Sound quality characteristics of refrigerator noise in real living environments with relation to psychoacoustical and autocorrelation function parameters.

PubMed

Sato, Shin-ichi; You, Jin; Jeon, Jin Yong

2007-07-01

Psychoacoustical and autocorrelation function (ACF) parameters were employed to describe the temporal fluctuations of refrigerator noise during starting, transition into/from the stationary phase and termination of operation. The temporal fluctuations of refrigerator noise include a click at start-up, followed by a rapid increase in volume, a change of pitch, and termination of the operation. Subjective evaluations of the noise of 24 different refrigerators were conducted in a real living environment. The relationship between objective measures and perceived noisiness was examined by multiple regression analysis. Sound quality indices were developed based on psychoacoustical and ACF parameters. The psychoacoustical parameters found to be important for evaluating noisiness in the stationary phase were loudness and roughness. The relationship between noisiness and ACF parameters shows that sound energy and its fluctuations are important for evaluating noisiness. Also, refrigerator sounds that had a fluctuation of pitch were rated as more annoying. The tolerance level for the starting phase of refrigerator noise was found to be 33 dBA, which is the level where 65% of the participants in the subjective tests were satisfied.

Probabilistic density function method for nonlinear dynamical systems driven by colored noise

DOE Office of Scientific and Technical Information (OSTI.GOV)

Barajas-Solano, David A.; Tartakovsky, Alexandre M.

2016-05-01

We present a probability density function (PDF) method for a system of nonlinear stochastic ordinary differential equations driven by colored noise. The method provides an integro-differential equation for the temporal evolution of the joint PDF of the system's state, which we close by means of a modified Large-Eddy-Diffusivity-type closure. Additionally, we introduce the generalized local linearization (LL) approximation for deriving a computable PDF equation in the form of the second-order partial differential equation (PDE). We demonstrate the proposed closure and localization accurately describe the dynamics of the PDF in phase space for systems driven by noise with arbitrary auto-correlation time.more » We apply the proposed PDF method to the analysis of a set of Kramers equations driven by exponentially auto-correlated Gaussian colored noise to study the dynamics and stability of a power grid.« less

Quantifying temporal change in biodiversity: challenges and opportunities

PubMed Central

Dornelas, Maria; Magurran, Anne E.; Buckland, Stephen T.; Chao, Anne; Chazdon, Robin L.; Colwell, Robert K.; Curtis, Tom; Gaston, Kevin J.; Gotelli, Nicholas J.; Kosnik, Matthew A.; McGill, Brian; McCune, Jenny L.; Morlon, Hélène; Mumby, Peter J.; Øvreås, Lise; Studeny, Angelika; Vellend, Mark

2013-01-01

Growing concern about biodiversity loss underscores the need to quantify and understand temporal change. Here, we review the opportunities presented by biodiversity time series, and address three related issues: (i) recognizing the characteristics of temporal data; (ii) selecting appropriate statistical procedures for analysing temporal data; and (iii) inferring and forecasting biodiversity change. With regard to the first issue, we draw attention to defining characteristics of biodiversity time series—lack of physical boundaries, uni-dimensionality, autocorrelation and directionality—that inform the choice of analytic methods. Second, we explore methods of quantifying change in biodiversity at different timescales, noting that autocorrelation can be viewed as a feature that sheds light on the underlying structure of temporal change. Finally, we address the transition from inferring to forecasting biodiversity change, highlighting potential pitfalls associated with phase-shifts and novel conditions. PMID:23097514

Extracting the time scales of conformational dynamics from single-molecule single-photon fluorescence statistics.

PubMed

Shang, Jianyuan; Geva, Eitan

2007-04-26

The quenching rate of a fluorophore attached to a macromolecule can be rather sensitive to its conformational state. The decay of the corresponding fluorescence lifetime autocorrelation function can therefore provide unique information on the time scales of conformational dynamics. The conventional way of measuring the fluorescence lifetime autocorrelation function involves evaluating it from the distribution of delay times between photoexcitation and photon emission. However, the time resolution of this procedure is limited by the time window required for collecting enough photons in order to establish this distribution with sufficient signal-to-noise ratio. Yang and Xie have recently proposed an approach for improving the time resolution, which is based on the argument that the autocorrelation function of the delay time between photoexcitation and photon emission is proportional to the autocorrelation function of the square of the fluorescence lifetime [Yang, H.; Xie, X. S. J. Chem. Phys. 2002, 117, 10965]. In this paper, we show that the delay-time autocorrelation function is equal to the autocorrelation function of the square of the fluorescence lifetime divided by the autocorrelation function of the fluorescence lifetime. We examine the conditions under which the delay-time autocorrelation function is approximately proportional to the autocorrelation function of the square of the fluorescence lifetime. We also investigate the correlation between the decay of the delay-time autocorrelation function and the time scales of conformational dynamics. The results are demonstrated via applications to a two-state model and an off-lattice model of a polypeptide.

Perception and psychological evaluation for visual and auditory environment based on the correlation mechanisms

NASA Astrophysics Data System (ADS)

Fujii, Kenji

2002-06-01

In this dissertation, the correlation mechanism in modeling the process in the visual perception is introduced. It has been well described that the correlation mechanism is effective for describing subjective attributes in auditory perception. The main result is that it is possible to apply the correlation mechanism to the process in temporal vision and spatial vision, as well as in audition. (1) The psychophysical experiment was performed on subjective flicker rates for complex waveforms. A remarkable result is that the phenomenon of missing fundamental is found in temporal vision as analogous to the auditory pitch perception. This implies the existence of correlation mechanism in visual system. (2) For spatial vision, the autocorrelation analysis provides useful measures for describing three primary perceptual properties of visual texture: contrast, coarseness, and regularity. Another experiment showed that the degree of regularity is a salient cue for texture preference judgment. (3) In addition, the autocorrelation function (ACF) and inter-aural cross-correlation function (IACF) were applied for analysis of the temporal and spatial properties of environmental noise. It was confirmed that the acoustical properties of aircraft noise and traffic noise are well described. These analyses provided useful parameters extracted from the ACF and IACF in assessing the subjective annoyance for noise. Thesis advisor: Yoichi Ando Copies of this thesis written in English can be obtained from Junko Atagi, 6813 Mosonou, Saijo-cho, Higashi-Hiroshima 739-0024, Japan. E-mail address: atagi\\@urban.ne.jp.

Effect of confounding variables on hemodynamic response function estimation using averaging and deconvolution analysis: An event-related NIRS study.

PubMed

Aarabi, Ardalan; Osharina, Victoria; Wallois, Fabrice

2017-07-15

Slow and rapid event-related designs are used in fMRI and functional near-infrared spectroscopy (fNIRS) experiments to temporally characterize the brain hemodynamic response to discrete events. Conventional averaging (CA) and the deconvolution method (DM) are the two techniques commonly used to estimate the Hemodynamic Response Function (HRF) profile in event-related designs. In this study, we conducted a series of simulations using synthetic and real NIRS data to examine the effect of the main confounding factors, including event sequence timing parameters, different types of noise, signal-to-noise ratio (SNR), temporal autocorrelation and temporal filtering on the performance of these techniques in slow and rapid event-related designs. We also compared systematic errors in the estimates of the fitted HRF amplitude, latency and duration for both techniques. We further compared the performance of deconvolution methods based on Finite Impulse Response (FIR) basis functions and gamma basis sets. Our results demonstrate that DM was much less sensitive to confounding factors than CA. Event timing was the main parameter largely affecting the accuracy of CA. In slow event-related designs, deconvolution methods provided similar results to those obtained by CA. In rapid event-related designs, our results showed that DM outperformed CA for all SNR, especially above -5 dB regardless of the event sequence timing and the dynamics of background NIRS activity. Our results also show that periodic low-frequency systemic hemodynamic fluctuations as well as phase-locked noise can markedly obscure hemodynamic evoked responses. Temporal autocorrelation also affected the performance of both techniques by inducing distortions in the time profile of the estimated hemodynamic response with inflated t-statistics, especially at low SNRs. We also found that high-pass temporal filtering could substantially affect the performance of both techniques by removing the low-frequency components of HRF profiles. Our results emphasize the importance of characterization of event timing, background noise and SNR when estimating HRF profiles using CA and DM in event-related designs. Copyright © 2017 Elsevier Inc. All rights reserved.

An Overdetermined System for Improved Autocorrelation Based Spectral Moment Estimator Performance

NASA Technical Reports Server (NTRS)

Keel, Byron M.

1996-01-01

Autocorrelation based spectral moment estimators are typically derived using the Fourier transform relationship between the power spectrum and the autocorrelation function along with using either an assumed form of the autocorrelation function, e.g., Gaussian, or a generic complex form and applying properties of the characteristic function. Passarelli has used a series expansion of the general complex autocorrelation function and has expressed the coefficients in terms of central moments of the power spectrum. A truncation of this series will produce a closed system of equations which can be solved for the central moments of interest. The autocorrelation function at various lags is estimated from samples of the random process under observation. These estimates themselves are random variables and exhibit a bias and variance that is a function of the number of samples used in the estimates and the operational signal-to-noise ratio. This contributes to a degradation in performance of the moment estimators. This dissertation investigates the use autocorrelation function estimates at higher order lags to reduce the bias and standard deviation in spectral moment estimates. In particular, Passarelli's series expansion is cast in terms of an overdetermined system to form a framework under which the application of additional autocorrelation function estimates at higher order lags can be defined and assessed. The solution of the overdetermined system is the least squares solution. Furthermore, an overdetermined system can be solved for any moment or moments of interest and is not tied to a particular form of the power spectrum or corresponding autocorrelation function. As an application of this approach, autocorrelation based variance estimators are defined by a truncation of Passarelli's series expansion and applied to simulated Doppler weather radar returns which are characterized by a Gaussian shaped power spectrum. The performance of the variance estimators determined from a closed system is shown to improve through the application of additional autocorrelation lags in an overdetermined system. This improvement is greater in the narrowband spectrum region where the information is spread over more lags of the autocorrelation function. The number of lags needed in the overdetermined system is a function of the spectral width, the number of terms in the series expansion, the number of samples used in estimating the autocorrelation function, and the signal-to-noise ratio. The overdetermined system provides a robustness to the chosen variance estimator by expanding the region of spectral widths and signal-to-noise ratios over which the estimator can perform as compared to the closed system.

Statistics of some atmospheric turbulence records relevant to aircraft response calculations

NASA Technical Reports Server (NTRS)

Mark, W. D.; Fischer, R. W.

1981-01-01

Methods for characterizing atmospheric turbulence are described. The methods illustrated include maximum likelihood estimation of the integral scale and intensity of records obeying the von Karman transverse power spectral form, constrained least-squares estimation of the parameters of a parametric representation of autocorrelation functions, estimation of the power spectra density of the instantaneous variance of a record with temporally fluctuating variance, and estimation of the probability density functions of various turbulence components. Descriptions of the computer programs used in the computations are given, and a full listing of these programs is included.

[Temporal and spatial characteristics of ecological risk in Shunyi, Beijing, China based on landscape structure.

PubMed

Qing, Feng Ting; Peng, Yu

2016-05-01

Based on the remote sensing data in 1997, 2001, 2005, 2009 and 2013, this article classified the landscape types of Shunyi, and the ecological risk index was built based on landscape disturbance index and landscape fragility. The spatial auto-correlation and geostatistical analysis by GS + and ArcGIS was used to study temporal and spatial changes of ecological risk. The results showed that eco-risk degree in the study region had positive spatial correlation which decreased with the increasing grain size. Within a certain grain range (<12 km), the spatial auto-correlation had an obvious dependence on scale. The random variation of spatial heterogeneity was less than spatial auto-correlation variation from 1997 to 2013, which meant the auto-correlation had a dominant role in spatial heterogeneity. The ecological risk of Shunyi was mainly at moderate level during the study period. The area of the district with higher and lower ecological risk increased, while that of mode-rate ecological risk decreased. The area with low ecological risk was mainly located in the airport region and forest of southeast Shunyi, while that with high ecological risk was mainly concentrated in the water landscape, such as the banks of Chaobai River.

Longitudinal and bulk viscosities of Lennard-Jones fluids

NASA Astrophysics Data System (ADS)

Tankeshwar, K.; Pathak, K. N.; Ranganathan, S.

1996-12-01

Expressions for the longitudinal and bulk viscosities have been derived using Green Kubo formulae involving the time integral of the longitudinal and bulk stress autocorrelation functions. The time evolution of stress autocorrelation functions are determined using the Mori formalism and a memory function which is obtained from the Mori equation of motion. The memory function is of hyperbolic secant form and involves two parameters which are related to the microscopic sum rules of the respective autocorrelation function. We have derived expressions for the zeroth-, second-and fourth- order sum rules of the longitudinal and bulk stress autocorrelation functions. These involve static correlation functions up to four particles. The final expressions for these have been put in a form suitable for numerical calculations using low- order decoupling approximations. The numerical results have been obtained for the sum rules of longitudinal and bulk stress autocorrelation functions. These have been used to calculate the longitudinal and bulk viscosities and time evolution of the longitudinal stress autocorrelation function of the Lennard-Jones fluids over wide ranges of densities and temperatures. We have compared our results with the available computer simulation data and found reasonable agreement.

Time-Frequency Based Instantaneous Frequency Estimation of Sparse Signals from an Incomplete Set of Samples

DTIC Science & Technology

2014-06-17

100 0 2 4 Wigner distribution 0 50 100 0 0.5 1 Auto-correlation function 0 50 100 0 2 4 L- Wigner distribution 0 50 100 0 0.5 1 Auto-correlation function ...bilinear or higher order autocorrelation functions will increase the number of missing samples, the analysis shows that accurate instantaneous...frequency estimation can be achieved even if we deal with only few samples, as long as the auto-correlation function is properly chosen to coincide with

Geographical and Temporal Body Size Variation in a Reptile: Roles of Sex, Ecology, Phylogeny and Ecology Structured in Phylogeny

PubMed Central

Aragón, Pedro; Fitze, Patrick S.

2014-01-01

Geographical body size variation has long interested evolutionary biologists, and a range of mechanisms have been proposed to explain the observed patterns. It is considered to be more puzzling in ectotherms than in endotherms, and integrative approaches are necessary for testing non-exclusive alternative mechanisms. Using lacertid lizards as a model, we adopted an integrative approach, testing different hypotheses for both sexes while incorporating temporal, spatial, and phylogenetic autocorrelation at the individual level. We used data on the Spanish Sand Racer species group from a field survey to disentangle different sources of body size variation through environmental and individual genetic data, while accounting for temporal and spatial autocorrelation. A variation partitioning method was applied to separate independent and shared components of ecology and phylogeny, and estimated their significance. Then, we fed-back our models by controlling for relevant independent components. The pattern was consistent with the geographical Bergmann's cline and the experimental temperature-size rule: adults were larger at lower temperatures (and/or higher elevations). This result was confirmed with additional multi-year independent data-set derived from the literature. Variation partitioning showed no sex differences in phylogenetic inertia but showed sex differences in the independent component of ecology; primarily due to growth differences. Interestingly, only after controlling for independent components did primary productivity also emerge as an important predictor explaining size variation in both sexes. This study highlights the importance of integrating individual-based genetic information, relevant ecological parameters, and temporal and spatial autocorrelation in sex-specific models to detect potentially important hidden effects. Our individual-based approach devoted to extract and control for independent components was useful to reveal hidden effects linked with alternative non-exclusive hypothesis, such as those of primary productivity. Also, including measurement date allowed disentangling and controlling for short-term temporal autocorrelation reflecting sex-specific growth plasticity. PMID:25090025

Autocorrelation factors and intelligibility of Japanese monosyllables in individuals with sensorineural hearing loss.

PubMed

Shimokura, Ryota; Akasaka, Sakie; Nishimura, Tadashi; Hosoi, Hiroshi; Matsui, Toshie

2017-02-01

Some Japanese monosyllables contain consonants that are not easily discernible for individuals with sensorineural hearing loss. However, the acoustic features that make these monosyllables difficult to discern have not been clearly identified. Here, this study used the autocorrelation function (ACF), which can capture temporal features of signals, to clarify the factors influencing speech intelligibility. For each monosyllable, five factors extracted from the ACF [Φ(0): total energy; τ 1 and ϕ 1 : delay time and amplitude of the maximum peak; τ e : effective duration; W ϕ (0) : spectral centroid], voice onset time, speech intelligibility index, and loudness level were compared with the percentage of correctly perceived articulations (144 ears) obtained by 50 Japanese vowel and consonant-vowel monosyllables produced by one female speaker. Results showed that median effective duration [(τ e ) med ] was strongly correlated with the percentage of correctly perceived articulations of the consonants (r = 0.87, p < 0.01). (τ e ) med values were computed by running ACFs with the time lag at which the magnitude of the logarithmic-ACF envelope had decayed to -10 dB. Effective duration is a measure of temporal pattern persistence, i.e., the duration over which the waveform maintains a stable pattern. The authors postulate that low recognition ability is related to degraded perception of temporal fluctuation patterns.

MATLAB-Based Program for Teaching Autocorrelation Function and Noise Concepts

ERIC Educational Resources Information Center

Jovanovic Dolecek, G.

2012-01-01

An attractive MATLAB-based tool for teaching the basics of autocorrelation function and noise concepts is presented in this paper. This tool enhances traditional in-classroom lecturing. The demonstrations of the tool described here highlight the description of the autocorrelation function (ACF) in a general case for wide-sense stationary (WSS)…

Disentangling the effects of forage, social rank, and risk on movement autocorrelation of elephants using Fourier and wavelet analyses

PubMed Central

Wittemyer, George; Polansky, Leo; Douglas-Hamilton, Iain; Getz, Wayne M.

2008-01-01

The internal state of an individual—as it relates to thirst, hunger, fear, or reproductive drive—can be inferred by referencing points on its movement path to external environmental and sociological variables. Using time-series approaches to characterize autocorrelative properties of step-length movements collated every 3 h for seven free-ranging African elephants, we examined the influence of social rank, predation risk, and seasonal variation in resource abundance on periodic properties of movement. The frequency domain methods of Fourier and wavelet analyses provide compact summaries of temporal autocorrelation and show both strong diurnal and seasonal based periodicities in the step-length time series. This autocorrelation is weaker during the wet season, indicating random movements are more common when ecological conditions are good. Periodograms of socially dominant individuals are consistent across seasons, whereas subordinate individuals show distinct differences diverging from that of dominants during the dry season. We link temporally localized statistical properties of movement to landscape features and find that diurnal movement correlation is more common within protected wildlife areas, and multiday movement correlations found among lower ranked individuals are typically outside of protected areas where predation risks are greatest. A frequency-related spatial analysis of movement-step lengths reveal that rest cycles related to the spatial distribution of critical resources (i.e., forage and water) are responsible for creating the observed patterns. Our approach generates unique information regarding the spatial-temporal interplay between environmental and individual characteristics, providing an original approach for understanding the movement ecology of individual animals and the spatial organization of animal populations. PMID:19060207

The Error Structure of the SMAP Single and Dual Channel Soil Moisture Retrievals

NASA Astrophysics Data System (ADS)

Dong, Jianzhi; Crow, Wade T.; Bindlish, Rajat

2018-01-01

Knowledge of the temporal error structure for remotely sensed surface soil moisture retrievals can improve our ability to exploit them for hydrologic and climate studies. This study employs a triple collocation analysis to investigate both the total variance and temporal autocorrelation of errors in Soil Moisture Active and Passive (SMAP) products generated from two separate soil moisture retrieval algorithms, the vertically polarized brightness temperature-based single-channel algorithm (SCA-V, the current baseline SMAP algorithm) and the dual-channel algorithm (DCA). A key assumption made in SCA-V is that real-time vegetation opacity can be accurately captured using only a climatology for vegetation opacity. Results demonstrate that while SCA-V generally outperforms DCA, SCA-V can produce larger total errors when this assumption is significantly violated by interannual variability in vegetation health and biomass. Furthermore, larger autocorrelated errors in SCA-V retrievals are found in areas with relatively large vegetation opacity deviations from climatological expectations. This implies that a significant portion of the autocorrelated error in SCA-V is attributable to the violation of its vegetation opacity climatology assumption and suggests that utilizing a real (as opposed to climatological) vegetation opacity time series in the SCA-V algorithm would reduce the magnitude of autocorrelated soil moisture retrieval errors.

A method to identify differential expression profiles of time-course gene data with Fourier transformation.

PubMed

Kim, Jaehee; Ogden, Robert Todd; Kim, Haseong

2013-10-18

Time course gene expression experiments are an increasingly popular method for exploring biological processes. Temporal gene expression profiles provide an important characterization of gene function, as biological systems are both developmental and dynamic. With such data it is possible to study gene expression changes over time and thereby to detect differential genes. Much of the early work on analyzing time series expression data relied on methods developed originally for static data and thus there is a need for improved methodology. Since time series expression is a temporal process, its unique features such as autocorrelation between successive points should be incorporated into the analysis. This work aims to identify genes that show different gene expression profiles across time. We propose a statistical procedure to discover gene groups with similar profiles using a nonparametric representation that accounts for the autocorrelation in the data. In particular, we first represent each profile in terms of a Fourier basis, and then we screen out genes that are not differentially expressed based on the Fourier coefficients. Finally, we cluster the remaining gene profiles using a model-based approach in the Fourier domain. We evaluate the screening results in terms of sensitivity, specificity, FDR and FNR, compare with the Gaussian process regression screening in a simulation study and illustrate the results by application to yeast cell-cycle microarray expression data with alpha-factor synchronization.The key elements of the proposed methodology: (i) representation of gene profiles in the Fourier domain; (ii) automatic screening of genes based on the Fourier coefficients and taking into account autocorrelation in the data, while controlling the false discovery rate (FDR); (iii) model-based clustering of the remaining gene profiles. Using this method, we identified a set of cell-cycle-regulated time-course yeast genes. The proposed method is general and can be potentially used to identify genes which have the same patterns or biological processes, and help facing the present and forthcoming challenges of data analysis in functional genomics.

Spatio-temporal patterns of Barmah Forest virus disease in Queensland, Australia.

PubMed

Naish, Suchithra; Hu, Wenbiao; Mengersen, Kerrie; Tong, Shilu

2011-01-01

Barmah Forest virus (BFV) disease is a common and wide-spread mosquito-borne disease in Australia. This study investigated the spatio-temporal patterns of BFV disease in Queensland, Australia using geographical information system (GIS) tools and geostatistical analysis. We calculated the incidence rates and standardised incidence rates of BFV disease. Moran's I statistic was used to assess the spatial autocorrelation of BFV incidences. Spatial dynamics of BFV disease was examined using semi-variogram analysis. Interpolation techniques were applied to visualise and display the spatial distribution of BFV disease in statistical local areas (SLAs) throughout Queensland. Mapping of BFV disease by SLAs reveals the presence of substantial spatio-temporal variation over time. Statistically significant differences in BFV incidence rates were identified among age groups (χ(2) = 7587, df = 7327,p<0.01). There was a significant positive spatial autocorrelation of BFV incidence for all four periods, with the Moran's I statistic ranging from 0.1506 to 0.2901 (p<0.01). Semi-variogram analysis and smoothed maps created from interpolation techniques indicate that the pattern of spatial autocorrelation was not homogeneous across the state. This is the first study to examine spatial and temporal variation in the incidence rates of BFV disease across Queensland using GIS and geostatistics. The BFV transmission varied with age and gender, which may be due to exposure rates or behavioural risk factors. There are differences in the spatio-temporal patterns of BFV disease which may be related to local socio-ecological and environmental factors. These research findings may have implications in the BFV disease control and prevention programs in Queensland.

Spatial and temporal changes in the structure of groundwater nitrate concentration time series (1935 1999) as demonstrated by autoregressive modelling

NASA Astrophysics Data System (ADS)

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.

Environmental drivers of spatial variation in whole-tree transpiration in an aspen-dominated upland-to-wetland forest gradient

NASA Astrophysics Data System (ADS)

Loranty, Michael M.; Mackay, D. Scott; Ewers, Brent E.; Adelman, Jonathan D.; Kruger, Eric L.

2008-02-01

Assumed representative center-of-stand measurements are typical inputs to models that scale forest transpiration to stand and regional extents. These inputs do not consider gradients in transpiration at stand boundaries or along moisture gradients and therefore potentially bias the large-scale estimates. We measured half-hourly sap flux (JS) for 173 trees in a spatially explicit cyclic sampling design across a topographically controlled gradient between a forested wetland and upland forest in northern Wisconsin. Our analyses focused on three dominant species in the site: quaking aspen (Populus tremuloides Michx), speckled alder (Alnus incana (DuRoi) Spreng), and white cedar (Thuja occidentalis L.). Sapwood area (AS) was used to scale JS to whole tree transpiration (EC). Because spatial patterns imply underlying processes, geostatistical analyses were employed to quantify patterns of spatial autocorrelation across the site. A simple Jarvis type model parameterized using a Monte Carlo sampling approach was used to simulate EC (EC-SIM). EC-SIM was compared with observed EC(EC-OBS) and found to reproduce both the temporal trends and spatial variance of canopy transpiration. EC-SIM was then used to examine spatial autocorrelation as a function of environmental drivers. We found no spatial autocorrelation in JS across the gradient from forested wetland to forested upland. EC was spatially autocorrelated and this was attributed to spatial variation in AS which suggests species spatial patterns are important for understanding spatial estimates of transpiration. However, the range of autocorrelation in EC-SIM decreased linearly with increasing vapor pressure deficit, implying that consideration of spatial variation in the sensitivity of canopy stomatal conductance to D is also key to accurately scaling up transpiration in space.

Modeling long correlation times using additive binary Markov chains: Applications to wind generation time series.

PubMed

Weber, Juliane; Zachow, Christopher; Witthaut, Dirk

2018-03-01

Wind power generation exhibits a strong temporal variability, which is crucial for system integration in highly renewable power systems. Different methods exist to simulate wind power generation but they often cannot represent the crucial temporal fluctuations properly. We apply the concept of additive binary Markov chains to model a wind generation time series consisting of two states: periods of high and low wind generation. The only input parameter for this model is the empirical autocorrelation function. The two-state model is readily extended to stochastically reproduce the actual generation per period. To evaluate the additive binary Markov chain method, we introduce a coarse model of the electric power system to derive backup and storage needs. We find that the temporal correlations of wind power generation, the backup need as a function of the storage capacity, and the resting time distribution of high and low wind events for different shares of wind generation can be reconstructed.

Modeling long correlation times using additive binary Markov chains: Applications to wind generation time series

NASA Astrophysics Data System (ADS)

Weber, Juliane; Zachow, Christopher; Witthaut, Dirk

2018-03-01

Wind power generation exhibits a strong temporal variability, which is crucial for system integration in highly renewable power systems. Different methods exist to simulate wind power generation but they often cannot represent the crucial temporal fluctuations properly. We apply the concept of additive binary Markov chains to model a wind generation time series consisting of two states: periods of high and low wind generation. The only input parameter for this model is the empirical autocorrelation function. The two-state model is readily extended to stochastically reproduce the actual generation per period. To evaluate the additive binary Markov chain method, we introduce a coarse model of the electric power system to derive backup and storage needs. We find that the temporal correlations of wind power generation, the backup need as a function of the storage capacity, and the resting time distribution of high and low wind events for different shares of wind generation can be reconstructed.

Impact of Autocorrelation on Functional Connectivity

PubMed Central

Arbabshirani, Mohammad R.; Damaraju, Eswar; Phlypo, Ronald; Plis, Sergey; Allen, Elena; Ma, Sai; Mathalon, Daniel; Preda, Adrian; Vaidya, Jatin G.; Adali, Tülay; Calhoun, Vince D.

2014-01-01

Although the impact of serial correlation (autocorrelation) in residuals of general linear models for fMRI time-series has been studied extensively, the effect of autocorrelation on functional connectivity studies has been largely neglected until recently. Some recent studies based on results from economics have questioned the conventional estimation of functional connectivity and argue that not correcting for autocorrelation in fMRI time-series results in “spurious” correlation coefficients. In this paper, first we assess the effect of autocorrelation on Pearson correlation coefficient through theoretical approximation and simulation. Then we present this effect on real fMRI data. To our knowledge this is the first work comprehensively investigating the effect of autocorrelation on functional connectivity estimates. Our results show that although FC values are altered, even following correction for autocorrelation, results of hypothesis testing on FC values remain very similar to those before correction. In real data we show this is true for main effects and also for group difference testing between healthy controls and schizophrenia patients. We further discuss model order selection in the context of autoregressive processes, effects of frequency filtering and propose a preprocessing pipeline for connectivity studies. PMID:25072392

A Search for Quasi-periodic Oscillations in the Blazar 1ES 1959+650

DOE Office of Scientific and Technical Information (OSTI.GOV)

Li, Xiao-Pan; Luo, Yu-Hui; Yang, Hai-Yan

We have searched quasi-periodic oscillations (QPOs) in the 15 GHz light curve of the BL Lac object 1ES 1959+650 monitored by the Owens Valley Radio Observatory 40 m telescope during the period from 2008 January to 2016 February, using the Lomb–Scargle Periodogram, power spectral density (PSD), discrete autocorrelation function, and phase dispersion minimization (PDM) techniques. The red noise background has been established via the PSD method, and no QPO can be derived at the 3 σ confidence level accounting for the impact of the red noise variability. We conclude that the light curve of 1ES 1959+650 can be explained bymore » a stochastic red noise process that contributes greatly to the total observed variability amplitude, dominates the power spectrum, causes spurious bumps and wiggles in the autocorrelation function and can result in the variance of the folded light curve decreasing toward lower temporal frequencies when few-cycle, sinusoid-like patterns are present. Moreover, many early supposed periodicity claims for blazar light curves need to be reevaluated assuming red noise.« less

Optimal periodic binary codes of lengths 28 to 64

NASA Technical Reports Server (NTRS)

Tyler, S.; Keston, R.

1980-01-01

Results from computer searches performed to find repeated binary phase coded waveforms with optimal periodic autocorrelation functions are discussed. The best results for lengths 28 to 64 are given. The code features of major concern are where (1) the peak sidelobe in the autocorrelation function is small and (2) the sum of the squares of the sidelobes in the autocorrelation function is small.

The error structure of the SMAP single and dual channel soil moisture retrievals

USDA-ARS?s Scientific Manuscript database

Knowledge of the temporal error structure for remotely-sensed surface soil moisture retrievals can improve our ability to exploit them for hydrology and climate studies. This study employs a triple collocation type analysis to investigate both the total variance and temporal auto-correlation of erro...

Climate reddening increases the chance of critical transitions

NASA Astrophysics Data System (ADS)

van der Bolt, Bregje; van Nes, Egbert H.; Bathiany, Sebastian; Vollebregt, Marlies E.; Scheffer, Marten

2018-06-01

Climate change research often focuses on trends in the mean and variance. However, analyses of palaeoclimatic and contemporary dynamics reveal that climate memory — as measured for instance by temporal autocorrelation — may also change substantially over time. Here, we show that elevated temporal autocorrelation in climatic variables should be expected to increase the chance of critical transitions in climate-sensitive systems with tipping points. We demonstrate that this prediction is consistent with evidence from forests, coral reefs, poverty traps, violent conflict and ice sheet instability. In each example, the duration of anomalous dry or warm events elevates chances of invoking a critical transition. Understanding the effects of climate variability thus requires research not only on variance, but also on climate memory.

Spatio-Temporal Patterns of Barmah Forest Virus Disease in Queensland, Australia

PubMed Central

Naish, Suchithra; Hu, Wenbiao; Mengersen, Kerrie; Tong, Shilu

2011-01-01

Background Barmah Forest virus (BFV) disease is a common and wide-spread mosquito-borne disease in Australia. This study investigated the spatio-temporal patterns of BFV disease in Queensland, Australia using geographical information system (GIS) tools and geostatistical analysis. Methods/Principal Findings We calculated the incidence rates and standardised incidence rates of BFV disease. Moran's I statistic was used to assess the spatial autocorrelation of BFV incidences. Spatial dynamics of BFV disease was examined using semi-variogram analysis. Interpolation techniques were applied to visualise and display the spatial distribution of BFV disease in statistical local areas (SLAs) throughout Queensland. Mapping of BFV disease by SLAs reveals the presence of substantial spatio-temporal variation over time. Statistically significant differences in BFV incidence rates were identified among age groups (χ2 = 7587, df = 7327,p<0.01). There was a significant positive spatial autocorrelation of BFV incidence for all four periods, with the Moran's I statistic ranging from 0.1506 to 0.2901 (p<0.01). Semi-variogram analysis and smoothed maps created from interpolation techniques indicate that the pattern of spatial autocorrelation was not homogeneous across the state. Conclusions/Significance This is the first study to examine spatial and temporal variation in the incidence rates of BFV disease across Queensland using GIS and geostatistics. The BFV transmission varied with age and gender, which may be due to exposure rates or behavioural risk factors. There are differences in the spatio-temporal patterns of BFV disease which may be related to local socio-ecological and environmental factors. These research findings may have implications in the BFV disease control and prevention programs in Queensland. PMID:22022430

Time-to-space mapping of femtosecond pulses.

PubMed

Nuss, M C; Li, M; Chiu, T H; Weiner, A M; Partovi, A

1994-05-01

We report time-to-space mapping of femtosecond light pulses in a temporal holography setup. By reading out a temporal hologram of a short optical pulse with a continuous-wave diode laser, we accurately convert temporal pulse-shape information into a spatial pattern that can be viewed with a camera. We demonstrate real-time acquisition of electric-field autocorrelation and cross correlation of femtosecond pulses with this technique.

Break point on the auto-correlation function of Elsässer variable z- in the super-Alfvénic solar wind fluctuations

NASA Astrophysics Data System (ADS)

Wang, X.; Tu, C. Y.; He, J.; Wang, L.

2017-12-01

It has been a longstanding debate on what the nature of Elsässer variables z- observed in the Alfvénic solar wind is. It is widely believed that z- represents inward propagating Alfvén waves and undergoes non-linear interaction with z+ to produce energy cascade. However, z- variations sometimes show nature of convective structures. Here we present a new data analysis on z- autocorrelation functions to get some definite information on its nature. We find that there is usually a break point on the z- auto-correlation function when the fluctuations show nearly pure Alfvénicity. The break point observed by Helios-2 spacecraft near 0.3 AU is at the first time lag ( 81 s), where the autocorrelation coefficient has the value less than that at zero-time lag by a factor of more than 0.4. The autocorrelation function breaks also appear in the WIND observations near 1 AU. The z- autocorrelation function is separated by the break into two parts: fast decreasing part and slowly decreasing part, which cannot be described in a whole by an exponential formula. The breaks in the z- autocorrelation function may represent that the z- time series are composed of high-frequency white noise and low-frequency apparent structures, which correspond to the flat and steep parts of the function, respectively. This explanation is supported by a simple test with a superposition of an artificial random data series and a smoothed random data series. Since in many cases z- autocorrelation functions do not decrease very quickly at large time lag and cannot be considered as the Lanczos type, no reliable value for correlation-time can be derived. Our results showed that in these cases with high Alfvénicity, z- should not be considered as inward-propagating wave. The power-law spectrum of z+ should be made by fluid turbulence cascade process presented by Kolmogorov.

3D Time-lapse Imaging and Quantification of Mitochondrial Dynamics

NASA Astrophysics Data System (ADS)

Sison, Miguel; Chakrabortty, Sabyasachi; Extermann, Jérôme; Nahas, Amir; James Marchand, Paul; Lopez, Antonio; Weil, Tanja; Lasser, Theo

2017-02-01

We present a 3D time-lapse imaging method for monitoring mitochondrial dynamics in living HeLa cells based on photothermal optical coherence microscopy and using novel surface functionalization of gold nanoparticles. The biocompatible protein-based biopolymer coating contains multiple functional groups which impart better cellular uptake and mitochondria targeting efficiency. The high stability of the gold nanoparticles allows continuous imaging over an extended time up to 3000 seconds without significant cell damage. By combining temporal autocorrelation analysis with a classical diffusion model, we quantify mitochondrial dynamics and cast these results into 3D maps showing the heterogeneity of diffusion parameters across the whole cell volume.

Correlation Factors Describing Primary and Spatial Sensations of Sound Fields

NASA Astrophysics Data System (ADS)

ANDO, Y.

2002-11-01

The theory of subjective preference of the sound field in a concert hall is established based on the model of human auditory-brain system. The model consists of the autocorrelation function (ACF) mechanism and the interaural crosscorrelation function (IACF) mechanism for signals arriving at two ear entrances, and the specialization of human cerebral hemispheres. This theory can be developed to describe primary sensations such as pitch or missing fundamental, loudness, timbre and, in addition, duration sensation which is introduced here as a fourth. These four primary sensations may be formulated by the temporal factors extracted from the ACF associated with the left hemisphere and, spatial sensations such as localization in the horizontal plane, apparent source width and subjective diffuseness are described by the spatial factors extracted from the IACF associated with the right hemisphere. Any important subjective responses of sound fields may be described by both temporal and spatial factors.

Theory of acoustic design of opera house and a design proposal

NASA Astrophysics Data System (ADS)

Ando, Yoichi

2004-05-01

First of all, the theory of subjective preference for sound fields based on the model of auditory-brain system is briefly mentioned. It consists of the temporal factors and spatial factors associated with the left and right cerebral hemispheres, respectively. The temporal criteria are the initial time delay gap between the direct sound and the first Reflection (Dt1) and the subsequent reverberation time (Tsub). These preferred conditions are related to the minimum value of effective duration of the running autocorrelation function of source signals (te)min. The spatial criteria are binaural listening level (LL) and the IACC, which may be extracted from the interaural crosscorrelation function. In the opera house, there are two different kind of sound sources, i.e., the vocal source of relatively short values of (te)min in the stage and the orchestra music of long values of (te)min in the pit. For these sources, a proposal is made here.

[Characteristics of temporal-spatial differentiation in landscape pattern vulnerability in Nansihu Lake wetland, China.

PubMed

Liang, Jia Xin; Li, Xin Ju

2018-02-01

With remote sensing images from 1985, 2000 Lantsat 5 TM and 2015 Lantsat 8 OLI as data sources, we tried to select the suitable research scale and examine the temporal-spatial diffe-rentiation with such scale in the Nansihu Lake wetland by using landscape pattern vulnerability index constructed by sensitivity index and adaptability index, and combined with space statistics such as semivariogram and spatial autocorrelation. The results showed that 1 km × 1 km equidistant grid was the suitable research scale, which could eliminate the influence of spatial heterogeneity induced by random factors. From 1985 to 2015, the landscape pattern vulnerability in the Nansihu Lake wetland deteriorated gradually. The high-risk area of landscape pattern vulnerability dramatically expanded with time. The spatial heterogeneity of landscape pattern vulnerability increased, and the influence of non-structural factors on landscape pattern vulnerability strengthened. Spatial variability affected by spatial autocorrelation slightly weakened. Landscape pattern vulnerability had strong general spatial positive correlation, with the significant form of spatial agglomeration. The positive spatial autocorrelation continued to increase and the phenomenon of spatial concentration was more and more obvious over time. The local autocorrelation mainly based on high-high accumulation zone and low-low accumulation zone had stronger spatial autocorrelation among neighboring space units. The high-high accumulation areas showed the strongest level of significance, and the significant level of low-low accumulation zone increased with time. Natural factors, such as temperature and precipitation, affected water-level and landscape distribution, and thus changed the landscape patterns vulnerability of Nansihu Lake wetland. The dominant driver for the deterioration of landscape patterns vulnerability was human activities, including social economy activity and policy system.

Analysis of Spatiotemporal Characteristics of Pandemic SARS Spread in Mainland China.

PubMed

Cao, Chunxiang; Chen, Wei; Zheng, Sheng; Zhao, Jian; Wang, Jinfeng; Cao, Wuchun

2016-01-01

Severe acute respiratory syndrome (SARS) is one of the most severe emerging infectious diseases of the 21st century so far. SARS caused a pandemic that spread throughout mainland China for 7 months, infecting 5318 persons in 194 administrative regions. Using detailed mainland China epidemiological data, we study spatiotemporal aspects of this person-to-person contagious disease and simulate its spatiotemporal transmission dynamics via the Bayesian Maximum Entropy (BME) method. The BME reveals that SARS outbreaks show autocorrelation within certain spatial and temporal distances. We use BME to fit a theoretical covariance model that has a sine hole spatial component and exponential temporal component and obtain the weights of geographical and temporal autocorrelation factors. Using the covariance model, SARS dynamics were estimated and simulated under the most probable conditions. Our study suggests that SARS transmission varies in its epidemiological characteristics and SARS outbreak distributions exhibit palpable clusters on both spatial and temporal scales. In addition, the BME modelling demonstrates that SARS transmission features are affected by spatial heterogeneity, so we analyze potential causes. This may benefit epidemiological control of pandemic infectious diseases.

Analysis of Spatiotemporal Characteristics of Pandemic SARS Spread in Mainland China

PubMed Central

Cao, Chunxiang; Zheng, Sheng; Zhao, Jian; Wang, Jinfeng; Cao, Wuchun

2016-01-01

Severe acute respiratory syndrome (SARS) is one of the most severe emerging infectious diseases of the 21st century so far. SARS caused a pandemic that spread throughout mainland China for 7 months, infecting 5318 persons in 194 administrative regions. Using detailed mainland China epidemiological data, we study spatiotemporal aspects of this person-to-person contagious disease and simulate its spatiotemporal transmission dynamics via the Bayesian Maximum Entropy (BME) method. The BME reveals that SARS outbreaks show autocorrelation within certain spatial and temporal distances. We use BME to fit a theoretical covariance model that has a sine hole spatial component and exponential temporal component and obtain the weights of geographical and temporal autocorrelation factors. Using the covariance model, SARS dynamics were estimated and simulated under the most probable conditions. Our study suggests that SARS transmission varies in its epidemiological characteristics and SARS outbreak distributions exhibit palpable clusters on both spatial and temporal scales. In addition, the BME modelling demonstrates that SARS transmission features are affected by spatial heterogeneity, so we analyze potential causes. This may benefit epidemiological control of pandemic infectious diseases. PMID:27597972

A spatio-temporal model for estimating the long-term effects of air pollution on respiratory hospital admissions in Greater London.

PubMed

Rushworth, Alastair; Lee, Duncan; Mitchell, Richard

2014-07-01

It has long been known that air pollution is harmful to human health, as many epidemiological studies have been conducted into its effects. Collectively, these studies have investigated both the acute and chronic effects of pollution, with the latter typically based on individual level cohort designs that can be expensive to implement. As a result of the increasing availability of small-area statistics, ecological spatio-temporal study designs are also being used, with which a key statistical problem is allowing for residual spatio-temporal autocorrelation that remains after the covariate effects have been removed. We present a new model for estimating the effects of air pollution on human health, which allows for residual spatio-temporal autocorrelation, and a study into the long-term effects of air pollution on human health in Greater London, England. The individual and joint effects of different pollutants are explored, via the use of single pollutant models and multiple pollutant indices. Copyright © 2014 The Authors. Published by Elsevier Ltd.. All rights reserved.

Copulas and time series with long-ranged dependencies.

PubMed

Chicheportiche, Rémy; Chakraborti, Anirban

2014-04-01

We review ideas on temporal dependencies and recurrences in discrete time series from several areas of natural and social sciences. We revisit existing studies and redefine the relevant observables in the language of copulas (joint laws of the ranks). We propose that copulas provide an appropriate mathematical framework to study nonlinear time dependencies and related concepts-like aftershocks, Omori law, recurrences, and waiting times. We also critically argue, using this global approach, that previous phenomenological attempts involving only a long-ranged autocorrelation function lacked complexity in that they were essentially monoscale.

Modified Beer-Lambert law for blood flow.

PubMed

Baker, Wesley B; Parthasarathy, Ashwin B; Busch, David R; Mesquita, Rickson C; Greenberg, Joel H; Yodh, A G

2014-11-01

We develop and validate a Modified Beer-Lambert law for blood flow based on diffuse correlation spectroscopy (DCS) measurements. The new formulation enables blood flow monitoring from temporal intensity autocorrelation function data taken at single or multiple delay-times. Consequentially, the speed of the optical blood flow measurement can be substantially increased. The scheme facilitates blood flow monitoring of highly scattering tissues in geometries wherein light propagation is diffusive or non-diffusive, and it is particularly well-suited for utilization with pressure measurement paradigms that employ differential flow signals to reduce contributions of superficial tissues.

A better understanding of long-range temporal dependence of traffic flow time series

NASA Astrophysics Data System (ADS)

Feng, Shuo; Wang, Xingmin; Sun, Haowei; Zhang, Yi; Li, Li

2018-02-01

Long-range temporal dependence is an important research perspective for modelling of traffic flow time series. Various methods have been proposed to depict the long-range temporal dependence, including autocorrelation function analysis, spectral analysis and fractal analysis. However, few researches have studied the daily temporal dependence (i.e. the similarity between different daily traffic flow time series), which can help us better understand the long-range temporal dependence, such as the origin of crossover phenomenon. Moreover, considering both types of dependence contributes to establishing more accurate model and depicting the properties of traffic flow time series. In this paper, we study the properties of daily temporal dependence by simple average method and Principal Component Analysis (PCA) based method. Meanwhile, we also study the long-range temporal dependence by Detrended Fluctuation Analysis (DFA) and Multifractal Detrended Fluctuation Analysis (MFDFA). The results show that both the daily and long-range temporal dependence exert considerable influence on the traffic flow series. The DFA results reveal that the daily temporal dependence creates crossover phenomenon when estimating the Hurst exponent which depicts the long-range temporal dependence. Furthermore, through the comparison of the DFA test, PCA-based method turns out to be a better method to extract the daily temporal dependence especially when the difference between days is significant.

Development of a local size hierarchy causes regular spacing of trees in an even-aged Abies forest: analyses using spatial autocorrelation and the mark correlation function.

PubMed

Suzuki, Satoshi N; Kachi, Naoki; Suzuki, Jun-Ichirou

2008-09-01

During the development of an even-aged plant population, the spatial distribution of individuals often changes from a clumped pattern to a random or regular one. The development of local size hierarchies in an Abies forest was analysed for a period of 47 years following a large disturbance in 1959. In 1980 all trees in an 8 x 8 m plot were mapped and their height growth after the disturbance was estimated. Their mortality and growth were then recorded at 1- to 4-year intervals between 1980 and 2006. Spatial distribution patterns of trees were analysed by the pair correlation function. Spatial correlations between tree heights were analysed with a spatial autocorrelation function and the mark correlation function. The mark correlation function was able to detect a local size hierarchy that could not be detected by the spatial autocorrelation function alone. The small-scale spatial distribution pattern of trees changed from clumped to slightly regular during the 47 years. Mortality occurred in a density-dependent manner, which resulted in regular spacing between trees after 1980. The spatial autocorrelation and mark correlation functions revealed the existence of tree patches consisting of large trees at the initial stage. Development of a local size hierarchy was detected within the first decade after the disturbance, although the spatial autocorrelation was not negative. Local size hierarchies that developed persisted until 2006, and the spatial autocorrelation became negative at later stages (after about 40 years). This is the first study to detect local size hierarchies as a prelude to regular spacing using the mark correlation function. The results confirm that use of the mark correlation function together with the spatial autocorrelation function is an effective tool to analyse the development of a local size hierarchy of trees in a forest.

Ecosystem functional assessment based on the "optical type" concept and self-similarity patterns: An application using MODIS-NDVI time series autocorrelation

NASA Astrophysics Data System (ADS)

Huesca, Margarita; Merino-de-Miguel, Silvia; Eklundh, Lars; Litago, Javier; Cicuéndez, Victor; Rodríguez-Rastrero, Manuel; Ustin, Susan L.; Palacios-Orueta, Alicia

2015-12-01

Remote sensing (RS) time series are an excellent operative source for information about the land surface across several scales and different levels of landscape heterogeneity. Ustin and Gamon (2010) proposed the new concept of "optical types" (OT), meaning "optically distinguishable functional types", as a way to better understand remote sensing signals related to the actual functional behavior of species that share common physiognomic forms but differ in functionality. Whereas the OT approach seems to be promising and consistent with ecological theory as a way to monitor vegetation derived from RS, it received little implementation. This work presents a method for implementing the OT concept for efficient monitoring of ecosystems based on RS time series. We propose relying on an ecosystem's repetitive pattern in the temporal domain (self-similarity) to assess its dynamics. Based on this approach, our main hypothesis is that distinct dynamics are intrinsic to a specific OT. Self-similarity level in the temporal domain within a broadleaf forest class was quantitatively assessed using the auto-correlation function (ACF), from statistical time series analysis. A vector comparison classification method, spectral angle mapper, and principal component analysis were used to identify general patterns related to forest dynamics. Phenological metrics derived from MODIS NDVI time series using the TIMESAT software, together with information from the National Forest Map were used to explain the different dynamics found. Results showed significant and highly stable self-similarity patterns in OTs that corresponded to forests under non-moisture-limited environments with an adaptation strategy based on a strong phenological synchrony with climate seasonality. These forests are characterized by dense closed canopy deciduous forests associated with high productivity and low biodiversity in terms of dominant species. Forests in transitional areas were associated with patterns of less temporal stability probably due to mixtures of different adaptation strategies (i.e., deciduous, marcescent and evergreen species) and higher functional diversity related to climate variability at long and short terms. A less distinct seasonality and even a double season appear in the OT of the broadleaf Mediterranean forest characterized by an open canopy dominated by evergreen-sclerophyllous formations. Within this forest, understory and overstory dynamics maximize functional diversity resulting in contrasting traits adapted to summer drought, winter frosts, and high precipitation variability.

