used to estimate the parameters of the SVR and ARIMA models. The proposed hybrid model is used to forecast the property crime rates of the United State based on economic indicators. The experimental results show that the proposed hybrid model is able to produce more accurate forecasting results as compared to the individual models.
Directory of Open Access Journals (Sweden)
VIAN RISKA AYUNING TYAS
2014-01-01
Full Text Available The Arbitrage Pricing Theory (APT is an alternative model to estimate the price of securities based of arbitrage concept. In APT, the returns of securities are affected by several factors. This research is aimed to estimate the expected returns of securities using APT model and Vector Autoregressive model. There are ten stocks incorporated in Kompas100 index and four macroeconomic variables, these are inflation, exchange rates, the amountof circulate money (JUB, and theinterest rateof Bank Indonesia(SBI are applied in this research. The first step in using VAR is to test the stationary of the data using colerogram and the results indicate that all data are stationary. The second step is to select the optimal lag based on the smallest value of AIC. The Granger causality test shows that the LPKR stock is affected by the inflation and the exchange rate while the nine other stocks do not show the existence of the expected causality. The results of causality test are then estimated by the VAR models in order to obtain expected returnof macroeconomic factors. The expected return of macroeconomic factors obtained is used in the APT model, then the expected return stock LPKR is calculated. It shows that the expected return of LPKR is 3,340%
Directory of Open Access Journals (Sweden)
VIAN RISKA AYUNING TYAS
2014-08-01
Full Text Available The Arbitrage Pricing Theory (APT is an alternative model to estimate the price of securities based of arbitrage concept. In APT, the returns of securities are affected by several factors. This research is aimed to estimate the expected returns of securities using APT model and Vector Autoregressive model. There are ten stocks incorporated in Kompas100 index and four macroeconomic variables, these are inflation, exchange rates, the amountof circulate money (JUB, and theinterest rateof Bank Indonesia(SBI are applied in this research. The first step in using VAR is to test the stationary of the data using colerogram and the results indicate that all data are stationary. The second step is to select the optimal lag based on the smallest value of AIC. The Granger causality test shows that the LPKR stock is affected by the inflation and the exchange rate while the nine other stocks do not show the existence of the expected causality. The results of causality test are then estimated by the VAR models in order to obtain expected returnof macroeconomic factors. The expected return of macroeconomic factors obtained is used in the APT model, then the expected return stock LPKR is calculated. It shows that the expected return of LPKR is 3,340%
International Nuclear Information System (INIS)
Yousufzai, M.A.K; Aansari, M.R.K.; Quamar, J.; Iqbal, J.; Hussain, M.A.
2010-01-01
This communication presents the development of a comprehensive characterization of ozone layer depletion (OLD) phenomenon as a physical process in the form of mathematical models that comprise the usual regression, multiple or polynomial regression and stochastic strategy. The relevance of these models has been illuminated using predicted values of different parameters under a changing environment. The information obtained from such analysis can be employed to alter the possible factors and variables to achieve optimum performance. This kind of analysis initiates a study towards formulating the phenomenon of OLD as a physical process with special reference to the stratospheric region of Pakistan. The data presented here establishes that the Auto regressive (AR) nature of modeling OLD as a physical process is an appropriate scenario rather than using usual regression. The data reported in literature suggest quantitatively the OLD is occurring in our region. For this purpose we have modeled this phenomenon using the data recorded at the Geophysical Centre Quetta during the period 1960-1999. The predictions made by this analysis are useful for public, private and other relevant organizations. (author)
will link to the zone forecast and then allow further zooming to the point of interest whereas on the Honolulu, HI Chicago, IL Northern Indiana, IN Lake Charles, LA New Orleans, LA Boston, MA Caribou, ME
International Nuclear Information System (INIS)
Clark, R.M.
2004-01-01
It has been suggested that a change of nuclear shape may be described in terms of a phase transition and that specific nuclei may lie close to the critical point of the transition. Analytical descriptions of such critical-point nuclei have been introduced recently and they are described briefly. The results of extensive searches for possible examples of critical-point behavior are presented. Alternative pictures, such as describing bands in the candidate nuclei using simple ΔK = 0 and ΔK = 2 rotational-coupling models, are discussed, and the limitations of the different approaches highlighted. A possible critical-point description of the transition from a vibrational to rotational pairing phase is suggested
National Wetlands Inventory Points
Minnesota Department of Natural Resources — Wetland point features (typically wetlands that are too small to be as area features at the data scale) mapped as part of the National Wetlands Inventory (NWI). The...
Allegheny County Address Points
Allegheny County / City of Pittsburgh / Western PA Regional Data Center — This dataset contains address points which represent physical address locations assigned by the Allegheny County addressing authority. Data is updated by County...
Iowa State University GIS Support and Research Facility — Point locations of geologic samples/files in the IGS repository. Types of samples include well cuttings, outcrop samples, cores, drillers logs, measured sections,...