Magnetoencephalographic responses in relation to temporal and spatial factors of sound fields

NASA Astrophysics Data System (ADS)

Soeta, Yoshiharu; Nakagawa, Seiji; Tonoike, Mitsuo; Hotehama, Takuya; Ando, Yoichi

2004-05-01

To establish the guidelines based on brain functions for designing sound fields such as a concert hall and an opera house, the activities of the human brain to the temporal and spatial factors of the sound field have been investigated using magnetoencephalography (MEG). MEG is a noninvasive technique for investigating neuronal activity in human brain. First of all, the auditory evoked responses in change of the magnitude of the interaural cross-correlation (IACC) were analyzed. IACC is one of the spatial factors, which has great influence on the degree of subjective preference and diffuseness for sound fields. The results indicated that the peak amplitude of N1m, which was found over the left and right temporal lobes around 100 ms after the stimulus onset, decreased with increasing the IACC. Second, the responses corresponding to subjective preference for one of the typical temporal factors, i.e., the initial delay gap between a direct sound and the first reflection, were investigated. The results showed that the effective duration of the autocorrelation function of MEG between 8 and 13 Hz became longer during presentations of a preferred stimulus. These results indicate that the brain may be relaxed, and repeat a similar temporal rhythm under preferred sound fields.

Autocorrelation structure at rest predicts value correlates of single neurons during reward-guided choice

PubMed Central

Cavanagh, Sean E; Wallis, Joni D; Kennerley, Steven W; Hunt, Laurence T

2016-01-01

Correlates of value are routinely observed in the prefrontal cortex (PFC) during reward-guided decision making. In previous work (Hunt et al., 2015), we argued that PFC correlates of chosen value are a consequence of varying rates of a dynamical evidence accumulation process. Yet within PFC, there is substantial variability in chosen value correlates across individual neurons. Here we show that this variability is explained by neurons having different temporal receptive fields of integration, indexed by examining neuronal spike rate autocorrelation structure whilst at rest. We find that neurons with protracted resting temporal receptive fields exhibit stronger chosen value correlates during choice. Within orbitofrontal cortex, these neurons also sustain coding of chosen value from choice through the delivery of reward, providing a potential neural mechanism for maintaining predictions and updating stored values during learning. These findings reveal that within PFC, variability in temporal specialisation across neurons predicts involvement in specific decision-making computations. DOI: http://dx.doi.org/10.7554/eLife.18937.001 PMID:27705742

Assessment of sacrococcygeal pressure ulcers using diffuse correlation spectroscopy

NASA Astrophysics Data System (ADS)

Diaz, David; Lafontant, Alec; Neidrauer, Michael; Weingarten, Michael S.; DiMaria-Ghalili, Rose Ann; Fried, Guy W.; Rece, Julianne; Lewin, Peter A.; Zubkov, Leonid

2016-03-01

Microcirculation is essential for proper supply of oxygen and nutritive substances to the biological tissue and the removal of waste products of metabolism. The determination of microcirculatory blood flow (mBF) is therefore of substantial interest to clinicians for assessing tissue health; particularly in pressure ulceration and suspected deep tissue injury. The goal of this pilot clinical study was to assess deep-tissue pressure ulceration by non-invasively measuring mBF using Diffuse Correlation Spectroscopy (DCS). DCS provides information about the flow of red blood cells in the capillary network by measuring the temporal autocorrelation function of scattering light intensity. A novel optical probe was developed in order to obtain measurements under the load of the subject's body as pressure is applied (ischemia) and then released (reperfusion) on sacrococcygeal tissue in a hospital bed. Prior to loading measurements, baseline readings of the sacral region were obtained by measuring the subjects in a side-lying position. DCS measurements from the sacral region of twenty healthy volunteers have been compared to those of two patients who initially had similar non-blanchable redness. The temporal autocorrelation function of scattering light intensity of the patient whose redness later disappeared was similar to that of the average healthy subject. The second patient, whose redness developed into an advanced pressure ulcer two weeks later, had a substantial decrease in blood flow while under the loading position compared to healthy subjects. Preliminary results suggest the developed system may potentially predict whether non-blanchable redness will manifest itself as advanced ulceration or dissipate over time.

High Frequency Sampling of TTL Pulses on a Raspberry Pi for Diffuse Correlation Spectroscopy Applications.

PubMed

Tivnan, Matthew; Gurjar, Rajan; Wolf, David E; Vishwanath, Karthik

2015-08-12

Diffuse Correlation Spectroscopy (DCS) is a well-established optical technique that has been used for non-invasive measurement of blood flow in tissues. Instrumentation for DCS includes a correlation device that computes the temporal intensity autocorrelation of a coherent laser source after it has undergone diffuse scattering through a turbid medium. Typically, the signal acquisition and its autocorrelation are performed by a correlation board. These boards have dedicated hardware to acquire and compute intensity autocorrelations of rapidly varying input signal and usually are quite expensive. Here we show that a Raspberry Pi minicomputer can acquire and store a rapidly varying time-signal with high fidelity. We show that this signal collected by a Raspberry Pi device can be processed numerically to yield intensity autocorrelations well suited for DCS applications. DCS measurements made using the Raspberry Pi device were compared to those acquired using a commercial hardware autocorrelation board to investigate the stability, performance, and accuracy of the data acquired in controlled experiments. This paper represents a first step toward lowering the instrumentation cost of a DCS system and may offer the potential to make DCS become more widely used in biomedical applications.

High Frequency Sampling of TTL Pulses on a Raspberry Pi for Diffuse Correlation Spectroscopy Applications

PubMed Central

Tivnan, Matthew; Gurjar, Rajan; Wolf, David E.; Vishwanath, Karthik

2015-01-01

Diffuse Correlation Spectroscopy (DCS) is a well-established optical technique that has been used for non-invasive measurement of blood flow in tissues. Instrumentation for DCS includes a correlation device that computes the temporal intensity autocorrelation of a coherent laser source after it has undergone diffuse scattering through a turbid medium. Typically, the signal acquisition and its autocorrelation are performed by a correlation board. These boards have dedicated hardware to acquire and compute intensity autocorrelations of rapidly varying input signal and usually are quite expensive. Here we show that a Raspberry Pi minicomputer can acquire and store a rapidly varying time-signal with high fidelity. We show that this signal collected by a Raspberry Pi device can be processed numerically to yield intensity autocorrelations well suited for DCS applications. DCS measurements made using the Raspberry Pi device were compared to those acquired using a commercial hardware autocorrelation board to investigate the stability, performance, and accuracy of the data acquired in controlled experiments. This paper represents a first step toward lowering the instrumentation cost of a DCS system and may offer the potential to make DCS become more widely used in biomedical applications. PMID:26274961

A method to identify differential expression profiles of time-course gene data with Fourier transformation

PubMed Central

2013-01-01

Background Time course gene expression experiments are an increasingly popular method for exploring biological processes. Temporal gene expression profiles provide an important characterization of gene function, as biological systems are both developmental and dynamic. With such data it is possible to study gene expression changes over time and thereby to detect differential genes. Much of the early work on analyzing time series expression data relied on methods developed originally for static data and thus there is a need for improved methodology. Since time series expression is a temporal process, its unique features such as autocorrelation between successive points should be incorporated into the analysis. Results This work aims to identify genes that show different gene expression profiles across time. We propose a statistical procedure to discover gene groups with similar profiles using a nonparametric representation that accounts for the autocorrelation in the data. In particular, we first represent each profile in terms of a Fourier basis, and then we screen out genes that are not differentially expressed based on the Fourier coefficients. Finally, we cluster the remaining gene profiles using a model-based approach in the Fourier domain. We evaluate the screening results in terms of sensitivity, specificity, FDR and FNR, compare with the Gaussian process regression screening in a simulation study and illustrate the results by application to yeast cell-cycle microarray expression data with alpha-factor synchronization. The key elements of the proposed methodology: (i) representation of gene profiles in the Fourier domain; (ii) automatic screening of genes based on the Fourier coefficients and taking into account autocorrelation in the data, while controlling the false discovery rate (FDR); (iii) model-based clustering of the remaining gene profiles. Conclusions Using this method, we identified a set of cell-cycle-regulated time-course yeast genes. The proposed method is general and can be potentially used to identify genes which have the same patterns or biological processes, and help facing the present and forthcoming challenges of data analysis in functional genomics. PMID:24134721

Velocity and stress autocorrelation decay in isothermal dissipative particle dynamics

NASA Astrophysics Data System (ADS)

Chaudhri, Anuj; Lukes, Jennifer R.

2010-02-01

The velocity and stress autocorrelation decay in a dissipative particle dynamics ideal fluid model is analyzed in this paper. The autocorrelation functions are calculated at three different friction parameters and three different time steps using the well-known Groot/Warren algorithm and newer algorithms including self-consistent leap-frog, self-consistent velocity Verlet and Shardlow first and second order integrators. At low friction values, the velocity autocorrelation function decays exponentially at short times, shows slower-than exponential decay at intermediate times, and approaches zero at long times for all five integrators. As friction value increases, the deviation from exponential behavior occurs earlier and is more pronounced. At small time steps, all the integrators give identical decay profiles. As time step increases, there are qualitative and quantitative differences between the integrators. The stress correlation behavior is markedly different for the algorithms. The self-consistent velocity Verlet and the Shardlow algorithms show very similar stress autocorrelation decay with change in friction parameter, whereas the Groot/Warren and leap-frog schemes show variations at higher friction factors. Diffusion coefficients and shear viscosities are calculated using Green-Kubo integration of the velocity and stress autocorrelation functions. The diffusion coefficients match well-known theoretical results at low friction limits. Although the stress autocorrelation function is different for each integrator, fluctuates rapidly, and gives poor statistics for most of the cases, the calculated shear viscosities still fall within range of theoretical predictions and nonequilibrium studies.

Increasing market efficiency in the stock markets

NASA Astrophysics Data System (ADS)

Yang, Jae-Suk; Kwak, Wooseop; Kaizoji, Taisei; Kim, In-Mook

2008-01-01

We study the temporal evolutions of three stock markets; Standard and Poor's 500 index, Nikkei 225 Stock Average, and the Korea Composite Stock Price Index. We observe that the probability density function of the log-return has a fat tail but the tail index has been increasing continuously in recent years. We have also found that the variance of the autocorrelation function, the scaling exponent of the standard deviation, and the statistical complexity decrease, but that the entropy density increases as time goes over time. We introduce a modified microscopic spin model and simulate the model to confirm such increasing and decreasing tendencies in statistical quantities. These findings indicate that these three stock markets are becoming more efficient.

Gamma-Ray Light Curves And Variability Of Bright Fermi -Detected Blazars

DOE PAGES

Abdo, A. A.

2010-09-22

This paper presents light curves as well as the first systematic characterization of variability of the 106 objects in the high-confidence Fermi Large Area Telescope Bright AGN Sample (LBAS). Weekly light curves of this sample, obtained during the first 11 months of the Fermi survey (2008 August 4-2009 July 4), are tested for variability and their properties are quantified through autocorrelation function and structure function analysis. For the brightest sources, 3 or 4 day binned light curves are extracted in order to determine power density spectra (PDSs) and to fit the temporal structure of major flares. More than 50% ofmore » the sources are found to be variable with high significance, where high states do not exceed 1/4 of the total observation range. Variation amplitudes are larger for flat spectrum radio quasars and low/intermediate synchrotron frequency peaked BL Lac objects. Autocorrelation timescales derived from weekly light curves vary from four to a dozen of weeks. Variable sources of the sample have weekly and 3-4 day bin light curves that can be described by 1/f α PDS, and show two kinds of gamma-ray variability: (1) rather constant baseline with sporadic flaring activity characterized by flatter PDS slopes resembling flickering and red noise with occasional intermittence and (2)—measured for a few blazars showing strong activity—complex and structured temporal profiles characterized by long-term memory and steeper PDS slopes, reflecting a random walk underlying mechanism. The average slope of the PDS of the brightest 22 FSRQs and of the 6 brightest BL Lacs is 1.5 and 1.7, respectively. The study of temporal profiles of well-resolved flares observed in the 10 brightest LBAS sources shows that they generally have symmetric profiles and that their total duration vary between 10 and 100 days. Results presented here can assist in source class recognition for unidentified sources and can serve as reference for more detailed analysis of the brightest gamma-ray blazars.« less

Accounting for Non-Gaussian Sources of Spatial Correlation in Parametric Functional Magnetic Resonance Imaging Paradigms II: A Method to Obtain First-Level Analysis Residuals with Uniform and Gaussian Spatial Autocorrelation Function and Independent and Identically Distributed Time-Series.

PubMed

Gopinath, Kaundinya; Krishnamurthy, Venkatagiri; Lacey, Simon; Sathian, K

2018-02-01

In a recent study Eklund et al. have shown that cluster-wise family-wise error (FWE) rate-corrected inferences made in parametric statistical method-based functional magnetic resonance imaging (fMRI) studies over the past couple of decades may have been invalid, particularly for cluster defining thresholds less stringent than p < 0.001; principally because the spatial autocorrelation functions (sACFs) of fMRI data had been modeled incorrectly to follow a Gaussian form, whereas empirical data suggest otherwise. Hence, the residuals from general linear model (GLM)-based fMRI activation estimates in these studies may not have possessed a homogenously Gaussian sACF. Here we propose a method based on the assumption that heterogeneity and non-Gaussianity of the sACF of the first-level GLM analysis residuals, as well as temporal autocorrelations in the first-level voxel residual time-series, are caused by unmodeled MRI signal from neuronal and physiological processes as well as motion and other artifacts, which can be approximated by appropriate decompositions of the first-level residuals with principal component analysis (PCA), and removed. We show that application of this method yields GLM residuals with significantly reduced spatial correlation, nearly Gaussian sACF and uniform spatial smoothness across the brain, thereby allowing valid cluster-based FWE-corrected inferences based on assumption of Gaussian spatial noise. We further show that application of this method renders the voxel time-series of first-level GLM residuals independent, and identically distributed across time (which is a necessary condition for appropriate voxel-level GLM inference), without having to fit ad hoc stochastic colored noise models. Furthermore, the detection power of individual subject brain activation analysis is enhanced. This method will be especially useful for case studies, which rely on first-level GLM analysis inferences.

Modified Beer-Lambert law for blood flow

PubMed Central

Baker, Wesley B.; Parthasarathy, Ashwin B.; Busch, David R.; Mesquita, Rickson C.; Greenberg, Joel H.; Yodh, A. G.

2014-01-01

We develop and validate a Modified Beer-Lambert law for blood flow based on diffuse correlation spectroscopy (DCS) measurements. The new formulation enables blood flow monitoring from temporal intensity autocorrelation function data taken at single or multiple delay-times. Consequentially, the speed of the optical blood flow measurement can be substantially increased. The scheme facilitates blood flow monitoring of highly scattering tissues in geometries wherein light propagation is diffusive or non-diffusive, and it is particularly well-suited for utilization with pressure measurement paradigms that employ differential flow signals to reduce contributions of superficial tissues. PMID:25426330

Concentration dependence of the wings of a dipole-broadened magnetic resonance line in magnetically diluted lattices

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zobov, V. E., E-mail: rsa@iph.krasn.ru; Kucherov, M. M.

2017-01-15

The singularities of the time autocorrelation functions (ACFs) of magnetically diluted spin systems with dipole–dipole interaction (DDI), which determine the high-frequency asymptotics of autocorrelation functions and the wings of a magnetic resonance line, are studied. Using the self-consistent fluctuating local field approximation, nonlinear equations are derived for autocorrelation functions averaged over the independent random arrangement of spins (magnetic atoms) in a diamagnetic lattice with different spin concentrations. The equations take into account the specificity of the dipole–dipole interaction. First, due to its axial symmetry in a strong static magnetic field, the autocorrelation functions of longitudinal and transverse spin components aremore » described by different equations. Second, the long-range type of the dipole–dipole interaction is taken into account by separating contributions into the local field from distant and near spins. The recurrent equations are obtained for the expansion coefficients of autocorrelation functions in power series in time. From them, the numerical value of the coordinate of the nearest singularity of the autocorrelation function is found on the imaginary time axis, which is equal to the radius of convergence of these expansions. It is shown that in the strong dilution case, the logarithmic concentration dependence of the coordinate of the singularity is observed, which is caused by the presence of a cluster of near spins whose fraction is small but contribution to the modulation frequency is large. As an example a silicon crystal with different {sup 29}Si concentrations in magnetic fields directed along three crystallographic axes is considered.« less

0.1 Trend analysis of δ18O composition of precipitation in Germany: Combining Mann-Kendall trend test and ARIMA models to correct for higher order serial correlation

NASA Astrophysics Data System (ADS)

Klaus, Julian; Pan Chun, Kwok; Stumpp, Christine

2015-04-01

Spatio-temporal dynamics of stable oxygen (18O) and hydrogen (2H) isotopes in precipitation can be used as proxies for changing hydro-meteorological and regional and global climate patterns. While spatial patterns and distributions gained much attention in recent years the temporal trends in stable isotope time series are rarely investigated and our understanding of them is still limited. These might be a result of a lack of proper trend detection tools and effort for exploring trend processes. Here we make use of an extensive data set of stable isotope in German precipitation. In this study we investigate temporal trends of δ18O in precipitation at 17 observation station in Germany between 1978 and 2009. For that we test different approaches for proper trend detection, accounting for first and higher order serial correlation. We test if significant trends in the isotope time series based on different models can be observed. We apply the Mann-Kendall trend tests on the isotope series, using general multiplicative seasonal autoregressive integrate moving average (ARIMA) models which account for first and higher order serial correlations. With the approach we can also account for the effects of temperature, precipitation amount on the trend. Further we investigate the role of geographic parameters on isotope trends. To benchmark our proposed approach, the ARIMA results are compared to a trend-free prewhiting (TFPW) procedure, the state of the art method for removing the first order autocorrelation in environmental trend studies. Moreover, we explore whether higher order serial correlations in isotope series affects our trend results. The results show that three out of the 17 stations have significant changes when higher order autocorrelation are adjusted, and four stations show a significant trend when temperature and precipitation effects are considered. Significant trends in the isotope time series are generally observed at low elevation stations (≤315 m a.s.l.). Higher order autoregressive processes are important in the isotope time series analysis. Our results show that the widely used trend analysis with only the first order autocorrelation adjustment may not adequately take account of the high order autocorrelated processes in the stable isotope series. The investigated time series analysis method including higher autocorrelation and external climate variable adjustments is shown to be a better alternative.

Determination of modulation transfer function of a printer by measuring the autocorrelation of the transmission function of a printed Ronchi grating

DOE Office of Scientific and Technical Information (OSTI.GOV)

Madanipour, Khosro; Tavassoly, Mohammad T

2009-02-01

We show theoretically and verify experimentally that the modulation transfer function (MTF) of a printing system can be determined by measuring the autocorrelation of a printed Ronchi grating. In practice, two similar Ronchi gratings are printed on two transparencies and the transparencies are superimposed with parallel grating lines. Then, the gratings are uniformly illuminated and the transmitted light from a large section is measured versus the displacement of one grating with respect to the other in a grating pitch interval. This measurement provides the required autocorrelation function for determination of the MTF.

An advanced analysis and modelling the air pollutant concentration temporal dynamics in atmosphere of the industrial cities: Odessa city

NASA Astrophysics Data System (ADS)

Buyadzhi, V. V.; Glushkov, A. V.; Khetselius, O. Yu; Ternovsky, V. B.; Serga, I. N.; Bykowszczenko, N.

2017-10-01

Results of analysis and modelling the air pollutant (dioxide of nitrogen) concentration temporal dynamics in atmosphere of the industrial city Odessa are presented for the first time and based on computing by nonlinear methods of the chaos and dynamical systems theories. A chaotic behaviour is discovered and investigated. To reconstruct the corresponding strange chaotic attractor, the time delay and embedding dimension are computed. The former is determined by the methods of autocorrelation function and average mutual information, and the latter is calculated by means of correlation dimension method and algorithm of false nearest neighbours. It is shown that low-dimensional chaos exists in the nitrogen dioxide concentration time series under investigation. Further, the Lyapunov’s exponents spectrum, Kaplan-Yorke dimension and Kolmogorov entropy are computed.

Atomistic observation and simulation analysis of spatio-temporal fluctuations during radiation-induced amorphization.

PubMed

Watanabe, Seiichi; Hoshino, Misaki; Koike, Takuto; Suda, Takanori; Ohnuki, Soumei; Takahashi, Heishichirou; Lam, Nighi Q

2003-01-01

We performed a dynamical-atomistic study of radiation-induced amorphization in the NiTi intermetallic compound using in situ high-resolution high-voltage electron microscopy and molecular dynamics simulations in connection with image simulation. Spatio-temporal fluctuations as non-equilibrium fluctuations in an energy-dissipative system, due to transient atom-cluster formation during amorphization, were revealed by the present spatial autocorrelation analysis.

Forecasting intentional wildfires using temporal and spatiotemporal autocorrelations

Treesearch

Jeffrey P. Prestemon; María L. Chas-Amil; Julia M. Touza; Scott L. Goodrick

2012-01-01

We report daily time series models containing both temporal and spatiotemporal lags, which are applied to forecasting intentional wildfires in Galicia, Spain. Models are estimated independently for each of the 19 forest districts in Galicia using a 1999–2003 training dataset and evaluated out-of-sample with a 2004–06 dataset. Poisson autoregressive models of order P –...

Simultaneous and co-localized acousto-optic measurements of spectral and temporal properties of diffusive media

NASA Astrophysics Data System (ADS)

Balberg, Michal; Shechter, Revital; Girshovitz, Pinhas; Breskin, Ilan; Fantini, Sergio

2017-02-01

Acousto-optic (AO) modulation of light is used to extract both temporal and spectral information of diffusive media such as biological tissue, where they provide measures of blood flow and oxygen saturation of hemoglobin, respectively. The temporal information is extracted from the width of the power spectrum of the light intensity, whereas the spectral information is calculated from the spatial decay of the cross correlation between the light intensity and the generated ultrasonic signal. The ultrasonic signal is a coded phase modulated signal with a narrow autocorrelation, enabling localization of the measurement volume. Two different liquid phantoms are used, with similar scattering but different absorption properties. The difference in absorption calculated with the AO signal is compared to calculations based on the modified Beer Lambert law. As the same AO signal is used to extract both modalities, it might be used to extract hemodynamic related changes in the brain for diagnostic and functional assessment.

Temporal trend and climate factors of hemorrhagic fever with renal syndrome epidemic in Shenyang City, China

PubMed Central

2011-01-01

Background Hemorrhagic fever with renal syndrome (HFRS) is an important infectious disease caused by different species of hantaviruses. As a rodent-borne disease with a seasonal distribution, external environmental factors including climate factors may play a significant role in its transmission. The city of Shenyang is one of the most seriously endemic areas for HFRS. Here, we characterized the dynamic temporal trend of HFRS, and identified climate-related risk factors and their roles in HFRS transmission in Shenyang, China. Methods The annual and monthly cumulative numbers of HFRS cases from 2004 to 2009 were calculated and plotted to show the annual and seasonal fluctuation in Shenyang. Cross-correlation and autocorrelation analyses were performed to detect the lagged effect of climate factors on HFRS transmission and the autocorrelation of monthly HFRS cases. Principal component analysis was constructed by using climate data from 2004 to 2009 to extract principal components of climate factors to reduce co-linearity. The extracted principal components and autocorrelation terms of monthly HFRS cases were added into a multiple regression model called principal components regression model (PCR) to quantify the relationship between climate factors, autocorrelation terms and transmission of HFRS. The PCR model was compared to a general multiple regression model conducted only with climate factors as independent variables. Results A distinctly declining temporal trend of annual HFRS incidence was identified. HFRS cases were reported every month, and the two peak periods occurred in spring (March to May) and winter (November to January), during which, nearly 75% of the HFRS cases were reported. Three principal components were extracted with a cumulative contribution rate of 86.06%. Component 1 represented MinRH0, MT1, RH1, and MWV1; component 2 represented RH2, MaxT3, and MAP3; and component 3 represented MaxT2, MAP2, and MWV2. The PCR model was composed of three principal components and two autocorrelation terms. The association between HFRS epidemics and climate factors was better explained in the PCR model (F = 446.452, P < 0.001, adjusted R2 = 0.75) than in the general multiple regression model (F = 223.670, P < 0.000, adjusted R2 = 0.51). Conclusion The temporal distribution of HFRS in Shenyang varied in different years with a distinctly declining trend. The monthly trends of HFRS were significantly associated with local temperature, relative humidity, precipitation, air pressure, and wind velocity of the different previous months. The model conducted in this study will make HFRS surveillance simpler and the control of HFRS more targeted in Shenyang. PMID:22133347

The temporal representation of the delay of dynamic iterated rippled noise with positive and negative gain by single units in the ventral cochlear nucleus.

PubMed

Sayles, Mark; Winter, Ian Michael

2007-09-26

Spike trains were recorded from single units in the ventral cochlear nucleus of the anaesthetised guinea-pig in response to dynamic iterated rippled noise with positive and negative gain. The short-term running waveform autocorrelation functions of these stimuli show peaks at integer multiples of the time-varying delay when the gain is +1, and troughs at odd-integer multiples and peaks at even-integer multiples of the time-varying delay when the gain is -1. In contrast, the short-term autocorrelation of the Hilbert envelope shows peaks at integer multiples of the time-varying delay for both positive and negative gain stimuli. A running short-term all-order interspike interval analysis demonstrates the ability of single units to represent the modulated pitch contour in their short-term interval statistics. For units with low best frequency (approximate < or = 1.1 kHz) the temporal discharge pattern reflected the waveform fine structure regardless of unit classification (Primary-like, Chopper). For higher best frequency units the pattern of response varied according to unit type. Chopper units with best frequency approximate > or = 1.1 kHz responded to envelope modulation; showing no difference between their response to stimuli with positive and negative gain. Primary-like units with best frequencies in the range 1-3 kHz were still able to represent the difference in the temporal fine structure between dynamic rippled noise with positive and negative gain. No unit with a best frequency above 3 kHz showed a response to the temporal fine structure. Chopper units in this high frequency group showed significantly greater representation of envelope modulation relative to primary-like units with the same range of best frequencies. These results show that at the level of the cochlear nucleus there exists sufficient information in the time domain to represent the time-varying pitch associated with dynamic iterated rippled noise.

Superaging and Subaging Phenomena in a Nonequilibrium Critical Behavior of the Structurally Disordered Two-Dimensional XY Model

NASA Astrophysics Data System (ADS)

Prudnikov, V. V.; Prudnikov, P. V.; Popov, I. S.

2018-03-01

A Monte Carlo numerical simulation of the specific features of nonequilibrium critical behavior is carried out for the two-dimensional structurally disordered XY model during its evolution from a low-temperature initial state. On the basis of the analysis of the two-time dependence of autocorrelation functions and dynamic susceptibility for systems with spin concentrations of p = 1.0, 0.9, and 0.6, aging phenomena characterized by a slowing down of the relaxation system with increasing waiting time and the violation of the fluctuation-dissipation theorem (FDT) are revealed. The values of the universal limiting fluctuation-dissipation ratio (FDR) are obtained for the systems considered. As a result of the analysis of the two-time scaling dependence for spin-spin and connected spin autocorrelation functions, it is found that structural defects lead to subaging phenomena in the behavior of the spin-spin autocorrelation function and superaging phenomena in the behavior of the connected spin autocorrelation function.

Using PPI network autocorrelation in hierarchical multi-label classification trees for gene function prediction.

PubMed

Stojanova, Daniela; Ceci, Michelangelo; Malerba, Donato; Dzeroski, Saso

2013-09-26

Ontologies and catalogs of gene functions, such as the Gene Ontology (GO) and MIPS-FUN, assume that functional classes are organized hierarchically, that is, general functions include more specific ones. This has recently motivated the development of several machine learning algorithms for gene function prediction that leverages on this hierarchical organization where instances may belong to multiple classes. In addition, it is possible to exploit relationships among examples, since it is plausible that related genes tend to share functional annotations. Although these relationships have been identified and extensively studied in the area of protein-protein interaction (PPI) networks, they have not received much attention in hierarchical and multi-class gene function prediction. Relations between genes introduce autocorrelation in functional annotations and violate the assumption that instances are independently and identically distributed (i.i.d.), which underlines most machine learning algorithms. Although the explicit consideration of these relations brings additional complexity to the learning process, we expect substantial benefits in predictive accuracy of learned classifiers. This article demonstrates the benefits (in terms of predictive accuracy) of considering autocorrelation in multi-class gene function prediction. We develop a tree-based algorithm for considering network autocorrelation in the setting of Hierarchical Multi-label Classification (HMC). We empirically evaluate the proposed algorithm, called NHMC (Network Hierarchical Multi-label Classification), on 12 yeast datasets using each of the MIPS-FUN and GO annotation schemes and exploiting 2 different PPI networks. The results clearly show that taking autocorrelation into account improves the predictive performance of the learned models for predicting gene function. Our newly developed method for HMC takes into account network information in the learning phase: When used for gene function prediction in the context of PPI networks, the explicit consideration of network autocorrelation increases the predictive performance of the learned models. Overall, we found that this holds for different gene features/ descriptions, functional annotation schemes, and PPI networks: Best results are achieved when the PPI network is dense and contains a large proportion of function-relevant interactions.

Hydrodynamics of confined colloidal fluids in two dimensions

NASA Astrophysics Data System (ADS)

Sané, Jimaan; Padding, Johan T.; Louis, Ard A.

2009-05-01

We apply a hybrid molecular dynamics and mesoscopic simulation technique to study the dynamics of two-dimensional colloidal disks in confined geometries. We calculate the velocity autocorrelation functions and observe the predicted t-1 long-time hydrodynamic tail that characterizes unconfined fluids, as well as more complex oscillating behavior and negative tails for strongly confined geometries. Because the t-1 tail of the velocity autocorrelation function is cut off for longer times in finite systems, the related diffusion coefficient does not diverge but instead depends logarithmically on the overall size of the system. The Langevin equation gives a poor approximation to the velocity autocorrelation function at both short and long times.

Periodicity in the autocorrelation function as a mechanism for regularly occurring zero crossings or extreme values of a Gaussian process.

PubMed

Wilson, Lorna R M; Hopcraft, Keith I

2017-12-01

The problem of zero crossings is of great historical prevalence and promises extensive application. The challenge is to establish precisely how the autocorrelation function or power spectrum of a one-dimensional continuous random process determines the density function of the intervals between the zero crossings of that process. This paper investigates the case where periodicities are incorporated into the autocorrelation function of a smooth process. Numerical simulations, and statistics about the number of crossings in a fixed interval, reveal that in this case the zero crossings segue between a random and deterministic point process depending on the relative time scales of the periodic and nonperiodic components of the autocorrelation function. By considering the Laplace transform of the density function, we show that incorporating correlation between successive intervals is essential to obtaining accurate results for the interval variance. The same method enables prediction of the density function tail in some regions, and we suggest approaches for extending this to cover all regions. In an ever-more complex world, the potential applications for this scale of regularity in a random process are far reaching and powerful.

Periodicity in the autocorrelation function as a mechanism for regularly occurring zero crossings or extreme values of a Gaussian process

NASA Astrophysics Data System (ADS)

Wilson, Lorna R. M.; Hopcraft, Keith I.

2017-12-01

The problem of zero crossings is of great historical prevalence and promises extensive application. The challenge is to establish precisely how the autocorrelation function or power spectrum of a one-dimensional continuous random process determines the density function of the intervals between the zero crossings of that process. This paper investigates the case where periodicities are incorporated into the autocorrelation function of a smooth process. Numerical simulations, and statistics about the number of crossings in a fixed interval, reveal that in this case the zero crossings segue between a random and deterministic point process depending on the relative time scales of the periodic and nonperiodic components of the autocorrelation function. By considering the Laplace transform of the density function, we show that incorporating correlation between successive intervals is essential to obtaining accurate results for the interval variance. The same method enables prediction of the density function tail in some regions, and we suggest approaches for extending this to cover all regions. In an ever-more complex world, the potential applications for this scale of regularity in a random process are far reaching and powerful.

Autocorrelation of the susceptible-infected-susceptible process on networks

NASA Astrophysics Data System (ADS)

Liu, Qiang; Van Mieghem, Piet

2018-06-01

In this paper, we focus on the autocorrelation of the susceptible-infected-susceptible (SIS) process on networks. The N -intertwined mean-field approximation (NIMFA) is applied to calculate the autocorrelation properties of the exact SIS process. We derive the autocorrelation of the infection state of each node and the fraction of infected nodes both in the steady and transient states as functions of the infection probabilities of nodes. Moreover, we show that the autocorrelation can be used to estimate the infection and curing rates of the SIS process. The theoretical results are compared with the simulation of the exact SIS process. Our work fully utilizes the potential of the mean-field method and shows that NIMFA can indeed capture the autocorrelation properties of the exact SIS process.

Sources of variation in Landsat autocorrelation

NASA Technical Reports Server (NTRS)

Craig, R. G.; Labovitz, M. L.

1980-01-01

Analysis of sixty-four scan lines representing diverse conditions across satellites, channels, scanners, locations and cloud cover confirms that Landsat data are autocorrelated and consistently follow an Arima (1,0,1) pattern. The AR parameter varies significantly with location and the MA coefficient with cloud cover. Maximum likelihood classification functions are considerably in error unless this autocorrelation is compensated for in sampling.

Temporal and spatial distribution characteristics in the natural plague foci of Chinese Mongolian gerbils based on spatial autocorrelation.

PubMed

Du, Hai-Wen; Wang, Yong; Zhuang, Da-Fang; Jiang, Xiao-San

2017-08-07

The nest flea index of Meriones unguiculatus is a critical indicator for the prevention and control of plague, which can be used not only to detect the spatial and temporal distributions of Meriones unguiculatus, but also to reveal its cluster rule. This research detected the temporal and spatial distribution characteristics of the plague natural foci of Mongolian gerbils by body flea index from 2005 to 2014, in order to predict plague outbreaks. Global spatial autocorrelation was used to describe the entire spatial distribution pattern of the body flea index in the natural plague foci of typical Chinese Mongolian gerbils. Cluster and outlier analysis and hot spot analysis were also used to detect the intensity of clusters based on geographic information system methods. The quantity of M. unguiculatus nest fleas in the sentinel surveillance sites from 2005 to 2014 and host density data of the study area from 2005 to 2010 used in this study were provided by Chinese Center for Disease Control and Prevention. The epidemic focus regions of the Mongolian gerbils remain the same as the hot spot regions relating to the body flea index. High clustering areas possess a similar pattern as the distribution pattern of the body flea index indicating that the transmission risk of plague is relatively high. In terms of time series, the area of the epidemic focus gradually increased from 2005 to 2007, declined rapidly in 2008 and 2009, and then decreased slowly and began trending towards stability from 2009 to 2014. For the spatial change, the epidemic focus regions began moving northward from the southwest epidemic focus of the Mongolian gerbils from 2005 to 2007, and then moved from north to south in 2007 and 2008. The body flea index of Chinese gerbil foci reveals significant spatial and temporal aggregation characteristics through the employing of spatial autocorrelation. The diversity of temporary and spatial distribution is mainly affected by seasonal variation, the human activity and natural factors.

Analysis of spatial autocorrelation patterns of heavy and super-heavy rainfall in Iran

NASA Astrophysics Data System (ADS)

Rousta, Iman; Doostkamian, Mehdi; Haghighi, Esmaeil; Ghafarian Malamiri, Hamid Reza; Yarahmadi, Parvane

2017-09-01

Rainfall is a highly variable climatic element, and rainfall-related changes occur in spatial and temporal dimensions within a regional climate. The purpose of this study is to investigate the spatial autocorrelation changes of Iran's heavy and super-heavy rainfall over the past 40 years. For this purpose, the daily rainfall data of 664 meteorological stations between 1971 and 2011 are used. To analyze the changes in rainfall within a decade, geostatistical techniques like spatial autocorrelation analysis of hot spots, based on the Getis-Ord G i statistic, are employed. Furthermore, programming features in MATLAB, Surfer, and GIS are used. The results indicate that the Caspian coast, the northwest and west of the western foothills of the Zagros Mountains of Iran, the inner regions of Iran, and southern parts of Southeast and Northeast Iran, have the highest likelihood of heavy and super-heavy rainfall. The spatial pattern of heavy rainfall shows that, despite its oscillation in different periods, the maximum positive spatial autocorrelation pattern of heavy rainfall includes areas of the west, northwest and west coast of the Caspian Sea. On the other hand, a negative spatial autocorrelation pattern of heavy rainfall is observed in central Iran and parts of the east, particularly in Zabul. Finally, it is found that patterns of super-heavy rainfall are similar to those of heavy rainfall.

Inference for local autocorrelations in locally stationary models.

PubMed

Zhao, Zhibiao

2015-04-01

For non-stationary processes, the time-varying correlation structure provides useful insights into the underlying model dynamics. We study estimation and inferences for local autocorrelation process in locally stationary time series. Our constructed simultaneous confidence band can be used to address important hypothesis testing problems, such as whether the local autocorrelation process is indeed time-varying and whether the local autocorrelation is zero. In particular, our result provides an important generalization of the R function acf() to locally stationary Gaussian processes. Simulation studies and two empirical applications are developed. For the global temperature series, we find that the local autocorrelations are time-varying and have a "V" shape during 1910-1960. For the S&P 500 index, we conclude that the returns satisfy the efficient-market hypothesis whereas the magnitudes of returns show significant local autocorrelations.

Dynamic Assessment on the Landscape Patterns and Spatio-temporal Change in the mainstream of Tarim River

NASA Astrophysics Data System (ADS)

Zhang, Hui; Xue, Lianqing; Yang, Changbing; Chen, Xinfang; Zhang, Luochen; Wei, Guanghui

2018-01-01

The Tarim River (TR), as the longest inland river at an arid area in China, is a typical regions of vegetation variation research and plays a crucial role in the sustainable development of regional ecological environment. In this paper, the newest dataset of MODND1M NDVI, at a resolution of 500m, were applied to calculate vegetation index in growing season during the period 2000-2015. Using a vegetation coverage index, a trend line analysis, and the local spatial autocorrelation analysis, this paper investigated the landscape patterns and spatio-temporal variation of vegetation coverage at regional and pixel scales over mainstream of the Tarim River, Xinjiang. The results showed that (1) The bare land area on both sides of Tarim River appeared to have a fluctuated downward trend and there were two obvious valley values in 2005 and 2012. (2) Spatially, the vegetation coverage improved areas is mostly distributed in upstream and the degraded areas is mainly distributed in the left bank of midstream and the end of Tarim River during 2000-2005. (3) The local spatial auto-correlation analysis revealed that vegetation coverage was spatially positive autocorrelated and spatial concentrated. The high-high self-related areas are mainly distributed in upstream, where vegetation cover are relatively good, and the low-low self-related areas are mostly with lower vegetation cover in the lower reaches of Tarim River.

Quantitative approaches in climate change ecology

PubMed Central

Brown, Christopher J; Schoeman, David S; Sydeman, William J; Brander, Keith; Buckley, Lauren B; Burrows, Michael; Duarte, Carlos M; Moore, Pippa J; Pandolfi, John M; Poloczanska, Elvira; Venables, William; Richardson, Anthony J

2011-01-01

Contemporary impacts of anthropogenic climate change on ecosystems are increasingly being recognized. Documenting the extent of these impacts requires quantitative tools for analyses of ecological observations to distinguish climate impacts in noisy data and to understand interactions between climate variability and other drivers of change. To assist the development of reliable statistical approaches, we review the marine climate change literature and provide suggestions for quantitative approaches in climate change ecology. We compiled 267 peer-reviewed articles that examined relationships between climate change and marine ecological variables. Of the articles with time series data (n = 186), 75% used statistics to test for a dependency of ecological variables on climate variables. We identified several common weaknesses in statistical approaches, including marginalizing other important non-climate drivers of change, ignoring temporal and spatial autocorrelation, averaging across spatial patterns and not reporting key metrics. We provide a list of issues that need to be addressed to make inferences more defensible, including the consideration of (i) data limitations and the comparability of data sets; (ii) alternative mechanisms for change; (iii) appropriate response variables; (iv) a suitable model for the process under study; (v) temporal autocorrelation; (vi) spatial autocorrelation and patterns; and (vii) the reporting of rates of change. While the focus of our review was marine studies, these suggestions are equally applicable to terrestrial studies. Consideration of these suggestions will help advance global knowledge of climate impacts and understanding of the processes driving ecological change.

Temporal pattern and memory in sediment transport in an experimental step-pool channel

NASA Astrophysics Data System (ADS)

Saletti, Matteo; Molnar, Peter; Zimmermann, André; Hassan, Marwan A.; Church, Michael; Burlando, Paolo

2015-04-01

In this work we study the complex dynamics of sediment transport and bed morphology in steep streams, using a dataset of experiments performed in a steep flume with natural sediment. High-resolution (1 sec) time series of sediment transport were measured for individual size classes at the outlet of the flume for different combinations of sediment input rates, discharges, and flume slopes. The data show that the relation between instantaneous discharge and sediment transport exhibits large variability on different levels. After dividing the time series into segments of constant water discharge, we quantify the statistical properties of transport rates by fitting the data with a Generalized Extreme Value distribution, whose 3 parameters are related to the average sediment flux. We analyze separately extreme events of transport rate in terms of their fractional composition; if only events of high magnitude are considered, coarse grains become the predominant component of the total sediment yield. We quantify the memory in grain size dependent sediment transport with variance scaling and autocorrelation analyses; more specifically, we study how the variance changes with different aggregation scales and how the autocorrelation coefficient changes with different time lags. Our results show that there is a tendency to an infinite memory regime in transport rate signals, which is limited by the intermittency of the largest fractions. Moreover, the structure of memory is both grain size-dependent and magnitude-dependent: temporal autocorrelation is stronger for small grain size fractions and when the average sediment transport rate is large. The short-term memory in coarse grain transport increases with temporal aggregation and this reveals the importance of the sampling frequency of bedload transport rates in natural streams, especially for large fractions.

Dynamical Resilience Indicators in Time Series of Self-Rated Health Correspond to Frailty Levels in Older Adults.

PubMed

Gijzel, Sanne M W; van de Leemput, Ingrid A; Scheffer, Marten; Roppolo, Mattia; Olde Rikkert, Marcel G M; Melis, René J F

2017-07-01

We currently still lack valid methods to dynamically measure resilience for stressors before the appearance of adverse health outcomes that hamper well-being. Quantifying an older adult's resilience in an early stage would aid complex decision-making in health care. Translating complex dynamical systems theory to humans, we hypothesized that three dynamical indicators of resilience (variance, temporal autocorrelation, and cross-correlation) in time series of self-rated physical, mental, and social health were associated with frailty levels in older adults. We monitored self-rated physical, mental, and social health during 100 days using daily visual analogue scale questions in 22 institutionalized older adults (mean age 84.0, SD: 5.9 years). Frailty was determined by the Survey of Health, Ageing and Retirement in Europe (SHARE) frailty index. The resilience indicators (variance, temporal autocorrelation, and cross-correlation) were calculated using multilevel models. The self-rated health time series of frail elderly exhibited significantly elevated variance in the physical, mental, and social domain, as well as significantly stronger cross-correlations between all three domains, as compared to the nonfrail group (all P < 0.001). Temporal autocorrelation was not significantly associated with frailty. We found supporting evidence for two out of three hypothesized resilience indicators to be related to frailty levels in older adults. By mirroring the dynamical resilience indicators to a frailty index, we delivered a first empirical base to validate and quantify the construct of systemic resilience in older adults in a dynamic way. © The Author 2017. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Temporal Variation and Scaling of Hydrological Variables in a Typical Watershed

NASA Astrophysics Data System (ADS)

Yang, C.; Zhang, Y. K.; Liang, X.; Liu, J.

2016-12-01

Temporal variations of the main hydrological variables over 16 years were systematically investigated based on the results from an integrated hydrological modeling at the Sagehen Creek Watershed in northern Sierra Nevada. Temporal scaling of these variables and damping effects of the hydrological system as well as its subsystems, i.e., the land surface, unsaturated zone, and saturated zone, were analyzed with spectral analyses. It was found that the hydrological system may act as a cascade of hierarchical fractal filters which sequentially transfer a non-fractal or less correlated fractal hydrological signal to a more correlated fractal signal. Temporal scaling of infiltration (I), actual evapotraspiration (ET), recharge (R), baseflow (BF), streamflow (SF) exist and the temporal autocorrelation of these variables increase as water moves through the system. The degree of the damping effect of the subsystems is different and is strongest in the unsaturated zone compared with that of the land surface and saturated zone. The temporal scaling of the groundwater levels (h) also exists and is strongly affected by the river: the temporal autocorrelation of h near the river is similar to that of the river stage fluctuations and increases away from the river. There is a break in the temporal scaling of h near the river at low frequencies due to the effect of the river. Temporal variations of the soil moisture (θ) is more complicated: the value of the scaling exponent (β) for θ increases with depth as water moves downwards and its high-frequency fluctuations are damped by the unsaturated zone. The temporal fluctuations of precipitation (P) and I are fractional Gauss noise (fGn), those of ET, R, BF, and SF are fractional Brownian motion (fBm), and those of h away from the river are 2nd-order fBm based on the values of β obtained in this study. Keywords: Temporal variations, Scaling, Damping effect, Hydrological system.

An empirical analysis of the distribution of the duration of overshoots in a stationary gaussian stochastic process

NASA Technical Reports Server (NTRS)

Parrish, R. S.; Carter, M. C.

1974-01-01

This analysis utilizes computer simulation and statistical estimation. Realizations of stationary gaussian stochastic processes with selected autocorrelation functions are computer simulated. Analysis of the simulated data revealed that the mean and the variance of a process were functionally dependent upon the autocorrelation parameter and crossing level. Using predicted values for the mean and standard deviation, by the method of moments, the distribution parameters was estimated. Thus, given the autocorrelation parameter, crossing level, mean, and standard deviation of a process, the probability of exceeding the crossing level for a particular length of time was calculated.

A model relating Eulerian spatial and temporal velocity correlations

NASA Astrophysics Data System (ADS)

Cholemari, Murali R.; Arakeri, Jaywant H.

2006-03-01

In this paper we propose a model to relate Eulerian spatial and temporal velocity autocorrelations in homogeneous, isotropic and stationary turbulence. We model the decorrelation as the eddies of various scales becoming decorrelated. This enables us to connect the spatial and temporal separations required for a certain decorrelation through the ‘eddy scale’. Given either the spatial or the temporal velocity correlation, we obtain the ‘eddy scale’ and the rate at which the decorrelation proceeds. This leads to a spatial separation from the temporal correlation and a temporal separation from the spatial correlation, at any given value of the correlation relating the two correlations. We test the model using experimental data from a stationary axisymmetric turbulent flow with homogeneity along the axis.

Coherence solution for bidirectional reflectance distributions of surfaces with wavelength-scale statistics.

PubMed

Hoover, Brian G; Gamiz, Victor L

2006-02-01

The scalar bidirectional reflectance distribution function (BRDF) due to a perfectly conducting surface with roughness and autocorrelation width comparable with the illumination wavelength is derived from coherence theory on the assumption of a random reflective phase screen and an expansion valid for large effective roughness. A general quadratic expansion of the two-dimensional isotropic surface autocorrelation function near the origin yields representative Cauchy and Gaussian BRDF solutions and an intermediate general solution as the sum of an incoherent component and a nonspecular coherent component proportional to an integral of the plasma dispersion function in the complex plane. Plots illustrate agreement of the derived general solution with original bistatic BRDF data due to a machined aluminum surface, and comparisons are drawn with previously published data in the examination of variations with incident angle, roughness, illumination wavelength, and autocorrelation coefficients in the bistatic and monostatic geometries. The general quadratic autocorrelation expansion provides a BRDF solution that smoothly interpolates between the well-known results of the linear and parabolic approximations.

Spatial autocorrelation of radiation measured by the Earth Radiation Budget Experiment: Scene inhomogeneity and reciprocity violation

NASA Technical Reports Server (NTRS)

Davies, Roger

1994-01-01

The spatial autocorrelation functions of broad-band longwave and shortwave radiances measured by the Earth Radiation Budget Experiment (ERBE) are analyzed as a function of view angle in an investigation of the general effects of scene inhomogeneity on radiation. For nadir views, the correlation distance of the autocorrelation function is about 900 km for longwave radiance and about 500 km for shortwave radiance, consistent with higher degrees of freedom in shortwave reflection. Both functions rise monotonically with view angle, but there is a substantial difference in the relative angular dependence of the shortwave and longwave functions, especially for view angles less than 50 deg. In this range, the increase with angle of the longwave functions is found to depend only on the expansion of pixel area with angle, whereas the shortwave functions show an additional dependence on angle that is attributed to the occlusion of inhomogeneities by cloud height variations. Beyond a view angle of about 50 deg, both longwave and shortwave functions appear to be affected by cloud sides. The shortwave autocorrelation functions do not satisfy the principle of directional reciprocity, thereby proving that the average scene is horizontally inhomogeneous over the scale of an ERBE pixel (1500 sq km). Coarse stratification of the measurements by cloud amount, however, indicates that the average cloud-free scene does satisfy directional reciprocity on this scale.

DETAILED DATA ANALYSIS OF ECHO I, ECHO II AND MOON REFLECTED SIGNALS. VOLUME 2. AUTOCORRELATION FUNCTIONS OF ECHO II REFLECTED SIGNALS,

DTIC Science & Technology

techniques is presented. Two methods for linearizing the data are given. An expression for the specular-to-spattered power ratio is derived, and the inverse ... transform of the autocorrelation function is discussed. The surface roughness of the reflector, the discrete fading rates, and fading frequencies

Usage Autocorrelation Function in the Capacity of Indicator Shape of the Signal in Acoustic Emission Testing of Intricate Castings

NASA Astrophysics Data System (ADS)

Popkov, Artem

2016-01-01

The article contains information about acoustic emission signals analysing using autocorrelation function. Operation factors were analysed, such as shape of signal, the origins time and carrier frequency. The purpose of work is estimating the validity of correlations methods analysing signals. Acoustic emission signal consist of different types of waves, which propagate on different trajectories in object of control. Acoustic emission signal is amplitude-, phase- and frequency-modeling signal. It was described by carrier frequency at a given point of time. Period of signal make up 12.5 microseconds and carrier frequency make up 80 kHz for analysing signal. Usage autocorrelation function like indicator the origin time of acoustic emission signal raises validity localization of emitters.

Environmental variability uncovers disruptive effects of species' interactions on population dynamics.

PubMed

Gudmundson, Sara; Eklöf, Anna; Wennergren, Uno

2015-08-07

How species respond to changes in environmental variability has been shown for single species, but the question remains whether these results are transferable to species when incorporated in ecological communities. Here, we address this issue by analysing the same species exposed to a range of environmental variabilities when (i) isolated or (ii) embedded in a food web. We find that all species in food webs exposed to temporally uncorrelated environments (white noise) show the same type of dynamics as isolated species, whereas species in food webs exposed to positively autocorrelated environments (red noise) can respond completely differently compared with isolated species. This is owing to species following their equilibrium densities in a positively autocorrelated environment that in turn enables species-species interactions to come into play. Our results give new insights into species' response to environmental variation. They especially highlight the importance of considering both species' interactions and environmental autocorrelation when studying population dynamics in a fluctuating environment. © 2015 The Author(s).

Environmental variability uncovers disruptive effects of species' interactions on population dynamics

PubMed Central

Gudmundson, Sara; Eklöf, Anna; Wennergren, Uno

2015-01-01

How species respond to changes in environmental variability has been shown for single species, but the question remains whether these results are transferable to species when incorporated in ecological communities. Here, we address this issue by analysing the same species exposed to a range of environmental variabilities when (i) isolated or (ii) embedded in a food web. We find that all species in food webs exposed to temporally uncorrelated environments (white noise) show the same type of dynamics as isolated species, whereas species in food webs exposed to positively autocorrelated environments (red noise) can respond completely differently compared with isolated species. This is owing to species following their equilibrium densities in a positively autocorrelated environment that in turn enables species–species interactions to come into play. Our results give new insights into species' response to environmental variation. They especially highlight the importance of considering both species' interactions and environmental autocorrelation when studying population dynamics in a fluctuating environment. PMID:26224705

Decorrelation Times of Photospheric Fields and Flows

NASA Technical Reports Server (NTRS)

Welsch, B. T.; Kusano, K.; Yamamoto, T. T.; Muglach, K.

2012-01-01

We use autocorrelation to investigate evolution in flow fields inferred by applying Fourier Local Correlation Tracking (FLCT) to a sequence of high-resolution (0.3 "), high-cadence (approx = 2 min) line-of-sight magnetograms of NOAA active region (AR) 10930 recorded by the Narrowband Filter Imager (NFI) of the Solar Optical Telescope (SOT) aboard the Hinode satellite over 12 - 13 December 2006. To baseline the timescales of flow evolution, we also autocorrelated the magnetograms, at several spatial binnings, to characterize the lifetimes of active region magnetic structures versus spatial scale. Autocorrelation of flow maps can be used to optimize tracking parameters, to understand tracking algorithms f susceptibility to noise, and to estimate flow lifetimes. Tracking parameters varied include: time interval Delta t between magnetogram pairs tracked, spatial binning applied to the magnetograms, and windowing parameter sigma used in FLCT. Flow structures vary over a range of spatial and temporal scales (including unresolved scales), so tracked flows represent a local average of the flow over a particular range of space and time. We define flow lifetime to be the flow decorrelation time, tau . For Delta t > tau, tracking results represent the average velocity over one or more flow lifetimes. We analyze lifetimes of flow components, divergences, and curls as functions of magnetic field strength and spatial scale. We find a significant trend of increasing lifetimes of flow components, divergences, and curls with field strength, consistent with Lorentz forces partially governing flows in the active photosphere, as well as strong trends of increasing flow lifetime and decreasing magnitudes with increases in both spatial scale and Delta t.

Early-warning signals for catastrophic soil degradation

NASA Astrophysics Data System (ADS)

Karssenberg, Derek

2010-05-01

Many earth systems have critical thresholds at which the system shifts abruptly from one state to another. Such critical transitions have been described, among others, for climate, vegetation, animal populations, and geomorphology. Predicting the timing of critical transitions before they are reached is of importance because of the large impact on nature and society associated with the transition. However, it is notably difficult to predict the timing of a transition. This is because the state variables of the system show little change before the threshold is reached. As a result, the precision of field observations is often too low to provide predictions of the timing of a transition. A possible solution is the use of spatio-temporal patterns in state variables as leading indicators of a transition. It is becoming clear that the critically slowing down of a system causes spatio-temporal autocorrelation and variance to increase before the transition. Thus, spatio-temporal patterns are important candidates for early-warning signals. In this research we will show that these early-warning signals also exist in geomorphological systems. We consider a modelled vegetation-soil system under a gradually increasing grazing pressure causing an abrupt shift towards extensive soil degradation. It is shown that changes in spatio-temporal patterns occur well ahead of this catastrophic transition. A distributed model describing the coupled processes of vegetation growth and geomorphological denudation is adapted. The model uses well-studied simple process representations for vegetation and geomorphology. A logistic growth model calculates vegetation cover as a function of grazing pressure and vegetation growth rate. Evolution of the soil thickness is modelled by soil creep and wash processes, as a function of net rain reaching the surface. The vegetation and soil system are coupled by 1) decreasing vegetation growth with decreasing soil thickness and 2) increasing soil wash with decreasing vegetation cover. The model describes a critical, catastrophic transition of an underexploited system with low grazing pressure towards an overexploited system. The underexploited state has high vegetation cover and well developed soils, while the overexploited state has low vegetation cover and largely degraded soils. We first show why spatio-temporal patterns in vegetation cover, morphology, erosion rate, and sediment load should be expected to change well before the critical transition towards the overexploited state. Subsequently, spatio-temporal patterns are quantified by calculating statistics, in particular first order statistics and autocorrelation in space and time. It is shown that these statistics gradually change before the transition is reached. This indicates that the statistics may serve as early-warning signals in real-world applications. We also discuss the potential use of remote sensing to predict the critical transition in real-world landscapes.

Spatio-temporal Eigenvector Filtering: Application on Bioenergy Crop Impacts

NASA Astrophysics Data System (ADS)

Wang, M.; Kamarianakis, Y.; Georgescu, M.

2017-12-01

A suite of 10-year ensemble-based simulations was conducted to investigate the hydroclimatic impacts due to large-scale deployment of perennial bioenergy crops across the continental United States. Given the large size of the simulated dataset (about 60Tb), traditional hierarchical spatio-temporal statistical modelling cannot be implemented for the evaluation of physics parameterizations and biofuel impacts. In this work, we propose a filtering algorithm that takes into account the spatio-temporal autocorrelation structure of the data while avoiding spatial confounding. This method is used to quantify the robustness of simulated hydroclimatic impacts associated with bioenergy crops to alternative physics parameterizations and observational datasets. Results are evaluated against those obtained from three alternative Bayesian spatio-temporal specifications.

Total ozone trend significance from space time variability of daily Dobson data

NASA Technical Reports Server (NTRS)

Wilcox, R. W.

1981-01-01

Estimates of standard errors of total ozone time and area means, as derived from ozone's natural temporal and spatial variability and autocorrelation in middle latitudes determined from daily Dobson data are presented. Assessing the significance of apparent total ozone trends is equivalent to assessing the standard error of the means. Standard errors of time averages depend on the temporal variability and correlation of the averaged parameter. Trend detectability is discussed, both for the present network and for satellite measurements.

Geostatistical Prediction of Microbial Water Quality Throughout a Stream Network Using Meteorology, Land Cover, and Spatiotemporal Autocorrelation.

PubMed

Holcomb, David A; Messier, Kyle P; Serre, Marc L; Rowny, Jakob G; Stewart, Jill R

2018-06-25

Predictive modeling is promising as an inexpensive tool to assess water quality. We developed geostatistical predictive models of microbial water quality that empirically modeled spatiotemporal autocorrelation in measured fecal coliform (FC) bacteria concentrations to improve prediction. We compared five geostatistical models featuring different autocorrelation structures, fit to 676 observations from 19 locations in North Carolina's Jordan Lake watershed using meteorological and land cover predictor variables. Though stream distance metrics (with and without flow-weighting) failed to improve prediction over the Euclidean distance metric, incorporating temporal autocorrelation substantially improved prediction over the space-only models. We predicted FC throughout the stream network daily for one year, designating locations "impaired", "unimpaired", or "unassessed" if the probability of exceeding the state standard was ≥90%, ≤10%, or >10% but <90%, respectively. We could assign impairment status to more of the stream network on days any FC were measured, suggesting frequent sample-based monitoring remains necessary, though implementing spatiotemporal predictive models may reduce the number of concurrent sampling locations required to adequately assess water quality. Together, these results suggest that prioritizing sampling at different times and conditions using geographically sparse monitoring networks is adequate to build robust and informative geostatistical models of water quality impairment.

Interferometric Near-Infrared Spectroscopy (iNIRS) for determination of optical and dynamical properties of turbid media

PubMed Central

Borycki, Dawid; Kholiqov, Oybek; Chong, Shau Poh; Srinivasan, Vivek J.

2016-01-01

We introduce and implement interferometric near-infrared spectroscopy (iNIRS), which simultaneously extracts optical and dynamical properties of turbid media through analysis of a spectral interference fringe pattern. The spectral interference fringe pattern is measured using a Mach-Zehnder interferometer with a frequency-swept narrow linewidth laser. Fourier analysis of the detected signal is used to determine time-of-flight (TOF)-resolved intensity, which is then analyzed over time to yield TOF-resolved intensity autocorrelations. This approach enables quantification of optical properties, which is not possible in conventional, continuous-wave near-infrared spectroscopy (NIRS). Furthermore, iNIRS quantifies scatterer motion based on TOF-resolved autocorrelations, which is a feature inaccessible by well-established diffuse correlation spectroscopy (DCS) techniques. We prove this by determining TOF-resolved intensity and temporal autocorrelations for light transmitted through diffusive fluid phantoms with optical thicknesses of up to 55 reduced mean free paths (approximately 120 scattering events). The TOF-resolved intensity is used to determine optical properties with time-resolved diffusion theory, while the TOF-resolved intensity autocorrelations are used to determine dynamics with diffusing wave spectroscopy. iNIRS advances the capabilities of diffuse optical methods and is suitable for in vivo tissue characterization. Moreover, iNIRS combines NIRS and DCS capabilities into a single modality. PMID:26832264

Employing the Hilbert-Huang Transform to analyze observed natural complex signals: Calm wind meandering cases

NASA Astrophysics Data System (ADS)

Martins, Luis Gustavo Nogueira; Stefanello, Michel Baptistella; Degrazia, Gervásio Annes; Acevedo, Otávio Costa; Puhales, Franciano Scremin; Demarco, Giuliano; Mortarini, Luca; Anfossi, Domenico; Roberti, Débora Regina; Costa, Felipe Denardin; Maldaner, Silvana

2016-11-01

In this study we analyze natural complex signals employing the Hilbert-Huang spectral analysis. Specifically, low wind meandering meteorological data are decomposed into turbulent and non turbulent components. These non turbulent movements, responsible for the absence of a preferential direction of the horizontal wind, provoke negative lobes in the meandering autocorrelation functions. The meandering characteristic time scales (meandering periods) are determined from the spectral peak provided by the Hilbert-Huang marginal spectrum. The magnitudes of the temperature and horizontal wind meandering period obtained agree with the results found from the best fit of the heuristic meandering autocorrelation functions. Therefore, the new method represents a new procedure to evaluate meandering periods that does not employ mathematical expressions to represent observed meandering autocorrelation functions.

Spatial and temporal patterns of locally-acquired dengue transmission in northern Queensland, Australia, 1993-2012.

PubMed

Naish, Suchithra; Dale, Pat; Mackenzie, John S; McBride, John; Mengersen, Kerrie; Tong, Shilu

2014-01-01

Dengue has been a major public health concern in Australia since it re-emerged in Queensland in 1992-1993. We explored spatio-temporal characteristics of locally-acquired dengue cases in northern tropical Queensland, Australia during the period 1993-2012. Locally-acquired notified cases of dengue were collected for northern tropical Queensland from 1993 to 2012. Descriptive spatial and temporal analyses were conducted using geographic information system tools and geostatistical techniques. 2,398 locally-acquired dengue cases were recorded in northern tropical Queensland during the study period. The areas affected by the dengue cases exhibited spatial and temporal variation over the study period. Notified cases of dengue occurred more frequently in autumn. Mapping of dengue by statistical local areas (census units) reveals the presence of substantial spatio-temporal variation over time and place. Statistically significant differences in dengue incidence rates among males and females (with more cases in females) (χ(2) = 15.17, d.f.  = 1, p<0.01). Differences were observed among age groups, but these were not statistically significant. There was a significant positive spatial autocorrelation of dengue incidence for the four sub-periods, with the Moran's I statistic ranging from 0.011 to 0.463 (p<0.01). Semi-variogram analysis and smoothed maps created from interpolation techniques indicate that the pattern of spatial autocorrelation was not homogeneous across the northern Queensland. Tropical areas are potential high-risk areas for mosquito-borne diseases such as dengue. This study demonstrated that the locally-acquired dengue cases have exhibited a spatial and temporal variation over the past twenty years in northern tropical Queensland, Australia. Therefore, this study provides an impetus for further investigation of clusters and risk factors in these high-risk areas.

Spatial and Temporal Patterns of Locally-Acquired Dengue Transmission in Northern Queensland, Australia, 1993–2012

PubMed Central

Naish, Suchithra; Dale, Pat; Mackenzie, John S.; McBride, John; Mengersen, Kerrie; Tong, Shilu

2014-01-01

Background Dengue has been a major public health concern in Australia since it re-emerged in Queensland in 1992–1993. We explored spatio-temporal characteristics of locally-acquired dengue cases in northern tropical Queensland, Australia during the period 1993–2012. Methods Locally-acquired notified cases of dengue were collected for northern tropical Queensland from 1993 to 2012. Descriptive spatial and temporal analyses were conducted using geographic information system tools and geostatistical techniques. Results 2,398 locally-acquired dengue cases were recorded in northern tropical Queensland during the study period. The areas affected by the dengue cases exhibited spatial and temporal variation over the study period. Notified cases of dengue occurred more frequently in autumn. Mapping of dengue by statistical local areas (census units) reveals the presence of substantial spatio-temporal variation over time and place. Statistically significant differences in dengue incidence rates among males and females (with more cases in females) (χ2 = 15.17, d.f. = 1, p<0.01). Differences were observed among age groups, but these were not statistically significant. There was a significant positive spatial autocorrelation of dengue incidence for the four sub-periods, with the Moran's I statistic ranging from 0.011 to 0.463 (p<0.01). Semi-variogram analysis and smoothed maps created from interpolation techniques indicate that the pattern of spatial autocorrelation was not homogeneous across the northern Queensland. Conclusions Tropical areas are potential high-risk areas for mosquito-borne diseases such as dengue. This study demonstrated that the locally-acquired dengue cases have exhibited a spatial and temporal variation over the past twenty years in northern tropical Queensland, Australia. Therefore, this study provides an impetus for further investigation of clusters and risk factors in these high-risk areas. PMID:24691549

The acquired immunodeficiency syndrome in the State of Rio de Janeiro, Brazil: a spatio-temporal analysis of cases reported in the period 2001-2010.

PubMed

Alves, André T J; Nobre, Flávio F

2014-05-01

Despite increased funding for research on the human immunodeficiency virus (HIV) and the acquired immunodeficiency syndrome (AIDS), neither vaccine nor cure is yet in sight. Surveillance and prevention are essential for disease intervention, and it is recognised that spatio-temporal analysis of AIDS cases can assist the decision-making process for control of the disease. This study investigated the dynamic, spatial distribution of notified AIDS cases in the State of Rio de Janeiro, Brazil, between 2001 and 2010, based on the annual incidence in each municipality. Sequential choropleth maps were developed and used to analyse the incidence distribution and Moran's I spatial autocorrelation statistics was applied for characterisation of the spatio-temporal distribution pattern. A significant, positive spatial autocorrelation of AIDS incidence was observed indicating that municipalities with high incidence are likely to be close to other municipalities with similarly high incidence and, conversely, municipalities with low incidence are likely to be surrounded by municipalities with low incidence. Two clusters were identified; one hotspot related to the State Capital and the other with low to intermediate AIDS incidence comprising municipalities in the north-eastern region of the State of Rio de Janeiro.

Autocorrelations of stellar light and mass at z˜ 0 and ˜1: from SDSS to DEEP2

NASA Astrophysics Data System (ADS)

Li, Cheng; White, Simon D. M.; Chen, Yanmei; Coil, Alison L.; Davis, Marc; De Lucia, Gabriella; Guo, Qi; Jing, Y. P.; Kauffmann, Guinevere; Willmer, Christopher N. A.; Zhang, Wei

2012-01-01

We present measurements of projected autocorrelation functions wp(rp) for the stellar mass of galaxies and for their light in the U, B and V bands, using data from the third data release of the DEEP2 Galaxy Redshift Survey and the final data release of the Sloan Digital Sky Survey (SDSS). We investigate the clustering bias of stellar mass and light by comparing these to projected autocorrelations of dark matter estimated from the Millennium Simulations (MS) at z= 1 and 0.07, the median redshifts of our galaxy samples. All of the autocorrelation and bias functions show systematic trends with spatial scale and waveband which are impressively similar at the two redshifts. This shows that the well-established environmental dependence of stellar populations in the local Universe is already in place at z= 1. The recent MS-based galaxy formation simulation of Guo et al. reproduces the scale-dependent clustering of luminosity to an accuracy better than 30 per cent in all bands and at both redshifts, but substantially overpredicts mass autocorrelations at separations below about 2 Mpc. Further comparison of the shapes of our stellar mass bias functions with those predicted by the model suggests that both the SDSS and DEEP2 data prefer a fluctuation amplitude of σ8˜ 0.8 rather than the σ8= 0.9 assumed by the MS.

Testing Pairwise Association between Spatially Autocorrelated Variables: A New Approach Using Surrogate Lattice Data

PubMed Central

Deblauwe, Vincent; Kennel, Pol; Couteron, Pierre

2012-01-01

Background Independence between observations is a standard prerequisite of traditional statistical tests of association. This condition is, however, violated when autocorrelation is present within the data. In the case of variables that are regularly sampled in space (i.e. lattice data or images), such as those provided by remote-sensing or geographical databases, this problem is particularly acute. Because analytic derivation of the null probability distribution of the test statistic (e.g. Pearson's r) is not always possible when autocorrelation is present, we propose instead the use of a Monte Carlo simulation with surrogate data. Methodology/Principal Findings The null hypothesis that two observed mapped variables are the result of independent pattern generating processes is tested here by generating sets of random image data while preserving the autocorrelation function of the original images. Surrogates are generated by matching the dual-tree complex wavelet spectra (and hence the autocorrelation functions) of white noise images with the spectra of the original images. The generated images can then be used to build the probability distribution function of any statistic of association under the null hypothesis. We demonstrate the validity of a statistical test of association based on these surrogates with both actual and synthetic data and compare it with a corrected parametric test and three existing methods that generate surrogates (randomization, random rotations and shifts, and iterative amplitude adjusted Fourier transform). Type I error control was excellent, even with strong and long-range autocorrelation, which is not the case for alternative methods. Conclusions/Significance The wavelet-based surrogates are particularly appropriate in cases where autocorrelation appears at all scales or is direction-dependent (anisotropy). We explore the potential of the method for association tests involving a lattice of binary data and discuss its potential for validation of species distribution models. An implementation of the method in Java for the generation of wavelet-based surrogates is available online as supporting material. PMID:23144961

Studies of the micromorphology of sputtered TiN thin films by autocorrelation techniques

NASA Astrophysics Data System (ADS)

Smagoń, Kamil; Stach, Sebastian; Ţălu, Ştefan; Arman, Ali; Achour, Amine; Luna, Carlos; Ghobadi, Nader; Mardani, Mohsen; Hafezi, Fatemeh; Ahmadpourian, Azin; Ganji, Mohsen; Grayeli Korpi, Alireza

2017-12-01

Autocorrelation techniques are crucial tools for the study of the micromorphology of surfaces: They provide the description of anisotropic properties and the identification of repeated patterns on the surface, facilitating the comparison of samples. In the present investigation, some fundamental concepts of these techniques including the autocorrelation function and autocorrelation length have been reviewed and applied in the study of titanium nitride thin films by atomic force microscopy (AFM). The studied samples were grown on glass substrates by reactive magnetron sputtering at different substrate temperatures (from 25 {}°C to 400 {}°C , and their micromorphology was studied by AFM. The obtained AFM data were analyzed using MountainsMap Premium software obtaining the correlation function, the structure of isotropy and the spatial parameters according to ISO 25178 and EUR 15178N. These studies indicated that the substrate temperature during the deposition process is an important parameter to modify the micromorphology of sputtered TiN thin films and to find optimized surface properties. For instance, the autocorrelation length exhibited a maximum value for the sample prepared at a substrate temperature of 300 {}°C , and the sample obtained at 400 {}°C presented a maximum angle of the direction of the surface structure.

A general statistical test for correlations in a finite-length time series.

PubMed

Hanson, Jeffery A; Yang, Haw

2008-06-07

The statistical properties of the autocorrelation function from a time series composed of independently and identically distributed stochastic variables has been studied. Analytical expressions for the autocorrelation function's variance have been derived. It has been found that two common ways of calculating the autocorrelation, moving-average and Fourier transform, exhibit different uncertainty characteristics. For periodic time series, the Fourier transform method is preferred because it gives smaller uncertainties that are uniform through all time lags. Based on these analytical results, a statistically robust method has been proposed to test the existence of correlations in a time series. The statistical test is verified by computer simulations and an application to single-molecule fluorescence spectroscopy is discussed.

Single-channel autocorrelation functions: the effects of time interval omission.

PubMed Central

Ball, F G; Sansom, M S

1988-01-01

We present a general mathematical framework for analyzing the dynamic aspects of single channel kinetics incorporating time interval omission. An algorithm for computing model autocorrelation functions, incorporating time interval omission, is described. We show, under quite general conditions, that the form of these autocorrelations is identical to that which would be obtained if time interval omission was absent. We also show, again under quite general conditions, that zero correlations are necessarily a consequence of the underlying gating mechanism and not an artefact of time interval omission. The theory is illustrated by a numerical study of an allosteric model for the gating mechanism of the locust muscle glutamate receptor-channel. PMID:2455553

On the temporal window of auditory-brain system in connection with subjective responses

NASA Astrophysics Data System (ADS)

Mouri, Kiminori

2003-08-01

The human auditory-brain system processes information extracted from autocorrelation function (ACF) of the source signal and interaural cross correlation function (IACF) of binaural sound signals which are associated with the left and right cerebral hemispheres, respectively. The purpose of this dissertation is to determine the desirable temporal window (2T: integration interval) for ACF and IACF mechanisms. For the ACF mechanism, the visual change of Φ(0), i.e., the power of ACF, was associated with the change of loudness, and it is shown that the recommended temporal window is given as about 30(τe)min [s]. The value of (τe)min is the minimum value of effective duration of the running ACF of the source signal. It is worth noticing from the experiment of EEG that the most preferred delay time of the first reflection sound is determined by the piece indicating (τe)min in the source signal. For the IACF mechanism, the temporal window is determined as below: The measured range of τIACC corresponding to subjective angle for the moving image sound depends on the temporal window. Here, the moving image was simulated by the use of two loudspeakers located at +/-20° in the horizontal plane, reproducing amplitude modulated band-limited noise alternatively. It is found that the temporal window has a wide range of values from 0.03 to 1 [s] for the modulation frequency below 0.2 Hz. Thesis advisor: Yoichi Ando Copies of this thesis written in English can be obtained from Kiminori Mouri, 5-3-3-1110 Harayama-dai, Sakai city, Osaka 590-0132, Japan. E-mail address: km529756@aol.com

Quantifying Temporal Autocorrelations for the Expression of Geobacter species mRNA Gene Transcripts at Variable Ammonium Levels during in situ U(VI) Bioremediation

NASA Astrophysics Data System (ADS)

Mouser, P. J.

2010-12-01

In order to develop decision-making tools for the prediction and optimization of subsurface bioremediation strategies, we must be able to link the molecular-scale activity of microorganisms involved in remediation processes with biogeochemical processes observed at the field-scale. This requires the ability to quantify changes in the in situ metabolic condition of dominant microbes and associate these changes to fluctuations in nutrient levels throughout the bioremediation process. It also necessitates a need to understand the spatiotemporal variability of the molecular-scale information to develop meaningful parameters and constraint ranges in complex bio-physio-chemical models. The expression of three Geobacter species genes (ammonium transporter (amtB), nitrogen fixation (nifD), and a housekeeping gene (recA)) were tracked at two monitoring locations that differed significantly in ammonium (NH4+) concentrations during a field-scale experiment where acetate was injected into the subsurface to simulate Geobacteraceae in a uranium-contaminated aquifer. Analysis of amtB and nifD mRNA transcript levels indicated that NH4+ was the primary form of fixed nitrogen during bioremediation. Overall expression levels of amtB were on average 8-fold higher at NH4+ concentrations of 300 μM or more than at lower NH4+ levels (average 60 μM). The degree of temporal correlation in Geobacter species mRNA expression levels was calculated at both locations using autocorrelation methods that describe the relationship between sample semi-variance and time lag. At the monitoring location with lower NH4+, a temporal correlation lag of 8 days was observed for both amtB and nifD transcript patterns. At the location where higher NH4+ levels were observed, no discernable temporal correlation lag above the sampling frequency (approximately every 2 days) was observed for amtB or nifD transcript fluctuations. Autocorrelation trends in recA expression levels at both locations indicated that while a temporal correlation in the general metabolic activity of Geobacter species may exist, considerable variability in transcript levels masked these correlations at the sampled scale. These findings suggest that when Geobacter species are dependent upon a particular nutrient such as NH4+, the time length for which their activity level relating to this nutrient condition can be predicted is significantly enhanced.

Performance of signal-to-noise ratio estimation for scanning electron microscope using autocorrelation Levinson-Durbin recursion model.

PubMed

Sim, K S; Lim, M S; Yeap, Z X

2016-07-01

A new technique to quantify signal-to-noise ratio (SNR) value of the scanning electron microscope (SEM) images is proposed. This technique is known as autocorrelation Levinson-Durbin recursion (ACLDR) model. To test the performance of this technique, the SEM image is corrupted with noise. The autocorrelation function of the original image and the noisy image are formed. The signal spectrum based on the autocorrelation function of image is formed. ACLDR is then used as an SNR estimator to quantify the signal spectrum of noisy image. The SNR values of the original image and the quantified image are calculated. The ACLDR is then compared with the three existing techniques, which are nearest neighbourhood, first-order linear interpolation and nearest neighbourhood combined with first-order linear interpolation. It is shown that ACLDR model is able to achieve higher accuracy in SNR estimation. © 2016 The Authors Journal of Microscopy © 2016 Royal Microscopical Society.

Spectra of empirical autocorrelation matrices: A random-matrix-theory-inspired perspective

NASA Astrophysics Data System (ADS)

Jamali, Tayeb; Jafari, G. R.

2015-07-01

We construct an autocorrelation matrix of a time series and analyze it based on the random-matrix theory (RMT) approach. The autocorrelation matrix is capable of extracting information which is not easily accessible by the direct analysis of the autocorrelation function. In order to provide a precise conclusion based on the information extracted from the autocorrelation matrix, the results must be first evaluated. In other words they need to be compared with some sort of criterion to provide a basis for the most suitable and applicable conclusions. In the context of the present study, the criterion is selected to be the well-known fractional Gaussian noise (fGn). We illustrate the applicability of our method in the context of stock markets. For the former, despite the non-Gaussianity in returns of the stock markets, a remarkable agreement with the fGn is achieved.

Why GPS makes distances bigger than they are

PubMed Central

Ranacher, Peter; Brunauer, Richard; Trutschnig, Wolfgang; Van der Spek, Stefan; Reich, Siegfried

2016-01-01

ABSTRACT Global navigation satellite systems such as the Global Positioning System (GPS) is one of the most important sensors for movement analysis. GPS is widely used to record the trajectories of vehicles, animals and human beings. However, all GPS movement data are affected by both measurement and interpolation errors. In this article we show that measurement error causes a systematic bias in distances recorded with a GPS; the distance between two points recorded with a GPS is – on average – bigger than the true distance between these points. This systematic ‘overestimation of distance’ becomes relevant if the influence of interpolation error can be neglected, which in practice is the case for movement sampled at high frequencies. We provide a mathematical explanation of this phenomenon and illustrate that it functionally depends on the autocorrelation of GPS measurement error (C). We argue that C can be interpreted as a quality measure for movement data recorded with a GPS. If there is a strong autocorrelation between any two consecutive position estimates, they have very similar error. This error cancels out when average speed, distance or direction is calculated along the trajectory. Based on our theoretical findings we introduce a novel approach to determine C in real-world GPS movement data sampled at high frequencies. We apply our approach to pedestrian trajectories and car trajectories. We found that the measurement error in the data was strongly spatially and temporally autocorrelated and give a quality estimate of the data. Most importantly, our findings are not limited to GPS alone. The systematic bias and its implications are bound to occur in any movement data collected with absolute positioning if interpolation error can be neglected. PMID:27019610

Spectrum sensing algorithm based on autocorrelation energy in cognitive radio networks

NASA Astrophysics Data System (ADS)

Ren, Shengwei; Zhang, Li; Zhang, Shibing

2016-10-01

Cognitive radio networks have wide applications in the smart home, personal communications and other wireless communication. Spectrum sensing is the main challenge in cognitive radios. This paper proposes a new spectrum sensing algorithm which is based on the autocorrelation energy of signal received. By taking the autocorrelation energy of the received signal as the statistics of spectrum sensing, the effect of the channel noise on the detection performance is reduced. Simulation results show that the algorithm is effective and performs well in low signal-to-noise ratio. Compared with the maximum generalized eigenvalue detection (MGED) algorithm, function of covariance matrix based detection (FMD) algorithm and autocorrelation-based detection (AD) algorithm, the proposed algorithm has 2 11 dB advantage.

Modeling correlated bursts by the bursty-get-burstier mechanism

NASA Astrophysics Data System (ADS)

Jo, Hang-Hyun

2017-12-01

Temporal correlations of time series or event sequences in natural and social phenomena have been characterized by power-law decaying autocorrelation functions with decaying exponent γ . Such temporal correlations can be understood in terms of power-law distributed interevent times with exponent α and/or correlations between interevent times. The latter, often called correlated bursts, has recently been studied by measuring power-law distributed bursty trains with exponent β . A scaling relation between α and γ has been established for the uncorrelated interevent times, while little is known about the effects of correlated interevent times on temporal correlations. In order to study these effects, we devise the bursty-get-burstier model for correlated bursts, by which one can tune the degree of correlations between interevent times, while keeping the same interevent time distribution. We numerically find that sufficiently strong correlations between interevent times could violate the scaling relation between α and γ for the uncorrelated case. A nontrivial dependence of γ on β is also found for some range of α . The implication of our results is discussed in terms of the hierarchical organization of bursty trains at various time scales.

A technique to detect periodic and non-periodic ultra-rapid flux time variations with standard radio-astronomical data

NASA Astrophysics Data System (ADS)

Borra, Ermanno F.; Romney, Jonathan D.; Trottier, Eric

2018-06-01

We demonstrate that extremely rapid and weak periodic and non-periodic signals can easily be detected by using the autocorrelation of intensity as a function of time. We use standard radio-astronomical observations that have artificial periodic and non-periodic signals generated by the electronics of terrestrial origin. The autocorrelation detects weak signals that have small amplitudes because it averages over long integration times. Another advantage is that it allows a direct visualization of the shape of the signals, while it is difficult to see the shape with a Fourier transform. Although Fourier transforms can also detect periodic signals, a novelty of this work is that we demonstrate another major advantage of the autocorrelation, that it can detect non-periodic signals while the Fourier transform cannot. Another major novelty of our work is that we use electric fields taken in a standard format with standard instrumentation at a radio observatory and therefore no specialized instrumentation is needed. Because the electric fields are sampled every 15.625 ns, they therefore allow detection of very rapid time variations. Notwithstanding the long integration times, the autocorrelation detects very rapid intensity variations as a function of time. The autocorrelation could also detect messages from Extraterrestrial Intelligence as non-periodic signals.

Characterizing local traffic contributions to particulate air pollution in street canyons using mobile monitoring techniques

NASA Astrophysics Data System (ADS)

Zwack, Leonard M.; Paciorek, Christopher J.; Spengler, John D.; Levy, Jonathan I.

2011-05-01

Traffic within urban street canyons can contribute significantly to ambient concentrations of particulate air pollution. In these settings, it is challenging to separate within-canyon source contributions from urban and regional background concentrations given the highly variable and complex emissions and dispersion characteristics. In this study, we used continuous mobile monitoring of traffic-related particulate air pollutants to assess the contribution to concentrations, above background, of traffic in the street canyons of midtown Manhattan. Concentrations of both ultrafine particles (UFP) and fine particles (PM 2.5) were measured at street level using portable instruments. Statistical modeling techniques accounting for autocorrelation were used to investigate the presence of spatial heterogeneity of pollutant concentrations as well as to quantify the contribution of within-canyon traffic sources. Measurements were also made within Central Park, to examine the impact of offsets from major roadways in this urban environment. On average, an approximate 11% increase in concentrations of UFP and 8% increase in concentrations of PM 2.5 over urban background was estimated during high-traffic periods in street canyons as opposed to low traffic periods. Estimates were 8% and 5%, respectively, after accounting for temporal autocorrelation. Within Central Park, concentrations were 40% higher than background (5% after accounting for temporal autocorrelation) within the first 100 m from the nearest roadway for UFP, with a smaller but statistically significant increase for PM 2.5. Our findings demonstrate the viability of a mobile monitoring protocol coupled with spatiotemporal modeling techniques in characterizing local source contributions in a setting with street canyons.

Establishing the diffuse correlation spectroscopy signal relationship with blood flow.

PubMed

Boas, David A; Sakadžić, Sava; Selb, Juliette; Farzam, Parisa; Franceschini, Maria Angela; Carp, Stefan A

2016-07-01

Diffuse correlation spectroscopy (DCS) measurements of blood flow rely on the sensitivity of the temporal autocorrelation function of diffusively scattered light to red blood cell (RBC) mean square displacement (MSD). For RBCs flowing with convective velocity [Formula: see text], the autocorrelation is expected to decay exponentially with [Formula: see text], where [Formula: see text] is the delay time. RBCs also experience shear-induced diffusion with a diffusion coefficient [Formula: see text] and an MSD of [Formula: see text]. Surprisingly, experimental data primarily reflect diffusive behavior. To provide quantitative estimates of the relative contributions of convective and diffusive movements, we performed Monte Carlo simulations of light scattering through tissue of varying vessel densities. We assumed laminar vessel flow profiles and accounted for shear-induced diffusion effects. In agreement with experimental data, we found that diffusive motion dominates the correlation decay for typical DCS measurement parameters. Furthermore, our model offers a quantitative relationship between the RBC diffusion coefficient and absolute tissue blood flow. We thus offer, for the first time, theoretical support for the empirically accepted ability of the DCS blood flow index ([Formula: see text]) to quantify tissue perfusion. We find [Formula: see text] to be linearly proportional to blood flow, but with a proportionality modulated by the hemoglobin concentration and the average blood vessel diameter.

Partial correlation properties of pseudonoise /PN/ codes in noncoherent synchronization/detection schemes

NASA Technical Reports Server (NTRS)

Cartier, D. E.

1976-01-01

This concise paper considers the effect on the autocorrelation function of a pseudonoise (PN) code when the acquisition scheme only integrates coherently over part of the code and then noncoherently combines these results. The peak-to-null ratio of the effective PN autocorrelation function is shown to degrade to the square root of n, where n is the number of PN symbols over which coherent integration takes place.

Exploring fractal behaviour of blood oxygen saturation in preterm babies

NASA Astrophysics Data System (ADS)

Zahari, Marina; Hui, Tan Xin; Zainuri, Nuryazmin Ahmat; Darlow, Brian A.

2017-04-01

Recent evidence has been emerging that oxygenation instability in preterm babies could lead to an increased risk of retinal injury such as retinopathy of prematurity. There is a potential that disease severity could be better understood using nonlinear methods for time series data such as fractal theories [1]. Theories on fractal behaviours have been employed by researchers in various disciplines who were motivated to look into the behaviour or structure of irregular fluctuations in temporal data. In this study, an investigation was carried out to examine whether fractal behaviour could be detected in blood oxygen time series. Detection for the presence of fractals in oxygen data of preterm infants was performed using the methods of power spectrum, empirical probability distribution function and autocorrelation function. The results from these fractal identification methods indicate the possibility that these data exhibit fractal nature. Subsequently, a fractal framework for future research was suggested for oxygen time series.

Analysis of data from NASA B-57B gust gradient program

NASA Technical Reports Server (NTRS)

Frost, W.; Lin, M. C.; Chang, H. P.; Ringnes, E.

1985-01-01

Statistical analysis of the turbulence measured in flight 6 of the NASA B-57B over Denver, Colorado, from July 7 to July 23, 1982 included the calculations of average turbulence parameters, integral length scales, probability density functions, single point autocorrelation coefficients, two point autocorrelation coefficients, normalized autospectra, normalized two point autospectra, and two point cross sectra for gust velocities. The single point autocorrelation coefficients were compared with the theoretical model developed by von Karman. Theoretical analyses were developed which address the effects spanwise gust distributions, using two point spatial turbulence correlations.

Imaging the Lower Crust and Moho Beneath Long Beach, CA Using Autocorrelations

NASA Astrophysics Data System (ADS)

Clayton, R. W.

2017-12-01

Three-dimensional images of the lower crust and Moho in a 10x10 km region beneath Long Beach, CA are constructed from autocorrelations of ambient noise. The results show the Moho at a depth of 15 km at the coast and dipping at 45 degrees inland to a depth of 25 km. The shape of the Moho interface is irregular in both the coast perpendicular and parallel directions. The lower crust appears as a zone of enhanced reflectivity with numerous small-scale structures. The autocorrelations are constructed from virtual source gathers that were computed from the dense Long Beach array that were used in the Lin et al (2013) study. All near zero-offset traces within a 200 m disk are stacked to produce a single autocorrelation at that point. The stack typically is over 50-60 traces. To convert the auto correlation to reflectivity as in Claerbout (1968), the noise source autocorrelation, which is estimated as the average of all autocorrelations is subtracted from each trace. The subsurface image is then constructed with a 0.1-2 Hz filter and AGC scaling. The main features of the image are confirmed with broadband receiver functions from the LASSIE survey (Ma et al, 2016). The use of stacked autocorrelations extends ambient noise into the lower crust.

Aging Wiener-Khinchin theorem and critical exponents of 1/f^{β} noise.

PubMed

Leibovich, N; Dechant, A; Lutz, E; Barkai, E

2016-11-01

The power spectrum of a stationary process may be calculated in terms of the autocorrelation function using the Wiener-Khinchin theorem. We here generalize the Wiener-Khinchin theorem for nonstationary processes and introduce a time-dependent power spectrum 〈S_{t_{m}}(ω)〉 where t_{m} is the measurement time. For processes with an aging autocorrelation function of the form 〈I(t)I(t+τ)〉=t^{Υ}ϕ_{EA}(τ/t), where ϕ_{EA}(x) is a nonanalytic function when x is small, we find aging 1/f^{β} noise. Aging 1/f^{β} noise is characterized by five critical exponents. We derive the relations between the scaled autocorrelation function and these exponents. We show that our definition of the time-dependent spectrum retains its interpretation as a density of Fourier modes and discuss the relation to the apparent infrared divergence of 1/f^{β} noise. We illustrate our results for blinking-quantum-dot models, single-file diffusion, and Brownian motion in a logarithmic potential.

Diffusion and viscosity of liquid tin: Green-Kubo relationship-based calculations from molecular dynamics simulations

NASA Astrophysics Data System (ADS)

Mouas, Mohamed; Gasser, Jean-Georges; Hellal, Slimane; Grosdidier, Benoît; Makradi, Ahmed; Belouettar, Salim

2012-03-01

Molecular dynamics (MD) simulations of liquid tin between its melting point and 1600 °C have been performed in order to interpret and discuss the ionic structure. The interactions between ions are described by a new accurate pair potential built within the pseudopotential formalism and the linear response theory. The calculated structure factor that reflects the main information on the local atomic order in liquids is compared to diffraction measurements. Having some confidence in the ability of this pair potential to give a good representation of the atomic structure, we then focused our attention on the investigation of the atomic transport properties through the MD computations of the velocity autocorrelation function and stress autocorrelation function. Using the Green-Kubo formula (for the first time to our knowledge for liquid tin) we determine the macroscopic transport properties from the corresponding microscopic time autocorrelation functions. The selfdiffusion coefficient and the shear viscosity as functions of temperature are found to be in good agreement with the experimental data.

Dissecting components of population-level variation in seed production and the evolution of masting behavior.

Treesearch

W. D. Koenig; D. Kelly; V. L. Sork; R. P. Duncan; J. S. Elkinton; M.S. Peltonen; R. D. Westfall

2003-01-01

Mast-fruiting or masting behavior is the cumulative result of the reproductive patterns of individuals within a population and thus involves components of individual variability, between-individual synchrony, and endogenous cycles of temporal autocorrelation. Extending prior work by Herrera, we explore the interrelationships of these components using data on individual...

Spatiotemporal distribution patterns of forest fires in northern Mexico

Treesearch

Gustavo Pérez-Verdin; M. A. Márquez-Linares; A. Cortes-Ortiz; M. Salmerón-Macias

2013-01-01

Using the 2000-2011 CONAFOR databases, a spatiotemporal analysis of the occurrence of forest fires in Durango, one of the most affected States in Mexico, was conducted. The Moran's index was used to determine a spatial distribution pattern; also, an analysis of seasonal and temporal autocorrelation of the data collected was completed. The geographically weighted...

Using Exponential Smoothing to Specify Intervention Models for Interrupted Time Series.

ERIC Educational Resources Information Center

Mandell, Marvin B.; Bretschneider, Stuart I.

1984-01-01

The authors demonstrate how exponential smoothing can play a role in the identification of the intervention component of an interrupted time-series design model that is analogous to the role that the sample autocorrelation and partial autocorrelation functions serve in the identification of the noise portion of such a model. (Author/BW)

Identification of AR(I)MA processes for modelling temporal correlations of GPS observations

NASA Astrophysics Data System (ADS)

Luo, X.; Mayer, M.; Heck, B.

2009-04-01

In many geodetic applications observations of the Global Positioning System (GPS) are routinely processed by means of the least-squares method. However, this algorithm delivers reliable estimates of unknown parameters und realistic accuracy measures only if both the functional and stochastic models are appropriately defined within GPS data processing. One deficiency of the stochastic model used in many GPS software products consists in neglecting temporal correlations of GPS observations. In practice the knowledge of the temporal stochastic behaviour of GPS observations can be improved by analysing time series of residuals resulting from the least-squares evaluation. This paper presents an approach based on the theory of autoregressive (integrated) moving average (AR(I)MA) processes to model temporal correlations of GPS observations using time series of observation residuals. A practicable integration of AR(I)MA models in GPS data processing requires the determination of the order parameters of AR(I)MA processes at first. In case of GPS, the identification of AR(I)MA processes could be affected by various factors impacting GPS positioning results, e.g. baseline length, multipath effects, observation weighting, or weather variations. The influences of these factors on AR(I)MA identification are empirically analysed based on a large amount of representative residual time series resulting from differential GPS post-processing using 1-Hz observation data collected within the permanent SAPOS® (Satellite Positioning Service of the German State Survey) network. Both short and long time series are modelled by means of AR(I)MA processes. The final order parameters are determined based on the whole residual database; the corresponding empirical distribution functions illustrate that multipath and weather variations seem to affect the identification of AR(I)MA processes much more significantly than baseline length and observation weighting. Additionally, the modelling results of temporal correlations using high-order AR(I)MA processes are compared with those by means of first order autoregressive (AR(1)) processes and empirically estimated autocorrelation functions.

Dangers and uses of cross-correlation in analyzing time series in perception, performance, movement, and neuroscience: The importance of constructing transfer function autoregressive models.

PubMed

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.

On the wrong inference of long-range correlations in climate data; the case of the solar and volcanic forcing over the Tropical Pacific

NASA Astrophysics Data System (ADS)

Varotsos, Costas A.; Efstathiou, Maria N.

2017-05-01

A substantial weakness of several climate studies on long-range dependence is the conclusion of long-term memory of the climate conditions, without considering it necessary to establish the power-law scaling and to reject a simple exponential decay of the autocorrelation function. We herewith show one paradigmatic case, where a strong long-range dependence could be wrongly inferred from incomplete data analysis. We firstly apply the DFA method on the solar and volcanic forcing time series over the tropical Pacific, during the past 1000 years and the results obtained show that a statistically significant straight line fit to the fluctuation function in a log-log representation is revealed with slope higher than 0.5, which wrongly may be assumed as an indication of persistent long-range correlations in the time series. We argue that the long-range dependence cannot be concluded just from this straight line fit, but it requires the fulfilment of the two additional prerequisites i.e. reject the exponential decay of the autocorrelation function and establish the power-law scaling. In fact, the investigation of the validity of these prerequisites showed that the DFA exponent higher than 0.5 does not justify the existence of persistent long-range correlations in the temporal evolution of the solar and volcanic forcing during last millennium. In other words, we show that empirical analyses, based on these two prerequisites must not be considered as panacea for a direct proof of scaling, but only as evidence that the scaling hypothesis is plausible. We also discuss the scaling behaviour of solar and volcanic forcing data based on the Haar tool, which recently proved its ability to reliably detect the existence of the scaling effect in climate series.

Animal movement constraints improve resource selection inference in the presence of telemetry error

USGS Publications Warehouse

Brost, Brian M.; Hooten, Mevin B.; Hanks, Ephraim M.; Small, Robert J.

2016-01-01

Multiple factors complicate the analysis of animal telemetry location data. Recent advancements address issues such as temporal autocorrelation and telemetry measurement error, but additional challenges remain. Difficulties introduced by complicated error structures or barriers to animal movement can weaken inference. We propose an approach for obtaining resource selection inference from animal location data that accounts for complicated error structures, movement constraints, and temporally autocorrelated observations. We specify a model for telemetry data observed with error conditional on unobserved true locations that reflects prior knowledge about constraints in the animal movement process. The observed telemetry data are modeled using a flexible distribution that accommodates extreme errors and complicated error structures. Although constraints to movement are often viewed as a nuisance, we use constraints to simultaneously estimate and account for telemetry error. We apply the model to simulated data, showing that it outperforms common ad hoc approaches used when confronted with measurement error and movement constraints. We then apply our framework to an Argos satellite telemetry data set on harbor seals (Phoca vitulina) in the Gulf of Alaska, a species that is constrained to move within the marine environment and adjacent coastlines.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Davies, R.

The spatial autocorrelation functions of broad-band longwave and shortwave radiances measured by the Earth Radiation Budget Experiment (ERBE) are analyzed as a function of view angle in an investigation of the general effects of scene inhomogeneity on radiation. For nadir views, the correlation distance of the autocorrelation function is about 900 km for longwave radiance and about 500 km for shortwave radiance, consistent with higher degrees of freedom in shortwave reflection. Both functions rise monotonically with view angle, but there is a substantial difference in the relative angular dependence of the shortwave and longwave functions, especially for view angles lessmore » than 50 deg. In this range, the increase with angle of the longwave functions is found to depend only on the expansion of pixel area with angle, whereas the shortwave functions show an additional dependence on angle that is attributed to the occlusion of inhomogeneities by cloud height variations. Beyond a view angle of about 50 deg, both longwave and shortwave functions appear to be affected by cloud sides. The shortwave autocorrelation functions do not satisfy the principle of directional reciprocity, thereby proving that the average scene is horizontally inhomogeneous over the scale of an ERBE pixel (1500 sq km). Coarse stratification of the measurements by cloud amount, however, indicates that the average cloud-free scene does satisfy directional reciprocity on this scale.« less

Local orientational mobility in regular hyperbranched polymers.

PubMed

Dolgushev, Maxim; Markelov, Denis A; Fürstenberg, Florian; Guérin, Thomas

2016-07-01

We study the dynamics of local bond orientation in regular hyperbranched polymers modeled by Vicsek fractals. The local dynamics is investigated through the temporal autocorrelation functions of single bonds and the corresponding relaxation forms of the complex dielectric susceptibility. We show that the dynamic behavior of single segments depends on their remoteness from the periphery rather than on the size of the whole macromolecule. Remarkably, the dynamics of the core segments (which are most remote from the periphery) shows a scaling behavior that differs from the dynamics obtained after structural average. We analyze the most relevant processes of single segment motion and provide an analytic approximation for the corresponding relaxation times. Furthermore, we describe an iterative method to calculate the orientational dynamics in the case of very large macromolecular sizes.

Autocorrelation Function for Monitoring the Gap between The Steel Plates During Laser Welding

NASA Astrophysics Data System (ADS)

Mrna, Libor; Hornik, Petr

Proper alignment of the plates prior to laser welding represents an important factor that determines the quality of the resulting weld. A gap between the plates in a butt or overlap joint affects the oscillations of the keyhole and the surrounding weld pool. We present an experimental study of the butt and overlap welds with the artificial gap of the different thickness of the plates. The welds were made on a 2 kW fiber laser machine for the steel plates and the various welding parameters settings. The eigenfrequency of the keyhole oscillations and its changes were determined from the light emissions of the plasma plume using an autocorrelation function. As a result, we describe the relations between the autocorrelation characteristics, the thickness of the gap between plates and the weld geometry.

Autocorrelation exponent of conserved spin systems in the scaling regime following a critical quench.

PubMed

Sire, Clément

2004-09-24

We study the autocorrelation function of a conserved spin system following a quench at the critical temperature. Defining the correlation length L(t) approximately t(1/z), we find that for times t' and t satisfying L(t')infinity limit, we show that lambda(')(c)=d+2 and phi=z/2. We give a heuristic argument suggesting that this result is, in fact, valid for any dimension d and spin vector dimension n. We present numerical simulations for the conserved Ising model in d=1 and d=2, which are fully consistent with the present theory.