Directory of Open Access Journals (Sweden)
Sanjo Zlobec
2017-04-01
Full Text Available A set of sufficient conditions which guarantee the existence of a point x⋆ such that f(x⋆ = x⋆ is called a "fixed point theorem". Many such theorems are named after well-known mathematicians and economists. Fixed point theorems are among most useful ones in applied mathematics, especially in economics and game theory. Particularly important theorem in these areas is Kakutani's fixed point theorem which ensures existence of fixed point for point-to-set mappings, e.g., [2, 3, 4]. John Nash developed and applied Kakutani's ideas to prove the existence of (what became known as "Nash equilibrium" for finite games with mixed strategies for any number of players. This work earned him a Nobel Prize in Economics that he shared with two mathematicians. Nash's life was dramatized in the movie "Beautiful Mind" in 2001. In this paper, we approach the system f(x = x differently. Instead of studying existence of its solutions our objective is to determine conditions which are both necessary and sufficient that an arbitrary point x⋆ is a fixed point, i.e., that it satisfies f(x⋆ = x⋆. The existence of solutions for continuous function f of the single variable is easy to establish using the Intermediate Value Theorem of Calculus. However, characterizing fixed points x⋆, i.e., providing answers to the question of finding both necessary and sufficient conditions for an arbitrary given x⋆ to satisfy f(x⋆ = x⋆, is not simple even for functions of the single variable. It is possible that constructive answers do not exist. Our objective is to find them. Our work may require some less familiar tools. One of these might be the "quadratic envelope characterization of zero-derivative point" recalled in the next section. The results are taken from the author's current research project "Studying the Essence of Fixed Points". They are believed to be original. The author has received several feedbacks on the preliminary report and on parts of the project
International Nuclear Information System (INIS)
Calasso, Irio G.; Craig, Walter; Diebold, Gerald J.
2001-01-01
We investigate the photoacoustic effect generated by heat deposition at a point in space in an inviscid fluid. Delta-function and long Gaussian optical pulses are used as sources in the wave equation for the displacement potential to determine the fluid motion. The linear sound-generation mechanism gives bipolar photoacoustic waves, whereas the nonlinear mechanism produces asymmetric tripolar waves. The salient features of the photoacoustic point source are that rapid heat deposition and nonlinear thermal expansion dominate the production of ultrasound
Unconventional Quantum Critical Points
Xu, Cenke
2012-01-01
In this paper we review the theory of unconventional quantum critical points that are beyond the Landau's paradigm. Three types of unconventional quantum critical points will be discussed: (1). The transition between topological order and semiclassical spin ordered phase; (2). The transition between topological order and valence bond solid phase; (3). The direct second order transition between different competing orders. We focus on the field theory and universality class of these unconventio...
Ali, Mudassar
2013-01-01
Masteroppgave i informasjons- og kommunikasjonsteknologi IKT590 2013 – Universitetet i Agder, Grimstad SharePoint is a web-based business collaboration platform from Microsoft which is very robust and dynamic in nature. The platform has been in the market for more than a decade and has been adapted by large number of organisations in the world. The platform has become larger in scale, richer in features and is improving consistently with every new version. However, SharePoint ...
Schermerhorn, Alice C; Cummings, E Mark; Davies, Patrick T
2008-02-01
The authors examine mutual family influence processes at the level of children's representations of multiple family relationships, as well as the structure of those representations. From a community sample with 3 waves, each spaced 1 year apart, kindergarten-age children (105 boys and 127 girls) completed a story-stem completion task, tapping representations of multiple family relationships. Structural equation modeling with autoregressive controls indicated that representational processes involving different family relationships were interrelated over time, including links between children's representations of marital conflict and reactions to conflict, between representations of security about marital conflict and parent-child relationships, and between representations of security in father-child and mother-child relationships. Mixed support was found for notions of increasing stability in representations during this developmental period. Results are discussed in terms of notions of transactional family dynamics, including family-wide perspectives on mutual influence processes attributable to multiple family relationships.
Zhang, Dongliang
2013-01-01
To increase the illumination of the subsurface and to eliminate the dependency of FWI on the source wavelet, we propose multiples waveform inversion (MWI) that transforms each hydrophone into a virtual point source with a time history equal to that of the recorded data. These virtual sources are used to numerically generate downgoing wavefields that are correlated with the backprojected surface-related multiples to give the migration image. Since the recorded data are treated as the virtual sources, knowledge of the source wavelet is not required, and the subsurface illumination is greatly enhanced because the entire free surface acts as an extended source compared to the radiation pattern of a traditional point source. Numerical tests on the Marmousi2 model show that the convergence rate and the spatial resolution of MWI is, respectively, faster and more accurate then FWI. The potential pitfall with this method is that the multiples undergo more than one roundtrip to the surface, which increases attenuation and reduces spatial resolution. This can lead to less resolved tomograms compared to conventional FWI. The possible solution is to combine both FWI and MWI in inverting for the subsurface velocity distribution.