Modeling continuous seismic velocity changes due to ground shaking in Chile

NASA Astrophysics Data System (ADS)

Gassenmeier, Martina; Richter, Tom; Sens-Schönfelder, Christoph; Korn, Michael; Tilmann, Frederik

2015-04-01

In order to investigate temporal seismic velocity changes due to earthquake related processes and environmental forcing, we analyze 8 years of ambient seismic noise recorded by the Integrated Plate Boundary Observatory Chile (IPOC) network in northern Chile between 18° and 25° S. The Mw 7.7 Tocopilla earthquake in 2007 and the Mw 8.1 Iquique earthquake in 2014 as well as numerous smaller events occurred in this area. By autocorrelation of the ambient seismic noise field, approximations of the Green's functions are retrieved. The recovered function represents backscattered or multiply scattered energy from the immediate neighborhood of the station. To detect relative changes of the seismic velocities we apply the stretching method, which compares individual autocorrelation functions to stretched or compressed versions of a long term averaged reference autocorrelation function. We use time windows in the coda of the autocorrelations, that contain scattered waves which are highly sensitive to minute changes in the velocity. At station PATCX we observe seasonal changes in seismic velocity as well as temporary velocity reductions in the frequency range of 4-6 Hz. The seasonal changes can be attributed to thermal stress changes in the subsurface related to variations of the atmospheric temperature. This effect can be modeled well by a sine curve and is subtracted for further analysis of short term variations. Temporary velocity reductions occur at the time of ground shaking usually caused by earthquakes and are followed by a recovery. We present an empirical model that describes the seismic velocity variations based on continuous observations of the local ground acceleration. Our hypothesis is that not only the shaking of earthquakes provokes velocity drops, but any small vibrations continuously induce minor velocity variations that are immediately compensated by healing in the steady state. We show that the shaking effect is accumulated over time and best described by the integrated envelope of the ground acceleration over 1 day which is the discretization interval of the velocity measurements. In our model the amplitude of the velocity reduction as well as the recovery time are proportional to the size of the excitation. This model with the two free scaling parameters for the shaking induced velocity variation fits the data in remarkable detail. Additionally, a linear trend is observed that might be related to a recovery process from one or more earthquakes before our measurement period. For the Tocopilla earthquake in 2007 and the Iquique earthquake in 2014 velocity reductions are also observed at other stations of the IPOC network. However, a clear relationship between the ground shaking and the induced velocity reductions is not visible at other stations. We attribute the outstanding sensitivity of PATCX to ground shaking to the special geological setting of the station, where the material consists of relatively loose conglomerate with high pore volume.

Power-law Exponent in Multiplicative Langevin Equation with Temporally Correlated Noise

NASA Astrophysics Data System (ADS)

Morita, Satoru

2018-05-01

Power-law distributions are ubiquitous in nature. Random multiplicative processes are a basic model for the generation of power-law distributions. For discrete-time systems, the power-law exponent is known to decrease as the autocorrelation time of the multiplier increases. However, for continuous-time systems, it is not yet clear how the temporal correlation affects the power-law behavior. Herein, we analytically investigated a multiplicative Langevin equation with colored noise. We show that the power-law exponent depends on the details of the multiplicative noise, in contrast to the case of discrete-time systems.

Spatial design and strength of spatial signal: Effects on covariance estimation

USGS Publications Warehouse

Irvine, Kathryn M.; Gitelman, Alix I.; Hoeting, Jennifer A.

2007-01-01

In a spatial regression context, scientists are often interested in a physical interpretation of components of the parametric covariance function. For example, spatial covariance parameter estimates in ecological settings have been interpreted to describe spatial heterogeneity or “patchiness” in a landscape that cannot be explained by measured covariates. In this article, we investigate the influence of the strength of spatial dependence on maximum likelihood (ML) and restricted maximum likelihood (REML) estimates of covariance parameters in an exponential-with-nugget model, and we also examine these influences under different sampling designs—specifically, lattice designs and more realistic random and cluster designs—at differing intensities of sampling (n=144 and 361). We find that neither ML nor REML estimates perform well when the range parameter and/or the nugget-to-sill ratio is large—ML tends to underestimate the autocorrelation function and REML produces highly variable estimates of the autocorrelation function. The best estimates of both the covariance parameters and the autocorrelation function come under the cluster sampling design and large sample sizes. As a motivating example, we consider a spatial model for stream sulfate concentration.

Modified Beer-Lambert law for blood flow

NASA Astrophysics Data System (ADS)

Baker, Wesley B.; Parthasarathy, Ashwin B.; Busch, David R.; Mesquita, Rickson C.; Greenberg, Joel H.; Yodh, A. G.

2015-03-01

The modified Beer-Lambert law is among the most widely used approaches for analysis of near-infrared spectroscopy (NIRS) reflectance signals for measurements of tissue blood volume and oxygenation. Briefly, the modified Beer-Lambert paradigm is a scheme to derive changes in tissue optical properties based on continuous-wave (CW) diffuse optical intensity measurements. In its simplest form, the scheme relates differential changes in light transmission (in any geometry) to differential changes in tissue absorption. Here we extend this paradigm to the measurement of tissue blood flow by diffuse correlation spectroscopy (DCS). In the new approach, differential changes of the intensity temporal auto-correlation function at a single delay-time are related to differential changes in blood flow. The key theoretical results for measurement of blood flow changes in any tissue geometry are derived, and we demonstrate the new method to monitor cerebral blood flow in a pig under conditions wherein the semi-infinite geometry approximation is fairly good. Specifically, the drug dinitrophenol was injected in the pig to induce a gradual 200% increase in cerebral blood flow, as measured with MRI velocity flow mapping and by DCS. The modified Beer-Lambert law for flow accurately recovered these flow changes using only a single delay-time in the intensity auto-correlation function curve. The scheme offers increased DCS measurement speed of blood flow. Further, the same techniques using the modified Beer-Lambert law to filter out superficial tissue effects in NIRS measurements of deep tissues can be applied to the DCS modified Beer-Lambert law for blood flow monitoring of deep tissues.

Spatial and temporal structure of typhoid outbreaks in Washington, D.C., 1906–1909: evaluating local clustering with the Gi* statistic

PubMed Central

Hinman, Sarah E; Blackburn, Jason K; Curtis, Andrew

2006-01-01

Background To better understand the distribution of typhoid outbreaks in Washington, D.C., the U.S. Public Health Service (PHS) conducted four investigations of typhoid fever. These studies included maps of cases reported between 1 May – 31 October 1906 – 1909. These data were entered into a GIS database and analyzed using Ripley's K-function followed by the Gi* statistic in yearly intervals to evaluate spatial clustering, the scale of clustering, and the temporal stability of these clusters. Results The Ripley's K-function indicated no global spatial autocorrelation. The Gi* statistic indicated clustering of typhoid at multiple scales across the four year time period, refuting the conclusions drawn in all four PHS reports concerning the distribution of cases. While the PHS reports suggested an even distribution of the disease, this study quantified both areas of localized disease clustering, as well as mobile larger regions of clustering. Thus, indicating both highly localized and periodic generalized sources of infection within the city. Conclusion The methodology applied in this study was useful for evaluating the spatial distribution and annual-level temporal patterns of typhoid outbreaks in Washington, D.C. from 1906 to 1909. While advanced spatial analyses of historical data sets must be interpreted with caution, this study does suggest that there is utility in these types of analyses and that they provide new insights into the urban patterns of typhoid outbreaks during the early part of the twentieth century. PMID:16566830

Spatial and temporal structure of typhoid outbreaks in Washington, D.C., 1906-1909: evaluating local clustering with the Gi* statistic.

PubMed

Hinman, Sarah E; Blackburn, Jason K; Curtis, Andrew

2006-03-27

To better understand the distribution of typhoid outbreaks in Washington, D.C., the U.S. Public Health Service (PHS) conducted four investigations of typhoid fever. These studies included maps of cases reported between 1 May - 31 October 1906 - 1909. These data were entered into a GIS database and analyzed using Ripley's K-function followed by the Gi* statistic in yearly intervals to evaluate spatial clustering, the scale of clustering, and the temporal stability of these clusters. The Ripley's K-function indicated no global spatial autocorrelation. The Gi* statistic indicated clustering of typhoid at multiple scales across the four year time period, refuting the conclusions drawn in all four PHS reports concerning the distribution of cases. While the PHS reports suggested an even distribution of the disease, this study quantified both areas of localized disease clustering, as well as mobile larger regions of clustering. Thus, indicating both highly localized and periodic generalized sources of infection within the city. The methodology applied in this study was useful for evaluating the spatial distribution and annual-level temporal patterns of typhoid outbreaks in Washington, D.C. from 1906 to 1909. While advanced spatial analyses of historical data sets must be interpreted with caution, this study does suggest that there is utility in these types of analyses and that they provide new insights into the urban patterns of typhoid outbreaks during the early part of the twentieth century.

Seasonal Patterns in Microbial Community Composition in Denitrifying Bioreactors Treating Subsurface Agricultural Drainage.

PubMed

Porter, Matthew D; Andrus, J Malia; Bartolerio, Nicholas A; Rodriguez, Luis F; Zhang, Yuanhui; Zilles, Julie L; Kent, Angela D

2015-10-01

Denitrifying bioreactors, consisting of water flow control structures and a woodchip-filled trench, are a promising approach for removing nitrate from agricultural subsurface or tile drainage systems. To better understand the seasonal dynamics and the ecological drivers of the microbial communities responsible for denitrification in these bioreactors, we employed microbial community "fingerprinting" techniques in a time-series examination of three denitrifying bioreactors over 2 years, looking at bacteria, fungi, and the denitrifier functional group responsible for the final step of complete denitrification. Our analysis revealed that microbial community composition responds to depth and seasonal variation in moisture content and inundation of the bioreactor media, as well as temperature. Using a geostatistical analysis approach, we observed recurring temporal patterns in bacterial and denitrifying bacterial community composition in these bioreactors, consistent with annual cycling. The fungal communities were more stable, having longer temporal autocorrelations, and did not show significant annual cycling. These results suggest a recurring seasonal cycle in the denitrifying bioreactor microbial community, likely due to seasonal variation in moisture content.

New approaches for calculating Moran's index of spatial autocorrelation.

PubMed

Chen, Yanguang

2013-01-01

Spatial autocorrelation plays an important role in geographical analysis; however, there is still room for improvement of this method. The formula for Moran's index is complicated, and several basic problems remain to be solved. Therefore, I will reconstruct its mathematical framework using mathematical derivation based on linear algebra and present four simple approaches to calculating Moran's index. Moran's scatterplot will be ameliorated, and new test methods will be proposed. The relationship between the global Moran's index and Geary's coefficient will be discussed from two different vantage points: spatial population and spatial sample. The sphere of applications for both Moran's index and Geary's coefficient will be clarified and defined. One of theoretical findings is that Moran's index is a characteristic parameter of spatial weight matrices, so the selection of weight functions is very significant for autocorrelation analysis of geographical systems. A case study of 29 Chinese cities in 2000 will be employed to validate the innovatory models and methods. This work is a methodological study, which will simplify the process of autocorrelation analysis. The results of this study will lay the foundation for the scaling analysis of spatial autocorrelation.

Asymmetric multiscale multifractal analysis of wind speed signals

NASA Astrophysics Data System (ADS)

Zhang, Xiaonei; Zeng, Ming; Meng, Qinghao

We develop a new method called asymmetric multiscale multifractal analysis (A-MMA) to explore the multifractality and asymmetric autocorrelations of the signals with a variable scale range. Three numerical experiments are provided to demonstrate the effectiveness of our approach. Then, the proposed method is applied to investigate multifractality and asymmetric autocorrelations of difference sequences between wind speed fluctuations with uptrends or downtrends. The results show that these sequences appear to be far more complex and contain abundant fractal dynamics information. Through analyzing the Hurst surfaces of nine difference sequences, we found that all series exhibit multifractal properties and multiscale structures. Meanwhile, the asymmetric autocorrelations are observed in all variable scale ranges and the asymmetry results are of good consistency within a certain spatial range. The sources of multifractality and asymmetry in nine difference series are further discussed using the corresponding shuffled series and surrogate series. We conclude that the multifractality of these series is due to both long-range autocorrelation and broad probability density function, but the major source of multifractality is long-range autocorrelation, and the source of asymmetry is affected by the spatial distance.

Creation of Synthetic Surface Temperature and Precipitation Ensembles Through A Computationally Efficient, Mixed Method Approach

NASA Astrophysics Data System (ADS)

Hartin, C.; Lynch, C.; Kravitz, B.; Link, R. P.; Bond-Lamberty, B. P.

2017-12-01

Typically, uncertainty quantification of internal variability relies on large ensembles of climate model runs under multiple forcing scenarios or perturbations in a parameter space. Computationally efficient, standard pattern scaling techniques only generate one realization and do not capture the complicated dynamics of the climate system (i.e., stochastic variations with a frequency-domain structure). In this study, we generate large ensembles of climate data with spatially and temporally coherent variability across a subselection of Coupled Model Intercomparison Project Phase 5 (CMIP5) models. First, for each CMIP5 model we apply a pattern emulation approach to derive the model response to external forcing. We take all the spatial and temporal variability that isn't explained by the emulator and decompose it into non-physically based structures through use of empirical orthogonal functions (EOFs). Then, we perform a Fourier decomposition of the EOF projection coefficients to capture the input fields' temporal autocorrelation so that our new emulated patterns reproduce the proper timescales of climate response and "memory" in the climate system. Through this 3-step process, we derive computationally efficient climate projections consistent with CMIP5 model trends and modes of variability, which address a number of deficiencies inherent in the ability of pattern scaling to reproduce complex climate model behavior.

Numerical analysis for finite-range multitype stochastic contact financial market dynamic systems

NASA Astrophysics Data System (ADS)

Yang, Ge; Wang, Jun; Fang, Wen

2015-04-01

In an attempt to reproduce and study the dynamics of financial markets, a random agent-based financial price model is developed and investigated by the finite-range multitype contact dynamic system, in which the interaction and dispersal of different types of investment attitudes in a stock market are imitated by viruses spreading. With different parameters of birth rates and finite-range, the normalized return series are simulated by Monte Carlo simulation method and numerical studied by power-law distribution analysis and autocorrelation analysis. To better understand the nonlinear dynamics of the return series, a q-order autocorrelation function and a multi-autocorrelation function are also defined in this work. The comparisons of statistical behaviors of return series from the agent-based model and the daily historical market returns of Shanghai Composite Index and Shenzhen Component Index indicate that the proposed model is a reasonable qualitative explanation for the price formation process of stock market systems.

General simulation algorithm for autocorrelated binary processes.

PubMed

Serinaldi, Francesco; Lombardo, Federico

2017-02-01

The apparent ubiquity of binary random processes in physics and many other fields has attracted considerable attention from the modeling community. However, generation of binary sequences with prescribed autocorrelation is a challenging task owing to the discrete nature of the marginal distributions, which makes the application of classical spectral techniques problematic. We show that such methods can effectively be used if we focus on the parent continuous process of beta distributed transition probabilities rather than on the target binary process. This change of paradigm results in a simulation procedure effectively embedding a spectrum-based iterative amplitude-adjusted Fourier transform method devised for continuous processes. The proposed algorithm is fully general, requires minimal assumptions, and can easily simulate binary signals with power-law and exponentially decaying autocorrelation functions corresponding, for instance, to Hurst-Kolmogorov and Markov processes. An application to rainfall intermittency shows that the proposed algorithm can also simulate surrogate data preserving the empirical autocorrelation.

Data Analysis Methods for Synthetic Polymer Mass Spectrometry: Autocorrelation

PubMed Central

Wallace, William E.; Guttman, Charles M.

2002-01-01

Autocorrelation is shown to be useful in describing the periodic patterns found in high- resolution mass spectra of synthetic polymers. Examples of this usefulness are described for a simple linear homopolymer to demonstrate the method fundamentals, a condensation polymer to demonstrate its utility in understanding complex spectra with multiple repeating patterns on different mass scales, and a condensation copolymer to demonstrate how it can elegantly and efficiently reveal unexpected phenomena. It is shown that using autocorrelation to determine where the signal devolves into noise can be useful in determining molecular mass distributions of synthetic polymers, a primary focus of the NIST synthetic polymer mass spectrometry effort. The appendices describe some of the effects of transformation from time to mass space when time-of-flight mass separation is used, as well as the effects of non-trivial baselines on the autocorrelation function. PMID:27446716

Synchronous scattering and diffraction from gold nanotextured surfaces with structure factors

NASA Astrophysics Data System (ADS)

Gu, Min-Jhong; Lee, Ming-Tsang; Huang, Chien-Hsun; Wu, Chi-Chun; Chen, Yu-Bin

2018-05-01

Synchronous scattering and diffraction were demonstrated using reflectance from gold nanotextured surfaces at oblique (θi = 15° and 60°) incidence of wavelength λ = 405 nm. Two samples of unique auto-correlation functions were cost-effectively fabricated. Multiple structure factors of their profiles were confirmed with Fourier expansions. Bi-directional reflectance function (BRDF) from these samples provided experimental proofs. On the other hand, standard deviation of height and unique auto-correlation function of each sample were used to generate surfaces numerically. Comparing their BRDF with those of totally random rough surfaces further suggested that structure factors in profile could reduce specular reflection more than totally random roughness.

An empirical analysis of the distribution of overshoots in a stationary Gaussian stochastic process

NASA Technical Reports Server (NTRS)

Carter, M. C.; Madison, M. W.

1973-01-01

The frequency distribution of overshoots in a stationary Gaussian stochastic process is analyzed. The primary processes involved in this analysis are computer simulation and statistical estimation. Computer simulation is used to simulate stationary Gaussian stochastic processes that have selected autocorrelation functions. An analysis of the simulation results reveals a frequency distribution for overshoots with a functional dependence on the mean and variance of the process. Statistical estimation is then used to estimate the mean and variance of a process. It is shown that for an autocorrelation function, the mean and the variance for the number of overshoots, a frequency distribution for overshoots can be estimated.

Comparison between satellite and instrumental solar irradiance data at the city of Athens, Greece

NASA Astrophysics Data System (ADS)

Markonis, Yannis; Dimoulas, Thanos; Atalioti, Athina; Konstantinou, Charalampos; Kontini, Anna; Pipini, Magdalini-Io; Skarlatou, Eleni; Sarantopoulos, Vasilis; Tzouka, Katerina; Papalexiou, Simon; Koutsoyiannis, Demetris

2015-04-01

In this study, we examine and compare the statistical properties of satellite and instrumental solar irradiance data at the capital of Greece, Athens. Our aim is to determine whether satellite data are sufficient for the requirements of solar energy modelling applications. To this end we estimate the corresponding probability density functions, the auto-correlation functions and the parameters of some fitted simple stochastic models. We also investigate the effect of sample size to the variance in the temporal interpolation of daily time series. Finally, as an alternative, we examine if temperature can be used as a better predictor for the daily irradiance non-seasonal component instead of the satellite data. Acknowledgement: This research is conducted within the frame of the undergraduate course "Stochastic Methods in Water Resources" of the National Technical University of Athens (NTUA). The School of Civil Engineering of NTUA provided moral support for the participation of the students in the Assembly.

The time resolved measurement of ultrashort terahertz-band electric fields without an ultrashort probe

DOE Office of Scientific and Technical Information (OSTI.GOV)

Walsh, D. A., E-mail: david.walsh@stfc.ac.uk; Snedden, E. W.; Jamison, S. P.

The time-resolved detection of ultrashort pulsed THz-band electric field temporal profiles without an ultrashort laser probe is demonstrated. A non-linear interaction between a narrow-bandwidth optical probe and the THz pulse transposes the THz spectral intensity and phase information to the optical region, thereby generating an optical pulse whose temporal electric field envelope replicates the temporal profile of the real THz electric field. This optical envelope is characterised via an autocorrelation based FROG (frequency resolved optical gating) measurement, hence revealing the THz temporal profile. The combination of a narrow-bandwidth, long duration, optical probe, and self-referenced FROG makes the technique inherently immunemore » to timing jitter between the optical probe and THz pulse and may find particular application where the THz field is not initially generated via ultrashort laser methods, such as the measurement of longitudinal electron bunch profiles in particle accelerators.« less

Intensity autocorrelation measurements of frequency combs in the terahertz range

NASA Astrophysics Data System (ADS)

Benea-Chelmus, Ileana-Cristina; Rösch, Markus; Scalari, Giacomo; Beck, Mattias; Faist, Jérôme

2017-09-01

We report on direct measurements of the emission character of quantum cascade laser based frequency combs, using intensity autocorrelation. Our implementation is based on fast electro-optic sampling, with a detection spectral bandwidth matching the emission bandwidth of the comb laser, around 2.5 THz. We find the output of these frequency combs to be continuous even in the locked regime, but accompanied by a strong intensity modulation. Moreover, with our record temporal resolution of only few hundreds of femtoseconds, we can resolve correlated intensity modulation occurring on time scales as short as the gain recovery time, about 4 ps. By direct comparison with pulsed terahertz light originating from a photoconductive emitter, we demonstrate the peculiar emission pattern of these lasers. The measurement technique is self-referenced and ultrafast, and requires no reconstruction. It will be of significant importance in future measurements of ultrashort pulses from quantum cascade lasers.

A new estimator method for GARCH models

NASA Astrophysics Data System (ADS)

Onody, R. N.; Favaro, G. M.; Cazaroto, E. R.

2007-06-01

The GARCH (p, q) model is a very interesting stochastic process with widespread applications and a central role in empirical finance. The Markovian GARCH (1, 1) model has only 3 control parameters and a much discussed question is how to estimate them when a series of some financial asset is given. Besides the maximum likelihood estimator technique, there is another method which uses the variance, the kurtosis and the autocorrelation time to determine them. We propose here to use the standardized 6th moment. The set of parameters obtained in this way produces a very good probability density function and a much better time autocorrelation function. This is true for both studied indexes: NYSE Composite and FTSE 100. The probability of return to the origin is investigated at different time horizons for both Gaussian and Laplacian GARCH models. In spite of the fact that these models show almost identical performances with respect to the final probability density function and to the time autocorrelation function, their scaling properties are, however, very different. The Laplacian GARCH model gives a better scaling exponent for the NYSE time series, whereas the Gaussian dynamics fits better the FTSE scaling exponent.

Genetic evolution, plasticity, and bet-hedging as adaptive responses to temporally autocorrelated fluctuating selection: A quantitative genetic model.

PubMed

Tufto, Jarle

2015-08-01

Adaptive responses to autocorrelated environmental fluctuations through evolution in mean reaction norm elevation and slope and an independent component of the phenotypic variance are analyzed using a quantitative genetic model. Analytic approximations expressing the mutual dependencies between all three response modes are derived and solved for the joint evolutionary outcome. Both genetic evolution in reaction norm elevation and plasticity are favored by slow temporal fluctuations, with plasticity, in the absence of microenvironmental variability, being the dominant evolutionary outcome for reasonable parameter values. For fast fluctuations, tracking of the optimal phenotype through genetic evolution and plasticity is limited. If residual fluctuations in the optimal phenotype are large and stabilizing selection is strong, selection then acts to increase the phenotypic variance (bet-hedging adaptive). Otherwise, canalizing selection occurs. If the phenotypic variance increases with plasticity through the effect of microenvironmental variability, this shifts the joint evolutionary balance away from plasticity in favor of genetic evolution. If microenvironmental deviations experienced by each individual at the time of development and selection are correlated, however, more plasticity evolves. The adaptive significance of evolutionary fluctuations in plasticity and the phenotypic variance, transient evolution, and the validity of the analytic approximations are investigated using simulations. © 2015 The Author(s). Evolution © 2015 The Society for the Study of Evolution.

New Approaches for Calculating Moran’s Index of Spatial Autocorrelation

PubMed Central

Chen, Yanguang

2013-01-01

Spatial autocorrelation plays an important role in geographical analysis; however, there is still room for improvement of this method. The formula for Moran’s index is complicated, and several basic problems remain to be solved. Therefore, I will reconstruct its mathematical framework using mathematical derivation based on linear algebra and present four simple approaches to calculating Moran’s index. Moran’s scatterplot will be ameliorated, and new test methods will be proposed. The relationship between the global Moran’s index and Geary’s coefficient will be discussed from two different vantage points: spatial population and spatial sample. The sphere of applications for both Moran’s index and Geary’s coefficient will be clarified and defined. One of theoretical findings is that Moran’s index is a characteristic parameter of spatial weight matrices, so the selection of weight functions is very significant for autocorrelation analysis of geographical systems. A case study of 29 Chinese cities in 2000 will be employed to validate the innovatory models and methods. This work is a methodological study, which will simplify the process of autocorrelation analysis. The results of this study will lay the foundation for the scaling analysis of spatial autocorrelation. PMID:23874592

Long-range correlations in time series generated by time-fractional diffusion: A numerical study

NASA Astrophysics Data System (ADS)

Barbieri, Davide; Vivoli, Alessandro

2005-09-01

Time series models showing power law tails in autocorrelation functions are common in econometrics. A special non-Markovian model for such kind of time series is provided by the random walk introduced by Gorenflo et al. as a discretization of time fractional diffusion. The time series so obtained are analyzed here from a numerical point of view in terms of autocorrelations and covariance matrices.

[Correlation coefficient-based classification method of hydrological dependence variability: With auto-regression model as example].

PubMed

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.

Monte Carlo errors with less errors

NASA Astrophysics Data System (ADS)

Wolff, Ulli; Alpha Collaboration

2004-01-01

We explain in detail how to estimate mean values and assess statistical errors for arbitrary functions of elementary observables in Monte Carlo simulations. The method is to estimate and sum the relevant autocorrelation functions, which is argued to produce more certain error estimates than binning techniques and hence to help toward a better exploitation of expensive simulations. An effective integrated autocorrelation time is computed which is suitable to benchmark efficiencies of simulation algorithms with regard to specific observables of interest. A Matlab code is offered for download that implements the method. It can also combine independent runs (replica) allowing to judge their consistency.

Characterization, parameter estimation, and aircraft response statistics of atmospheric turbulence

NASA Technical Reports Server (NTRS)

Mark, W. D.

1981-01-01

A nonGaussian three component model of atmospheric turbulence is postulated that accounts for readily observable features of turbulence velocity records, their autocorrelation functions, and their spectra. Methods for computing probability density functions and mean exceedance rates of a generic aircraft response variable are developed using nonGaussian turbulence characterizations readily extracted from velocity recordings. A maximum likelihood method is developed for optimal estimation of the integral scale and intensity of records possessing von Karman transverse of longitudinal spectra. Formulas for the variances of such parameter estimates are developed. The maximum likelihood and least-square approaches are combined to yield a method for estimating the autocorrelation function parameters of a two component model for turbulence.

Skewness, long-time memory, and non-stationarity: Application to leverage effect in financial time series

NASA Astrophysics Data System (ADS)

Roman, H. E.; Porto, M.; Dose, C.

2008-10-01

We analyze daily log-returns data for a set of 1200 stocks, taken from US stock markets, over a period of 2481 trading days (January 1996-November 2005). We estimate the degree of non-stationarity in daily market volatility employing a polynomial fit, used as a detrending function. We find that the autocorrelation function of absolute detrended log-returns departs strongly from the corresponding original data autocorrelation function, while the observed leverage effect depends only weakly on trends. Such effect is shown to occur when both skewness and long-time memory are simultaneously present. A fractional derivative random walk model is discussed yielding a quantitative agreement with the empirical results.

Microchannel plate cross-talk mitigation for spatial autocorrelation measurements

NASA Astrophysics Data System (ADS)

Lipka, Michał; Parniak, Michał; Wasilewski, Wojciech

2018-05-01

Microchannel plates (MCP) are the basis for many spatially resolved single-particle detectors such as ICCD or I-sCMOS cameras employing image intensifiers (II), MCPs with delay-line anodes for the detection of cold gas particles or Cherenkov radiation detectors. However, the spatial characterization provided by an MCP is severely limited by cross-talk between its microchannels, rendering MCP and II ill-suited for autocorrelation measurements. Here, we present a cross-talk subtraction method experimentally exemplified for an I-sCMOS based measurement of pseudo-thermal light second-order intensity autocorrelation function at the single-photon level. The method merely requires a dark counts measurement for calibration. A reference cross-correlation measurement certifies the cross-talk subtraction. While remaining universal for MCP applications, the presented cross-talk subtraction, in particular, simplifies quantum optical setups. With the possibility of autocorrelation measurements, the signal needs no longer to be divided into two camera regions for a cross-correlation measurement, reducing the experimental setup complexity and increasing at least twofold the simultaneously employable camera sensor region.

Numerical analysis for finite-range multitype stochastic contact financial market dynamic systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yang, Ge; Wang, Jun; Fang, Wen, E-mail: fangwen@bjtu.edu.cn

In an attempt to reproduce and study the dynamics of financial markets, a random agent-based financial price model is developed and investigated by the finite-range multitype contact dynamic system, in which the interaction and dispersal of different types of investment attitudes in a stock market are imitated by viruses spreading. With different parameters of birth rates and finite-range, the normalized return series are simulated by Monte Carlo simulation method and numerical studied by power-law distribution analysis and autocorrelation analysis. To better understand the nonlinear dynamics of the return series, a q-order autocorrelation function and a multi-autocorrelation function are also definedmore » in this work. The comparisons of statistical behaviors of return series from the agent-based model and the daily historical market returns of Shanghai Composite Index and Shenzhen Component Index indicate that the proposed model is a reasonable qualitative explanation for the price formation process of stock market systems.« less

Method to manage integration error in the Green-Kubo method.

PubMed

Oliveira, Laura de Sousa; Greaney, P Alex

2017-02-01

The Green-Kubo method is a commonly used approach for predicting transport properties in a system from equilibrium molecular dynamics simulations. The approach is founded on the fluctuation dissipation theorem and relates the property of interest to the lifetime of fluctuations in its thermodynamic driving potential. For heat transport, the lattice thermal conductivity is related to the integral of the autocorrelation of the instantaneous heat flux. A principal source of error in these calculations is that the autocorrelation function requires a long averaging time to reduce remnant noise. Integrating the noise in the tail of the autocorrelation function becomes conflated with physically important slow relaxation processes. In this paper we present a method to quantify the uncertainty on transport properties computed using the Green-Kubo formulation based on recognizing that the integrated noise is a random walk, with a growing envelope of uncertainty. By characterizing the noise we can choose integration conditions to best trade off systematic truncation error with unbiased integration noise, to minimize uncertainty for a given allocation of computational resources.

Method to manage integration error in the Green-Kubo method

NASA Astrophysics Data System (ADS)

Oliveira, Laura de Sousa; Greaney, P. Alex

2017-02-01

The Green-Kubo method is a commonly used approach for predicting transport properties in a system from equilibrium molecular dynamics simulations. The approach is founded on the fluctuation dissipation theorem and relates the property of interest to the lifetime of fluctuations in its thermodynamic driving potential. For heat transport, the lattice thermal conductivity is related to the integral of the autocorrelation of the instantaneous heat flux. A principal source of error in these calculations is that the autocorrelation function requires a long averaging time to reduce remnant noise. Integrating the noise in the tail of the autocorrelation function becomes conflated with physically important slow relaxation processes. In this paper we present a method to quantify the uncertainty on transport properties computed using the Green-Kubo formulation based on recognizing that the integrated noise is a random walk, with a growing envelope of uncertainty. By characterizing the noise we can choose integration conditions to best trade off systematic truncation error with unbiased integration noise, to minimize uncertainty for a given allocation of computational resources.

Possible Noise Nature of Elsässer Variable z- in Highly Alfvénic Solar Wind Fluctuations

NASA Astrophysics Data System (ADS)

Wang, X.; Tu, C.-Y.; He, J.-S.; Wang, L.-H.; Yao, S.; Zhang, L.

2018-01-01

It has been a long-standing debate on the nature of Elsässer variable z- observed in the solar wind fluctuations. It is widely believed that z- represents inward propagating Alfvén waves and interacts nonlinearly with z+ (outward propagating Alfvén waves) to generate energy cascade. However, z- variations sometimes show a feature of convective structures. Here we present a new data analysis on autocorrelation functions of z- in order to get some definite information on its nature. We find that there is usually a large drop on the z- autocorrelation function when the solar wind fluctuations are highly Alfvénic. The large drop observed by Helios 2 spacecraft near 0.3 AU appears at the first nonzero time lag τ = 81 s, where the value of the autocorrelation coefficient drops to 25%-65% of that at τ = 0 s. Beyond the first nonzero time lag, the autocorrelation coefficient decreases gradually to zero. The drop of z- correlation function also appears in the Wind observations near 1 AU. These features of the z- correlation function may suggest that z- fluctuations consist of two components: high-frequency white noise and low-frequency pseudo structures, which correspond to flat and steep parts of z- power spectrum, respectively. This explanation is confirmed by doing a simple test on an artificial time series, which is obtained from the superposition of a random data series on its smoothed sequence. Our results suggest that in highly Alfvénic fluctuations, z- may not contribute importantly to the interactions with z+ to produce energy cascade.

Reconfigurable wavefront sensor for ultrashort pulses.

PubMed

Bock, Martin; Das, Susanta Kumar; Fischer, Carsten; Diehl, Michael; Börner, Peter; Grunwald, Ruediger

2012-04-01

A highly flexible Shack-Hartmann wavefront sensor for ultrashort pulse diagnostics is presented. The temporal system performance is studied in detail. Reflective operation is enabled by programming tilt-tolerant microaxicons into a liquid-crystal-on-silicon spatial light modulator. Nearly undistorted pulse transfer is obtained by generating nondiffracting needle beams as subbeams. Reproducible wavefront analysis and spatially resolved second-order autocorrelation are demonstrated at incident angles up to 50° and pulse durations down to 6 fs.

Time correlation functions of simple liquids: A new insight on the underlying dynamical processes

NASA Astrophysics Data System (ADS)

Garberoglio, Giovanni; Vallauri, Renzo; Bafile, Ubaldo

2018-05-01

Extensive molecular dynamics simulations of liquid sodium have been carried out to evaluate correlation functions of several dynamical quantities. We report the results of a novel analysis of the longitudinal and transverse correlation functions obtained by evaluating directly their self- and distinct contributions at different wavevectors k. It is easily recognized that the self-contribution remains close to its k → 0 limit, which turns out to be exactly the autocorrelation function of the single particle velocity. The wavevector dependence of the longitudinal and transverse spectra and their self- and distinct parts is also presented. By making use of the decomposition of the velocity autocorrelation spectrum in terms of longitudinal and transverse parts, our analysis is able to recognize the effect of different dynamical processes in different frequency ranges.

A reverberation-time-aware DNN approach leveraging spatial information for microphone array dereverberation

NASA Astrophysics Data System (ADS)

Wu, Bo; Yang, Minglei; Li, Kehuang; Huang, Zhen; Siniscalchi, Sabato Marco; Wang, Tong; Lee, Chin-Hui

2017-12-01

A reverberation-time-aware deep-neural-network (DNN)-based multi-channel speech dereverberation framework is proposed to handle a wide range of reverberation times (RT60s). There are three key steps in designing a robust system. First, to accomplish simultaneous speech dereverberation and beamforming, we propose a framework, namely DNNSpatial, by selectively concatenating log-power spectral (LPS) input features of reverberant speech from multiple microphones in an array and map them into the expected output LPS features of anechoic reference speech based on a single deep neural network (DNN). Next, the temporal auto-correlation function of received signals at different RT60s is investigated to show that RT60-dependent temporal-spatial contexts in feature selection are needed in the DNNSpatial training stage in order to optimize the system performance in diverse reverberant environments. Finally, the RT60 is estimated to select the proper temporal and spatial contexts before feeding the log-power spectrum features to the trained DNNs for speech dereverberation. The experimental evidence gathered in this study indicates that the proposed framework outperforms the state-of-the-art signal processing dereverberation algorithm weighted prediction error (WPE) and conventional DNNSpatial systems without taking the reverberation time into account, even for extremely weak and severe reverberant conditions. The proposed technique generalizes well to unseen room size, array geometry and loudspeaker position, and is robust to reverberation time estimation error.

Temporal variability and memory in sediment transport in an experimental step-pool channel

NASA Astrophysics Data System (ADS)

Saletti, Matteo; Molnar, Peter; Zimmermann, André; Hassan, Marwan A.; Church, Michael

2015-11-01

Temporal dynamics of sediment transport in steep channels using two experiments performed in a steep flume (8%) with natural sediment composed of 12 grain sizes are studied. High-resolution (1 s) time series of sediment transport were measured for individual grain-size classes at the outlet of the flume for different combinations of sediment input rates and flow discharges. Our aim in this paper is to quantify (a) the relation of discharge and sediment transport and (b) the nature and strength of memory in grain-size-dependent transport. None of the simple statistical descriptors of sediment transport (mean, extreme values, and quantiles) display a clear relation with water discharge, in fact a large variability between discharge and sediment transport is observed. Instantaneous transport rates have probability density functions with heavy tails. Bed load bursts have a coarser grain-size distribution than that of the entire experiment. We quantify the strength and nature of memory in sediment transport rates by estimating the Hurst exponent and the autocorrelation coefficient of the time series for different grain sizes. Our results show the presence of the Hurst phenomenon in transport rates, indicating long-term memory which is grain-size dependent. The short-term memory in coarse grain transport increases with temporal aggregation and this reveals the importance of the sampling duration of bed load transport rates in natural streams, especially for large fractions.

Detection of Subtle Hydromechanical Medium Changes Caused By a Small-Magnitude Earthquake Swarm in NE Brazil

NASA Astrophysics Data System (ADS)

D'Hour, V.; Schimmel, M.; Do Nascimento, A. F.; Ferreira, J. M.; Lima Neto, H. C.

2016-04-01

Ambient noise correlation analyses are largely used in seismology to map heterogeneities and to monitor the temporal evolution of seismic velocity changes associated mostly with stress field variations and/or fluid movements. Here we analyse a small earthquake swarm related to a main mR 3.7 intraplate earthquake in North-East of Brazil to study the corresponding post-seismic effects on the medium. So far, post-seismic effects have been observed mainly for large magnitude events. In our study, we show that we were able to detect localized structural changes even for a small earthquake swarm in an intraplate setting. Different correlation strategies are presented and their performances are also shown. We compare the classical auto-correlation with and without pre-processing, including 1-bit normalization and spectral whitening, and the phase auto-correlation. The worst results were obtained for the pre-processed data due to the loss of waveform details. The best results were achieved with the phase cross-correlation which is amplitude unbiased and sensitive to small amplitude changes as long as there exist waveform coherence superior to other unrelated signals and noise. The analysis of 6 months of data using phase auto-correlation and cross-correlation resulted in the observation of a progressive medium change after the major recorded event. The progressive medium change is likely related to the swarm activity through opening new path ways for pore fluid diffusion. We further observed for the auto-correlations a lag time frequency-dependent change which likely indicates that the medium change is localized in depth. As expected, the main change is observed along the fault.

Assessment of drug-induced arrhythmic risk using limit cycle and autocorrelation analysis of human iPSC-cardiomyocyte contractility

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kirby, R. Jason

2016-08-15

Cardiac safety assays incorporating label-free detection of human stem-cell derived cardiomyocyte contractility provide human relevance and medium throughput screening to assess compound-induced cardiotoxicity. In an effort to provide quantitative analysis of the large kinetic datasets resulting from these real-time studies, we applied bioinformatic approaches based on nonlinear dynamical system analysis, including limit cycle analysis and autocorrelation function, to systematically assess beat irregularity. The algorithms were integrated into a software program to seamlessly generate results for 96-well impedance-based data. Our approach was validated by analyzing dose- and time-dependent changes in beat patterns induced by known proarrhythmic compounds and screening a cardiotoxicitymore » library to rank order compounds based on their proarrhythmic potential. We demonstrate a strong correlation for dose-dependent beat irregularity monitored by electrical impedance and quantified by autocorrelation analysis to traditional manual patch clamp potency values for hERG blockers. In addition, our platform identifies non-hERG blockers known to cause clinical arrhythmia. Our method provides a novel suite of medium-throughput quantitative tools for assessing compound effects on cardiac contractility and predicting compounds with potential proarrhythmia and may be applied to in vitro paradigms for pre-clinical cardiac safety evaluation. - Highlights: • Impedance-based monitoring of human iPSC-derived cardiomyocyte contractility • Limit cycle analysis of impedance data identifies aberrant oscillation patterns. • Nonlinear autocorrelation function quantifies beat irregularity. • Identification of hERG and non-hERG inhibitors with known risk of arrhythmia • Automated software processes limit cycle and autocorrelation analyses of 96w data.« less

Non-isotropic noise correlation in PET data reconstructed by FBP but not by OSEM demonstrated using auto-correlation function.

PubMed

Razifar, Pasha; Lubberink, Mark; Schneider, Harald; Långström, Bengt; Bengtsson, Ewert; Bergström, Mats

2005-05-13

BACKGROUND: Positron emission tomography (PET) is a powerful imaging technique with the potential of obtaining functional or biochemical information by measuring distribution and kinetics of radiolabelled molecules in a biological system, both in vitro and in vivo. PET images can be used directly or after kinetic modelling to extract quantitative values of a desired physiological, biochemical or pharmacological entity. Because such images are generally noisy, it is essential to understand how noise affects the derived quantitative values. A pre-requisite for this understanding is that the properties of noise such as variance (magnitude) and texture (correlation) are known. METHODS: In this paper we explored the pattern of noise correlation in experimentally generated PET images, with emphasis on the angular dependence of correlation, using the autocorrelation function (ACF). Experimental PET data were acquired in 2D and 3D acquisition mode and reconstructed by analytical filtered back projection (FBP) and iterative ordered subsets expectation maximisation (OSEM) methods. The 3D data was rebinned to a 2D dataset using FOurier REbinning (FORE) followed by 2D reconstruction using either FBP or OSEM. In synthetic images we compared the ACF results with those from covariance matrix. The results were illustrated as 1D profiles and also visualized as 2D ACF images. RESULTS: We found that the autocorrelation images from PET data obtained after FBP were not fully rotationally symmetric or isotropic if the object deviated from a uniform cylindrical radioactivity distribution. In contrast, similar autocorrelation images obtained after OSEM reconstruction were isotropic even when the phantom was not circular. Simulations indicated that the noise autocorrelation is non-isotropic in images created by FBP when the level of noise in projections is angularly variable. Comparison between 1D cross profiles on autocorrelation images obtained by FBP reconstruction and covariance matrices produced almost identical results in a simulation study. CONCLUSION: With asymmetric radioactivity distribution in PET, reconstruction using FBP, in contrast to OSEM, generates images in which the noise correlation is non-isotropic when the noise magnitude is angular dependent, such as in objects with asymmetric radioactivity distribution. In this respect, iterative reconstruction is superior since it creates isotropic noise correlations in the images.

Differential Effects of Noise and Music Signals on the Behavior of Children

NASA Astrophysics Data System (ADS)

ANDO, Y.

2001-03-01

A theory based on the model of how the auditory-brain system perceive primary sensations is used to explain the differential effects of noise and music signals on the sleep of babies and on the performance of mental tasks by children. In a previous study by Ando and Hattori, [1], it was found that sleeping babies (2-4 months old) whose mothers had begun living in a noisy area before conception or during the first five months of pregnancy did not react to daily aircraft noise but did react to music. In another previous study by Ando et al.[2], the percentage of the pupils in "V-type relaxation" state during an adding task in a quiet living area was much greater when pupils heard music than when they heard noise. These phenomena are explained here by the difference between the temporal factors extracted from the running autocorrelation function of the noise and music signals.

Dynamic speckle - Interferometry of micro-displacements

NASA Astrophysics Data System (ADS)

Vladimirov, A. P.

2012-06-01

The problem of the dynamics of speckles in the image plane of the object, caused by random movements of scattering centers is solved. We consider three cases: 1) during the observation the points move at random, but constant speeds, and 2) the relative displacement of any pair of points is a continuous random process, and 3) the motion of the centers is the sum of a deterministic movement and random displacement. For the cases 1) and 2) the characteristics of temporal and spectral autocorrelation function of the radiation intensity can be used for determining of individually and the average relative displacement of the centers, their dispersion and the relaxation time. For the case 3) is showed that under certain conditions, the optical signal contains a periodic component, the number of periods is proportional to the derivations of the deterministic displacements. The results of experiments conducted to test and application of theory are given.

Studies in astronomical time series analysis. III - Fourier transforms, autocorrelation functions, and cross-correlation functions of unevenly spaced data

NASA Technical Reports Server (NTRS)

Scargle, Jeffrey D.

1989-01-01

This paper develops techniques to evaluate the discrete Fourier transform (DFT), the autocorrelation function (ACF), and the cross-correlation function (CCF) of time series which are not evenly sampled. The series may consist of quantized point data (e.g., yes/no processes such as photon arrival). The DFT, which can be inverted to recover the original data and the sampling, is used to compute correlation functions by means of a procedure which is effectively, but not explicitly, an interpolation. The CCF can be computed for two time series not even sampled at the same set of times. Techniques for removing the distortion of the correlation functions caused by the sampling, determining the value of a constant component to the data, and treating unequally weighted data are also discussed. FORTRAN code for the Fourier transform algorithm and numerical examples of the techniques are given.

Molecular dynamics test of the Brownian description of Na(+) motion in water

NASA Technical Reports Server (NTRS)

Wilson, M. A.; Pohorille, A.; Pratt, L. R.

1985-01-01

The present paper provides the results of molecular dynamics calculations on a Na(+) ion in aqueous solution. Attention is given to the sodium-oxygen and sodium-hydrogen radial distribution functions, the velocity autocorrelation function for the Na(+) ion, the autocorrelation function of the force on the stationary ion, and the accuracy of Brownian motion assumptions which are basic to hydrodynamic models of ion dyanmics in solution. It is pointed out that the presented calculations provide accurate data for testing theories of ion dynamics in solution. The conducted tests show that it is feasible to calculate Brownian friction constants for ions in aqueous solutions. It is found that for Na(+) under the considered conditions the Brownian mobility is in error by only 60 percent.

Polarization-correlation study of biotissue multifractal structure

NASA Astrophysics Data System (ADS)

Olar, O. I.; Ushenko, A. G.

2003-09-01

This paper presents the results of polarization-correlation study of multifractal collagen structure of physiologically normal and pathologically changed tissues of women"s reproductive sphere and skin. The technique of polarization selection of coherent images of biotissues with further determination of their autocorrelation functions and spectral densities is suggested. The correlation-optical criteria of early diagnostics of appearance of pathological changes in the cases of myometry (forming the germ of fibromyoma) and skin (psoriasis) are determined. This study is directed to investigate the possibilities of recognition of pathological changes of biotissue morphological structure by determining the polarization-dependent autocorrelation functions (ACF) and corresponding spectral densities of tissue coherent images.

Polarization-correlation investigation of biotissue multifractal structure and diagnostics of its pathological change

NASA Astrophysics Data System (ADS)

Angelsky, Oleg V.; Pishak, Vasyl P.; Ushenko, Alexander G.; Burkovets, Dimitry N.; Pishak, Olga V.

2001-05-01

The paper presents the results of polarization-correlation investigation of multifractal collagen structure of physiologically normal and pathologically changed tissues of women's reproductive sphere and of skin. The technique of polarization selection of coherent biotissues' images followed by determination of their autocorrelation functions and spectral densities is suggested. The correlation- optical criteria of early diagnostics of pathological changes' appearance of myometry (forming of the germ of fibromyoma) and of skin (psoriasis) are determined. The present paper examines the possibilities of diagnostics of pathological changes of biotissues' morphological structure by means of determining the polarizationally filtered autocorrelation functions (ACF) and corresponding spectral densities of their coherent images.

Dynamical analyses of the time series for three foreign exchange rates

NASA Astrophysics Data System (ADS)

Kim, Sehyun; Kim, Soo Yong; Jung, Jae-Won; Kim, Kyungsik

2012-05-01

In this study, we investigate the multifractal properties of three foreign exchange rates (USD-KRW, USD-JPY, and EUR-USD) that are quoted with different economic scales. We estimate and analyze both the generalized Hurst exponent and the autocorrelation function in three foreign exchange rates. The USD-KRW is shown to have the strongest of the Hurst exponents when compared with the other two foreign exchange rates. In particular, the autocorrelation function of the USD-KRW has the largest memory behavior among three foreign exchange rates. It also exhibits a long-memory property in the first quarter, more than those in the other quarters.

Integrated autocorrelator based on superconducting nanowires.

PubMed

Sahin, Döndü; Gaggero, Alessandro; Hoang, Thang Ba; Frucci, Giulia; Mattioli, Francesco; Leoni, Roberto; Beetz, Johannes; Lermer, Matthias; Kamp, Martin; Höfling, Sven; Fiore, Andrea

2013-05-06

We demonstrate an integrated autocorrelator based on two superconducting single-photon detectors patterned on top of a GaAs ridge waveguide. This device enables the on-chip measurement of the second-order intensity correlation function g(2)(τ). A polarization-independent device quantum efficiency in the 1% range is reported, with a timing jitter of 88 ps at 1300 nm. g(2)(τ) measurements of continuous-wave and pulsed laser excitations are demonstrated with no measurable crosstalk within our measurement accuracy.

Temporal Variability and Statistics of the Strehl Ratio in Adaptive-Optics Images

DTIC Science & Technology

2010-01-01

with the appropriate models and the residuals were extracted. This was done using the ARIMA modelling (Box & Jenkins 1970). ARIMA stands for...It was used here for the opposite goal – to obtain the values of the i.i.d. “noise” and test its distribution. Mixed ARIMA models of order 2 were...often sufficient to ensure non- significant autocorrelation of the residuals. Table 2 lists the stationary sequences with their respective ARIMA models

Research and Development of a High Power-Laser Driven Electron-Accelerator Suitable for Applications

DTIC Science & Technology

2011-06-12

autocorrelator to measure the temporal duration, an optical imaging system to correct for phase front tilt and a FROG device to measure and optimize the... Phase II Task Summary . . . . . . . . . . . . . . . . . . . . . 4 D.1 Module I: High-Energy Electron Accelerator . . . . . . 4 D.2 Module II: High-Energy...During Phase I of the HRS program, the team from the University of Ne- braska, Lincoln (UNL) made use of the unique capabilities of their high-power

A Possible Application of Coherent Light Scattering on Biological Fluids

NASA Astrophysics Data System (ADS)

Chicea, Dan; Chicea, Liana Maria

2007-04-01

Human urine from both healthy patients and patients with different diseases was used as scattering medium in a coherent light scattering experiment. The time variation of the light intensity in the far field speckle image was acquired using a data acquisition system on a PC and a time series resulted for each sample. The autocorrelation function for each sample was calculated and the autocorrelation time was determined. The same samples were analyzed in a medical laboratory using the standard procedure. We found so far that the autocorrelation time is differently modified by the presence of pus, albumin, urobilin and sediments. The results suggest a fast procedure that can be used as laboratory test to detect the presence not of each individual component in suspensions but of big conglomerates as albumin, cylinders, oxalate crystals.

Damage detection and isolation via autocorrelation: a step toward passive sensing

NASA Astrophysics Data System (ADS)

Chang, Y. S.; Yuan, F. G.

2018-03-01

Passive sensing technique may eliminate the need of expending power from actuators and thus provide a means of developing a compact and simple structural health monitoring system. More importantly, it may provide a solution for monitoring the aircraft subjected to environmental loading from air flow during operation. In this paper, a non-contact auto-correlation based technique is exploited as a feasibility study for passive sensing application to detect damage and isolate the damage location. Its theoretical basis bears some resemblance to reconstructing Green's function from diffusive wavefield through cross-correlation. Localized high pressure air from air compressor are randomly and continuously applied on the one side surface of the aluminum panels through the air blow gun. A laser Doppler vibrometer (LDV) was used to scan a 90 mm × 90 mm area to create a 6 × 6 2D-array signals from the opposite side of the panels. The scanned signals were auto-correlated to reconstruct a "selfimpulse response" (or Green's function). The premise for stably reconstructing the accurate Green's function requires long sensing times. For a 609.6 mm × 609.6 mm flat aluminum panel, the sensing times roughly at least four seconds is sufficient to establish converged Green's function through correlation. For the integral stiffened aluminum panel, the geometrical features of the panel expedite the formation of the diffusive wavefield and thus shorten the sensing times. The damage is simulated by gluing a magnet onto the panels. Reconstructed Green's functions (RGFs) are used for damage detection and damage isolation based on an imaging condition with mean square deviation of the RGFs from the pristine and the damaged structure and the results are shown in color maps. The auto-correlation based technique is shown to consistently detect the simulated damage, image and isolate the damage in the structure subjected to high pressure air excitation. This technique may be transformed into passive sensing applied on the aircraft during operation.

Autocorrelation measurement of femtosecond laser pulses based on two-photon absorption in GaP photodiode

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chong, E. Z.; Watson, T. F.; Festy, F., E-mail: frederic.festy@kcl.ac.uk

2014-08-11

Semiconductor materials which exhibit two-photon absorption characteristic within a spectral region of interest can be useful in building an ultra-compact interferometric autocorrelator. In this paper, we report on the evidence of a nonlinear absorption process in GaP photodiodes which was exploited to measure the temporal profile of femtosecond Ti:sapphire laser pulses with a tunable peak wavelength above 680 nm. The two-photon mediated conductivity measurements were performed at an average laser power of less than a few tenths of milliwatts. Its suitability as a single detector in a broadband autocorrelator setup was assessed by investigating the nonlinear spectral sensitivity bandwidth of amore » GaP photodiode. The highly favourable nonlinear response was found to cover the entire tuning range of our Ti:sapphire laser and can potentially be extended to wavelengths below 680 nm. We also demonstrated the flexibility of GaP in determining the optimum compensation value of the group delay dispersion required to restore the positively chirped pulses inherent in our experimental optical system to the shortest pulse width possible. With the rise in the popularity of nonlinear microscopy, the broad two-photon response of GaP and the simplicity of this technique can provide an alternative way of measuring the excitation laser pulse duration at the focal point of any microscopy systems.« less

Analysis of intermediate period correlations of coda from deep earthquakes

NASA Astrophysics Data System (ADS)

Poli, Piero; Campillo, Michel; de Hoop, Maarten

2017-11-01

We aim at assessing quantitatively the nature of the signals that appear in coda wave correlations at periods >20 s. These signals contain transient constituents with arrival times corresponding to deep seismic phases. These (body-wave) constituents can be used for imaging. To evaluate this approach, we calculate the autocorrelations of the vertical component seismograms for the Mw 8.4 sea of Okhotsk earthquake at 400 stations in the Eastern US, using data from 1 h before to 50 h after the earthquake. By using array analysis and modes identification, we discover the dominant role played by high quality factor normal modes in the emergence of strong coherent phases as ScS-like, and P'P'df-like. We then make use of geometrical quantization to derive the constituent rays associated with particular modes, and gain insights about the ballistic reverberation of the rays that contributes to the emergence of body waves. Our study indicates that the signals measured in the spatially averaged autocorrelations have a physical significance, but a direct interpretation of ScS-like and P'P'df-like is not trivial. Indeed, even a single simple measurement of long period late coda in a limited period band could provide valuable information on the deep structure by using the temporal information of its autocorrelation, a procedure that could be also useful for planetary exploration.

Asymptotic neutron scattering laws for anomalously diffusing quantum particles

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kneller, Gerald R.; Université d’Orléans, Chateau de la Source-Ave. du Parc Floral, 45067 Orléans; Synchrotron-SOLEIL, L’Orme de Merisiers, 91192 Gif-sur-Yvette

2016-07-28

The paper deals with a model-free approach to the analysis of quasielastic neutron scattering intensities from anomalously diffusing quantum particles. All quantities are inferred from the asymptotic form of their time-dependent mean square displacements which grow ∝t{sup α}, with 0 ≤ α < 2. Confined diffusion (α = 0) is here explicitly included. We discuss in particular the intermediate scattering function for long times and the Fourier spectrum of the velocity autocorrelation function for small frequencies. Quantum effects enter in both cases through the general symmetry properties of quantum time correlation functions. It is shown that the fractional diffusion constantmore » can be expressed by a Green-Kubo type relation involving the real part of the velocity autocorrelation function. The theory is exact in the diffusive regime and at moderate momentum transfers.« less

A rigorous statistical framework for spatio-temporal pollution prediction and estimation of its long-term impact on health.

PubMed

Lee, Duncan; Mukhopadhyay, Sabyasachi; Rushworth, Alastair; Sahu, Sujit K

2017-04-01

In the United Kingdom, air pollution is linked to around 40000 premature deaths each year, but estimating its health effects is challenging in a spatio-temporal study. The challenges include spatial misalignment between the pollution and disease data; uncertainty in the estimated pollution surface; and complex residual spatio-temporal autocorrelation in the disease data. This article develops a two-stage model that addresses these issues. The first stage is a spatio-temporal fusion model linking modeled and measured pollution data, while the second stage links these predictions to the disease data. The methodology is motivated by a new five-year study investigating the effects of multiple pollutants on respiratory hospitalizations in England between 2007 and 2011, using pollution and disease data relating to local and unitary authorities on a monthly time scale. © The Author 2016. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Microfluidic volumetric flow determination using optical coherence tomography speckle: An autocorrelation approach

DOE Office of Scientific and Technical Information (OSTI.GOV)

De Pretto, Lucas R., E-mail: lucas.de.pretto@usp.br; Nogueira, Gesse E. C.; Freitas, Anderson Z.

2016-04-28

Functional modalities of Optical Coherence Tomography (OCT) based on speckle analysis are emerging in the literature. We propose a simple approach to the autocorrelation of OCT signal to enable volumetric flow rate differentiation, based on decorrelation time. Our results show that this technique could distinguish flows separated by 3 μl/min, limited by the acquisition speed of the system. We further perform a B-scan of gradient flow inside a microchannel, enabling the visualization of the drag effect on the walls.

Temporal measurement on and using pulses from laser-like emission obtained from styrylpyridinium cyanine dye

NASA Astrophysics Data System (ADS)

Dharmadhikari, Aditya; Bhowmik, Achintya; Ahyi, Ayayi; Thakur, Mrinal

2000-03-01

We have recently reported observation of spectral narrowing and high-conversion laser-like emission in a solution of styrylpyridinium cynanine dye (SPCD) at a low threshold energy, pumped with the second-harmonic of a picosecond Nd:YAG laser. Fundamental and second-harmonic pulses from a Nd:YAG laser of 80 ps duration at 10 Hz repetition rate were used to pump 0.1 mol/l concentration of SPCD in methanol in two separate pumping arrangements. A highly directional emission was observed in both the pumping arrangements without incorporating any mirrors. The pulse duration of spectrally narrowed emission in both cases was measured by background-free SHG intensity autocorrelation technique. A BBO crystal was used for the autocorrelation measurement. The measured duration of the pulses was 40 ps. These pulses having a spectral linewidth of 10 nm (FWHM) were used as a probe to measure the gain in SPCD solution in a pump-probe set up. The results will be discussed.

Geomagnetic storm under laboratory conditions: randomized experiment

NASA Astrophysics Data System (ADS)

Gurfinkel, Yu I.; Vasin, A. L.; Pishchalnikov, R. Yu; Sarimov, R. M.; Sasonko, M. L.; Matveeva, T. A.

2017-10-01

The influence of the previously recorded geomagnetic storm (GS) on human cardiovascular system and microcirculation has been studied under laboratory conditions. Healthy volunteers in lying position were exposed under two artificially created conditions: quiet (Q) and storm (S). The Q regime playbacks a noise-free magnetic field (MF) which is closed to the natural geomagnetic conditions on Moscow's latitude. The S regime playbacks the initially recorded 6-h geomagnetic storm which is repeated four times sequentially. The cardiovascular response to the GS impact was assessed by measuring capillary blood velocity (CBV) and blood pressure (BP) and by the analysis of the 24-h ECG recording. A storm-to-quiet ratio for the cardio intervals (CI) and the heart rate variability (HRV) was introduced in order to reveal the average over group significant differences of HRV. An individual sensitivity to the GS was estimated using the autocorrelation function analysis of the high-frequency (HF) part of the CI spectrum. The autocorrelation analysis allowed for detection a group of subjects of study which autocorrelation functions (ACF) react differently in the Q and S regimes of exposure.

Geomagnetic storm under laboratory conditions: randomized experiment.

PubMed

Gurfinkel, Yu I; Vasin, A L; Pishchalnikov, R Yu; Sarimov, R M; Sasonko, M L; Matveeva, T A

2018-04-01

The influence of the previously recorded geomagnetic storm (GS) on human cardiovascular system and microcirculation has been studied under laboratory conditions. Healthy volunteers in lying position were exposed under two artificially created conditions: quiet (Q) and storm (S). The Q regime playbacks a noise-free magnetic field (MF) which is closed to the natural geomagnetic conditions on Moscow's latitude. The S regime playbacks the initially recorded 6-h geomagnetic storm which is repeated four times sequentially. The cardiovascular response to the GS impact was assessed by measuring capillary blood velocity (CBV) and blood pressure (BP) and by the analysis of the 24-h ECG recording. A storm-to-quiet ratio for the cardio intervals (CI) and the heart rate variability (HRV) was introduced in order to reveal the average over group significant differences of HRV. An individual sensitivity to the GS was estimated using the autocorrelation function analysis of the high-frequency (HF) part of the CI spectrum. The autocorrelation analysis allowed for detection a group of subjects of study which autocorrelation functions (ACF) react differently in the Q and S regimes of exposure.

Autocorrelation-based time synchronous averaging for condition monitoring of planetary gearboxes in wind turbines

NASA Astrophysics Data System (ADS)

Ha, Jong M.; Youn, Byeng D.; Oh, Hyunseok; Han, Bongtae; Jung, Yoongho; Park, Jungho

2016-03-01

We propose autocorrelation-based time synchronous averaging (ATSA) to cope with the challenges associated with the current practice of time synchronous averaging (TSA) for planet gears in planetary gearboxes of wind turbine (WT). An autocorrelation function that represents physical interactions between the ring, sun, and planet gears in the gearbox is utilized to define the optimal shape and range of the window function for TSA using actual kinetic responses. The proposed ATSA offers two distinctive features: (1) data-efficient TSA processing and (2) prevention of signal distortion during the TSA process. It is thus expected that an order analysis with the ATSA signals significantly improves the efficiency and accuracy in fault diagnostics of planet gears in planetary gearboxes. Two case studies are presented to demonstrate the effectiveness of the proposed method: an analytical signal from a simulation and a signal measured from a 2 kW WT testbed. It can be concluded from the results that the proposed method outperforms conventional TSA methods in condition monitoring of the planetary gearbox when the amount of available stationary data is limited.

Spatial and Temporal Changes of Aerosol Optical Depth and its Driving Factors Based on Modis in Jiangsu Province

NASA Astrophysics Data System (ADS)

Jiang, C.; Xu, Q.; Gu, Y. K.; Qian, X. Y.; He, J. N.

2018-04-01

Aerosol Optical Depth (AOD) is of great value for studying air mass and its changes. In this paper, we studied the spatial-temporal changes of AOD and its driving factors based on spatial autocorrelation model, gravity model and multiple regression analysis in Jiangsu Province from 2007 to 2016. The results showed that in terms of spatial distribution, the southern AOD value is higher, and the high-value aggregation areas are significant, while the northern AOD value is lower, but the low-value aggregation areas constantly change. The AOD gravity centers showed a clear point-like aggregation. In terms of temporal changes, the overall AOD in Jiangsu Province increased year by year in fluctuation. In terms of driving factors, the total amount of vehicles, precipitation and temperature are important factors for the growth of AOD.

Research on the Spatial-Temporal Distribution Pattern of the Network Attention of Fog and Haze in China

NASA Astrophysics Data System (ADS)

Weng, Lingyan; Han, Xugao

2018-01-01

Understanding the spatial-temporal distribution pattern of fog and haze is the base to deal with them by adjusting measures to local conditions. Taking 31 provinces in China mainland as the research areas, this paper collected data from Baidu index on the network attention of fog and haze in relevant areas from 2011 to 2016, and conducted an analysis of their spatial-temporal distribution pattern by using autocorrelation analysis. The results show that the network attention of fog and haze has an overall spatial distribution pattern of “higher in the eastern and central, lower in the western China”. There are regional differences in different provinces in terms of network attention. Network attention of fog and haze indicates an obvious geographical agglomeration phenomenon, which is a gradual enlargement of the agglomeration area of higher value with a slight shrinking of those lower value agglomeration areas.

Noninvasive observation of skeletal muscle contraction using near-infrared time-resolved reflectance and diffusing-wave spectroscopy

NASA Astrophysics Data System (ADS)

Belau, Markus; Ninck, Markus; Hering, Gernot; Spinelli, Lorenzo; Contini, Davide; Torricelli, Alessandro; Gisler, Thomas

2010-09-01

We introduce a method for noninvasively measuring muscle contraction in vivo, based on near-infrared diffusing-wave spectroscopy (DWS). The method exploits the information about time-dependent shear motions within the contracting muscle that are contained in the temporal autocorrelation function g(1)(τ,t) of the multiply scattered light field measured as a function of lag time, τ, and time after stimulus, t. The analysis of g(1)(τ,t) measured on the human M. biceps brachii during repetitive electrical stimulation, using optical properties measured with time-resolved reflectance spectroscopy, shows that the tissue dynamics giving rise to the speckle fluctuations can be described by a combination of diffusion and shearing. The evolution of the tissue Cauchy strain e(t) shows a strong correlation with the force, indicating that a significant part of the shear observed with DWS is due to muscle contraction. The evolution of the DWS decay time shows quantitative differences between the M. biceps brachii and the M. gastrocnemius, suggesting that DWS allows to discriminate contraction of fast- and slow-twitch muscle fibers.

Characteristic Exponent of Normal and Oblique Rolls in Homeotropically Aligned Nematic Liquid Crystal

NASA Astrophysics Data System (ADS)

Saraswati, V.; Nugroho, F.

2018-04-01

Soft-mode turbulence (SMT) is one of an experimental example of spatiotemporal chaos, observed in electroconvection system of homeotropically aligned nematic liquid crystal (NLC), due to a non-linear interaction between Nambu-Goldstone mode denoted by the C(r)- director and the convective mode q(r). There are two types of stripe patterns in the SMT, namely normal rolls (NR) and oblique rolls (OR) which separated by a point of applied frequency, called the Lifshitz frequency (f L ). We report a study of the phase transition from normal to oblique rolls by observing the patterns with an applied frequency below and beyond of fL . The temporal fluctuations of the pattern images had been analyzed using autocorrelation function. It fits with Kohlrausch Williams Watts (KWW) function, showing there is a dynamical glass-forming liquid in the transition of NR-OR regime. Also, we found a new type of defect in the NR regime which never been reported before, a dynamic defect which takes the shape of a ring first to a spot in the end.

Distribution of Reynolds stress carried by mesoscale variability in the Antarctic Circumpolar Current

NASA Technical Reports Server (NTRS)

Johnson, Thomas J.; Stewart, Robert H.; Shum, C. K.; Tapley, Byron D.

1992-01-01

Satellite altimeter data collected by the Geosat Exact Repeat Mission were used to investigate turbulent stress resulting from the variability of surface geostrophic currents in the Antarctic Circumpolar Current. The altimeter measured sea level along the subsatellite track. The variability of the along-track slope of sea level is directly proportional to the variability of surface geostrophic currents in the cross-track direction. Because the grid of crossover points is dense at high latitudes, the satellite data could be used for mapping the temporal and spatial variability of the current. Two and a half years of data were used to compute the statistical structure of the variability. The statistics included the probability distribution functions for each component of the current, the time-lagged autocorrelation functions of the variability, and the Reynolds stress produced by the variability. The results demonstrate that stress is correlated with bathymetry. In some areas the distribution of negative stress indicate that eddies contribute to an acceleration of the mean flow, strengthening the hypothesis that baroclinic instability makes important contributions to strong oceanic currents.

Errors in radial velocity variance from Doppler wind lidar

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, H.; Barthelmie, R. J.; Doubrawa, P.

A high-fidelity lidar turbulence measurement technique relies on accurate estimates of radial velocity variance that are subject to both systematic and random errors determined by the autocorrelation function of radial velocity, the sampling rate, and the sampling duration. Our paper quantifies the effect of the volumetric averaging in lidar radial velocity measurements on the autocorrelation function and the dependence of the systematic and random errors on the sampling duration, using both statistically simulated and observed data. For current-generation scanning lidars and sampling durations of about 30 min and longer, during which the stationarity assumption is valid for atmospheric flows, themore » systematic error is negligible but the random error exceeds about 10%.« less

Sign reversals of the output autocorrelation function for the stochastic Bernoulli-Verhulst equation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lumi, N., E-mail: Neeme.Lumi@tlu.ee; Mankin, R., E-mail: Romi.Mankin@tlu.ee

2015-10-28

We consider a stochastic Bernoulli-Verhulst equation as a model for population growth processes. The effect of fluctuating environment on the carrying capacity of a population is modeled as colored dichotomous noise. Relying on the composite master equation an explicit expression for the stationary autocorrelation function (ACF) of population sizes is found. On the basis of this expression a nonmonotonic decay of the ACF by increasing lag-time is shown. Moreover, in a certain regime of the noise parameters the ACF demonstrates anticorrelation as well as related sign reversals at some values of the lag-time. The conditions for the appearance of thismore » highly unexpected effect are also discussed.« less

Pathogenic changes of dispersion and contrast of coherent images of biotissues

NASA Astrophysics Data System (ADS)

Pishak, Olga V.

2002-02-01

The paper presents the results of polarization-correlation investigation of multifractal collagen structure of physiologically normal and pathologically changed tissues of women's reproductive sphere and of skin. The technique of polarization selection of coherent biotissues' images with the following determination of their autocorrelation functions and spectral densities is suggested. The correlation-optical criteria of early diagnostics of pathological changes' appearance of myometry (forming of the germ of fibromyoma) and of skin(psoriasis) are determined. The suggested paper is directed to investigation of the possibilities of pathological changes of biotissues' morphological structure by means of determining the polarizationally filtered autocorrelation functions (ACF) and corresponding spectral densities of their coherent images.

The calculation of transport properties in quantum liquids using the maximum entropy numerical analytic continuation method: Application to liquid para-hydrogen

PubMed Central

Rabani, Eran; Reichman, David R.; Krilov, Goran; Berne, Bruce J.

2002-01-01

We present a method based on augmenting an exact relation between a frequency-dependent diffusion constant and the imaginary time velocity autocorrelation function, combined with the maximum entropy numerical analytic continuation approach to study transport properties in quantum liquids. The method is applied to the case of liquid para-hydrogen at two thermodynamic state points: a liquid near the triple point and a high-temperature liquid. Good agreement for the self-diffusion constant and for the real-time velocity autocorrelation function is obtained in comparison to experimental measurements and other theoretical predictions. Improvement of the methodology and future applications are discussed. PMID:11830656

Errors in radial velocity variance from Doppler wind lidar

DOE PAGES

Wang, H.; Barthelmie, R. J.; Doubrawa, P.; ...

2016-08-29

A high-fidelity lidar turbulence measurement technique relies on accurate estimates of radial velocity variance that are subject to both systematic and random errors determined by the autocorrelation function of radial velocity, the sampling rate, and the sampling duration. Our paper quantifies the effect of the volumetric averaging in lidar radial velocity measurements on the autocorrelation function and the dependence of the systematic and random errors on the sampling duration, using both statistically simulated and observed data. For current-generation scanning lidars and sampling durations of about 30 min and longer, during which the stationarity assumption is valid for atmospheric flows, themore » systematic error is negligible but the random error exceeds about 10%.« less

Diffuse correlation tomography in the transport regime: A theoretical study of the sensitivity to Brownian motion.

PubMed

Tricoli, Ugo; Macdonald, Callum M; Durduran, Turgut; Da Silva, Anabela; Markel, Vadim A

2018-02-01

Diffuse correlation tomography (DCT) uses the electric-field temporal autocorrelation function to measure the mean-square displacement of light-scattering particles in a turbid medium over a given exposure time. The movement of blood particles is here estimated through a Brownian-motion-like model in contrast to ordered motion as in blood flow. The sensitivity kernel relating the measurable field correlation function to the mean-square displacement of the particles can be derived by applying a perturbative analysis to the correlation transport equation (CTE). We derive an analytical expression for the CTE sensitivity kernel in terms of the Green's function of the radiative transport equation, which describes the propagation of the intensity. We then evaluate the kernel numerically. The simulations demonstrate that, in the transport regime, the sensitivity kernel provides sharper spatial information about the medium as compared with the correlation diffusion approximation. Also, the use of the CTE allows one to explore some additional degrees of freedom in the data such as the collimation direction of sources and detectors. Our results can be used to improve the spatial resolution of DCT, in particular, with applications to blood flow imaging in regions where the Brownian motion is dominant.

Diffuse correlation tomography in the transport regime: A theoretical study of the sensitivity to Brownian motion

NASA Astrophysics Data System (ADS)

Tricoli, Ugo; Macdonald, Callum M.; Durduran, Turgut; Da Silva, Anabela; Markel, Vadim A.

2018-02-01

Diffuse correlation tomography (DCT) uses the electric-field temporal autocorrelation function to measure the mean-square displacement of light-scattering particles in a turbid medium over a given exposure time. The movement of blood particles is here estimated through a Brownian-motion-like model in contrast to ordered motion as in blood flow. The sensitivity kernel relating the measurable field correlation function to the mean-square displacement of the particles can be derived by applying a perturbative analysis to the correlation transport equation (CTE). We derive an analytical expression for the CTE sensitivity kernel in terms of the Green's function of the radiative transport equation, which describes the propagation of the intensity. We then evaluate the kernel numerically. The simulations demonstrate that, in the transport regime, the sensitivity kernel provides sharper spatial information about the medium as compared with the correlation diffusion approximation. Also, the use of the CTE allows one to explore some additional degrees of freedom in the data such as the collimation direction of sources and detectors. Our results can be used to improve the spatial resolution of DCT, in particular, with applications to blood flow imaging in regions where the Brownian motion is dominant.

Stochastic modelling of the monthly average maximum and minimum temperature patterns in India 1981-2015

NASA Astrophysics Data System (ADS)

Narasimha Murthy, K. V.; Saravana, R.; Vijaya Kumar, K.

2018-04-01

The paper investigates the stochastic modelling and forecasting of monthly average maximum and minimum temperature patterns through suitable seasonal auto regressive integrated moving average (SARIMA) model for the period 1981-2015 in India. The variations and distributions of monthly maximum and minimum temperatures are analyzed through Box plots and cumulative distribution functions. The time series plot indicates that the maximum temperature series contain sharp peaks in almost all the years, while it is not true for the minimum temperature series, so both the series are modelled separately. The possible SARIMA model has been chosen based on observing autocorrelation function (ACF), partial autocorrelation function (PACF), and inverse autocorrelation function (IACF) of the logarithmic transformed temperature series. The SARIMA (1, 0, 0) × (0, 1, 1)12 model is selected for monthly average maximum and minimum temperature series based on minimum Bayesian information criteria. The model parameters are obtained using maximum-likelihood method with the help of standard error of residuals. The adequacy of the selected model is determined using correlation diagnostic checking through ACF, PACF, IACF, and p values of Ljung-Box test statistic of residuals and using normal diagnostic checking through the kernel and normal density curves of histogram and Q-Q plot. Finally, the forecasting of monthly maximum and minimum temperature patterns of India for the next 3 years has been noticed with the help of selected model.

Crustal thickness across the Trans-European Suture Zone from ambient noise autocorrelations

NASA Astrophysics Data System (ADS)

Becker, G.; Knapmeyer-Endrun, B.

2018-02-01

We derive autocorrelations from ambient seismic noise to image the reflectivity of the subsurface and to extract the Moho depth beneath the stations for two different data sets in Central Europe. The autocorrelations are calculated by smoothing the spectrum of the data in order to suppress high amplitude, narrow-band signals of industrial origin, applying a phase autocorrelation algorithm and time-frequency domain phase-weighted stacking. The stacked autocorrelation results are filtered and analysed predominantly in the frequency range of 1-2 Hz. Moho depth is automatically picked inside uncertainty windows obtained from prior information. The processing scheme we developed is applied to data from permanent seismic stations located in different geological provinces across Europe, with varying Moho depths between 25 and 50 km, and to the mainly short period temporary PASSEQ stations along seismic profile POLONAISE P4. The autocorrelation results are spatially and temporarily stable, but show a clear correlation with the existence of cultural noise. On average, a minimum of six months of data is needed to obtain stable results. The obtained Moho depth results are in good agreement with the subsurface model provided by seismic profiling, receiver function estimates and the European Moho depth map. In addition to extracting the Moho depth, it is possible to identify an intracrustal layer along the profile, again closely matching the seismic model. For more than half of the broad-band stations, another change in reflectivity within the mantle is observed and can be correlated with the lithosphere-asthenosphere boundary to the west and a mid-lithospheric discontinuity beneath the East European Craton. With the application of the developed autocorrelation processing scheme to different stations with varying crustal thicknesses, it is shown that Moho depth can be extracted independent of subsurface structure, when station coverage is low, when no strong seismic sources are present, and when only limited amounts of data are available.

Temporal Variation in Single-Cell Power-Law Rheology Spans the Ensemble Variation of Cell Population.

PubMed

Cai, PingGen; Takahashi, Ryosuke; Kuribayashi-Shigetomi, Kaori; Subagyo, Agus; Sueoka, Kazuhisa; Maloney, John M; Van Vliet, Krystyn J; Okajima, Takaharu

2017-08-08

Changes in the cytoskeletal organization within cells can be characterized by large spatial and temporal variations in rheological properties of the cell (e.g., the complex shear modulus G ∗ ). Although the ensemble variation in G ∗ of single cells has been elucidated, the detailed temporal variation of G ∗ remains unknown. In this study, we investigated how the rheological properties of individual fibroblast cells change under a spatially confined environment in which the cell translational motion is highly restricted and the whole cell shape remains unchanged. The temporal evolution of single-cell rheology was probed at the same measurement location within the cell, using atomic force microscopy-based oscillatory deformation. The measurements reveal that the temporal variation in the power-law rheology of cells is quantitatively consistent with the ensemble variation, indicating that the cell system satisfies an ergodic hypothesis in which the temporal statistics are identical to the ensemble statistics. The autocorrelation of G ∗ implies that the cell mechanical state evolves in the ensemble of possible states with a characteristic timescale. Copyright © 2017 Biophysical Society. Published by Elsevier Inc. All rights reserved.

Correlation range in a supercooled liquid via Green-Kubo expression for viscosity, local atomic stresses, and MD simulations

NASA Astrophysics Data System (ADS)

Levashov, Valentin A.; Egami, Takeshi; Morris, James R.

2009-03-01

We present a new approach to the issue of correlation range in supercooled liquids based on Green-Kubo expression for viscosity. The integrand of this expression is the average stress-stress autocorrelation function. This correlation function could be rewritten in terms of correlations among local atomic stresses at different times and distances. The features of the autocorrelation function decay with time depend on temperature and correlation range. Through this approach we can study the development of spatial correlation with time, thus directly addressing the question of dynamic heterogeneity. We performed MD simulations on a single component system of particles interacting through short range pair potential. Our results indicate that even above the crossover temperature correlations extend well beyond the nearest neighbors. Surprisingly we found that the system size effects exist even on relatively large systems. We also address the role of diffusion in decay of stress-stress correlation function.

How cosmic microwave background correlations at large angles relate to mass autocorrelations in space

NASA Technical Reports Server (NTRS)

Blumenthal, George R.; Johnston, Kathryn V.

1994-01-01

The Sachs-Wolfe effect is known to produce large angular scale fluctuations in the cosmic microwave background radiation (CMBR) due to gravitational potential fluctuations. We show how the angular correlation function of the CMBR can be expressed explicitly in terms of the mass autocorrelation function xi(r) in the universe. We derive analytic expressions for the angular correlation function and its multipole moments in terms of integrals over xi(r) or its second moment, J(sub 3)(r), which does not need to satisfy the sort of integral constraint that xi(r) must. We derive similar expressions for bulk flow velocity in terms of xi and J(sub 3). One interesting result that emerges directly from this analysis is that, for all angles theta, there is a substantial contribution to the correlation function from a wide range of distance r and that radial shape of this contribution does not vary greatly with angle.

Exploring Lightning Jump Characteristics

NASA Technical Reports Server (NTRS)

Chronis, Themis; Carey, Larry D.; Schultz, Christopher J.; Schultz, Elise; Calhoun, Kristin; Goodman, Steven J.

2014-01-01

This study is concerned with the characteristics of storms exhibiting an abrupt temporal increase in the total lightning flash rate (i.e., lightning jump, LJ). An automated storm tracking method is used to identify storm "clusters" and total lightning activity from three different lightning detection systems over Oklahoma, northern Alabama and Washington, D.C. On average and for different employed thresholds, the clusters that encompass at least one LJ (LJ1) last longer, relate to higher Maximum Expected Size of Hail, Vertical Integrated Liquid and lightning flash rates (area-normalized) than the clusters that did not exhibit any LJ (LJ0). The respective mean values for LJ1 (LJ0) clusters are 80 min (35 min), 14 mm (8 mm), 25 kg per square meter (18 kg per square meter) and 0.05 flash per min per square kilometer (0.01 flash per min per square kilometer). Furthermore, the LJ1 clusters are also characterized by slower decaying autocorrelation functions, a result that implies a less "random" behavior in the temporal flash rate evolution. In addition, the temporal occurrence of the last LJ provides an estimate of the time remaining to the storm's dissipation. Depending of the LJ strength (i.e., varying thresholds), these values typically range between 20-60 min, with stronger jumps indicating more time until storm decay. This study's results support the hypothesis that the LJ is a proxy for the storm's kinematic and microphysical state rather than a coincidental value.

Structure of the North Anatolian Fault Zone from the Auto-Correlation of Ambient Seismic Noise Recorded at a Dense Seismometer Array

NASA Astrophysics Data System (ADS)

Taylor, D. G.; Rost, S.; Houseman, G.

2015-12-01

In recent years the technique of cross-correlating the ambient seismic noise wavefield at two seismometers to reconstruct empirical Green's Functions for the determination of Earth structure has been a powerful tool to study the Earth's interior without earthquake or man-made sources. However, far less attention has been paid to using auto-correlations of seismic noise to reveal body wave reflections from interfaces in the subsurface. In principle, the Green's functions thus derived should be comparable to the Earth's impulse response to a co-located source and receiver. We use data from a dense seismic array (Dense Array for Northern Anatolia - DANA) deployed across the northern branch of the North Anatolian Fault Zone (NAFZ) in the region of the 1999 magnitude 7.6 Izmit earthquake in western Turkey. The NAFZ is a major strike-slip system that extends ~1200 km across northern Turkey and continues to pose a high level of seismic hazard, in particular to the mega-city of Istanbul. We construct reflection images for the entire crust and upper mantle over the ~35 km by 70 km footprint of the 70-station DANA array. Using auto-correlations of vertical and horizontal components of ground motion, both P- and S-wave velocity information can be retrieved from the wavefield to constrain crustal structure further to established methods. We show that clear P-wave reflections from the crust-mantle boundary (Moho) can be retrieved using the autocorrelation technique, indicating topography on the Moho on horizontal scales of less than 10 km. Offsets in crustal structure can be identified that seem to be correlated with the surface expression of the fault zone in the region. The combined analysis of auto-correlations using vertical and horizontal components will lead to further insight into the fault zone structure throughout the crust and upper mantle.

Velocity autocorrelation function in supercooled liquids: Long-time tails and anomalous shear-wave propagation.

PubMed

Peng, H L; Schober, H R; Voigtmann, Th

2016-12-01

Molecular dynamic simulations are performed to reveal the long-time behavior of the velocity autocorrelation function (VAF) by utilizing the finite-size effect in a Lennard-Jones binary mixture. Whereas in normal liquids the classical positive t^{-3/2} long-time tail is observed, we find in supercooled liquids a negative tail. It is strongly influenced by the transfer of the transverse current wave across the period boundary. The t^{-5/2} decay of the negative long-time tail is confirmed in the spectrum of VAF. Modeling the long-time transverse current within a generalized Maxwell model, we reproduce the negative long-time tail of the VAF, but with a slower algebraic t^{-2} decay.

Spatial and Temporal Variation of Japanese encephalitis Disease and Detection of Disease Hotspots: a Case Study of Gorakhpur District, Uttar Pradesh, India

NASA Astrophysics Data System (ADS)

Verma, S.; Gupta, R. D.

2014-11-01

In recent times, Japanese Encephalitis (JE) has emerged as a serious public health problem. In India, JE outbreaks were recently reported in Uttar Pradesh, Gorakhpur. The present study presents an approach to use GIS for analyzing the reported cases of JE in the Gorakhpur district based on spatial analysis to bring out the spatial and temporal dynamics of the JE epidemic. The study investigates spatiotemporal pattern of the occurrence of disease and detection of the JE hotspot. Spatial patterns of the JE disease can provide an understanding of geographical changes. Geospatial distribution of the JE disease outbreak is being investigated since 2005 in this study. The JE incidence data for the years 2005 to 2010 is used. The data is then geo-coded at block level. Spatial analysis is used to evaluate autocorrelation in JE distribution and to test the cases that are clustered or dispersed in space. The Inverse Distance Weighting interpolation technique is used to predict the pattern of JE incidence distribution prevalent across the study area. Moran's I Index (Moran's I) statistics is used to evaluate autocorrelation in spatial distribution. The Getis-Ord Gi*(d) is used to identify the disease areas. The results represent spatial disease patterns from 2005 to 2010, depicting spatially clustered patterns with significant differences between the blocks. It is observed that the blocks on the built up areas reported higher incidences.

Statistical assessment of optical phase fluctuations through turbulent mixing layers

NASA Astrophysics Data System (ADS)

Gardner, Patrick J.; Roggemann, Michael C.; Welsh, Byron M.; Bowersox, Rodney D.

1995-09-01

A lateral shearing interferometer is used to measure the slope of perturbed wavefronts after propagating through turbulent shear flows. This provides a two-dimensional flow visualization technique which is nonintrusive. The slope measurements are used to reconstruct the phase of the turbulence-corrupted wave front. Experiments were performed on a plane shear mixing layer of helium and nitrogen gas at fixed velocities, for five locations in the flow development. The two gases, having a density ratio of approximately seven, provide an effective means of simulating compressible shear layers. Statistical autocorrelation functions and structure functions are computed on the reconstructed phase maps. The autocorrelation function results indicate that the turbulence-induced phase fluctuations are not wide-sense stationary. The structure functions exhibit statistical homogeneity, indicating the phase fluctuation are stationary in first increments. However, the turbulence-corrupted phase is not isotropic. A five-thirds power law is shown to fit one-dimensional, orthogonal slices of the structure function, with scaling coefficients related to the location in the flow.

Reliabilities of Intraindividual Variability Indicators with Autocorrelated Longitudinal Data: Implications for Longitudinal Study Designs.

PubMed

Du, Han; Wang, Lijuan

2018-04-23

Intraindividual variability can be measured by the intraindividual standard deviation ([Formula: see text]), intraindividual variance ([Formula: see text]), estimated hth-order autocorrelation coefficient ([Formula: see text]), and mean square successive difference ([Formula: see text]). Unresolved issues exist in the research on reliabilities of intraindividual variability indicators: (1) previous research only studied conditions with 0 autocorrelations in the longitudinal responses; (2) the reliabilities of [Formula: see text] and [Formula: see text] have not been studied. The current study investigates reliabilities of [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], and the intraindividual mean, with autocorrelated longitudinal data. Reliability estimates of the indicators were obtained through Monte Carlo simulations. The impact of influential factors on reliabilities of the intraindividual variability indicators is summarized, and the reliabilities are compared across the indicators. Generally, all the studied indicators of intraindividual variability were more reliable with a more reliable measurement scale and more assessments. The reliabilities of [Formula: see text] were generally lower than those of [Formula: see text] and [Formula: see text], the reliabilities of [Formula: see text] were usually between those of [Formula: see text] and [Formula: see text] unless the scale reliability was large and/or the interindividual standard deviation in autocorrelation coefficients was large, and the reliabilities of the intraindividual mean were generally the highest. An R function is provided for planning longitudinal studies to ensure sufficient reliabilities of the intraindividual indicators are achieved.

Bayesian Estimates of Autocorrelations in Single-Case Designs

ERIC Educational Resources Information Center

Shadish, William R.; Rindskopf, David M.; Hedges, Larry V.; Sullivan, Kristynn J.

2012-01-01

Researchers in the single-case design tradition have debated the size and importance of the observed autocorrelations in those designs. All of the past estimates of the autocorrelation in that literature have taken the observed autocorrelation estimates as the data to be used in the debate. However, estimates of the autocorrelation are subject to…

Spatio-temporal patterns of key exploited marine species in the Northwestern Mediterranean Sea.

PubMed

Morfin, Marie; Fromentin, Jean-Marc; Jadaud, Angélique; Bez, Nicolas

2012-01-01

This study analyzes the temporal variability/stability of the spatial distributions of key exploited species in the Gulf of Lions (Northwestern Mediterranean Sea). To do so, we analyzed data from the MEDITS bottom-trawl scientific surveys from 1994 to 2010 at 66 fixed stations and selected 12 key exploited species. We proposed a geostatistical approach to handle zero-inflated and non-stationary distributions and to test for the temporal stability of the spatial structures. Empirical Orthogonal Functions and other descriptors were then applied to investigate the temporal persistence and the characteristics of the spatial patterns. The spatial structure of the distribution (i.e. the pattern of spatial autocorrelation) of the 12 key species studied remained highly stable over the time period sampled. The spatial distributions of all species obtained through kriging also appeared to be stable over time, while each species displayed a specific spatial distribution. Furthermore, adults were generally more densely concentrated than juveniles and occupied areas included in the distribution of juveniles. Despite the strong persistence of spatial distributions, we also observed that the area occupied by each species was correlated to its abundance: the more abundant the species, the larger the occupation area. Such a result tends to support MacCall's basin theory, according to which density-dependence responses would drive the expansion of those 12 key species in the Gulf of Lions. Further analyses showed that these species never saturated their habitats, suggesting that they are below their carrying capacity; an assumption in agreement with the overexploitation of several of these species. Finally, the stability of their spatial distributions over time and their potential ability to diffuse outside their main habitats give support to Marine Protected Areas as a potential pertinent management tool.

Quantitative fluorescence correlation spectroscopy on DNA in living cells

NASA Astrophysics Data System (ADS)

Hodges, Cameron; Kafle, Rudra P.; Meiners, Jens-Christian

2017-02-01

FCS is a fluorescence technique conventionally used to study the kinetics of fluorescent molecules in a dilute solution. Being a non-invasive technique, it is now drawing increasing interest for the study of more complex systems like the dynamics of DNA or proteins in living cells. Unlike an ordinary dye solution, the dynamics of macromolecules like proteins or entangled DNA in crowded environments is often slow and subdiffusive in nature. This in turn leads to longer residence times of the attached fluorophores in the excitation volume of the microscope and artifacts from photobleaching abound that can easily obscure the signature of the molecular dynamics of interest and make quantitative analysis challenging.We discuss methods and procedures to make FCS applicable to quantitative studies of the dynamics of DNA in live prokaryotic and eukaryotic cells. The intensity autocorrelation is computed function from weighted arrival times of the photons on the detector that maximizes the information content while simultaneously correcting for the effect of photobleaching to yield an autocorrelation function that reflects only the underlying dynamics of the sample. This autocorrelation function in turn is used to calculate the mean square displacement of the fluorophores attached to DNA. The displacement data is more amenable to further quantitative analysis than the raw correlation functions. By using a suitable integral transform of the mean square displacement, we can then determine the viscoelastic moduli of the DNA in its cellular environment. The entire analysis procedure is extensively calibrated and validated using model systems and computational simulations.

A statistical physics view of pitch fluctuations in the classical music from Bach to Chopin: evidence for scaling.

PubMed

Liu, Lu; Wei, Jianrong; Zhang, Huishu; Xin, Jianhong; Huang, Jiping

2013-01-01

Because classical music has greatly affected our life and culture in its long history, it has attracted extensive attention from researchers to understand laws behind it. Based on statistical physics, here we use a different method to investigate classical music, namely, by analyzing cumulative distribution functions (CDFs) and autocorrelation functions of pitch fluctuations in compositions. We analyze 1,876 compositions of five representative classical music composers across 164 years from Bach, to Mozart, to Beethoven, to Mendelsohn, and to Chopin. We report that the biggest pitch fluctuations of a composer gradually increase as time evolves from Bach time to Mendelsohn/Chopin time. In particular, for the compositions of a composer, the positive and negative tails of a CDF of pitch fluctuations are distributed not only in power laws (with the scale-free property), but also in symmetry (namely, the probability of a treble following a bass and that of a bass following a treble are basically the same for each composer). The power-law exponent decreases as time elapses. Further, we also calculate the autocorrelation function of the pitch fluctuation. The autocorrelation function shows a power-law distribution for each composer. Especially, the power-law exponents vary with the composers, indicating their different levels of long-range correlation of notes. This work not only suggests a way to understand and develop music from a viewpoint of statistical physics, but also enriches the realm of traditional statistical physics by analyzing music.

Sub-femtosecond quantum dynamics of the strong-field ionization of water to the X ̃(2)B1 and Ã(2)A1 states of the cation.

PubMed

Jayachander Rao, B; Varandas, A J C

2015-03-07

Motivated by recent efforts to achieve sub-femtosecond structural resolution in various molecular systems, we have performed a femtosecond quantum dynamics study of the water cation in the X ̃(2)B1 and Ã(2)A1 electronic states. Autocorrelation functions for H2O(+) and D2O(+) are calculated for such electronic states by solving numerically the time-dependent Schrödinger equation. From the ratio of the squared autocorrelation functions of D2O(+) and H2O(+), the high-order harmonic generation signals are calculated. Substantial vibrational dynamics is found in the Ã(2)A1 state as compared to the one in X ̃(2)B1, which supports recent experimental findings of Farrell et al., Phys. Rev. Lett., 2011, 107, 083001. Maxima in the above ratio are also predicted at ∼1.1 fs and ∼1.6 fs for the X ̃(2)B1 and Ã(2)A1 states, respectively. The expectation values of the positions of the atoms in H2O(+) as a function of time reveal a strong excitation of the bending mode in the Ã(2)A1 state, which explains the observed vibrational dynamics. The peaks in the ratios of the squared autocorrelation functions are also explained in terms of the evolving geometries of the water cation.

Spatio-temporal autocorrelation of Neogene-Quaternary volcanic and clastic sedimentary rocks in SW Montana and SE Idaho: Relationship to Cenozoic tectonic and thermally induced extensional events

NASA Astrophysics Data System (ADS)

Davarpanah, A.; Babaie, H. A.; Dai, D.

2013-12-01

Two systems of full and half grabens have been forming since the mid-Tertiary through tectonic and thermally induced extensional events in SW Montana and neighboring SE Idaho. The earlier mid-Tertiary Basin and Range (BR) tectonic event formed the NW- and NE-striking mountains around the Snake River Plain (SRP) in Idaho and SW Montana, respectively. Since the mid-Tertiary, partially synchronous with the BR event, diachronous bulging and subsidence due to the thermally induced stress field of the Yellowstone hotspot (YHS) has produced the second system of variably-oriented grabens through faulting across the older BR fault blocks. The track of the migration of the YHS is defined by the presence of six prominent volcanic calderas along the SRP which become younger toward the present location of the YHS. Graben basins bounded by both the BR faults and thermally induced cross-faults (CF) systems are now filled with Tertiary-Quaternary clastic sedimentary and volcanic-volcaniclastic rocks. Neogene mafic and felsic lava which erupted along the SRP and clastic sedimentary units (Sixmile Creek Fm., Ts) deposited in both types of graben basins were classified based on their lithology and age, and mapped in ArcGIS 10 as polygon using a combination of MBMG and USGS databases and geological maps at scales of 1:250.000, 1:100,000, and 1:48,000. The spatio-temporal distributions of the lava polygons were then analyzed applying the Global and Local Moran`s I methods to detect any possible spatial or temporal autocorrelation relative to the track of the YHS. The results reveal the spatial autocorrelation of the lithology and age of the Neogene lavas, and suggest a spatio-temporal sequence of eruption of extrusive rocks between Miocene and late Pleistocene along the SRP. The sequence of eruptions, which progressively becomes younger toward the Yellowstone National Park, may track the migration of the YSH. The sub-parallelism of the trend of the SRP with the long axis of the standard deviation ellipses (SDEs), that give the trend of the dispersion of the centroids of lavas erupted at different times, and the spatio-temporally ordered overlap of older lavas by younger ones which were progressively erupted to the northeast of the older lavas, indicate the spatio-temporal migration of the centers of eruption along the SRP. Prominent graben basins which formed and filled during and after the BR normal faulting event were identified from those that formed during and after the cross faulting event based on cross cutting relationships and the trend of their long dimension (determined by applying the Dissolve and Minimum Bounding Geometry tools in ArcGIS 10) relative to the linear directional mean (LDM) of the BR and CF sets. The parallelism of the mean trend of the Ts graben fill polygons with the linear directional mean (LDM) of each of the two BR fault trace sets in the eastern SRP indicates that the Neogene deposition of the Ts is post-BR and pre-to syn-cross faulting. Cross-fault-bounded graben valleys filled with Ts roughly sub-parallel the mean trend of the CF sets, indicating that they formed after the BR faulting event.

Advanced Sine Wave Modulation of Continuous Wave Laser System for Atmospheric CO2 Differential Absorption Measurements

NASA Technical Reports Server (NTRS)

Campbell, Joel F.; Lin, Bing; Nehrir, Amin R.

2014-01-01

NASA Langley Research Center in collaboration with ITT Exelis have been experimenting with Continuous Wave (CW) laser absorption spectrometer (LAS) as a means of performing atmospheric CO2 column measurements from space to support the Active Sensing of CO2 Emissions over Nights, Days, and Seasons (ASCENDS) mission.Because range resolving Intensity Modulated (IM) CW lidar techniques presented here rely on matched filter correlations, autocorrelation properties without side lobes or other artifacts are highly desirable since the autocorrelation function is critical for the measurements of lidar return powers, laser path lengths, and CO2 column amounts. In this paper modulation techniques are investigated that improve autocorrelation properties. The modulation techniques investigated in this paper include sine waves modulated by maximum length (ML) sequences in various hardware configurations. A CW lidar system using sine waves modulated by ML pseudo random noise codes is described, which uses a time shifting approach to separate channels and make multiple, simultaneous online/offline differential absorption measurements. Unlike the pure ML sequence, this technique is useful in hardware that is band pass filtered as the IM sine wave carrier shifts the main power band. Both amplitude and Phase Shift Keying (PSK) modulated IM carriers are investigated that exibit perfect autocorrelation properties down to one cycle per code bit. In addition, a method is presented to bandwidth limit the ML sequence based on a Gaussian filter implemented in terms of Jacobi theta functions that does not seriously degrade the resolution or introduce side lobes as a means of reducing aliasing and IM carrier bandwidth.

NMR spin-rotation relaxation and diffusion of methane

NASA Astrophysics Data System (ADS)

Singer, P. M.; Asthagiri, D.; Chapman, W. G.; Hirasaki, G. J.

2018-05-01

The translational diffusion-coefficient and the spin-rotation contribution to the 1H NMR relaxation rate for methane (CH4) are investigated using MD (molecular dynamics) simulations, over a wide range of densities and temperatures, spanning the liquid, supercritical, and gas phases. The simulated diffusion-coefficients agree well with measurements, without any adjustable parameters in the interpretation of the simulations. A minimization technique is developed to compute the angular velocity for non-rigid spherical molecules, which is used to simulate the autocorrelation function for spin-rotation interactions. With increasing diffusivity, the autocorrelation function shows increasing deviations from the single-exponential decay predicted by the Langevin theory for rigid spheres, and the deviations are quantified using inverse Laplace transforms. The 1H spin-rotation relaxation rate derived from the autocorrelation function using the "kinetic model" agrees well with measurements in the supercritical/gas phase, while the relaxation rate derived using the "diffusion model" agrees well with measurements in the liquid phase. 1H spin-rotation relaxation is shown to dominate over the MD-simulated 1H-1H dipole-dipole relaxation at high diffusivity, while the opposite is found at low diffusivity. At high diffusivity, the simulated spin-rotation correlation time agrees with the kinetic collision time for gases, which is used to derive a new expression for 1H spin-rotation relaxation, without any adjustable parameters.

Decoherence in yeast cell populations and its implications for genome-wide expression noise.

PubMed

Briones, M R S; Bosco, F

2009-01-20

Gene expression "noise" is commonly defined as the stochastic variation of gene expression levels in different cells of the same population under identical growth conditions. Here, we tested whether this "noise" is amplified with time, as a consequence of decoherence in global gene expression profiles (genome-wide microarrays) of synchronized cells. The stochastic component of transcription causes fluctuations that tend to be amplified as time progresses, leading to a decay of correlations of expression profiles, in perfect analogy with elementary relaxation processes. Measuring decoherence, defined here as a decay in the auto-correlation function of yeast genome-wide expression profiles, we found a slowdown in the decay of correlations, opposite to what would be expected if, as in mixing systems, correlations decay exponentially as the equilibrium state is reached. Our results indicate that the populational variation in gene expression (noise) is a consequence of temporal decoherence, in which the slow decay of correlations is a signature of strong interdependence of the transcription dynamics of different genes.

Fast blood flow monitoring in deep tissues with real-time software correlators

PubMed Central

Wang, Detian; Parthasarathy, Ashwin B.; Baker, Wesley B.; Gannon, Kimberly; Kavuri, Venki; Ko, Tiffany; Schenkel, Steven; Li, Zhe; Li, Zeren; Mullen, Michael T.; Detre, John A.; Yodh, Arjun G.

2016-01-01

We introduce, validate and demonstrate a new software correlator for high-speed measurement of blood flow in deep tissues based on diffuse correlation spectroscopy (DCS). The software correlator scheme employs standard PC-based data acquisition boards to measure temporal intensity autocorrelation functions continuously at 50 – 100 Hz, the fastest blood flow measurements reported with DCS to date. The data streams, obtained in vivo for typical source-detector separations of 2.5 cm, easily resolve pulsatile heart-beat fluctuations in blood flow which were previously considered to be noise. We employ the device to separate tissue blood flow from tissue absorption/scattering dynamics and thereby show that the origin of the pulsatile DCS signal is primarily flow, and we monitor cerebral autoregulation dynamics in healthy volunteers more accurately than with traditional instrumentation as a result of increased data acquisition rates. Finally, we characterize measurement signal-to-noise ratio and identify count rate and averaging parameters needed for optimal performance. PMID:27231588

First results on quiet and magnetic granulation from SOUP

NASA Technical Reports Server (NTRS)

Title, A. M.; Tarbell, T. D.; Acton, L.; Duncan, D.; Ferguson, S. H.; Finch, M.; Frank, Z.; Kelly, G.; Lindgren, R.; Morrill, M.

1987-01-01

The flight of Solar Optical Universal Polarimeter (SOUP) on Spacelab 2 allowed the collection of time sequences of diffraction limited (0.5 arc sec) granulation images with excellent pointing (0.003 arc sec) and completely free of the distortion that plagues groundbased images. The p-mode oscillations are clearly seen in the data. Using Fourier transforms in the temporal and spatial domain, it was shown that the p-modes dominate the autocorrelation lifetime in magnetic regions. When these oscillations are removed the autocorrelation lifetime is found to be 500 sec in quiet and 950 sec in magnetic regions. In quiet areas exploding granules are seen to be common. It is speculated that a significant fraction of granule lifetimes are terminated by nearby explosions. Using local correlation tracking techniques it was able to measure horizontal displacements, and thus transverse velocities, in the magnetic field. In quiet sun it is possible to detect both super and mesogranulation. Horizontal velocities are as great as 1000 m/s and the average velocity is 400 m/s. In magnetic regions horizontal velocities are much less, about 100 m/s.

First results on quiet and magnetic granulation from SOUP

NASA Astrophysics Data System (ADS)

Title, A. M.; Tarbell, T. D.; Acton, L.; Duncan, D.; Ferguson, S. H.; Finch, M.; Frank, Z.; Kelly, G.; Lindgren, R.; Morrill, M.

1987-09-01

The flight of Solar Optical Universal Polarimeter (SOUP) on Spacelab 2 allowed the collection of time sequences of diffraction limited (0.5 arc sec) granulation images with excellent pointing (0.003 arc sec) and completely free of the distortion that plagues groundbased images. The p-mode oscillations are clearly seen in the data. Using Fourier transforms in the temporal and spatial domain, it was shown that the p-modes dominate the autocorrelation lifetime in magnetic regions. When these oscillations are removed the autocorrelation lifetime is found to be 500 sec in quiet and 950 sec in magnetic regions. In quiet areas exploding granules are seen to be common. It is speculated that a significant fraction of granule lifetimes are terminated by nearby explosions. Using local correlation tracking techniques it was able to measure horizontal displacements, and thus transverse velocities, in the magnetic field. In quiet sun it is possible to detect both super and mesogranulation. Horizontal velocities are as great as 1000 m/s and the average velocity is 400 m/s. In magnetic regions horizontal velocities are much less, about 100 m/s.

Stochastic environmental fluctuations drive epidemiology in experimental host–parasite metapopulations

PubMed Central

Duncan, Alison B.; Gonzalez, Andrew; Kaltz, Oliver

2013-01-01

Environmental fluctuations are important for parasite spread and persistence. However, the effects of the spatial and temporal structure of environmental fluctuations on host–parasite dynamics are not well understood. Temporal fluctuations can be random but positively autocorrelated, such that the environment is similar to the recent past (red noise), or random and uncorrelated with the past (white noise). We imposed red or white temporal temperature fluctuations on experimental metapopulations of Paramecium caudatum, experiencing an epidemic of the bacterial parasite Holospora undulata. Metapopulations (two subpopulations linked by migration) experienced fluctuations between stressful (5°C) and permissive (23°C) conditions following red or white temporal sequences. Spatial variation in temperature fluctuations was implemented by exposing subpopulations to the same (synchronous temperatures) or different (asynchronous temperatures) temporal sequences. Red noise, compared with white noise, enhanced parasite persistence. Despite this, red noise coupled with asynchronous temperatures allowed infected host populations to maintain sizes equivalent to uninfected populations. It is likely that this occurs because subpopulations in permissive conditions rescue declining subpopulations in stressful conditions. We show how patterns of temporal and spatial environmental fluctuations can impact parasite spread and host population abundance. We conclude that accurate prediction of parasite epidemics may require realistic models of environmental noise. PMID:23966645

nMoldyn: a program package for a neutron scattering oriented analysis of molecular dynamics simulations.

PubMed

Róg, T; Murzyn, K; Hinsen, K; Kneller, G R

2003-04-15

We present a new implementation of the program nMoldyn, which has been developed for the computation and decomposition of neutron scattering intensities from Molecular Dynamics trajectories (Comp. Phys. Commun 1995, 91, 191-214). The new implementation extends the functionality of the original version, provides a much more convenient user interface (both graphical/interactive and batch), and can be used as a tool set for implementing new analysis modules. This was made possible by the use of a high-level language, Python, and of modern object-oriented programming techniques. The quantities that can be calculated by nMoldyn are the mean-square displacement, the velocity autocorrelation function as well as its Fourier transform (the density of states) and its memory function, the angular velocity autocorrelation function and its Fourier transform, the reorientational correlation function, and several functions specific to neutron scattering: the coherent and incoherent intermediate scattering functions with their Fourier transforms, the memory function of the coherent scattering function, and the elastic incoherent structure factor. The possibility to compute memory function is a new and powerful feature that allows to relate simulation results to theoretical studies. Copyright 2003 Wiley Periodicals, Inc. J Comput Chem 24: 657-667, 2003

Forecasting coconut production in the Philippines with ARIMA model

NASA Astrophysics Data System (ADS)

Lim, Cristina Teresa

2015-02-01

The study aimed to depict the situation of the coconut industry in the Philippines for the future years applying Autoregressive Integrated Moving Average (ARIMA) method. Data on coconut production, one of the major industrial crops of the country, for the period of 1990 to 2012 were analyzed using time-series methods. Autocorrelation (ACF) and partial autocorrelation functions (PACF) were calculated for the data. Appropriate Box-Jenkins autoregressive moving average model was fitted. Validity of the model was tested using standard statistical techniques. The forecasting power of autoregressive moving average (ARMA) model was used to forecast coconut production for the eight leading years.

Networks of volatility spillovers among stock markets

NASA Astrophysics Data System (ADS)

Baumöhl, Eduard; Kočenda, Evžen; Lyócsa, Štefan; Výrost, Tomáš

2018-01-01

In our network analysis of 40 developed, emerging and frontier stock markets during the 2006-2014 period, we describe and model volatility spillovers during both the global financial crisis and tranquil periods. The resulting market interconnectedness is depicted by fitting a spatial model incorporating several exogenous characteristics. We document the presence of significant temporal proximity effects between markets and somewhat weaker temporal effects with regard to the US equity market - volatility spillovers decrease when markets are characterized by greater temporal proximity. Volatility spillovers also present a high degree of interconnectedness, which is measured by high spatial autocorrelation. This finding is confirmed by spatial regression models showing that indirect effects are much stronger than direct effects; i.e., market-related changes in 'neighboring' markets (within a network) affect volatility spillovers more than changes in the given market alone, suggesting that spatial effects simply cannot be ignored when modeling stock market relationships. Our results also link spillovers of escalating magnitude with increasing market size, market liquidity and economic openness.

The Superstatistical Nature and Interoccurrence Time of Atmospheric Mercury Concentration Fluctuations

EPA Science Inventory

The probability density function (PDF) of the time intervals between subsequent extreme events in atmospheric Hg0 concentration data series from different latitudes has been investigated. The Hg0 dynamic possesses a long-term memory autocorrelation function. Above a fixed thresh...

Diffraction and geometrical optical transfer functions: calculation time comparison

NASA Astrophysics Data System (ADS)

Díaz, José Antonio; Mahajan, Virendra N.

2017-08-01

In a recent paper, we compared the diffraction and geometrical optical transfer functions (OTFs) of an optical imaging system, and showed that the GOTF approximates the DOTF within 10% when a primary aberration is about two waves or larger [Appl. Opt., 55, 3241-3250 (2016)]. In this paper, we determine and compare the times to calculate the DOTF by autocorrelation or digital autocorrelation of the pupil function, and by a Fourier transform (FT) of the point-spread function (PSF); and the GOTF by a FT of the geometrical PSF and its approximation, the spot diagram. Our starting point for calculating the DOTF is the wave aberrations of the system in its pupil plane, and the ray aberrations in the image plane for the GOTF. The numerical results for primary aberrations and a typical imaging system show that the direct integrations are slow, but the calculation of the DOTF by a FT of the PSF is generally faster than the GOTF calculation by a FT of the spot diagram.

Spatio-Temporal Epidemiology of Viral Hepatitis in China (2003-2015): Implications for Prevention and Control Policies.

PubMed

Zhu, Bin; Liu, Jinlin; Fu, Yang; Zhang, Bo; Mao, Ying

2018-04-02

Viral hepatitis, as one of the most serious notifiable infectious diseases in China, takes heavy tolls from the infected and causes a severe economic burden to society, yet few studies have systematically explored the spatio-temporal epidemiology of viral hepatitis in China. This study aims to explore, visualize and compare the epidemiologic trends and spatial changing patterns of different types of viral hepatitis (A, B, C, E and unspecified, based on the classification of CDC) at the provincial level in China. The growth rates of incidence are used and converted to box plots to visualize the epidemiologic trends, with the linear trend being tested by chi-square linear by linear association test. Two complementary spatial cluster methods are used to explore the overall agglomeration level and identify spatial clusters: spatial autocorrelation analysis (measured by global and local Moran's I) and space-time scan analysis. Based on the spatial autocorrelation analysis, the hotspots of hepatitis A remain relatively stable and gradually shrunk, with Yunnan and Sichuan successively moving out the high-high (HH) cluster area. The HH clustering feature of hepatitis B in China gradually disappeared with time. However, the HH cluster area of hepatitis C has gradually moved towards the west, while for hepatitis E, the provincial units around the Yangtze River Delta region have been revealing HH cluster features since 2005. The space-time scan analysis also indicates the distinct spatial changing patterns of different types of viral hepatitis in China. It is easy to conclude that there is no one-size-fits-all plan for the prevention and control of viral hepatitis in all the provincial units. An effective response requires a package of coordinated actions, which should vary across localities regarding the spatial-temporal epidemic dynamics of each type of virus and the specific conditions of each provincial unit.

Time-series network analysis of civil aviation in Japan (1985-2005)

NASA Astrophysics Data System (ADS)

Michishita, Ryo; Xu, Bing; Yamada, Ikuho

2008-10-01

Due to the airline deregulation in 1985, a series of new airport developments in the 1990s and 2000s, and the reorganization of airline companies in the 2000s, Japan's air passenger transportation has been dramatically altered in the last two decades in many ways. In this paper, the authors examine how the network and flow structures of domestic air passenger transportation in Japan have geographically changed since 1985. For this purpose, passenger flow data in 1985, 1995, and 2005 were extracted from the Air Transportation Statistical Survey conducted by the Ministry of Land, Infrastructure and Transport, Japan. First, national and regional hub airports are identified via dominant flow and hub function analysis. Then the roles of the hub airports and individual connections over the network are examined with respect to their spatial and network autocorrelations. Spatial and network autocorrelations were evaluated both globally and locally using Moran's I and LISA statistics. The passenger flow data were first examined as a whole and then divided into 3 airline-based categories. Dominant flow and hub function enabled us to detect the hub airports. Structural processes of the hub-and-spoke network were confirmed in each airline through spatial autocorrelation analysis. Network autocorrelation analysis showed that all airlines ingeniously optimized their networks by connecting their routes with large numbers of passengers to other routes with large numbers of passengers, and routes with small numbers of passengers to other routes with small numbers of passengers. The effects of political events and the changes in the strategies of each airline on the whole networks were strongly reflected in the results of this study.

Critical Fluctuations in Cortical Models Near Instability

PubMed Central

Aburn, Matthew J.; Holmes, C. A.; Roberts, James A.; Boonstra, Tjeerd W.; Breakspear, Michael

2012-01-01

Computational studies often proceed from the premise that cortical dynamics operate in a linearly stable domain, where fluctuations dissipate quickly and show only short memory. Studies of human electroencephalography (EEG), however, have shown significant autocorrelation at time lags on the scale of minutes, indicating the need to consider regimes where non-linearities influence the dynamics. Statistical properties such as increased autocorrelation length, increased variance, power law scaling, and bistable switching have been suggested as generic indicators of the approach to bifurcation in non-linear dynamical systems. We study temporal fluctuations in a widely-employed computational model (the Jansen–Rit model) of cortical activity, examining the statistical signatures that accompany bifurcations. Approaching supercritical Hopf bifurcations through tuning of the background excitatory input, we find a dramatic increase in the autocorrelation length that depends sensitively on the direction in phase space of the input fluctuations and hence on which neuronal subpopulation is stochastically perturbed. Similar dependence on the input direction is found in the distribution of fluctuation size and duration, which show power law scaling that extends over four orders of magnitude at the Hopf bifurcation. We conjecture that the alignment in phase space between the input noise vector and the center manifold of the Hopf bifurcation is directly linked to these changes. These results are consistent with the possibility of statistical indicators of linear instability being detectable in real EEG time series. However, even in a simple cortical model, we find that these indicators may not necessarily be visible even when bifurcations are present because their expression can depend sensitively on the neuronal pathway of incoming fluctuations. PMID:22952464

SPATIO-TEMPORAL MODELING OF AGRICULTURAL YIELD DATA WITH AN APPLICATION TO PRICING CROP INSURANCE CONTRACTS

PubMed Central

Ozaki, Vitor A.; Ghosh, Sujit K.; Goodwin, Barry K.; Shirota, Ricardo

2009-01-01

This article presents a statistical model of agricultural yield data based on a set of hierarchical Bayesian models that allows joint modeling of temporal and spatial autocorrelation. This method captures a comprehensive range of the various uncertainties involved in predicting crop insurance premium rates as opposed to the more traditional ad hoc, two-stage methods that are typically based on independent estimation and prediction. A panel data set of county-average yield data was analyzed for 290 counties in the State of Paraná (Brazil) for the period of 1990 through 2002. Posterior predictive criteria are used to evaluate different model specifications. This article provides substantial improvements in the statistical and actuarial methods often applied to the calculation of insurance premium rates. These improvements are especially relevant to situations where data are limited. PMID:19890450

Detecting time-specific differences between temporal nonlinear curves: Analyzing data from the visual world paradigm

PubMed Central

Oleson, Jacob J; Cavanaugh, Joseph E; McMurray, Bob; Brown, Grant

2015-01-01

In multiple fields of study, time series measured at high frequencies are used to estimate population curves that describe the temporal evolution of some characteristic of interest. These curves are typically nonlinear, and the deviations of each series from the corresponding curve are highly autocorrelated. In this scenario, we propose a procedure to compare the response curves for different groups at specific points in time. The method involves fitting the curves, performing potentially hundreds of serially correlated tests, and appropriately adjusting the overall alpha level of the tests. Our motivating application comes from psycholinguistics and the visual world paradigm. We describe how the proposed technique can be adapted to compare fixation curves within subjects as well as between groups. Our results lead to conclusions beyond the scope of previous analyses. PMID:26400088

Building a three-dimensional model of CYP2C9 inhibition using the Autocorrelator: an autonomous model generator.

PubMed

Lardy, Matthew A; Lebrun, Laurie; Bullard, Drew; Kissinger, Charles; Gobbi, Alberto

2012-05-25

In modern day drug discovery campaigns, computational chemists have to be concerned not only about improving the potency of molecules but also reducing any off-target ADMET activity. There are a plethora of antitargets that computational chemists may have to consider. Fortunately many antitargets have crystal structures deposited in the PDB. These structures are immediately useful to our Autocorrelator: an automated model generator that optimizes variables for building computational models. This paper describes the use of the Autocorrelator to construct high quality docking models for cytochrome P450 2C9 (CYP2C9) from two publicly available crystal structures. Both models result in strong correlation coefficients (R² > 0.66) between the predicted and experimental determined log(IC₅₀) values. Results from the two models overlap well with each other, converging on the same scoring function, deprotonated charge state, and predicted the binding orientation for our collection of molecules.

Real-time autocorrelator for fluorescence correlation spectroscopy based on graphical-processor-unit architecture: method, implementation, and comparative studies

NASA Astrophysics Data System (ADS)

Laracuente, Nicholas; Grossman, Carl

2013-03-01

We developed an algorithm and software to calculate autocorrelation functions from real-time photon-counting data using the fast, parallel capabilities of graphical processor units (GPUs). Recent developments in hardware and software have allowed for general purpose computing with inexpensive GPU hardware. These devices are more suited for emulating hardware autocorrelators than traditional CPU-based software applications by emphasizing parallel throughput over sequential speed. Incoming data are binned in a standard multi-tau scheme with configurable points-per-bin size and are mapped into a GPU memory pattern to reduce time-expensive memory access. Applications include dynamic light scattering (DLS) and fluorescence correlation spectroscopy (FCS) experiments. We ran the software on a 64-core graphics pci card in a 3.2 GHz Intel i5 CPU based computer running Linux. FCS measurements were made on Alexa-546 and Texas Red dyes in a standard buffer (PBS). Software correlations were compared to hardware correlator measurements on the same signals. Supported by HHMI and Swarthmore College

Boundary versus bulk behavior of time-dependent correlation functions in one-dimensional quantum systems

NASA Astrophysics Data System (ADS)

Eliëns, I. S.; Ramos, F. B.; Xavier, J. C.; Pereira, R. G.

2016-05-01

We study the influence of reflective boundaries on time-dependent responses of one-dimensional quantum fluids at zero temperature beyond the low-energy approximation. Our analysis is based on an extension of effective mobile impurity models for nonlinear Luttinger liquids to the case of open boundary conditions. For integrable models, we show that boundary autocorrelations oscillate as a function of time with the same frequency as the corresponding bulk autocorrelations. This frequency can be identified as the band edge of elementary excitations. The amplitude of the oscillations decays as a power law with distinct exponents at the boundary and in the bulk, but boundary and bulk exponents are determined by the same coupling constant in the mobile impurity model. For nonintegrable models, we argue that the power-law decay of the oscillations is generic for autocorrelations in the bulk, but turns into an exponential decay at the boundary. Moreover, there is in general a nonuniversal shift of the boundary frequency in comparison with the band edge of bulk excitations. The predictions of our effective field theory are compared with numerical results obtained by time-dependent density matrix renormalization group (tDMRG) for both integrable and nonintegrable critical spin-S chains with S =1 /2 , 1, and 3 /2 .

Role of internal motions and molecular geometry on the NMR relaxation of hydrocarbons

NASA Astrophysics Data System (ADS)

Singer, P. M.; Asthagiri, D.; Chen, Z.; Valiya Parambathu, A.; Hirasaki, G. J.; Chapman, W. G.

2018-04-01

The role of internal motions and molecular geometry on 1H NMR relaxation rates in liquid-state hydrocarbons is investigated using MD (molecular dynamics) simulations of the autocorrelation functions for intramolecular and intermolecular 1H-1H dipole-dipole interactions. The effects of molecular geometry and internal motions on the functional form of the autocorrelation functions are studied by comparing symmetric molecules such as neopentane and benzene to corresponding straight-chain alkanes n-pentane and n-hexane, respectively. Comparison of rigid versus flexible molecules shows that internal motions cause the intramolecular and intermolecular correlation-times to get significantly shorter, and the corresponding relaxation rates to get significantly smaller, especially for longer-chain n-alkanes. Site-by-site simulations of 1H's across the chains indicate significant variations in correlation times and relaxation rates across the molecule, and comparison with measurements reveals insights into cross-relaxation effects. Furthermore, the simulations reveal new insights into the relative strength of intramolecular versus intermolecular relaxation as a function of internal motions, as a function of molecular geometry, and on a site-by-site basis across the chain.

Patterns and scaling properties of surface soil moisture in an agricultural landscape: An ecohydrological modeling study

NASA Astrophysics Data System (ADS)

Korres, W.; Reichenau, T. G.; Schneider, K.

2013-08-01

Soil moisture is a key variable in hydrology, meteorology and agriculture. Soil moisture, and surface soil moisture in particular, is highly variable in space and time. Its spatial and temporal patterns in agricultural landscapes are affected by multiple natural (precipitation, soil, topography, etc.) and agro-economic (soil management, fertilization, etc.) factors, making it difficult to identify unequivocal cause and effect relationships between soil moisture and its driving variables. The goal of this study is to characterize and analyze the spatial and temporal patterns of surface soil moisture (top 20 cm) in an intensively used agricultural landscape (1100 km2 northern part of the Rur catchment, Western Germany) and to determine the dominant factors and underlying processes controlling these patterns. A second goal is to analyze the scaling behavior of surface soil moisture patterns in order to investigate how spatial scale affects spatial patterns. To achieve these goals, a dynamically coupled, process-based and spatially distributed ecohydrological model was used to analyze the key processes as well as their interactions and feedbacks. The model was validated for two growing seasons for the three main crops in the investigation area: Winter wheat, sugar beet, and maize. This yielded RMSE values for surface soil moisture between 1.8 and 7.8 vol.% and average RMSE values for all three crops of 0.27 kg m-2 for total aboveground biomass and 0.93 for green LAI. Large deviations of measured and modeled soil moisture can be explained by a change of the infiltration properties towards the end of the growing season, especially in maize fields. The validated model was used to generate daily surface soil moisture maps, serving as a basis for an autocorrelation analysis of spatial patterns and scale. Outside of the growing season, surface soil moisture patterns at all spatial scales depend mainly upon soil properties. Within the main growing season, larger scale patterns that are induced by soil properties are superimposed by the small scale land use pattern and the resulting small scale variability of evapotranspiration. However, this influence decreases at larger spatial scales. Most precipitation events cause temporarily higher surface soil moisture autocorrelation lengths at all spatial scales for a short time even beyond the autocorrelation lengths induced by soil properties. The relation of daily spatial variance to the spatial scale of the analysis fits a power law scaling function, with negative values of the scaling exponent, indicating a decrease in spatial variability with increasing spatial resolution. High evapotranspiration rates cause an increase in the small scale soil moisture variability, thus leading to large negative values of the scaling exponent. Utilizing a multiple regression analysis, we found that 53% of the variance of the scaling exponent can be explained by a combination of an independent LAI parameter and the antecedent precipitation.

Identification of Piecewise Linear Uniform Motion Blur

NASA Astrophysics Data System (ADS)

Patanukhom, Karn; Nishihara, Akinori

A motion blur identification scheme is proposed for nonlinear uniform motion blurs approximated by piecewise linear models which consist of more than one linear motion component. The proposed scheme includes three modules that are a motion direction estimator, a motion length estimator and a motion combination selector. In order to identify the motion directions, the proposed scheme is based on a trial restoration by using directional forward ramp motion blurs along different directions and an analysis of directional information via frequency domain by using a Radon transform. Autocorrelation functions of image derivatives along several directions are employed for estimation of the motion lengths. A proper motion combination is identified by analyzing local autocorrelation functions of non-flat component of trial restored results. Experimental examples of simulated and real world blurred images are given to demonstrate a promising performance of the proposed scheme.

Autocorrelated residuals in inverse modelling of soil hydrological processes: a reason for concern or something that can safely be ignored?

NASA Astrophysics Data System (ADS)

Scharnagl, Benedikt; Durner, Wolfgang

2013-04-01

Models are inherently imperfect because they simplify processes that are themselves imperfectly known and understood. Moreover, the input variables and parameters needed to run a model are typically subject to various sources of error. As a consequence of these imperfections, model predictions will always deviate from corresponding observations. In most applications in soil hydrology, these deviations are clearly not random but rather show a systematic structure. From a statistical point of view, this systematic mismatch may be a reason for concern because it violates one of the basic assumptions made in inverse parameter estimation: the assumption of independence of the residuals. But what are the consequences of simply ignoring the autocorrelation in the residuals, as it is current practice in soil hydrology? Are the parameter estimates still valid even though the statistical foundation they are based on is partially collapsed? Theory and practical experience from other fields of science have shown that violation of the independence assumption will result in overconfident uncertainty bounds and that in some cases it may lead to significantly different optimal parameter values. In our contribution, we present three soil hydrological case studies, in which the effect of autocorrelated residuals on the estimated parameters was investigated in detail. We explicitly accounted for autocorrelated residuals using a formal likelihood function that incorporates an autoregressive model. The inverse problem was posed in a Bayesian framework, and the posterior probability density function of the parameters was estimated using Markov chain Monte Carlo simulation. In contrast to many other studies in related fields of science, and quite surprisingly, we found that the first-order autoregressive model, often abbreviated as AR(1), did not work well in the soil hydrological setting. We showed that a second-order autoregressive, or AR(2), model performs much better in these applications, leading to parameter and uncertainty estimates that satisfy all the underlying statistical assumptions. For theoretical reasons, these estimates are deemed more reliable than those estimates based on the neglect of autocorrelation in the residuals. In compliance with theory and results reported in the literature, our results showed that parameter uncertainty bounds were substantially wider if autocorrelation in the residuals was explicitly accounted for, and also the optimal parameter vales were slightly different in this case. We argue that the autoregressive model presented here should be used as a matter of routine in inverse modeling of soil hydrological processes.

On temporal stochastic modeling of precipitation, nesting models across scales

NASA Astrophysics Data System (ADS)

Paschalis, Athanasios; Molnar, Peter; Fatichi, Simone; Burlando, Paolo

2014-01-01

We analyze the performance of composite stochastic models of temporal precipitation which can satisfactorily reproduce precipitation properties across a wide range of temporal scales. The rationale is that a combination of stochastic precipitation models which are most appropriate for specific limited temporal scales leads to better overall performance across a wider range of scales than single models alone. We investigate different model combinations. For the coarse (daily) scale these are models based on Alternating renewal processes, Markov chains, and Poisson cluster models, which are then combined with a microcanonical Multiplicative Random Cascade model to disaggregate precipitation to finer (minute) scales. The composite models were tested on data at four sites in different climates. The results show that model combinations improve the performance in key statistics such as probability distributions of precipitation depth, autocorrelation structure, intermittency, reproduction of extremes, compared to single models. At the same time they remain reasonably parsimonious. No model combination was found to outperform the others at all sites and for all statistics, however we provide insight on the capabilities of specific model combinations. The results for the four different climates are similar, which suggests a degree of generality and wider applicability of the approach.

a Simple Spatially Weighted Measure of Temporal Stability for Data with Limited Temporal Observations

NASA Astrophysics Data System (ADS)

Piburn, J.; Stewart, R.; Morton, A.

2017-10-01

Identifying erratic or unstable time-series is an area of interest to many fields. Recently, there have been successful developments towards this goal. These new developed methodologies however come from domains where it is typical to have several thousand or more temporal observations. This creates a challenge when attempting to apply these methodologies to time-series with much fewer temporal observations such as for socio-cultural understanding, a domain where a typical time series of interest might only consist of 20-30 annual observations. Most existing methodologies simply cannot say anything interesting with so few data points, yet researchers are still tasked to work within in the confines of the data. Recently a method for characterizing instability in a time series with limitedtemporal observations was published. This method, Attribute Stability Index (ASI), uses an approximate entropy based method tocharacterize a time series' instability. In this paper we propose an explicitly spatially weighted extension of the Attribute StabilityIndex. By including a mechanism to account for spatial autocorrelation, this work represents a novel approach for the characterizationof space-time instability. As a case study we explore national youth male unemployment across the world from 1991-2014.

Using the thermal Gaussian approximation for the Boltzmann operator in semiclassical initial value time correlation functions.

PubMed

Liu, Jian; Miller, William H

2006-12-14

The thermal Gaussian approximation (TGA) recently developed by Frantsuzov et al. [Chem. Phys. Lett. 381, 117 (2003)] has been demonstrated to be a practical way for approximating the Boltzmann operator exp(-betaH) for multidimensional systems. In this paper the TGA is combined with semiclassical (SC) initial value representations (IVRs) for thermal time correlation functions. Specifically, it is used with the linearized SC-IVR (LSC-IVR, equivalent to the classical Wigner model), and the "forward-backward semiclassical dynamics" approximation developed by Shao and Makri [J. Phys. Chem. A 103, 7753 (1999); 103, 9749 (1999)]. Use of the TGA with both of these approximate SC-IVRs allows the oscillatory part of the IVR to be integrated out explicitly, providing an extremely simple result that is readily applicable to large molecular systems. Calculation of the force-force autocorrelation for a strongly anharmonic oscillator demonstrates its accuracy, and calculation of the velocity autocorrelation function (and thus the diffusion coefficient) of liquid neon demonstrates its applicability.

Computation and analysis of the transverse current autocorrelation function, Ct(k,t), for small wave vectors: A molecular-dynamics study for a Lennard-Jones fluid

NASA Astrophysics Data System (ADS)

Vogelsang, R.; Hoheisel, C.

1987-02-01

Molecular-dynamics (MD) calculations are reported for three thermodynamic states of a Lennard-Jones fluid. Systems of 2048 particles and 105 integration steps were used. The transverse current autocorrelation function, Ct(k,t), has been determined for wave vectors of the range 0.5<||k||σ<1.5. Ct(k,t) was fitted by hydrodynamic-type functions. The fits returned k-dependent decay times and shear viscosities which showed a systematic behavior as a function of k. Extrapolation to the hydrodynamic region at k=0 gave shear viscosity coefficients in good agreement with direct Green-Kubo results obtained in previous work. The two-exponential model fit for the memory function proposed by other authors does not provide a reasonable description of the MD results, as the fit parameters show no systematic wave-vector dependence, although the Ct(k,t) functions are somewhat better fitted. Similarly, the semiempirical interpolation formula for the decay time based on the viscoelastic concept proposed by Akcasu and Daniels fails to reproduce the correct k dependence for the wavelength range investigated herein.

Continuous Sub-daily Rainfall Simulation for Regional Flood Risk Assessment - Modelling of Spatio-temporal Correlation Structure of Extreme Precipitation in the Austrian Alps

NASA Astrophysics Data System (ADS)

Salinas, J. L.; Nester, T.; Komma, J.; Bloeschl, G.

2017-12-01

Generation of realistic synthetic spatial rainfall is of pivotal importance for assessing regional hydroclimatic hazard as the input for long term rainfall-runoff simulations. The correct reproduction of observed rainfall characteristics, such as regional intensity-duration-frequency curves, and spatial and temporal correlations is necessary to adequately model the magnitude and frequency of the flood peaks, by reproducing antecedent soil moisture conditions before extreme rainfall events, and joint probability of flood waves at confluences. In this work, a modification of the model presented by Bardossy and Platte (1992), where precipitation is first modeled on a station basis as a multivariate autoregressive model (mAr) in a Normal space. The spatial and temporal correlation structures are imposed in the Normal space, allowing for a different temporal autocorrelation parameter for each station, and simultaneously ensuring the positive-definiteness of the correlation matrix of the mAr errors. The Normal rainfall is then transformed to a Gamma-distributed space, with parameters varying monthly according to a sinusoidal function, in order to adapt to the observed rainfall seasonality. One of the main differences with the original model is the simulation time-step, reduced from 24h to 6h. Due to a larger availability of daily rainfall data, as opposite to sub-daily (e.g. hourly), the parameters of the Gamma distributions are calibrated to reproduce simultaneously a series of daily rainfall characteristics (mean daily rainfall, standard deviations of daily rainfall, and 24h intensity-duration-frequency [IDF] curves), as well as other aggregated rainfall measures (mean annual rainfall, and monthly rainfall). The calibration of the spatial and temporal correlation parameters is performed in a way that the catchment-averaged IDF curves aggregated at different temporal scales fit the measured ones. The rainfall model is used to generate 10.000 years of synthetic precipitation, fed into a rainfall-runoff model to derive the flood frequency in the Tirolean Alps in Austria. Given the number of generated events, the simulation framework is able to generate a large variety of rainfall patterns, as well as reproduce the variograms of relevant extreme rainfall events in the region of interest.

A comparison of chronologies from tree rings

Treesearch

Kurt H. Riitters

1990-01-01

Forty-five-year ring width index chronologies were estimated by five mean-value functions applied to 183 ring width series from four similar sites. The effects of autocorrelation on the comparisons among mean-value functions were explored by fitting box-Jenkins models to individual-tree index services prior to pooling (prewhitening), and to the pooled chronologies...

Statistical properties of a filtered Poisson process with additive random noise: distributions, correlations and moment estimation

NASA Astrophysics Data System (ADS)

Theodorsen, A.; E Garcia, O.; Rypdal, M.

2017-05-01

Filtered Poisson processes are often used as reference models for intermittent fluctuations in physical systems. Such a process is here extended by adding a noise term, either as a purely additive term to the process or as a dynamical term in a stochastic differential equation. The lowest order moments, probability density function, auto-correlation function and power spectral density are derived and used to identify and compare the effects of the two different noise terms. Monte-Carlo studies of synthetic time series are used to investigate the accuracy of model parameter estimation and to identify methods for distinguishing the noise types. It is shown that the probability density function and the three lowest order moments provide accurate estimations of the model parameters, but are unable to separate the noise types. The auto-correlation function and the power spectral density also provide methods for estimating the model parameters, as well as being capable of identifying the noise type. The number of times the signal crosses a prescribed threshold level in the positive direction also promises to be able to differentiate the noise type.

Spot auto-focusing and spot auto-stigmation methods with high-definition auto-correlation function in high-resolution TEM.

PubMed

Isakozawa, Shigeto; Fuse, Taishi; Amano, Junpei; Baba, Norio

2018-04-01

As alternatives to the diffractogram-based method in high-resolution transmission electron microscopy, a spot auto-focusing (AF) method and a spot auto-stigmation (AS) method are presented with a unique high-definition auto-correlation function (HD-ACF). The HD-ACF clearly resolves the ACF central peak region in small amorphous-thin-film images, reflecting the phase contrast transfer function. At a 300-k magnification for a 120-kV transmission electron microscope, the smallest areas used are 64 × 64 pixels (~3 nm2) for the AF and 256 × 256 pixels for the AS. A useful advantage of these methods is that the AF function has an allowable accuracy even for a low s/n (~1.0) image. A reference database on the defocus dependency of the HD-ACF by the pre-acquisition of through-focus amorphous-thin-film images must be prepared to use these methods. This can be very beneficial because the specimens are not limited to approximations of weak phase objects but can be extended to objects outside such approximations.

Subfemtosecond quantum nuclear dynamics in water isotopomers.

PubMed

Rao, B Jayachander; Varandas, A J C

2015-05-21

Subfemtosecond quantum dynamics studies of all water isotopomers in the X̃ (2)B1 and à (2)A1 electronic states of the cation formed by Franck-Condon ionization of the neutral ground electronic state are reported. Using the ratio of the autocorrelation functions for the isotopomers as obtained from the solution of the time-dependent Schrödinger equation in a grid representation, high-order harmonic generation signals are calculated as a function of time. The results are found to be in agreement with the available experimental findings and with our earlier study for D2O(+)/H2O(+). Maxima are predicted in the autocorrelation function ratio at various times. Their origin and occurrence is explained by calculating expectation values of the bond lengths and bond angle of the water isotopomers as a function of time. The values so calculated for the (2)B1 and (2)A1 electronic states of the cation show quasiperiodic oscillations that can be associated with the time at which the nuclear wave packet reaches the minima of the potential energy surface, there being responsible for the peaks in the HHG signals.

Simultaneous ocular and muscle artifact removal from EEG data by exploiting diverse statistics.

PubMed

Chen, Xun; Liu, Aiping; Chen, Qiang; Liu, Yu; Zou, Liang; McKeown, Martin J

2017-09-01

Electroencephalography (EEG) recordings are frequently contaminated by both ocular and muscle artifacts. These are normally dealt with separately, by employing blind source separation (BSS) techniques relying on either second-order or higher-order statistics (SOS & HOS respectively). When HOS-based methods are used, it is usually in the setting of assuming artifacts are statistically independent to the EEG. When SOS-based methods are used, it is assumed that artifacts have autocorrelation characteristics distinct from the EEG. In reality, ocular and muscle artifacts do not completely follow the assumptions of strict temporal independence to the EEG nor completely unique autocorrelation characteristics, suggesting that exploiting HOS or SOS alone may be insufficient to remove these artifacts. Here we employ a novel BSS technique, independent vector analysis (IVA), to jointly employ HOS and SOS simultaneously to remove ocular and muscle artifacts. Numerical simulations and application to real EEG recordings were used to explore the utility of the IVA approach. IVA was superior in isolating both ocular and muscle artifacts, especially for raw EEG data with low signal-to-noise ratio, and also integrated usually separate SOS and HOS steps into a single unified step. Copyright © 2017 Elsevier Ltd. All rights reserved.

A spatial model of bird abundance as adjusted for detection probability

USGS Publications Warehouse

Gorresen, P.M.; Mcmillan, G.P.; Camp, R.J.; Pratt, T.K.

2009-01-01

Modeling the spatial distribution of animals can be complicated by spatial and temporal effects (i.e. spatial autocorrelation and trends in abundance over time) and other factors such as imperfect detection probabilities and observation-related nuisance variables. Recent advances in modeling have demonstrated various approaches that handle most of these factors but which require a degree of sampling effort (e.g. replication) not available to many field studies. We present a two-step approach that addresses these challenges to spatially model species abundance. Habitat, spatial and temporal variables were handled with a Bayesian approach which facilitated modeling hierarchically structured data. Predicted abundance was subsequently adjusted to account for imperfect detection and the area effectively sampled for each species. We provide examples of our modeling approach for two endemic Hawaiian nectarivorous honeycreepers: 'i'iwi Vestiaria coccinea and 'apapane Himatione sanguinea. ?? 2009 Ecography.

Fetal source extraction from magnetocardiographic recordings by dependent component analysis

NASA Astrophysics Data System (ADS)

de Araujo, Draulio B.; Kardec Barros, Allan; Estombelo-Montesco, Carlos; Zhao, Hui; Roque da Silva Filho, A. C.; Baffa, Oswaldo; Wakai, Ronald; Ohnishi, Noboru

2005-10-01

Fetal magnetocardiography (fMCG) has been extensively reported in the literature as a non-invasive, prenatal technique that can be used to monitor various functions of the fetal heart. However, fMCG signals often have low signal-to-noise ratio (SNR) and are contaminated by strong interference from the mother's magnetocardiogram signal. A promising, efficient tool for extracting signals, even under low SNR conditions, is blind source separation (BSS), or independent component analysis (ICA). Herein we propose an algorithm based on a variation of ICA, where the signal of interest is extracted using a time delay obtained from an autocorrelation analysis. We model the system using autoregression, and identify the signal component of interest from the poles of the autocorrelation function. We show that the method is effective in removing the maternal signal, and is computationally efficient. We also compare our results to more established ICA methods, such as FastICA.

Diffusion in shear flow

NASA Astrophysics Data System (ADS)

Dufty, J. W.

1984-09-01

Diffusion of a tagged particle in a fluid with uniform shear flow is described. The continuity equation for the probability density describing the position of the tagged particle is considered. The diffusion tensor is identified by expanding the irreversible part of the probability current to first order in the gradient of the probability density, but with no restriction on the shear rate. The tensor is expressed as the time integral of a nonequilibrium autocorrelation function for the velocity of the tagged particle in its local fluid rest frame, generalizing the Green-Kubo expression to the nonequilibrium state. The tensor is evaluated from results obtained previously for the velocity autocorrelation function that are exact for Maxwell molecules in the Boltzmann limit. The effects of viscous heating are included and the dependence on frequency and shear rate is displayed explicitly. The mode-coupling contributions to the frequency and shear-rate dependent diffusion tensor are calculated.

Statistical spatial properties of speckle patterns generated by multiple laser beams

DOE Office of Scientific and Technical Information (OSTI.GOV)

Le Cain, A.; Sajer, J. M.; Riazuelo, G.

2011-08-15

This paper investigates hot spot characteristics generated by the superposition of multiple laser beams. First, properties of speckle statistics are studied in the context of only one laser beam by computing the autocorrelation function. The case of multiple laser beams is then considered. In certain conditions, it is shown that speckles have an ellipsoidal shape. Analytical expressions of hot spot radii generated by multiple laser beams are derived and compared to numerical estimates made from the autocorrelation function. They are also compared to numerical simulations performed within the paraxial approximation. Excellent agreement is found for the speckle width as wellmore » as for the speckle length. Application to the speckle patterns generated in the Laser MegaJoule configuration in the zone where all the beams overlap is presented. Influence of polarization on the size of the speckles as well as on their abundance is studied.« less

Quantifying uncertainty in soot volume fraction estimates using Bayesian inference of auto-correlated laser-induced incandescence measurements

NASA Astrophysics Data System (ADS)

Hadwin, Paul J.; Sipkens, T. A.; Thomson, K. A.; Liu, F.; Daun, K. J.

2016-01-01

Auto-correlated laser-induced incandescence (AC-LII) infers the soot volume fraction (SVF) of soot particles by comparing the spectral incandescence from laser-energized particles to the pyrometrically inferred peak soot temperature. This calculation requires detailed knowledge of model parameters such as the absorption function of soot, which may vary with combustion chemistry, soot age, and the internal structure of the soot. This work presents a Bayesian methodology to quantify such uncertainties. This technique treats the additional "nuisance" model parameters, including the soot absorption function, as stochastic variables and incorporates the current state of knowledge of these parameters into the inference process through maximum entropy priors. While standard AC-LII analysis provides a point estimate of the SVF, Bayesian techniques infer the posterior probability density, which will allow scientists and engineers to better assess the reliability of AC-LII inferred SVFs in the context of environmental regulations and competing diagnostics.

Momentum conserving Brownian dynamics propagator for complex soft matter fluids

DOE Office of Scientific and Technical Information (OSTI.GOV)

Padding, J. T.; Briels, W. J.

2014-12-28

We present a Galilean invariant, momentum conserving first order Brownian dynamics scheme for coarse-grained simulations of highly frictional soft matter systems. Friction forces are taken to be with respect to moving background material. The motion of the background material is described by locally averaged velocities in the neighborhood of the dissolved coarse coordinates. The velocity variables are updated by a momentum conserving scheme. The properties of the stochastic updates are derived through the Chapman-Kolmogorov and Fokker-Planck equations for the evolution of the probability distribution of coarse-grained position and velocity variables, by requiring the equilibrium distribution to be a stationary solution.more » We test our new scheme on concentrated star polymer solutions and find that the transverse current and velocity time auto-correlation functions behave as expected from hydrodynamics. In particular, the velocity auto-correlation functions display a long time tail in complete agreement with hydrodynamics.« less

Ring polymer dynamics in curved spaces

NASA Astrophysics Data System (ADS)

Wolf, S.; Curotto, E.

2012-07-01

We formulate an extension of the ring polymer dynamics approach to curved spaces using stereographic projection coordinates. We test the theory by simulating the particle in a ring, {T}^1, mapped by a stereographic projection using three potentials. Two of these are quadratic, and one is a nonconfining sinusoidal model. We propose a new class of algorithms for the integration of the ring polymer Hamilton equations in curved spaces. These are designed to improve the energy conservation of symplectic integrators based on the split operator approach. For manifolds, the position-position autocorrelation function can be formulated in numerous ways. We find that the position-position autocorrelation function computed from configurations in the Euclidean space {R}^2 that contains {T}^1 as a submanifold has the best statistical properties. The agreement with exact results obtained with vector space methods is excellent for all three potentials, for all values of time in the interval simulated, and for a relatively broad range of temperatures.

A Four Dimensional Spatio-Temporal Analysis of an Agricultural Dataset

PubMed Central

Donald, Margaret R.; Mengersen, Kerrie L.; Young, Rick R.

2015-01-01

While a variety of statistical models now exist for the spatio-temporal analysis of two-dimensional (surface) data collected over time, there are few published examples of analogous models for the spatial analysis of data taken over four dimensions: latitude, longitude, height or depth, and time. When taking account of the autocorrelation of data within and between dimensions, the notion of closeness often differs for each of the dimensions. Here, we consider a number of approaches to the analysis of such a dataset, which arises from an agricultural experiment exploring the impact of different cropping systems on soil moisture. The proposed models vary in their representation of the spatial correlation in the data, the assumed temporal pattern and choice of conditional autoregressive (CAR) and other priors. In terms of the substantive question, we find that response cropping is generally more effective than long fallow cropping in reducing soil moisture at the depths considered (100 cm to 220 cm). Thus, if we wish to reduce the possibility of deep drainage and increased groundwater salinity, the recommended cropping system is response cropping. PMID:26513746

Single-molecule fluorescence detection: autocorrelation criterion and experimental realization with phycoerythrin.

PubMed Central

Peck, K; Stryer, L; Glazer, A N; Mathies, R A

1989-01-01

A theory for single-molecule fluorescence detection is developed and then used to analyze data from subpicomolar solutions of B-phycoerythrin (PE). The distribution of detected counts is the convolution of a Poissonian continuous background with bursts arising from the passage of individual fluorophores through the focused laser beam. The autocorrelation function reveals single-molecule events and provides a criterion for optimizing experimental parameters. The transit time of fluorescent molecules through the 120-fl imaged volume was 800 microseconds. The optimal laser power (32 mW at 514.5 nm) gave an incident intensity of 1.8 x 10(23) photons.cm-2.s-1, corresponding to a mean time of 1.1 ns between absorptions. The mean incremental count rate was 1.5 per 100 microseconds for PE monomers and 3.0 for PE dimers above a background count rate of 1.0. The distribution of counts and the autocorrelation function for 200 fM monomer and 100 fM dimer demonstrate that single-molecule detection was achieved. At this concentration, the mean occupancy was 0.014 monomer molecules in the probed volume. A hard-wired version of this detection system was used to measure the concentration of PE down to 1 fM. This single-molecule counter is 3 orders of magnitude more sensitive than conventional fluorescence detection systems. PMID:2726766

Fluctuation analysis of proficient and dysgraphic handwriting in children

NASA Astrophysics Data System (ADS)

Rosenblum, S.; Roman, H. E.

2009-03-01

We analyze handwriting records from several school children with the aim of characterizing the fluctuating behavior of the writing speed. It will be concluded that remarkable differences exist between proficient and dysgraphic handwritings which were unknown so far. It is shown that in the case of proficient handwriting, the variations in handwriting speed are strongly autocorrelated within times corresponding to the completion of a single character or letter, while become uncorrelated at longer times. In the case of dysgraphia, such correlations persist on longer time scales and the autocorrelation function seems to display algebraic time decay, indicating the presence of strong anomalies in the handwriting process. Applications of the results in educational/clinical programs are envisaged.

A Comparison of Weights Matrices on Computation of Dengue Spatial Autocorrelation

NASA Astrophysics Data System (ADS)

Suryowati, K.; Bekti, R. D.; Faradila, A.

2018-04-01

Spatial autocorrelation is one of spatial analysis to identify patterns of relationship or correlation between locations. This method is very important to get information on the dispersal patterns characteristic of a region and linkages between locations. In this study, it applied on the incidence of Dengue Hemorrhagic Fever (DHF) in 17 sub districts in Sleman, Daerah Istimewa Yogyakarta Province. The link among location indicated by a spatial weight matrix. It describe the structure of neighbouring and reflects the spatial influence. According to the spatial data, type of weighting matrix can be divided into two types: point type (distance) and the neighbourhood area (contiguity). Selection weighting function is one determinant of the results of the spatial analysis. This study use queen contiguity based on first order neighbour weights, queen contiguity based on second order neighbour weights, and inverse distance weights. Queen contiguity first order and inverse distance weights shows that there is the significance spatial autocorrelation in DHF, but not by queen contiguity second order. Queen contiguity first and second order compute 68 and 86 neighbour list

Single-particle structure determination by correlations of snapshot X-ray diffraction patterns (CXIDB ID 20)

DOE Data Explorer

Starodub, D.

2013-03-25

This deposition includes the diffraction images generated by the paired polystyrene spheres in random orientations. These images were used to determine and phase the single particle diffraction volume from their autocorrelation functions.

Ecological dynamics of continuous and categorical decision-making: the regatta start in sailing.

PubMed

Araújo, Duarte; Davids, Keith; Diniz, Ana; Rocha, Luis; Santos, João Coelho; Dias, Gonçalo; Fernandes, Orlando

2015-01-01

Ecological dynamics of decision-making in the sport of sailing exemplifies emergent, conditionally coupled, co-adaptive behaviours. In this study, observation of the coupling dynamics of paired boats during competitive sailing showed that decision-making can be modelled as a self-sustained, co-adapting system of informationally coupled oscillators (boats). Bytracing the spatial-temporal displacements of the boats, time series analyses (autocorrelations, periodograms and running correlations) revealed that trajectories of match racing boats are coupled more than 88% of the time during a pre-start race, via continuous, competing co-adaptions between boats. Results showed that both the continuously selected trajectories of the sailors (12 years of age) and their categorical starting point locations were examples of emergent decisions. In this dynamical conception of decision-making behaviours, strategic positioning (categorical) and continuous displacement of a boat over the course in match-race sailing emerged as a function of interacting task, personal and environmental constraints. Results suggest how key interacting constraints could be manipulated in practice to enhance sailors' perceptual attunement to them in competition.

Non-parametric early seizure detection in an animal model of temporal lobe epilepsy

NASA Astrophysics Data System (ADS)

Talathi, Sachin S.; Hwang, Dong-Uk; Spano, Mark L.; Simonotto, Jennifer; Furman, Michael D.; Myers, Stephen M.; Winters, Jason T.; Ditto, William L.; Carney, Paul R.

2008-03-01

The performance of five non-parametric, univariate seizure detection schemes (embedding delay, Hurst scale, wavelet scale, nonlinear autocorrelation and variance energy) were evaluated as a function of the sampling rate of EEG recordings, the electrode types used for EEG acquisition, and the spatial location of the EEG electrodes in order to determine the applicability of the measures in real-time closed-loop seizure intervention. The criteria chosen for evaluating the performance were high statistical robustness (as determined through the sensitivity and the specificity of a given measure in detecting a seizure) and the lag in seizure detection with respect to the seizure onset time (as determined by visual inspection of the EEG signal by a trained epileptologist). An optimality index was designed to evaluate the overall performance of each measure. For the EEG data recorded with microwire electrode array at a sampling rate of 12 kHz, the wavelet scale measure exhibited better overall performance in terms of its ability to detect a seizure with high optimality index value and high statistics in terms of sensitivity and specificity.

Statistical anisotropy in free turbulence for mixing layers at high Reynolds numbers

NASA Astrophysics Data System (ADS)

Gardner, Patrick J.; Roggemann, Michael C.; Welsh, Byron M.; Bowersox, Rodney D.; Luke, Theodore E.

1996-08-01

A lateral shearing interferometer was used to measure the slope of perturbed wave fronts after propagating through free turbulent mixing layers. Shearing interferometers provide a two-dimensional flow visualization that is nonintrusive. Slope measurements were used to reconstruct the phase of the turbulence-corrupted wave front. The random phase fluctuations induced by the mixing layer were captured in a large ensemble of wave-front measurements. Experiments were performed on an unbounded, plane shear mixing layer of helium and nitrogen gas at fixed velocities and high Reynolds numbers for six locations in the flow development. Statistical autocorrelation functions and structure functions were computed on the reconstructed phase maps. The autocorrelation function results indicated that the turbulence-induced phase fluctuations were not wide-sense stationary. The structure functions exhibited statistical homogeneity, indicating that the phase fluctuations were stationary in first increments. However, the turbulence-corrupted phase was not isotropic. A five-thirds power law is shown to fit orthogonal slices of the structure function, analogous to the Kolmogorov model for isotropic turbulence. Strehl ratios were computed from the phase structure functions and compared with classical estimates that assume isotropy. The isotropic models are shown to overestimate the optical degradation by nearly 3 orders of magnitude compared with the structure function calculations.

Reflection Response of the Parnaíba Basin (NE Brazil) from Seismic Ambient Noise Autocorrelation Functions

NASA Astrophysics Data System (ADS)

Julià, Jordi; Schimmel, Martin; Cedraz, Victória

2017-04-01

Reflected-wave interferometry relies on the recording of transient seismic signals from random wavefields located beneath recording stations. Under vertical incidence, the recordings contain the full transmission response, which includes the direct wave as well as multiple reverberations from seismic discontinuities located between the wavefields and the receiver. It has been shown that, under those assumptions, the reflection response of the medium can be recovered from the autocorrelation function (ACF) of the transmission response at a given receiver, as if the wavefields had originated themselves at the free surface. This passive approach to seismic reflection profiling has the obvious advantage of being low-cost and non-invasive when compared to its active-source counterpart, and it has been successfully utilized in other sedimentary basins worldwide. In this paper we evaluate the ability of the autocorrelation of ambient seismic noise recorded in the Parnaíba basin - a large Paleozoic basin in NE Brazil - to recover the reflection response of the basin. The dataset was acquired by the Universidade Federal do Rio Grande do Norte during 2015 and 2016 under the Parnaíba Basin Analysis Project (PBAP), a multi-disciplinary and multi-institutional effort funded by BP Energy do Brasil aimed at improving our current understanding of the architecture of this cratonic basin. The dataset consists of about 1 year of continuous ground motion data from 10 short-period, 3-component stations located in the central portion of the basin. The stations were co-located with an existing (active-source) seismic reflection profile that was shot in 2012, making a linear array of about 100 km in aperture and about 10 km inter-station spacing. To develop the autocorrelation at a given station we considered the vertical component of ground motion only, which should result in the P-wave response. The vertical recordings were first split into 10 min-long windows, demeaned, de-trended, re-sampled, and band-pass filtered between 8 and 16 Hz before autocorrelation, and then stacked with phase-weighting to enhance coherency of the retrieved signal. The ACFs show coherent signal is recovered at lag times between 0.5 and 2 s, which we interpret as P- and S-wave energy reflected on top of an intra-sedimentary discontinuity. Our results are consistent, to first-order, with a previously developed active-source reflection response of the basin.

Dynamics of hard sphere colloidal dispersions

NASA Technical Reports Server (NTRS)

Zhu, J. X.; Chaikin, Paul M.; Phan, S.-E.; Russel, W. B.

1994-01-01

Our objective is to perform on homogeneous, fully equilibrated dispersions the full set of experiments characterizing the transition from fluid to solid and the properties of the crystalline and glassy solid. These include measurements quantifying the nucleation and growth of crystallites, the structure of the initial fluid and the fully crystalline solid, and Brownian motion of particles within the crystal, and the elasticity of the crystal and the glass. Experiments are being built and tested for ideal microgravity environment. Here we describe the ground based effort, which exploits a fluidized bed to create a homogeneous, steady dispersion for the studies. The differences between the microgravity environment and the fluidized bed is gauged by the Peclet number Pe, which measures the rate of convection/sedimentation relative to Brownian motion. We have designed our experiment to accomplish three types of measurements on hard sphere suspensions in a fluidized bed: the static scattering intensity as a function of angle to determine the structure factor, the temporal autocorrelation function at all scattering angles to probe the dynamics, and the amplitude of the response to an oscillatory forcing to deduce the low frequency viscoelasticity. Thus the scattering instrument and the colloidal dispersion were chosen such as that the important features of each physical property lie within the detectable range for each measurement.

Study of trabecular bone microstructure using spatial autocorrelation analysis

NASA Astrophysics Data System (ADS)

Wald, Michael J.; Vasilic, Branimir; Saha, Punam K.; Wehrli, Felix W.

2005-04-01

The spatial autocorrelation analysis method represents a powerful, new approach to quantitative characterization of structurally quasi-periodic anisotropic materials such as trabecular bone (TB). The method is applicable to grayscale images and thus does not require any preprocessing, such as segmentation which is difficult to achieve in the limited resolution regime of in vivo imaging. The 3D autocorrelation function (ACF) can be efficiently calculated using the Fourier transform. The resulting trabecular thickness and spacing measurements are robust to the presence of noise and produce values within the expected range as determined by other methods from μCT and μMRI datasets. TB features found from the ACF are shown to correlate well with those determined by the Fuzzy Distance transform (FDT) in the transverse plane, i.e. the plane orthogonal to bone"s major axis. The method is further shown to be applicable to in-vivo μMRI data. Using the ACF, we examine data acquired in a previous study aimed at evaluating the structural implications of male hypogonadism characterized by testosterone deficiency and reduced bone mass. Specifically, we consider the hypothesis that eugonadal and hypogonadal men differ in the anisotropy of their trabecular networks. The analysis indicates a significant difference in trabecular bone thickness and longitudinal spacing between the control group and the testosterone deficient group. We conclude that spatial autocorrelation analysis is able to characterize the 3D structure and anisotropy of trabecular bone and provides new insight into the structural changes associated with osteoporotic trabecular bone loss.

Old document image segmentation using the autocorrelation function and multiresolution analysis

NASA Astrophysics Data System (ADS)

Mehri, Maroua; Gomez-Krämer, Petra; Héroux, Pierre; Mullot, Rémy

2013-01-01

Recent progress in the digitization of heterogeneous collections of ancient documents has rekindled new challenges in information retrieval in digital libraries and document layout analysis. Therefore, in order to control the quality of historical document image digitization and to meet the need of a characterization of their content using intermediate level metadata (between image and document structure), we propose a fast automatic layout segmentation of old document images based on five descriptors. Those descriptors, based on the autocorrelation function, are obtained by multiresolution analysis and used afterwards in a specific clustering method. The method proposed in this article has the advantage that it is performed without any hypothesis on the document structure, either about the document model (physical structure), or the typographical parameters (logical structure). It is also parameter-free since it automatically adapts to the image content. In this paper, firstly, we detail our proposal to characterize the content of old documents by extracting the autocorrelation features in the different areas of a page and at several resolutions. Then, we show that is possible to automatically find the homogeneous regions defined by similar indices of autocorrelation without knowledge about the number of clusters using adapted hierarchical ascendant classification and consensus clustering approaches. To assess our method, we apply our algorithm on 316 old document images, which encompass six centuries (1200-1900) of French history, in order to demonstrate the performance of our proposal in terms of segmentation and characterization of heterogeneous corpus content. Moreover, we define a new evaluation metric, the homogeneity measure, which aims at evaluating the segmentation and characterization accuracy of our methodology. We find a 85% of mean homogeneity accuracy. Those results help to represent a document by a hierarchy of layout structure and content, and to define one or more signatures for each page, on the basis of a hierarchical representation of homogeneous blocks and their topology.

Structure of the North Anatolian Fault Zone from the Autocorrelation of Ambient Seismic Noise

NASA Astrophysics Data System (ADS)

Taylor, George; Rost, Sebastian; Houseman, Gregory

2016-04-01

In recent years the technique of cross-correlating the ambient seismic noise wavefield at two seismometers to reconstruct empirical Green's Functions for the determination of Earth structure has been a powerful tool to study the Earth's interior without earthquakes or man-made sources. However, far less attention has been paid to using auto-correlations of seismic noise to reveal body wave reflections from interfaces in the subsurface. In principle, the Green's functions thus derived should be comparable to the Earth's impulse response to a co-located source and receiver. We use data from a dense seismic array (Dense Array for Northern Anatolia - DANA) deployed across the northern branch of the North Anatolian Fault Zone (NAFZ) in the region of the 1999 magnitude 7.6 Izmit earthquake in western Turkey. The NAFZ is a major strike-slip system that extends ~1200 km across northern Turkey and continues to pose a high level of seismic hazard, in particular to the mega-city of Istanbul. We construct body wave images for the entire crust and the shallow upper mantle over the ~35 km by 70 km footprint of the 70-station DANA array. Using autocorrelations of the vertical component of ground motion, P-wave reflections can be retrieved from the wavefield to constrain crustal structure. We show that clear P-wave reflections from the crust-mantle boundary (Moho) can be retrieved using the autocorrelation technique, indicating topography on the Moho on horizontal scales of less than 10 km. Offsets in crustal structure can be identified that seem to be correlated with the surface expression of the northern branch of the fault zone, indicating that the NAFZ reaches the upper mantle as a narrow structure. The southern branch has a less clear effect on crustal structure. We also see evidence of several discontinuities in the mid-crust in addition to an upper mantle reflector that we interpret to represent the Hales discontinuity.

Identifying food deserts and swamps based on relative healthy food access: a spatio-temporal Bayesian approach.

PubMed

Luan, Hui; Law, Jane; Quick, Matthew

2015-12-30

Obesity and other adverse health outcomes are influenced by individual- and neighbourhood-scale risk factors, including the food environment. At the small-area scale, past research has analysed spatial patterns of food environments for one time period, overlooking how food environments change over time. Further, past research has infrequently analysed relative healthy food access (RHFA), a measure that is more representative of food purchasing and consumption behaviours than absolute outlet density. This research applies a Bayesian hierarchical model to analyse the spatio-temporal patterns of RHFA in the Region of Waterloo, Canada, from 2011 to 2014 at the small-area level. RHFA is calculated as the proportion of healthy food outlets (healthy outlets/healthy + unhealthy outlets) within 4-km from each small-area. This model measures spatial autocorrelation of RHFA, temporal trend of RHFA for the study region, and spatio-temporal trends of RHFA for small-areas. For the study region, a significant decreasing trend in RHFA is observed (-0.024), suggesting that food swamps have become more prevalent during the study period. For small-areas, significant decreasing temporal trends in RHFA were observed for all small-areas. Specific small-areas located in south Waterloo, north Kitchener, and southeast Cambridge exhibited the steepest decreasing spatio-temporal trends and are classified as spatio-temporal food swamps. This research demonstrates a Bayesian spatio-temporal modelling approach to analyse RHFA at the small-area scale. Results suggest that food swamps are more prevalent than food deserts in the Region of Waterloo. Analysing spatio-temporal trends of RHFA improves understanding of local food environment, highlighting specific small-areas where policies should be targeted to increase RHFA and reduce risk factors of adverse health outcomes such as obesity.

Multiresolution analysis of the spatiotemporal variability in global radiation observed by a dense network of 99 pyranometers

NASA Astrophysics Data System (ADS)

Lakshmi Madhavan, Bomidi; Deneke, Hartwig; Witthuhn, Jonas; Macke, Andreas

2017-03-01

The time series of global radiation observed by a dense network of 99 autonomous pyranometers during the HOPE campaign around Jülich, Germany, are investigated with a multiresolution analysis based on the maximum overlap discrete wavelet transform and the Haar wavelet. For different sky conditions, typical wavelet power spectra are calculated to quantify the timescale dependence of variability in global transmittance. Distinctly higher variability is observed at all frequencies in the power spectra of global transmittance under broken-cloud conditions compared to clear, cirrus, or overcast skies. The spatial autocorrelation function including its frequency dependence is determined to quantify the degree of similarity of two time series measurements as a function of their spatial separation. Distances ranging from 100 m to 10 km are considered, and a rapid decrease of the autocorrelation function is found with increasing frequency and distance. For frequencies above 1/3 min-1 and points separated by more than 1 km, variations in transmittance become completely uncorrelated. A method is introduced to estimate the deviation between a point measurement and a spatially averaged value for a surrounding domain, which takes into account domain size and averaging period, and is used to explore the representativeness of a single pyranometer observation for its surrounding region. Two distinct mechanisms are identified, which limit the representativeness; on the one hand, spatial averaging reduces variability and thus modifies the shape of the power spectrum. On the other hand, the correlation of variations of the spatially averaged field and a point measurement decreases rapidly with increasing temporal frequency. For a grid box of 10 km × 10 km and averaging periods of 1.5-3 h, the deviation of global transmittance between a point measurement and an area-averaged value depends on the prevailing sky conditions: 2.8 (clear), 1.8 (cirrus), 1.5 (overcast), and 4.2 % (broken clouds). The solar global radiation observed at a single station is found to deviate from the spatial average by as much as 14-23 (clear), 8-26 (cirrus), 4-23 (overcast), and 31-79 W m-2 (broken clouds) from domain averages ranging from 1 km × 1 km to 10 km × 10 km in area.

Multichannel heterodyning for wideband interferometry, correlation and signal processing

DOEpatents

Erskine, David J.

1999-01-01

A method of signal processing a high bandwidth signal by coherently subdividing it into many narrow bandwidth channels which are individually processed at lower frequencies in a parallel manner. Autocorrelation and correlations can be performed using reference frequencies which may drift slowly with time, reducing cost of device. Coordinated adjustment of channel phases alters temporal and spectral behavior of net signal process more precisely than a channel used individually. This is a method of implementing precision long coherent delays, interferometers, and filters for high bandwidth optical or microwave signals using low bandwidth electronics. High bandwidth signals can be recorded, mathematically manipulated, and synthesized.

Staffing Subsidies and the Quality of Care in Nursing Homes

PubMed Central

Foster, Andrew D.; Lee, Yong Suk

2015-01-01

Concerns about the quality of state-financed nursing home care has led to the wide-scale adoption by states of pass-through subsidies, in which Medicaid reimbursement rates are directly tied to staffing expenditure. We examine the effects of Medicaid pass-through on nursing home staffing and quality of care by adapting a two-step FGLS method that addresses clustering and state-level temporal autocorrelation. We find that pass-through subsidies increases staffing by about 1% on average and 2.7% in nursing homes with a low share of Medicaid patients. Furthermore, pass-through subsidies reduce the incidences of pressure ulcer worsening by about 0.9%. PMID:25814437

Multichannel heterodyning for wideband interferometry, correlation and signal processing

DOEpatents

Erskine, D.J.

1999-08-24

A method is disclosed of signal processing a high bandwidth signal by coherently subdividing it into many narrow bandwidth channels which are individually processed at lower frequencies in a parallel manner. Autocorrelation and correlations can be performed using reference frequencies which may drift slowly with time, reducing cost of device. Coordinated adjustment of channel phases alters temporal and spectral behavior of net signal process more precisely than a channel used individually. This is a method of implementing precision long coherent delays, interferometers, and filters for high bandwidth optical or microwave signals using low bandwidth electronics. High bandwidth signals can be recorded, mathematically manipulated, and synthesized. 50 figs.

Power quality analysis based on spatial correlation

NASA Astrophysics Data System (ADS)

Li, Jiangtao; Zhao, Gang; Liu, Haibo; Li, Fenghou; Liu, Xiaoli

2018-03-01

With the industrialization and urbanization, the status of electricity in the production and life is getting higher and higher. So the prediction of power quality is the more potential significance. Traditional power quality analysis methods include: power quality data compression, disturbance event pattern classification, disturbance parameter calculation. Under certain conditions, these methods can predict power quality. This paper analyses the temporal variation of power quality of one provincial power grid in China from time angle. The distribution of power quality was analyzed based on spatial autocorrelation. This paper tries to prove that the research idea of geography is effective for mining the potential information of power quality.

Temporal and spatial mapping of hand, foot and mouth disease in Sarawak, Malaysia.

PubMed

Sham, Noraishah M; Krishnarajah, Isthrinayagy; Ibrahim, Noor Akma; Lye, Munn-Sann

2014-05-01

Hand, foot and mouth disease (HFMD) is endemic in Sarawak, Malaysia. In this study, a geographical information system (GIS) was used to investigate the relationship between the reported HFMD cases and the spatial patterns in 11 districts of Sarawak from 2006 to 2012. Within this 7-years period, the highest number of reported HFMD cases occurred in 2006, followed by 2012, 2008, 2009, 2007, 2010 and 2011, in descending order. However, while there was no significant distribution pattern or clustering in the first part of the study period (2006 to 2011) based on Moran's I statistic, spatial autocorrelation (P = 0.068) was observed in 2012.

Spectral and temporal characterization of a fused-quartz-microresonator optical frequency comb

DOE Office of Scientific and Technical Information (OSTI.GOV)

Papp, Scott B.; Diddams, Scott A.

2011-11-15

We report on the fabrication of high-Q, fused-quartz microresonators and the parametric generation of a frequency comb with 36-GHz line spacing using them. We have characterized the intrinsic stability of the comb in both the time and frequency domains to assess its suitability for future precision metrology applications. Intensity autocorrelation measurements and line-by-line comb control reveal near-transform-limited picosecond pulse trains that are associated with good relative phase and amplitude stability of the comb lines. The comb's 36-GHz line spacing can be readily photodetected, which enables measurements of its intrinsic and absolute phase fluctuations.

Holographic Measurements of Fuel Droplet Diffusion in Isotropic Turbulence

NASA Astrophysics Data System (ADS)

Gopalan, Balaji

2005-11-01

High-speed digital holographic cinematography was used to investigate the diffusion of slightly buoyant fuel droplets in locally isotropic turbulence. High turbulence levels with a weak mean velocity was generated at the center of a tank by four rotating grids. 0.3-1.5mm droplets were injected here and tracked using in-line holography. To obtain all three components of velocity, we simultaneously recorded holograms of the central 37x37x37 mm^3 volume from two perpendicular directions. These were numerically reconstructed to provide focused images of the droplets. An automated code was developed to identify the 3-D droplet trajectories from the two views, and then calculate time series of their velocity. After subtracting the local mean fluid velocity, the time series were used to obtain the 3-D Lagrangian autocorrelation function of droplet velocity. Averaging over many trajectories provided the autocorrelation functions as a function of direction and droplet sizes. As expected, the correlation was higher in the vertical direction due to the effect of gravity. Data analysis is still in progress.

Anomalous diffusion due to the non-Markovian process of the dust particle velocity in complex plasmas

NASA Astrophysics Data System (ADS)

Ghannad, Z.; Hakimi Pajouh, H.

2017-12-01

In this work, the motion of a dust particle under the influence of the random force due to dust charge fluctuations is considered as a non-Markovian stochastic process. Memory effects in the velocity process of the dust particle are studied. A model is developed based on the fractional Langevin equation for the motion of the dust grain. The fluctuation-dissipation theorem for the dust grain is derived from this equation. The mean-square displacement and the velocity autocorrelation function of the dust particle are obtained in terms of the Mittag-Leffler functions. Their asymptotic behavior and the dust particle temperature due to charge fluctuations are studied in the long-time limit. As an interesting result, it is found that the presence of memory effects in the velocity process of the dust particle as a non-Markovian process can cause an anomalous diffusion in dusty plasmas. In this case, the velocity autocorrelation function of the dust particle has a power-law decay like t - α - 2, where the exponent α take values 0 < α < 1.

The temporal distribution of directional gradients under selection for an optimum.

PubMed

Chevin, Luis-Miguel; Haller, Benjamin C

2014-12-01

Temporal variation in phenotypic selection is often attributed to environmental change causing movements of the adaptive surface relating traits to fitness, but this connection is rarely established empirically. Fluctuating phenotypic selection can be measured by the variance and autocorrelation of directional selection gradients through time. However, the dynamics of these gradients depend not only on environmental changes altering the fitness surface, but also on evolution of the phenotypic distribution. Therefore, it is unclear to what extent variability in selection gradients can inform us about the underlying drivers of their fluctuations. To investigate this question, we derive the temporal distribution of directional gradients under selection for a phenotypic optimum that is either constant or fluctuates randomly in various ways in a finite population. Our analytical results, combined with population- and individual-based simulations, show that although some characteristic patterns can be distinguished, very different types of change in the optimum (including a constant optimum) can generate similar temporal distributions of selection gradients, making it difficult to infer the processes underlying apparent fluctuating selection. Analyzing changes in phenotype distributions together with changes in selection gradients should prove more useful for inferring the mechanisms underlying estimated fluctuating selection. © 2014 The Author(s). Evolution © 2014 The Society for the Study of Evolution.

Application of the Karhunen-Loeve transform temporal image filter to reduce noise in real-time cardiac cine MRI

NASA Astrophysics Data System (ADS)

Ding, Yu; Chung, Yiu-Cho; Raman, Subha V.; Simonetti, Orlando P.

2009-06-01

Real-time dynamic magnetic resonance imaging (MRI) typically sacrifices the signal-to-noise ratio (SNR) to achieve higher spatial and temporal resolution. Spatial and/or temporal filtering (e.g., low-pass filtering or averaging) of dynamic images improves the SNR at the expense of edge sharpness. We describe the application of a temporal filter for dynamic MR image series based on the Karhunen-Loeve transform (KLT) to remove random noise without blurring stationary or moving edges and requiring no training data. In this paper, we present several properties of this filter and their effects on filter performance, and propose an automatic way to find the filter cutoff based on the autocorrelation of the eigenimages. Numerical simulation and in vivo real-time cardiac cine MR image series spanning multiple cardiac cycles acquired using multi-channel sensitivity-encoded MRI, i.e., parallel imaging, are used to validate and demonstrate these properties. We found that in this application, the noise standard deviation was reduced to 42% of the original with no apparent image blurring by using the proposed filter cutoff. Greater noise reduction can be achieved by increasing the length of the image series. This advantage of KLT filtering provides flexibility in the form of another scan parameter to trade for SNR.

Chaotic dynamics of flexible Euler-Bernoulli beams

DOE Office of Scientific and Technical Information (OSTI.GOV)

Awrejcewicz, J., E-mail: awrejcew@p.lodz.pl; Krysko, A. V., E-mail: anton.krysko@gmail.com; Kutepov, I. E., E-mail: iekutepov@gmail.com

2013-12-15

Mathematical modeling and analysis of spatio-temporal chaotic dynamics of flexible simple and curved Euler-Bernoulli beams are carried out. The Kármán-type geometric non-linearity is considered. Algorithms reducing partial differential equations which govern the dynamics of studied objects and associated boundary value problems are reduced to the Cauchy problem through both Finite Difference Method with the approximation of O(c{sup 2}) and Finite Element Method. The obtained Cauchy problem is solved via the fourth and sixth-order Runge-Kutta methods. Validity and reliability of the results are rigorously discussed. Analysis of the chaotic dynamics of flexible Euler-Bernoulli beams for a series of boundary conditions ismore » carried out with the help of the qualitative theory of differential equations. We analyze time histories, phase and modal portraits, autocorrelation functions, the Poincaré and pseudo-Poincaré maps, signs of the first four Lyapunov exponents, as well as the compression factor of the phase volume of an attractor. A novel scenario of transition from periodicity to chaos is obtained, and a transition from chaos to hyper-chaos is illustrated. In particular, we study and explain the phenomenon of transition from symmetric to asymmetric vibrations. Vibration-type charts are given regarding two control parameters: amplitude q{sub 0} and frequency ω{sub p} of the uniformly distributed periodic excitation. Furthermore, we detected and illustrated how the so called temporal-space chaos is developed following the transition from regular to chaotic system dynamics.« less

Climatic variability of soil water in the American Midwest: Part 2. Spatio-temporal analysis

NASA Astrophysics Data System (ADS)

Georgakakos, Konstantine P.; Bae, Deg-Hyo

1994-11-01

A study of the model-estimated soil water, aggregated over three large drainage basins of the Midwestern USA, is reported. The basin areas are in the range from 2000 km 2 to 3500 km 2, and allow the study of mesoscale (1000-10000 km 2) soil water features. In each case, a conceptual hydrologic model was used to produce upper and lower soil water estimates that are consistent with the atmospheric forcing of daily precipitation, potential evapotranspiration and air temperature, and with the observed daily streamflow divergence over a 40 year period. It is shown that the water contents of the upper and lower soil reach peaks in different months, with the soil column being most saturated in June, when the area is prone to serious flooding. Temporal and spatial features of the variability of model-estimated soil water content are identified. The autocorrelation function of monthly averaged soil water shows that the upper soil water remains persistent for about a season, whereas the persistence of the lower soil water extends to several seasons. The soil water estimates of the three study basins exhibit strong similarities in annual cycles and interannual variability. It is shown that the frequency of significant positive (wet) soil water anomalies that extend over a 2° × 2° region is lower than that of significant negative (dry) ones of the same extent in this region of the USA.

Protein oligomerization monitored by fluorescence fluctuation spectroscopy: Self-assembly of Rubisco activase

USDA-ARS?s Scientific Manuscript database

A methodology is presented to characterize complex protein assembly pathways by fluorescence correlation spectroscopy. We have derived the total autocorrelation function describing the behavior of mixtures of labeled and unlabeled protein under equilibrium conditions. Our modeling approach allows us...

Statistical characteristics of MST radar echoes and its interpretation

NASA Technical Reports Server (NTRS)

Woodman, Ronald F.

1989-01-01

Two concepts of fundamental importance are reviewed: the autocorrelation function and the frequency power spectrum. In addition, some turbulence concepts, the relationship between radar signals and atmospheric medium statistics, partial reflection, and the characteristics of noise and clutter interference are discussed.

Pattern, growth, and aging in aggregation kinetics of a Vicsek-like active matter model

NASA Astrophysics Data System (ADS)

Das, Subir K.

2017-01-01

Via molecular dynamics simulations, we study kinetics in a Vicsek-like phase-separating active matter model. Quantitative results, for isotropic bicontinuous pattern, are presented on the structure, growth, and aging. These are obtained via the two-point equal-time density-density correlation function, the average domain length, and the two-time density autocorrelation function. Both the correlation functions exhibit basic scaling properties, implying self-similarity in the pattern dynamics, for which the average domain size exhibits a power-law growth in time. The equal-time correlation has a short distance behavior that provides reasonable agreement between the corresponding structure factor tail and the Porod law. The autocorrelation decay is a power-law in the average domain size. Apart from these basic similarities, the overall quantitative behavior of the above-mentioned observables is found to be vastly different from those of the corresponding passive limit of the model which also undergoes phase separation. The functional forms of these have been quantified. An exceptionally rapid growth in the active system occurs due to fast coherent motion of the particles, mean-squared-displacements of which exhibit multiple scaling regimes, including a long time ballistic one.

Coupling Poisson rectangular pulse and multiplicative microcanonical random cascade models to generate sub-daily precipitation timeseries

NASA Astrophysics Data System (ADS)

Pohle, Ina; Niebisch, Michael; Müller, Hannes; Schümberg, Sabine; Zha, Tingting; Maurer, Thomas; Hinz, Christoph

2018-07-01

To simulate the impacts of within-storm rainfall variabilities on fast hydrological processes, long precipitation time series with high temporal resolution are required. Due to limited availability of observed data such time series are typically obtained from stochastic models. However, most existing rainfall models are limited in their ability to conserve rainfall event statistics which are relevant for hydrological processes. Poisson rectangular pulse models are widely applied to generate long time series of alternating precipitation events durations and mean intensities as well as interstorm period durations. Multiplicative microcanonical random cascade (MRC) models are used to disaggregate precipitation time series from coarse to fine temporal resolution. To overcome the inconsistencies between the temporal structure of the Poisson rectangular pulse model and the MRC model, we developed a new coupling approach by introducing two modifications to the MRC model. These modifications comprise (a) a modified cascade model ("constrained cascade") which preserves the event durations generated by the Poisson rectangular model by constraining the first and last interval of a precipitation event to contain precipitation and (b) continuous sigmoid functions of the multiplicative weights to consider the scale-dependency in the disaggregation of precipitation events of different durations. The constrained cascade model was evaluated in its ability to disaggregate observed precipitation events in comparison to existing MRC models. For that, we used a 20-year record of hourly precipitation at six stations across Germany. The constrained cascade model showed a pronounced better agreement with the observed data in terms of both the temporal pattern of the precipitation time series (e.g. the dry and wet spell durations and autocorrelations) and event characteristics (e.g. intra-event intermittency and intensity fluctuation within events). The constrained cascade model also slightly outperformed the other MRC models with respect to the intensity-frequency relationship. To assess the performance of the coupled Poisson rectangular pulse and constrained cascade model, precipitation events were stochastically generated by the Poisson rectangular pulse model and then disaggregated by the constrained cascade model. We found that the coupled model performs satisfactorily in terms of the temporal pattern of the precipitation time series, event characteristics and the intensity-frequency relationship.

Time-scale effects on the gain-loss asymmetry in stock indices

NASA Astrophysics Data System (ADS)

Sándor, Bulcsú; Simonsen, Ingve; Nagy, Bálint Zsolt; Néda, Zoltán

2016-08-01

The gain-loss asymmetry, observed in the inverse statistics of stock indices is present for logarithmic return levels that are over 2 % , and it is the result of the non-Pearson-type autocorrelations in the index. These non-Pearson-type correlations can be viewed also as functionally dependent daily volatilities, extending for a finite time interval. A generalized time-window shuffling method is used to show the existence of such autocorrelations. Their characteristic time scale proves to be smaller (less than 25 trading days) than what was previously believed. It is also found that this characteristic time scale has decreased with the appearance of program trading in the stock market transactions. Connections with the leverage effect are also established.

Photon statistics and polarization correlations at telecommunications wavelengths from a warm atomic ensemble.

PubMed

Willis, R T; Becerra, F E; Orozco, L A; Rolston, S L

2011-07-18

We present measurements of the polarization correlation and photon statistics of photon pairs that emerge from a laser-pumped warm rubidium vapor cell. The photon pairs occur at 780 nm and 1367 nm and are polarization entangled. We measure the autocorrelation of each of the generated fields as well as the cross-correlation function, and observe a strong violation of the two-beam Cauchy-Schwartz inequality. We evaluate the performance of the system as source of heralded single photons at a telecommunication wavelength. We measure the heralded autocorrelation and see that coincidences are suppressed by a factor of ≈ 20 from a Poissonian source at a generation rate of 1500 s(-1), a heralding efficiency of 10%, and a narrow spectral width.

a Study of the Concentration Dependence of Macromolecular Diffusion Using Photon Correlation Spectroscopy.

NASA Astrophysics Data System (ADS)

Marlowe, Robert Lloyd

The dynamic light scattering technique of photon correlation spectroscopy has been used to investigate the dependence of the mutual diffusion coefficient of a macromolecular system upon concentration. The first part of the research was devoted to the design and construction of a single-clipping autocorrelator based on newly-developed integrated circuits. The resulting 128 channel instrument can perform real time autocorrelation for sample time intervals >(, )10 (mu)s, and batch processed autocorrelation for intervals down to 3 (mu)s. An improved design for a newer, all-digital autocorrelator is given. Homodyne light scattering experiments were then undertaken on monodisperse solutions of polystyrene spheres. The single-mode TEM(,oo) beam of an argon-ion laser ((lamda) = 5145 (ANGSTROM)) was used as the light source; all solutions were studied at room temperature. The scattering angle was varied from 30(DEGREES) to 110(DEGREES). Excellent agreement with the manufacturer's specification for the particle size was obtained from the photon correlation studies. Finally, aqueous solutions of the globular protein ovalbumin, ranging in concentration from 18.9 to 244.3 mg/ml, were illuminated under the same conditions of temperature and wavelength as before; the homodyne scattered light was detected at a fixed scattering angle of 30(DEGREES). The single-clipped photocount autocorrelation function was analyzed using the homodyne exponential integral method of Meneely et al. The resulting diffusion coefficients showed a general linear dependence upon concentration, as predicted by the generalized Stokes-Einstein equation. However, a clear peak in the data was evident at c (TURNEQ) 100 mg/ml, which could not be explained on the basis of a non -interacting particle theory. A semi-quantitative approach based on the Debye-Huckel theory of electrostatic interactions is suggested as the probable cause for the peak's rise, and an excluded volume effect for its decline.

ASSESSMENT OF SPATIAL AUTOCORRELATION IN EMPIRICAL MODELS IN ECOLOGY

EPA Science Inventory

Statistically assessing ecological models is inherently difficult because data are autocorrelated and this autocorrelation varies in an unknown fashion. At a simple level, the linking of a single species to a habitat type is a straightforward analysis. With some investigation int...

Stochastic characteristics and Second Law violations of atomic fluids in Couette flow

NASA Astrophysics Data System (ADS)

Raghavan, Bharath V.; Karimi, Pouyan; Ostoja-Starzewski, Martin

2018-04-01

Using Non-equilibrium Molecular Dynamics (NEMD) simulations, we study the statistical properties of an atomic fluid undergoing planar Couette flow, in which particles interact via a Lennard-Jones potential. We draw a connection between local density contrast and temporal fluctuations in the shear stress, which arise naturally through the equivalence between the dissipation function and entropy production according to the fluctuation theorem. We focus on the shear stress and the spatio-temporal density fluctuations and study the autocorrelations and spectral densities of the shear stress. The bispectral density of the shear stress is used to measure the degree of departure from a Gaussian model and the degree of nonlinearity induced in the system owing to the applied strain rate. More evidence is provided by the probability density function of the shear stress. We use the Information Theory to account for the departure from Gaussian statistics and to develop a more general probability distribution function that captures this broad range of effects. By accounting for negative shear stress increments, we show how this distribution preserves the violations of the Second Law of Thermodynamics observed in planar Couette flow of atomic fluids, and also how it captures the non-Gaussian nature of the system by allowing for non-zero higher moments. We also demonstrate how the temperature affects the band-width of the shear-stress and how the density affects its Power Spectral Density, thus determining the conditions under which the shear-stress acts is a narrow-band or wide-band random process. We show that changes in the statistical characteristics of the parameters of interest occur at a critical strain rate at which an ordering transition occurs in the fluid causing shear thinning and affecting its stability. A critical strain rate of this kind is also predicted by the Loose-Hess stability criterion.

A 3D Monte Carlo model of radiation affecting cells, and its application to neuronal cells and GCR irradiation

NASA Astrophysics Data System (ADS)

Ponomarev, Artem; Sundaresan, Alamelu; Kim, Angela; Vazquez, Marcelo E.; Guida, Peter; Kim, Myung-Hee; Cucinotta, Francis A.

A 3D Monte Carlo model of radiation transport in matter is applied to study the effect of heavy ion radiation on human neuronal cells. Central nervous system effects, including cognitive impairment, are suspected from the heavy ion component of galactic cosmic radiation (GCR) during space missions. The model can count, for instance, the number of direct hits from ions, which will have the most affect on the cells. For comparison, the remote hits, which are received through δ-rays from the projectile traversing space outside the volume of the cell, are also simulated and their contribution is estimated. To simulate tissue effects from irradiation, cellular matrices of neuronal cells, which were derived from confocal microscopy, were simulated in our model. To produce this realistic model of the brain tissue, image segmentation was used to identify cells in the images of cells cultures. The segmented cells were inserted pixel by pixel into the modeled physical space, which represents a volume of interacting cells with periodic boundary conditions (PBCs). PBCs were used to extrapolate the model results to the macroscopic tissue structures. Specific spatial patterns for cell apoptosis are expected from GCR, as heavy ions produce concentrated damage along their trajectories. The apoptotic cell patterns were modeled based on the action cross sections for apoptosis, which were estimated from the available experimental data. The cell patterns were characterized with an autocorrelation function, which values are higher for non-random cell patterns, and the values of the autocorrelation function were compared for X rays and Fe ion irradiations. The autocorrelation function indicates the directionality effects present in apoptotic neuronal cells from GCR.

Intrinsic autocorrelation time of picoseconds for thermal noise in water.

PubMed

Zhu, Zhi; Sheng, Nan; Wan, Rongzheng; Fang, Haiping

2014-10-02

Whether thermal noise is colored or white is of fundamental importance. In conventional theory, thermal noise is usually treated as white noise so that there are no directional transportations in the asymmetrical systems without external inputs, since only the colored fluctuations with appropriate autocorrelation time length can lead to directional transportations in the asymmetrical systems. Here, on the basis of molecular dynamics simulations, we show that the autocorrelation time length of thermal noise in water is ~10 ps at room temperature, which indicates that thermal noise is not white in the molecular scale while thermal noise can be reasonably assumed as white in macro- and meso-scale systems. The autocorrelation time length of thermal noise is intrinsic, since the value is almost unchanged for different temperature coupling methods. Interestingly, the autocorrelation time of thermal noise is correlated with the lifetime of hydrogen bonds, suggesting that the finite autocorrelation time length of thermal noise mainly comes from the finite lifetime of the interactions between neighboring water molecules.

Principal Components of Recurrence Quantification Analysis of EMG

DTIC Science & Technology

2001-10-25

Springer, 1981, pp. 366-381. 4. M. Fraser and H. L. Swinney, “ Independent coordinates for strange attractors from mutual information ,” Phys. Rev. A...autocorrelation function of s(n), although it has also been argued that the first local minimum of the auto mutual information function is more appropriate [4...recordings from a given subject. T was taken as the lag corresponding to the first minimum of the auto mutual information function, calculated as

Model Identification of Integrated ARMA Processes

ERIC Educational Resources Information Center

Stadnytska, Tetiana; Braun, Simone; Werner, Joachim

2008-01-01

This article evaluates the Smallest Canonical Correlation Method (SCAN) and the Extended Sample Autocorrelation Function (ESACF), automated methods for the Autoregressive Integrated Moving-Average (ARIMA) model selection commonly available in current versions of SAS for Windows, as identification tools for integrated processes. SCAN and ESACF can…

Physics behind the oscillation of pressure tensor autocorrelation function for nanocolloidal dispersions.

PubMed

Wang, Tao; Wang, Xinwei; Luo, Zhongyang; Cen, Kefa

2008-08-01

In this work, extensive equilibrium molecular dynamics simulations are conducted to explore the physics behind the oscillation of pressure tensor autocorrelation function (PTACF) for nanocolloidal dispersions, which leads to strong instability in viscosity calculation. By reducing the particle size and density, we find the intensity of the oscillation decreases while the frequency of the oscillation becomes higher. Careful analysis of the relationship between the oscillation and nanoparticle characteristics reveals that the stress wave scattering/reflection at the particle-liquid interface plays a critical role in PTACF oscillation while the Brownian motion/vibration of solid particles has little effect. Our modeling proves that it is practical to eliminate the PTACF oscillation through suppressing the acoustic mismatch at the solid-liquid interface by designing special nanoparticle materials. It is also found when the particle size is comparable with the wavelength of the stress wave, diffraction of stress wave happens at the interface. Such effect substantially reduces the PTACF oscillation and improves the stability of viscosity calculation.

Comparing phase-sensitive and phase-insensitive echolocation target images using a monaural audible sonar.

PubMed

Kuc, Roman

2018-04-01

This paper describes phase-sensitive and phase-insensitive processing of monaural echolocation waveforms to generate target maps. Composite waveforms containing both the emission and echoes are processed to estimate the target impulse response using an audible sonar. Phase-sensitive processing yields the composite signal envelope, while phase-insensitive processing that starts with the composite waveform power spectrum yields the envelope of the autocorrelation function. Analysis and experimental verification show that multiple echoes form an autocorrelation function that produces near-range phantom-reflector artifacts. These artifacts interfere with true target echoes when the first true echo occurs at a time that is less than the total duration of the target echoes. Initial comparison of phase-sensitive and phase-insensitive maps indicates that both display important target features, indicating that phase is not vital. A closer comparison illustrates the improved resolution of phase-sensitive processing, the near-range phantom-reflectors produced by phase-insensitive processing, and echo interference and multiple reflection artifacts that were independent of the processing.

Mesoscopic fluctuations and intermittency in aging dynamics

NASA Astrophysics Data System (ADS)

Sibani, P.

2006-01-01

Mesoscopic aging systems are characterized by large intermittent noise fluctuations. In a record dynamics scenario (Sibani P. and Dall J., Europhys. Lett., 64 (2003) 8) these events, quakes, are treated as a Poisson process with average αln (1 + t/tw), where t is the observation time, tw is the age and α is a parameter. Assuming for simplicity that quakes constitute the only source of de-correlation, we present a model for the probability density function (PDF) of the configuration autocorrelation function. Beside α, the model has the average quake size 1/q as a parameter. The model autocorrelation PDF has a Gumbel-like shape, which approaches a Gaussian for large t/tw and becomes sharply peaked in the thermodynamic limit. Its average and variance, which are given analytically, depend on t/tw as a power law and a power law with a logarithmic correction, respectively. Most predictions are in good agreement with data from the literature and with the simulations of the Edwards-Anderson spin-glass carried out as a test.

On the origin of the soft X-ray background. [in cosmological observations

NASA Technical Reports Server (NTRS)

Wang, Q. D.; Mccray, Richard

1993-01-01

The angular autocorrelation function and spectrum of the soft X-ray background is studied below a discrete source detection limit, using two deep images from the Rosat X-ray satellite. The average spectral shape of pointlike sources, which account for 40 to 60 percent of the background intensity, is determined by using the autocorrelation function. The background spectrum, in the 0.5-0.9 keV band (M band), is decomposed into a pointlike source component characterized by a power law and a diffuse component represented by a two-temperature plasma. These pointlike sources cannot contribute more than 60 percent of the X-ray background intensity in the M band without exceeding the total observed flux in the R7 band. Spectral analysis has shown that the local soft diffuse component, although dominating the background intensity at energies not greater than 0.3 keV, contributes only a small fraction of the M band background intensity. The diffuse component may represent an important constituent of the interstellar or intergalactic medium.

Scaling analysis and model estimation of solar corona index

NASA Astrophysics Data System (ADS)

Ray, Samujjwal; Ray, Rajdeep; Khondekar, Mofazzal Hossain; Ghosh, Koushik

2018-04-01

A monthly average solar green coronal index time series for the period from January 1939 to December 2008 collected from NOAA (The National Oceanic and Atmospheric Administration) has been analysed in this paper in perspective of scaling analysis and modelling. Smoothed and de-noising have been done using suitable mother wavelet as a pre-requisite. The Finite Variance Scaling Method (FVSM), Higuchi method, rescaled range (R/S) and a generalized method have been applied to calculate the scaling exponents and fractal dimensions of the time series. Autocorrelation function (ACF) is used to find autoregressive (AR) process and Partial autocorrelation function (PACF) has been used to get the order of AR model. Finally a best fit model has been proposed using Yule-Walker Method with supporting results of goodness of fit and wavelet spectrum. The results reveal an anti-persistent, Short Range Dependent (SRD), self-similar property with signatures of non-causality, non-stationarity and nonlinearity in the data series. The model shows the best fit to the data under observation.

Cross-section fluctuations in chaotic scattering systems.

PubMed

Ericson, Torleif E O; Dietz, Barbara; Richter, Achim

2016-10-01

Exact analytical expressions for the cross-section correlation functions of chaotic scattering systems have hitherto been derived only under special conditions. The objective of the present article is to provide expressions that are applicable beyond these restrictions. The derivation is based on a statistical model of Breit-Wigner type for chaotic scattering amplitudes which has been shown to describe the exact analytical results for the scattering (S)-matrix correlation functions accurately. Our results are given in the energy and in the time representations and apply in the whole range from isolated to overlapping resonances. The S-matrix contributions to the cross-section correlations are obtained in terms of explicit irreducible and reducible correlation functions. Consequently, the model can be used for a detailed exploration of the key features of the cross-section correlations and the underlying physical mechanisms. In the region of isolated resonances, the cross-section correlations contain a dominant contribution from the self-correlation term. For narrow states the self-correlations originate predominantly from widely spaced states with exceptionally large partial width. In the asymptotic region of well-overlapping resonances, the cross-section autocorrelation functions are given in terms of the S-matrix autocorrelation functions. For inelastic correlations, in particular, the Ericson fluctuations rapidly dominate in that region. Agreement with known analytical and experimental results is excellent.

Reexamination of relaxation of spins due to a magnetic field gradient: Identity of the Redfield and Torrey theories

DOE Office of Scientific and Technical Information (OSTI.GOV)

Golub, R.; Rohm, Ryan M.; Swank, C. M.

2011-02-15

There is an extensive literature on magnetic-gradient-induced spin relaxation. Cates, Schaefer, and Happer, in a seminal publication, have solved the problem in the regime where diffusion theory (the Torrey equation) is applicable using an expansion of the density matrix in diffusion equation eigenfunctions and angular momentum tensors. McGregor has solved the problem in the same regime using a slightly more general formulation using the Redfield theory formulated in terms of the autocorrelation function of the fluctuating field seen by the spins and calculating the correlation functions using the diffusion-theory Green's function. The results of both calculations were shown to agreemore » for a special case. In the present work, we show that the eigenfunction expansion of the Torrey equation yields the expansion of the Green's function for the diffusion equation, thus showing the identity of this approach with that of the Redfield theory. The general solution can also be obtained directly from the Torrey equation for the density matrix. Thus, the physical content of the Redfield and Torrey approaches are identical. We then introduce a more general expression for the position autocorrelation function of particles moving in a closed cell, extending the range of applicability of the theory.« less

Leading indicators of mosquito-borne disease elimination.

PubMed

O'Regan, Suzanne M; Lillie, Jonathan W; Drake, John M

Mosquito-borne diseases contribute significantly to the global disease burden. High-profile elimination campaigns are currently underway for many parasites, e.g., Plasmodium spp., the causal agent of malaria. Sustaining momentum near the end of elimination programs is often difficult to achieve and consequently quantitative tools that enable monitoring the effectiveness of elimination activities after the initial reduction of cases has occurred are needed. Documenting progress in vector-borne disease elimination is a potentially important application for the theory of critical transitions. Non-parametric approaches that are independent of model-fitting would advance infectious disease forecasting significantly. In this paper, we consider compartmental Ross-McDonald models that are slowly forced through a critical transition through gradually deployed control measures. We derive expressions for the behavior of candidate indicators, including the autocorrelation coefficient, variance, and coefficient of variation in the number of human cases during the approach to elimination. We conducted a simulation study to test the performance of each summary statistic as an early warning system of mosquito-borne disease elimination. Variance and coefficient of variation were highly predictive of elimination but autocorrelation performed poorly as an indicator in some control contexts. Our results suggest that tipping points (bifurcations) in mosquito-borne infectious disease systems may be foreshadowed by characteristic temporal patterns of disease prevalence.

Poor environmental tracking can make extinction risk insensitive to the colour of environmental noise

PubMed Central

van de Pol, Martijn; Vindenes, Yngvild; Sæther, Bernt-Erik; Engen, Steinar; Ens, Bruno J.; Oosterbeek, Kees; Tinbergen, Joost M.

2011-01-01

The relative importance of environmental colour for extinction risk compared with other aspects of environmental noise (mean and interannual variability) is poorly understood. Such knowledge is currently relevant, as climate change can cause the mean, variability and temporal autocorrelation of environmental variables to change. Here, we predict that the extinction risk of a shorebird population increases with the colour of a key environmental variable: winter temperature. However, the effect is weak compared with the impact of changes in the mean and interannual variability of temperature. Extinction risk was largely insensitive to noise colour, because demographic rates are poor in tracking the colour of the environment. We show that three mechanisms—which probably act in many species—can cause poor environmental tracking: (i) demographic rates that depend nonlinearly on environmental variables filter the noise colour, (ii) demographic rates typically depend on several environmental signals that do not change colour synchronously, and (iii) demographic stochasticity whitens the colour of demographic rates at low population size. We argue that the common practice of assuming perfect environmental tracking may result in overemphasizing the importance of noise colour for extinction risk. Consequently, ignoring environmental autocorrelation in population viability analysis could be less problematic than generally thought. PMID:21561978

Dispersion relation for electromagnetic propagation in stochastic dielectric and magnetic helical photonic crystals

NASA Astrophysics Data System (ADS)

Avendaño, Carlos G.; Reyes, Arturo

2017-03-01

We theoretically study the dispersion relation for axially propagating electromagnetic waves throughout a one-dimensional helical structure whose pitch and dielectric and magnetic properties are spatial random functions with specific statistical characteristics. In the system of coordinates rotating with the helix, by using a matrix formalism, we write the set of differential equations that governs the expected value of the electromagnetic field amplitudes and we obtain the corresponding dispersion relation. We show that the dispersion relation depends strongly on the noise intensity introduced in the system and the autocorrelation length. When the autocorrelation length increases at fixed fluctuation and when the fluctuation augments at fixed autocorrelation length, the band gap widens and the attenuation coefficient of electromagnetic waves propagating in the random medium gets larger. By virtue of the degeneracy in the imaginary part of the eigenvalues associated with the propagating modes, the random medium acts as a filter for circularly polarized electromagnetic waves, in which only the propagating backward circularly polarized wave can propagate with no attenuation. Our results are valid for any kind of dielectric and magnetic structures which possess a helical-like symmetry such as cholesteric and chiral smectic-C liquid crystals, structurally chiral materials, and stressed cholesteric elastomers.

Autocorrelated process control: Geometric Brownian Motion approach versus Box-Jenkins approach

NASA Astrophysics Data System (ADS)

Salleh, R. M.; Zawawi, N. I.; Gan, Z. F.; Nor, M. E.

2018-04-01

Existing of autocorrelation will bring a significant effect on the performance and accuracy of process control if the problem does not handle carefully. When dealing with autocorrelated process, Box-Jenkins method will be preferred because of the popularity. However, the computation of Box-Jenkins method is too complicated and challenging which cause of time-consuming. Therefore, an alternative method which known as Geometric Brownian Motion (GBM) is introduced to monitor the autocorrelated process. One real case of furnace temperature data is conducted to compare the performance of Box-Jenkins and GBM methods in monitoring autocorrelation process. Both methods give the same results in terms of model accuracy and monitoring process control. Yet, GBM is superior compared to Box-Jenkins method due to its simplicity and practically with shorter computational time.

Statistical regularities of Carbon emission trading market: Evidence from European Union allowances

NASA Astrophysics Data System (ADS)

Zheng, Zeyu; Xiao, Rui; Shi, Haibo; Li, Guihong; Zhou, Xiaofeng

2015-05-01

As an emerging financial market, the trading value of carbon emission trading market has definitely increased. In recent years, the carbon emission allowances have already become a way of investment. They are bought and sold not only by carbon emitters but also by investors. In this paper, we analyzed the price fluctuations of the European Union allowances (EUA) futures in European Climate Exchange (ECX) market from 2007 to 2011. The symmetric and power-law probability density function of return time series was displayed. We found that there are only short-range correlations in price changes (return), while long-range correlations in the absolute of price changes (volatility). Further, detrended fluctuation analysis (DFA) approach was applied with focus on long-range autocorrelations and Hurst exponent. We observed long-range power-law autocorrelations in the volatility that quantify risk, and found that they decay much more slowly than the autocorrelation of return time series. Our analysis also showed that the significant cross correlations exist between return time series of EUA and many other returns. These cross correlations exist in a wide range of fields, including stock markets, energy concerned commodities futures, and financial futures. The significant cross-correlations between energy concerned futures and EUA indicate the physical relationship between carbon emission and energy production process. Additionally, the cross-correlations between financial futures and EUA indicate that the speculation behavior may become an important factor that can affect the price of EUA. Finally we modeled the long-range volatility time series of EUA with a particular version of the GARCH process, and the result also suggests long-range volatility autocorrelations.

Retrieval of P wave Basin Response from Autocorrelation of Seismic Noise-Jakarta, Indonesia

NASA Astrophysics Data System (ADS)

Saygin, E.; Cummins, P. R.; Lumley, D. E.

2016-12-01

Indonesia's capital city, Jakarta, is home to a very large (over 10 million), vulnerable population and is proximate to known active faults, as well as to the subduction of Australian plate, which has a megathrust at abut 300 km distance, as well as intraslab seismicity extending to directly beneath the city. It is also located in a basin filled with a thick layer of unconsolidated and poorly consolidated sediment, which increases the seismic hazard the city is facing. Therefore, the information on the seismic velocity structure of the basin is crucial for increasing our knowledge of the seismic risk. We undertook a passive deployment of broadband seismographs throughout the city over a 3-month interval in 2013-2014, recording ambient seismic noise at over 90 sites for intervals of 1 month or more. Here we consider autocorrelations of the vertical component of the continuously recorded seismic wavefield across this dense network to image the shallow P wave velocity structure of Jakarta, Indonesia. Unlike the surface wave Green's functions used in ambient noise tomography, the vertical-component autocorrelograms are dominated by body wave energy that is potentially sensitive to sharp velocity contrasts, which makes them useful in seismic imaging. Results show autocorrelograms at different seismic stations with travel time variations that largely reflect changes in sediment thickness across the basin. We also confirm the validity our interpretation of the observed autocorrelation waveforms by conducting 2D finite difference full waveform numerical modeling for randomly distributed seismic sources to retrieve the reflection response through autocorrelation.

Strong Clustering of Lyman Break Galaxies around Luminous Quasars at Z ˜ 4

NASA Astrophysics Data System (ADS)

García-Vergara, Cristina; Hennawi, Joseph F.; Barrientos, L. Felipe; Rix, Hans-Walter

2017-10-01

In the standard picture of structure formation, the first massive galaxies are expected to form at the highest peaks of the density field, which constitute the cores of massive proto-clusters. Luminous quasars (QSOs) at z ˜ 4 are the most strongly clustered population known, and should thus reside in massive dark matter halos surrounded by large overdensities of galaxies, implying a strong QSO-galaxy cross-correlation function. We observed six z ˜ 4 QSO fields with VLT/FORS, exploiting a novel set of narrow-band filters custom designed to select Lyman Break Galaxies (LBGs) in a thin redshift slice of {{Δ }}z˜ 0.3, mitigating the projection effects that have limited the sensitivity of previous searches for galaxies around z≳ 4 QSOs. We find that LBGs are strongly clustered around QSOs, and present the first measurement of the QSO-LBG cross-correlation function at z ˜ 4, on scales of 0.1≲ R≲ 9 {h}-1 {Mpc} (comoving). Assuming a power-law form for the cross-correlation function ξ ={(r/{r}0{QG})}γ , we measure {r}0{QG}={8.83}-1.51+1.39 {h}-1 {Mpc} for a fixed slope of γ =2.0. This result is in agreement with the expected cross-correlation length deduced from measurements of the QSO and LBG auto-correlation function, and assuming a deterministic bias model. We also measure a strong auto-correlation of LBGs in our QSO fields, finding {r}0{GG}={21.59}-1.69+1.72 {h}-1 {Mpc} for a fixed slope of γ =1.5, which is ˜4 times larger than the LBG auto-correlation length in blank fields, providing further evidence that QSOs reside in overdensities of LBGs. Our results qualitatively support a picture where luminous QSOs inhabit exceptionally massive ({M}{halo}> {10}12 {M}⊙ ) dark matter halos at z ˜ 4.

Role of initial state and final quench temperature on aging properties in phase-ordering kinetics.

PubMed

Corberi, Federico; Villavicencio-Sanchez, Rodrigo

2016-05-01

We study numerically the two-dimensional Ising model with nonconserved dynamics quenched from an initial equilibrium state at the temperature T_{i}≥T_{c} to a final temperature T_{f} below the critical one. By considering processes initiating both from a disordered state at infinite temperature T_{i}=∞ and from the critical configurations at T_{i}=T_{c} and spanning the range of final temperatures T_{f}∈[0,T_{c}[ we elucidate the role played by T_{i} and T_{f} on the aging properties and, in particular, on the behavior of the autocorrelation C and of the integrated response function χ. Our results show that for any choice of T_{f}, while the autocorrelation function exponent λ_{C} takes a markedly different value for T_{i}=∞ [λ_{C}(T_{i}=∞)≃5/4] or T_{i}=T_{c} [λ_{C}(T_{i}=T_{c})≃1/8] the response function exponents are unchanged. Supported by the outcome of the analytical solution of the solvable spherical model we interpret this fact as due to the different contributions provided to autocorrelation and response by the large-scale properties of the system. As changing T_{f} is considered, although this is expected to play no role in the large-scale and long-time properties of the system, we show important effects on the quantitative behavior of χ. In particular, data for quenches to T_{f}=0 are consistent with a value of the response function exponent λ_{χ}=1/2λ_{C}(T_{i}=∞)=5/8 different from the one [λ_{χ}∈(0.5-0.56)] found in a wealth of previous numerical determinations in quenches to finite final temperatures. This is interpreted as due to important preasymptotic corrections associated to T_{f}>0.

Modeling vertebrate diversity in Oregon using satellite imagery

NASA Astrophysics Data System (ADS)

Cablk, Mary Elizabeth

Vertebrate diversity was modeled for the state of Oregon using a parametric approach to regression tree analysis. This exploratory data analysis effectively modeled the non-linear relationships between vertebrate richness and phenology, terrain, and climate. Phenology was derived from time-series NOAA-AVHRR satellite imagery for the year 1992 using two methods: principal component analysis and derivation of EROS data center greenness metrics. These two measures of spatial and temporal vegetation condition incorporated the critical temporal element in this analysis. The first three principal components were shown to contain spatial and temporal information about the landscape and discriminated phenologically distinct regions in Oregon. Principal components 2 and 3, 6 greenness metrics, elevation, slope, aspect, annual precipitation, and annual seasonal temperature difference were investigated as correlates to amphibians, birds, all vertebrates, reptiles, and mammals. Variation explained for each regression tree by taxa were: amphibians (91%), birds (67%), all vertebrates (66%), reptiles (57%), and mammals (55%). Spatial statistics were used to quantify the pattern of each taxa and assess validity of resulting predictions from regression tree models. Regression tree analysis was relatively robust against spatial autocorrelation in the response data and graphical results indicated models were well fit to the data.

Forecasting Temporal Dynamics of Cutaneous Leishmaniasis in Northeast Brazil

PubMed Central

Lewnard, Joseph A.; Jirmanus, Lara; Júnior, Nivison Nery; Machado, Paulo R.; Glesby, Marshall J.; Ko, Albert I.; Carvalho, Edgar M.; Schriefer, Albert; Weinberger, Daniel M.

2014-01-01

Introduction Cutaneous leishmaniasis (CL) is a vector-borne disease of increasing importance in northeastern Brazil. It is known that sandflies, which spread the causative parasites, have weather-dependent population dynamics. Routinely-gathered weather data may be useful for anticipating disease risk and planning interventions. Methodology/Principal Findings We fit time series models using meteorological covariates to predict CL cases in a rural region of Bahía, Brazil from 1994 to 2004. We used the models to forecast CL cases for the period 2005 to 2008. Models accounting for meteorological predictors reduced mean squared error in one, two, and three month-ahead forecasts by up to 16% relative to forecasts from a null model accounting only for temporal autocorrelation. Significance These outcomes suggest CL risk in northeastern Brazil might be partially dependent on weather. Responses to forecasted CL epidemics may include bolstering clinical capacity and disease surveillance in at-risk areas. Ecological mechanisms by which weather influences CL risk merit future research attention as public health intervention targets. PMID:25356734

Forecasting temporal dynamics of cutaneous leishmaniasis in Northeast Brazil.

PubMed

Lewnard, Joseph A; Jirmanus, Lara; Júnior, Nivison Nery; Machado, Paulo R; Glesby, Marshall J; Ko, Albert I; Carvalho, Edgar M; Schriefer, Albert; Weinberger, Daniel M

2014-10-01

Cutaneous leishmaniasis (CL) is a vector-borne disease of increasing importance in northeastern Brazil. It is known that sandflies, which spread the causative parasites, have weather-dependent population dynamics. Routinely-gathered weather data may be useful for anticipating disease risk and planning interventions. We fit time series models using meteorological covariates to predict CL cases in a rural region of Bahía, Brazil from 1994 to 2004. We used the models to forecast CL cases for the period 2005 to 2008. Models accounting for meteorological predictors reduced mean squared error in one, two, and three month-ahead forecasts by up to 16% relative to forecasts from a null model accounting only for temporal autocorrelation. These outcomes suggest CL risk in northeastern Brazil might be partially dependent on weather. Responses to forecasted CL epidemics may include bolstering clinical capacity and disease surveillance in at-risk areas. Ecological mechanisms by which weather influences CL risk merit future research attention as public health intervention targets.

Temporal and Cross Correlations in Business News

NASA Astrophysics Data System (ADS)

Mizuno, T.; Takei, K.; Ohnishi, T.; Watanabe, T.

We empirically investigate temporal and cross correlations inthe frequency of news reports on companies, using a dataset of more than 100 million news articles reported in English by around 500 press agencies worldwide for the period 2003--2009. Our first finding is that the frequency of news reports on a company does not follow a Poisson process, but instead exhibits long memory with a positive autocorrelation for longer than one year. The second finding is that there exist significant correlations in the frequency of news across companies. Specifically, on a daily time scale or longer the frequency of news is governed by external dynamics, while on a time scale of minutes it is governed by internal dynamics. These two findings indicate that the frequency of news reports on companies has statistical properties similar to trading volume or price volatility in stock markets, suggesting that the flow of information through company news plays an important role in price dynamics in stock markets.

Spatial autocorrelation in growth of undisturbed natural pine stands across Georgia

Treesearch

Raymond L. Czaplewski; Robin M. Reich; William A. Bechtold

1994-01-01

Moran's I statistic measures the spatial autocorrelation in a random variable measured at discrete locations in space. Permutation procedures test the null hypothesis that the observed Moran's I value is no greater than that expected by chance. The spatial autocorrelation of gross basal area increment is analyzed for undisturbed, naturally regenerated stands...

Propagation of mechanical waves through a stochastic medium with spherical symmetry

NASA Astrophysics Data System (ADS)

Avendaño, Carlos G.; Reyes, J. Adrián

2018-01-01

We theoretically analyze the propagation of outgoing mechanical waves through an infinite isotropic elastic medium possessing spherical symmetry whose Lamé coefficients and density are spatial random functions characterized by well-defined statistical parameters. We derive the differential equation that governs the average displacement for a system whose properties depend on the radial coordinate. We show that such an equation is an extended version of the well-known Bessel differential equation whose perturbative additional terms contain coefficients that depend directly on the squared noise intensities and the autocorrelation lengths in an exponential decay fashion. We numerically solve the second order differential equation for several values of noise intensities and autocorrelation lengths and compare the corresponding displacement profiles with that of the exact analytic solution for the case of absent inhomogeneities.

Constant-Envelope Waveform Design for Optimal Target-Detection and Autocorrelation Performances

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sen, Satyabrata

2013-01-01

We propose an algorithm to directly synthesize in time-domain a constant-envelope transmit waveform that achieves the optimal performance in detecting an extended target in the presence of signal-dependent interference. This approach is in contrast to the traditional indirect methods that synthesize the transmit signal following the computation of the optimal energy spectral density. Additionally, we aim to maintain a good autocorrelation property of the designed signal. Therefore, our waveform design technique solves a bi-objective optimization problem in order to simultaneously improve the detection and autocorrelation performances, which are in general conflicting in nature. We demonstrate this compromising characteristics of themore » detection and autocorrelation performances with numerical examples. Furthermore, in the absence of the autocorrelation criterion, our designed signal is shown to achieve a near-optimum detection performance.« less

A multi-level analysis of the relationship between environmental factors and questing Ixodes ricinus dynamics in Belgium

PubMed Central

2012-01-01

Background Ticks are the most important pathogen vectors in Europe. They are known to be influenced by environmental factors, but these links are usually studied at specific temporal or spatial scales. Focusing on Ixodes ricinus in Belgium, we attempt to bridge the gap between current “single-sided” studies that focus on temporal or spatial variation only. Here, spatial and temporal patterns of ticks are modelled together. Methods A multi-level analysis of the Ixodes ricinus patterns in Belgium was performed. Joint effects of weather, habitat quality and hunting on field sampled tick abundance were examined at two levels, namely, sampling level, which is associated with temporal dynamics, and site level, which is related to spatial dynamics. Independent variables were collected from standard weather station records, game management data and remote sensing-based land cover data. Results At sampling level, only a marginally significant effect of daily relative humidity and temperature on the abundance of questing nymphs was identified. Average wind speed of seven days prior to the sampling day was found important to both questing nymphs and adults. At site level, a group of landscape-level forest fragmentation indices were highlighted for both questing nymph and adult abundance, including the nearest-neighbour distance, the shape and the aggregation level of forest patches. No cross-level effects or spatial autocorrelation were found. Conclusions Nymphal and adult ticks responded differently to environmental variables at different spatial and temporal scales. Our results can advise spatio-temporal extents of environment data collection for continuing empirical investigations and potential parameters for biological tick models. PMID:22830528

Local Spatial and Temporal Processes of Influenza in Pennsylvania, USA: 2003–2009

PubMed Central

Stark, James H.; Sharma, Ravi; Ostroff, Stephen; Cummings, Derek A. T.; Ermentrout, Bard; Stebbins, Samuel; Burke, Donald S.; Wisniewski, Stephen R.

2012-01-01

Background Influenza is a contagious respiratory disease responsible for annual seasonal epidemics in temperate climates. An understanding of how influenza spreads geographically and temporally within regions could result in improved public health prevention programs. The purpose of this study was to summarize the spatial and temporal spread of influenza using data obtained from the Pennsylvania Department of Health's influenza surveillance system. Methodology and Findings We evaluated the spatial and temporal patterns of laboratory-confirmed influenza cases in Pennsylvania, United States from six influenza seasons (2003–2009). Using a test of spatial autocorrelation, local clusters of elevated risk were identified in the South Central region of the state. Multivariable logistic regression indicated that lower monthly precipitation levels during the influenza season (OR = 0.52, 95% CI: 0.28, 0.94), fewer residents over age 64 (OR = 0.27, 95% CI: 0.10, 0.73) and fewer residents with more than a high school education (OR = 0.76, 95% CI: 0.61, 0.95) were significantly associated with membership in this cluster. In addition, time series analysis revealed a temporal lag in the peak timing of the influenza B epidemic compared to the influenza A epidemic. Conclusions These findings illustrate a distinct spatial cluster of cases in the South Central region of Pennsylvania. Further examination of the regional transmission dynamics within these clusters may be useful in planning public health influenza prevention programs. PMID:22470544

Integrating Eddy Covariance, Penman-Monteith and METRIC based Evapotranspiration estimates to generate high resolution space-time ET over the Brazos River Basin

NASA Astrophysics Data System (ADS)

Mbabazi, D.; Mohanty, B.; Gaur, N.

2017-12-01

Evapotranspiration (ET) is an important component of the water and energy balance and accounts for 60 -70% of precipitation losses. However, accurate estimates of ET are difficult to quantify at varying spatial and temporal scales. Eddy covariance methods estimate ET at high temporal resolutions but without capturing the spatial variation in ET within its footprint. On the other hand, remote sensing methods using Landsat imagery provide ET with high spatial resolution but low temporal resolution (16 days). In this study, we used both eddy covariance and remote sensing methods to generate high space-time resolution ET. Daily, monthly and seasonal ET estimates were obtained using the eddy covariance (EC) method, Penman-Monteith (PM) and Mapping Evapotranspiration with Internalized Calibration (METRIC) models to determine cotton and native prairie ET dynamics in the Brazos river basin characterized by varying hydro-climatic and geological gradients. Daily estimates of spatially distributed ET (30 m resolution) were generated using spatial autocorrelation and temporal interpolations between the EC flux variable footprints and METRIC ET for the 2016 and 2017 growing seasons. A comparison of the 2016 and 2017 preliminary daily ET estimates showed similar ET dynamics/trends among the EC, PM and METRIC methods, and 5-20% differences in seasonal ET estimates. This study will improve the spatial estimates of EC ET and temporal resolution of satellite derived ET thus providing better ET data for water use management.

Correlation functions for Hermitian many-body systems: Necessary conditions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Brown, E.B.

1994-02-01

Lee [Phys. Rev. B 47, 8293 (1993)] has shown that the odd-numbered derivatives of the Kubo autocorrelation function vanish at [ital t]=0. We show that this condition is based on a more general property of nondiagonal Kubo correlation functions. This general property provides that certain functional forms (e.g., simple exponential decay) are not admissible for any symmetric or antisymmetric Kubo correlation function in a Hermitian many-body system. Lee's result emerges as a special case of this result. Applications to translationally invariant systems and systems with rotational symmetries are also demonstrated.

The Effects of Autocorrelation on the Curve-of-Factors Growth Model

ERIC Educational Resources Information Center

Murphy, Daniel L.; Beretvas, S. Natasha; Pituch, Keenan A.

2011-01-01

This simulation study examined the performance of the curve-of-factors model (COFM) when autocorrelation and growth processes were present in the first-level factor structure. In addition to the standard curve-of factors growth model, 2 new models were examined: one COFM that included a first-order autoregressive autocorrelation parameter, and a…

Carbonaceous aerosol at two rural locations in New York State: Characterization and behavior

NASA Astrophysics Data System (ADS)

Sunder Raman, Ramya; Hopke, Philip K.; Holsen, Thomas M.

2008-06-01

Fine particle samples were collected to determine the chemical constituents in PM2.5 at two rural background sites (Potsdam and Stockton, N. Y.) in the northeastern United States from November 2002 to August 2005. Samples were collected every third day for 24 h with a speciation network sampler. The measured carbonaceous species included thermal-optical organic carbon (OC), elemental carbon (EC), pyrolytic carbon (OP), black carbon (BC), and water-soluble, short-chain (WSSC) organic acids. Concentration time series, autocorrelations, and seasonal variations of the carbonaceous species were examined. During this multiyear period, the contributions of the total carbon (OC + EC) to the measured fine particle mass were 31.2% and 31.1% at Potsdam and Stockton, respectively. The average sum of the WSSC acids carbon accounted for approximately 2.5% of the organic carbon at Potsdam and 3.0% at Stockton. At Potsdam, the seasonal differences in the autocorrelation function (ACF) and partial autocorrelation function (PACF) values for carbonaceous species suggest that secondary formation may be an important contributor to the observed concentrations of species likely to be secondary in origin, particularly during the photochemically active time of the year (May to October). This study also investigated the relationships between carbonaceous species to better understand the behavior of carbonaceous aerosol and to assess the contribution of secondary organic carbon (SOC) to the total organic carbon mass (the EC tracer method was used to estimate SOC). At Potsdam the average SOC contribution to total OC varied between 66% and 72%, while at Stockton it varied between 58% and 64%.

Analysis of stochastic characteristics of the Benue River flow process

NASA Astrophysics Data System (ADS)

Otache, Martins Y.; Bakir, Mohammad; Li, Zhijia

2008-05-01

Stochastic characteristics of the Benue River streamflow process are examined under conditions of data austerity. The streamflow process is investigated for trend, non-stationarity and seasonality for a time period of 26 years. Results of trend analyses with Mann-Kendall test show that there is no trend in the annual mean discharges. Monthly flow series examined with seasonal Kendall test indicate the presence of positive change in the trend for some months, especially the months of August, January, and February. For the stationarity test, daily and monthly flow series appear to be stationary whereas at 1%, 5%, and 10% significant levels, the stationarity alternative hypothesis is rejected for the annual flow series. Though monthly flow appears to be stationary going by this test, because of high seasonality, it could be said to exhibit periodic stationarity based on the seasonality analysis. The following conclusions are drawn: (1) There is seasonality in both the mean and variance with unimodal distribution. (2) Days with high mean also have high variance. (3) Skewness coefficients for the months within the dry season period are greater than those of the wet season period, and seasonal autocorrelations for streamflow during dry season are generally larger than those of the wet season. Precisely, they are significantly different for most of the months. (4) The autocorrelation functions estimated “over time” are greater in the absolute value for data that have not been deseasonalised but were initially normalised by logarithmic transformation only, while autocorrelation functions for i = 1, 2, ..., 365 estimated “over realisations” have their coefficients significantly different from other coefficients.

Narayanaswamy's 1971 aging theory and material time

NASA Astrophysics Data System (ADS)

Dyre, Jeppe C.

2015-09-01

The Bochkov-Kuzovlev nonlinear fluctuation-dissipation theorem is used to derive Narayanaswamy's phenomenological theory of physical aging, in which this highly nonlinear phenomenon is described by a linear material-time convolution integral. A characteristic property of the Narayanaswamy aging description is material-time translational invariance, which is here taken as the basic assumption of the derivation. It is shown that only one possible definition of the material time obeys this invariance, namely, the square of the distance travelled from a configuration of the system far back in time. The paper concludes with suggestions for computer simulations that test for consequences of material-time translational invariance. One of these is the "unique-triangles property" according to which any three points on the system's path form a triangle such that two side lengths determine the third; this is equivalent to the well-known triangular relation for time-autocorrelation functions of aging spin glasses [L. F. Cugliandolo and J. Kurchan, J. Phys. A: Math. Gen. 27, 5749 (1994)]. The unique-triangles property implies a simple geometric interpretation of out-of-equilibrium time-autocorrelation functions, which extends to aging a previously proposed framework for such functions in equilibrium [J. C. Dyre, e-print arXiv:cond-mat/9712222 (1997)].

Factors Influencing Army Accessions.

DTIC Science & Technology

1982-12-01

partial autocorrelations were examined for significant lags or a recognizable pattern such as a damped exponential or a sine wave. The TSP prugrams...decreasing function indicating nonstation- *arity or a very long sine wave where only a small portion of the wave is plotted. The partial...plot of the raw data appeared (Appendix E-1) to be either the middle of a long sine wave or a linearly decreasing function. This pattern is recognized

An empirical comparison of SPM preprocessing parameters to the analysis of fMRI data.

PubMed

Della-Maggiore, Valeria; Chau, Wilkin; Peres-Neto, Pedro R; McIntosh, Anthony R

2002-09-01

We present the results from two sets of Monte Carlo simulations aimed at evaluating the robustness of some preprocessing parameters of SPM99 for the analysis of functional magnetic resonance imaging (fMRI). Statistical robustness was estimated by implementing parametric and nonparametric simulation approaches based on the images obtained from an event-related fMRI experiment. Simulated datasets were tested for combinations of the following parameters: basis function, global scaling, low-pass filter, high-pass filter and autoregressive modeling of serial autocorrelation. Based on single-subject SPM analysis, we derived the following conclusions that may serve as a guide for initial analysis of fMRI data using SPM99: (1) The canonical hemodynamic response function is a more reliable basis function to model the fMRI time series than HRF with time derivative. (2) Global scaling should be avoided since it may significantly decrease the power depending on the experimental design. (3) The use of a high-pass filter may be beneficial for event-related designs with fixed interstimulus intervals. (4) When dealing with fMRI time series with short interstimulus intervals (<8 s), the use of first-order autoregressive model is recommended over a low-pass filter (HRF) because it reduces the risk of inferential bias while providing a relatively good power. For datasets with interstimulus intervals longer than 8 seconds, temporal smoothing is not recommended since it decreases power. While the generalizability of our results may be limited, the methods we employed can be easily implemented by other scientists to determine the best parameter combination to analyze their data.

Precipitation data in a mountainous catchment in Honduras: quality assessment and spatiotemporal characteristics

NASA Astrophysics Data System (ADS)

Westerberg, I.; Walther, A.; Guerrero, J.-L.; Coello, Z.; Halldin, S.; Xu, C.-Y.; Chen, D.; Lundin, L.-C.

2010-08-01

An accurate description of temporal and spatial precipitation variability in Central America is important for local farming, water supply and flood management. Data quality problems and lack of consistent precipitation data impede hydrometeorological analysis in the 7,500 km2 Choluteca River basin in central Honduras, encompassing the capital Tegucigalpa. We used precipitation data from 60 daily and 13 monthly stations in 1913-2006 from five local authorities and NOAA's Global Historical Climatology Network. Quality control routines were developed to tackle the specific data quality problems. The quality-controlled data were characterised spatially and temporally, and compared with regional and larger-scale studies. Two gap-filling methods for daily data and three interpolation methods for monthly and mean annual precipitation were compared. The coefficient-of-correlation-weighting method provided the best results for gap-filling and the universal kriging method for spatial interpolation. In-homogeneity in the time series was the main quality problem, and 22% of the daily precipitation data were too poor to be used. Spatial autocorrelation for monthly precipitation was low during the dry season, and correlation increased markedly when data were temporally aggregated from a daily time scale to 4-5 days. The analysis manifested the high spatial and temporal variability caused by the diverse precipitation-generating mechanisms and the need for an improved monitoring network.

A Hybrid Model for Spatially and Temporally Resolved Ozone Exposures in the Continental United States

PubMed Central

Di, Qian; Rowland, Sebastian; Koutrakis, Petros; Schwartz, Joel

2017-01-01

Ground-level ozone is an important atmospheric oxidant, which exhibits considerable spatial and temporal variability in its concentration level. Existing modeling approaches for ground-level ozone include chemical transport models, land-use regression, Kriging, and data fusion of chemical transport models with monitoring data. Each of these methods has both strengths and weaknesses. Combining those complementary approaches could improve model performance. Meanwhile, satellite-based total column ozone, combined with ozone vertical profile, is another potential input. We propose a hybrid model that integrates the above variables to achieve spatially and temporally resolved exposure assessments for ground-level ozone. We used a neural network for its capacity to model interactions and nonlinearity. Convolutional layers, which use convolution kernels to aggregate nearby information, were added to the neural network to account for spatial and temporal autocorrelation. We trained the model with AQS 8-hour daily maximum ozone in the continental United States from 2000 to 2012 and tested it with left out monitoring sites. Cross-validated R2 on the left out monitoring sites ranged from 0.74 to 0.80 (mean 0.76) for predictions on 1 km×1 km grid cells, which indicates good model performance. Model performance remains good even at low ozone concentrations. The prediction results facilitate epidemiological studies to assess the health effect of ozone in the long term and the short term. PMID:27332675

Fine-scale genetic structure and cryptic associations reveal evidence of kin-based sociality in the African forest elephant.

PubMed

Schuttler, Stephanie G; Philbrick, Jessica A; Jeffery, Kathryn J; Eggert, Lori S

2014-01-01

Spatial patterns of relatedness within animal populations are important in the evolution of mating and social systems, and have the potential to reveal information on species that are difficult to observe in the wild. This study examines the fine-scale genetic structure and connectivity of groups within African forest elephants, Loxodonta cyclotis, which are often difficult to observe due to forest habitat. We tested the hypothesis that genetic similarity will decline with increasing geographic distance, as we expect kin to be in closer proximity, using spatial autocorrelation analyses and Tau K(r) tests. Associations between individuals were investigated through a non-invasive genetic capture-recapture approach using network models, and were predicted to be more extensive than the small groups found in observational studies, similar to fission-fusion sociality found in African savanna (Loxodonta africana) and Asian (Elephas maximus) species. Dung samples were collected in Lopé National Park, Gabon in 2008 and 2010 and genotyped at 10 microsatellite loci, genetically sexed, and sequenced at the mitochondrial DNA control region. We conducted analyses on samples collected at three different temporal scales: a day, within six-day sampling sessions, and within each year. Spatial autocorrelation and Tau K(r) tests revealed genetic structure, but results were weak and inconsistent between sampling sessions. Positive spatial autocorrelation was found in distance classes of 0-5 km, and was strongest for the single day session. Despite weak genetic structure, individuals within groups were significantly more related to each other than to individuals between groups. Social networks revealed some components to have large, extensive groups of up to 22 individuals, and most groups were composed of individuals of the same matriline. Although fine-scale population genetic structure was weak, forest elephants are typically found in groups consisting of kin and based on matrilines, with some individuals having more associates than observed from group sizes alone.

Level crossing statistics for optical beam wander in a turbulent atmosphere with applications to ground-to-space laser communications.

PubMed

Yura, Harold T; Fields, Renny A

2011-06-20

Level crossing statistics is applied to the complex problem of atmospheric turbulence-induced beam wander for laser propagation from ground to space. A comprehensive estimate of the single-axis wander angle temporal autocorrelation function and the corresponding power spectrum is used to develop, for the first time to our knowledge, analytic expressions for the mean angular level crossing rate and the mean duration of such crossings. These results are based on an extension and generalization of a previous seminal analysis of the beam wander variance by Klyatskin and Kon. In the geometrical optics limit, we obtain an expression for the beam wander variance that is valid for both an arbitrarily shaped initial beam profile and transmitting aperture. It is shown that beam wander can disrupt bidirectional ground-to-space laser communication systems whose small apertures do not require adaptive optics to deliver uniform beams at their intended target receivers in space. The magnitude and rate of beam wander is estimated for turbulence profiles enveloping some practical laser communication deployment options and suggesting what level of beam wander effects must be mitigated to demonstrate effective bidirectional laser communication systems.

Extraction of phase information in daily stock prices

NASA Astrophysics Data System (ADS)

Fujiwara, Yoshi; Maekawa, Satoshi

2000-06-01

It is known that, in an intermediate time-scale such as days, stock market fluctuations possess several statistical properties that are common to different markets. Namely, logarithmic returns of an asset price have (i) truncated Pareto-Lévy distribution, (ii) vanishing linear correlation, (iii) volatility clustering and its power-law autocorrelation. The fact (ii) is a consequence of nonexistence of arbitragers with simple strategies, but this does not mean statistical independence of market fluctuations. Little attention has been paid to temporal structure of higher-order statistics, although it contains some important information on market dynamics. We applied a signal separation technique, called Independent Component Analysis (ICA), to actual data of daily stock prices in Tokyo and New York Stock Exchange (TSE/NYSE). ICA does a linear transformation of lag vectors from time-series to find independent components by a nonlinear algorithm. We obtained a similar impulse response for these dataset. If it were a Martingale process, it can be shown that impulse response should be a delta-function under a few conditions that could be numerically checked and as was verified by surrogate data. This result would provide information on the market dynamics including speculative bubbles and arbitrating processes. .

Temporal Variation of the Rotation of the Solar Mean Magnetic Field

NASA Astrophysics Data System (ADS)

Xie, J. L.; Shi, X. J.; Xu, J. C.

2017-04-01

Based on continuous wavelet transformation analysis, the daily solar mean magnetic field (SMMF) from 1975 May 16 to 2014 July 31 is analyzed to reveal its rotational behavior. Both the recurrent plot in Bartels form and the continuous wavelet transformation analysis show the existence of rotational modulation in the variation of the daily SMMF. The dependence of the rotational cycle lengths on solar cycle phase is also studied, which indicates that the yearly mean rotational cycle lengths generally seem to be longer during the rising phase of solar cycles and shorter during the declining phase. The mean rotational cycle length for the rising phase of all of the solar cycles in the considered time is 28.28 ± 0.67 days, while for the declining phase it is 27.32 ± 0.64 days. The difference of the mean rotational cycle lengths between the rising phase and the declining phase is 0.96 days. The periodicity analysis, through the use of an auto-correlation function, indicates that the rotational cycle lengths have a significant period of about 10.1 years. Furthermore, the cross-correlation analysis indicates that there exists a phase difference between the rotational cycle lengths and solar activity.

Early seizure detection in an animal model of temporal lobe epilepsy

NASA Astrophysics Data System (ADS)

Talathi, Sachin S.; Hwang, Dong-Uk; Ditto, William; Carney, Paul R.

2007-11-01

The performance of five seizure detection schemes, i.e., Nonlinear embedding delay, Hurst scaling, Wavelet Scale, autocorrelation and gradient of accumulated energy, in their ability to detect EEG seizures close to the seizure onset time were evaluated to determine the feasibility of their application in the development of a real time closed loop seizure intervention program (RCLSIP). The criteria chosen for the performance evaluation were, high statistical robustness as determined through the predictability index, the sensitivity and the specificity of a given measure to detect an EEG seizure, the lag in seizure detection with respect to the EEG seizure onset time, as determined through visual inspection and the computational efficiency for each detection measure. An optimality function was designed to evaluate the overall performance of each measure dependent on the criteria chosen. While each of the above measures analyzed for seizure detection performed very well in terms of the statistical parameters, the nonlinear embedding delay measure was found to have the highest optimality index due to its ability to detect seizure very close to the EEG seizure onset time, thereby making it the most suitable dynamical measure in the development of RCLSIP in rat model with chronic limbic epilepsy.

Changes in scalp and cortical blood flow during hyperventilation measured with diffusing-wave spectroscopy

NASA Astrophysics Data System (ADS)

Li, Jun; Ninck, Markus; Gisler, Thomas

2009-07-01

Changes in scalp and cortical blood flow induced by voluntary hyperventilation are investigated by near-infrared diffusing-wave spectroscopy. The temporal intensity autocorrelation function g(2) (τ) of multiply scattered light is recorded from the forehead of subjects during hyperventilation. Blood flow within the sampled tissue volume is estimated by the mean decay rate of g(2) (τ) . Data measured from six subjects show that the pattern of the hemodynamic response during 50 s hyperventilation is rather complicated: within the first 10 s, in three subjects an initial increase in blood flow is observed; from 10 s to 20 s, the mean blood flow is smaller than its baseline value for all six subjects; for the duration from 20 s to 30 s, the blood flow increases again. However, after 30 s the change is not consistent across subjects. Further study on one of these subjects by using two receivers probing the blood flow in the cortex and in the superficial layers simultaneously, reveals that during hyperventilation, the direction of change in blood flow within the scalp is opposite to the one in the brain. This helps to understand the complicated hemodynamic response observed in our measurements.

Correction of motion artifacts and serial correlations for real-time functional near-infrared spectroscopy

PubMed Central

Barker, Jeffrey W.; Rosso, Andrea L.; Sparto, Patrick J.; Huppert, Theodore J.

2016-01-01

Abstract. Functional near-infrared spectroscopy (fNIRS) is a relatively low-cost, portable, noninvasive neuroimaging technique for measuring task-evoked hemodynamic changes in the brain. Because fNIRS can be applied to a wide range of populations, such as children or infants, and under a variety of study conditions, including those involving physical movement, gait, or balance, fNIRS data are often confounded by motion artifacts. Furthermore, the high sampling rate of fNIRS leads to high temporal autocorrelation due to systemic physiology. These two factors can reduce the sensitivity and specificity of detecting hemodynamic changes. In a previous work, we showed that these factors could be mitigated by autoregressive-based prewhitening followed by the application of an iterative reweighted least squares algorithm offline. This current work extends these same ideas to real-time analysis of brain signals by modifying the linear Kalman filter, resulting in an algorithm for online estimation that is robust to systemic physiology and motion artifacts. We evaluated the performance of the proposed method via simulations of evoked hemodynamics that were added to experimental resting-state data, which provided realistic fNIRS noise. Last, we applied the method post hoc to data from a standing balance task. Overall, the new method showed good agreement with the analogous offline algorithm, in which both methods outperformed ordinary least squares methods. PMID:27226974

Parallel auto-correlative statistics with VTK.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pebay, Philippe Pierre; Bennett, Janine Camille

2013-08-01

This report summarizes existing statistical engines in VTK and presents both the serial and parallel auto-correlative statistics engines. It is a sequel to [PT08, BPRT09b, PT09, BPT09, PT10] which studied the parallel descriptive, correlative, multi-correlative, principal component analysis, contingency, k-means, and order statistics engines. The ease of use of the new parallel auto-correlative statistics engine is illustrated by the means of C++ code snippets and algorithm verification is provided. This report justifies the design of the statistics engines with parallel scalability in mind, and provides scalability and speed-up analysis results for the autocorrelative statistics engine.

False alarms: How early warning signals falsely predict abrupt sea ice loss

NASA Astrophysics Data System (ADS)

Wagner, Till J. W.; Eisenman, Ian

2016-04-01

Uncovering universal early warning signals for critical transitions has become a coveted goal in diverse scientific disciplines, ranging from climate science to financial mathematics. There has been a flurry of recent research proposing such signals, with increasing autocorrelation and increasing variance being among the most widely discussed candidates. A number of studies have suggested that increasing autocorrelation alone may suffice to signal an impending transition, although some others have questioned this. Here we consider variance and autocorrelation in the context of sea ice loss in an idealized model of the global climate system. The model features no bifurcation, nor increased rate of retreat, as the ice disappears. Nonetheless, the autocorrelation of summer sea ice area is found to increase in a global warming scenario. The variance, by contrast, decreases. A simple physical mechanism is proposed to explain the occurrence of increasing autocorrelation but not variance when there is no approaching bifurcation. Additionally, a similar mechanism is shown to allow an increase in both indicators with no physically attainable bifurcation. This implies that relying on autocorrelation and variance as early warning signals can raise false alarms in the climate system, warning of "tipping points" that are not actually there.

Geometrical optical transfer function: is it worth calculating?

PubMed

Díaz, José A; Mahajan, Virendra N

2017-10-01

In this paper, we explore the merit of calculating the geometrical optical transfer function (GOTF) in optical design by comparing the time to calculate it with the time to calculate the diffraction optical transfer function (DOTF). We determine the DOTF by numerical integration of the pupil function autocorrelation (that reduces to an integration of a complex exponential of the aberration difference function), 2D digital autocorrelation of the pupil function, and the Fourier transform (FT) of the point-spread function (PSF); and we determine the GOTF by the FT of the geometrical PSF (that reduces to an integration over the pupil plane of a complex exponential that is a scalar product of the spatial frequency and transverse ray aberration vectors) and the FT of the spot diagram. Our starting point for calculating the DOTF is the wave aberrations of the system in its pupil plane, and the transverse ray aberrations in the image plane for the GOTF. Numerical results for primary aberrations and some typical imaging systems show that the direct numerical integrations are slow, but the GOTF calculation by a FT of the spot diagram is two or even three times slower than the DOTF calculation by an FT of the PSF, depending on the aberration. We conclude that the calculation of GOTF is, at best, an approximation of the DOTF and only for large aberrations; GOTF does not offer any advantage in the optical design process, and hence negates its utility.

Mixed memory, (non) Hurst effect, and maximum entropy of rainfall in the tropical Andes

NASA Astrophysics Data System (ADS)

Poveda, Germán

2011-02-01

Diverse linear and nonlinear statistical parameters of rainfall under aggregation in time and the kind of temporal memory are investigated. Data sets from the Andes of Colombia at different resolutions (15 min and 1-h), and record lengths (21 months and 8-40 years) are used. A mixture of two timescales is found in the autocorrelation and autoinformation functions, with short-term memory holding for time lags less than 15-30 min, and long-term memory onwards. Consistently, rainfall variance exhibits different temporal scaling regimes separated at 15-30 min and 24 h. Tests for the Hurst effect evidence the frailty of the R/ S approach in discerning the kind of memory in high resolution rainfall, whereas rigorous statistical tests for short-memory processes do reject the existence of the Hurst effect. Rainfall information entropy grows as a power law of aggregation time, S( T) ˜ Tβ with < β> = 0.51, up to a timescale, TMaxEnt (70-202 h), at which entropy saturates, with β = 0 onwards. Maximum entropy is reached through a dynamic Generalized Pareto distribution, consistently with the maximum information-entropy principle for heavy-tailed random variables, and with its asymptotically infinitely divisible property. The dynamics towards the limit distribution is quantified. Tsallis q-entropies also exhibit power laws with T, such that Sq( T) ˜ Tβ( q) , with β( q) ⩽ 0 for q ⩽ 0, and β( q) ≃ 0.5 for q ⩾ 1. No clear patterns are found in the geographic distribution within and among the statistical parameters studied, confirming the strong variability of tropical Andean rainfall.

A globally calibrated scheme for generating daily meteorology from monthly statistics: Global-WGEN (GWGEN) v1.0

NASA Astrophysics Data System (ADS)

Sommer, Philipp S.; Kaplan, Jed O.

2017-10-01

While a wide range of Earth system processes occur at daily and even subdaily timescales, many global vegetation and other terrestrial dynamics models historically used monthly meteorological forcing both to reduce computational demand and because global datasets were lacking. Recently, dynamic land surface modeling has moved towards resolving daily and subdaily processes, and global datasets containing daily and subdaily meteorology have become available. These meteorological datasets, however, cover only the instrumental era of the last approximately 120 years at best, are subject to considerable uncertainty, and represent extremely large data files with associated computational costs of data input/output and file transfer. For periods before the recent past or in the future, global meteorological forcing can be provided by climate model output, but the quality of these data at high temporal resolution is low, particularly for daily precipitation frequency and amount. Here, we present GWGEN, a globally applicable statistical weather generator for the temporal downscaling of monthly climatology to daily meteorology. Our weather generator is parameterized using a global meteorological database and simulates daily values of five common variables: minimum and maximum temperature, precipitation, cloud cover, and wind speed. GWGEN is lightweight, modular, and requires a minimal set of monthly mean variables as input. The weather generator may be used in a range of applications, for example, in global vegetation, crop, soil erosion, or hydrological models. While GWGEN does not currently perform spatially autocorrelated multi-point downscaling of daily weather, this additional functionality could be implemented in future versions.

Space-time description of dengue outbreaks in Cruzeiro, São Paulo, in 2006 and 2011.

PubMed

Carvalho, Renata Marzzano de; Nascimento, Luiz Fernando Costa

2014-01-01

to identify patterns in the spatial and temporal distribution of cases of dengue fever occurring in the city of Cruzeiro, state of São Paulo (SP). an ecological and exploratory study was undertaken using spatial analysis tools and data from dengue cases obtained on the SinanNet. The analysis was carried out by area, using the IBGE census sector as a unit. The months of March to June 2006 and 2011 were assessed, revealing progress of the disease. TerraView 3.3.1 was used to calculate the Global Moran's I, month to month, and the Kernel estimator. in the year 2006, 691 cases of dengue fever (rate of 864.2 cases/100,000 inhabitants) were georeferenced; and the Moran's I and p-values were significant in the months of April and May (IM = 0.28; p = 0.01; IM = 0.20; p = 0.01) with higher densities in the central, north, northeast and south regions. In the year 2011, 654 cases of dengue fever (rate of 886.8 cases/100,000 inhabitants) were georeferenced; and the Moran's I and p-values were significant in the months of April and May (IM = 0.28; p = 0.01; IM = 0.16; p = 0.05) with densities in the same regions as 2006. The Global Moran's I is a global measure of spatial autocorrelation, which indicates the degree of spatial association in the set of information from the product in relation to the average. The I varies between -1 and +1 and can be attributed to a level of significance (p-value). The positive value points to a positive or direct spatial autocorrelation. we were able to identify patterns in the spatial and temporal distribution of dengue cases occurring in the city of Cruzeiro, SP, and locate the census sectors where the outbreak began and how it evolved.

Statistical, time series, and fractal analysis of full stretch of river Yamuna (India) for water quality management.

PubMed

Parmar, Kulwinder Singh; Bhardwaj, Rashmi

2015-01-01

River water is a major resource of drinking water on earth. Management of river water is highly needed for surviving. Yamuna is the main river of India, and monthly variation of water quality of river Yamuna, using statistical methods have been compared at different sites for each water parameters. Regression, correlation coefficient, autoregressive integrated moving average (ARIMA), box-Jenkins, residual autocorrelation function (ACF), residual partial autocorrelation function (PACF), lag, fractal, Hurst exponent, and predictability index have been estimated to analyze trend and prediction of water quality. Predictive model is useful at 95% confidence limits and all water parameters reveal platykurtic curve. Brownian motion (true random walk) behavior exists at different sites for BOD, AMM, and total Kjeldahl nitrogen (TKN). Quality of Yamuna River water at Hathnikund is good, declines at Nizamuddin, Mazawali, Agra D/S, and regains good quality again at Juhikha. For all sites, almost all parameters except potential of hydrogen (pH), water temperature (WT) crosses the prescribed limits of World Health Organization (WHO)/United States Environmental Protection Agency (EPA).

DYNAMICS OF SELF-GRAVITY WAKES IN DENSE PLANETARY RINGS. I. PITCH ANGLE

DOE Office of Scientific and Technical Information (OSTI.GOV)

Michikoshi, Shugo; Kokubo, Eiichiro; Fujii, Akihiko

2015-10-20

We investigate the dynamics of self-gravity wakes in dense planetary rings. In particular, we examine how the pitch angles of self-gravity wakes depend on ring parameters using N-body simulations. We calculate the pitch angles using the two-dimensional autocorrelation function of the ring surface density. We obtain the pitch angles for the inner and outer parts of the autocorrelation function separately. We confirm that the pitch angles are 15°–30° for reasonable ring parameters, which are consistent with previous studies. We find that the inner pitch angle increases with the Saturnicentric distance, while it barely depends on the optical depth and themore » restitution coefficient of ring particles. The increase of the inner pitch angle with the Saturnicentric distance is consistent with the observations of the A ring. The outer pitch angle does not have a clear dependence on any ring parameters and is about 10°–15°. This value is consistent with the pitch angle of spiral arms in collisionless systems.« less

Statistical Approach To Extraction Of Texture In SAR

NASA Technical Reports Server (NTRS)

Rignot, Eric J.; Kwok, Ronald

1992-01-01

Improved statistical method of extraction of textural features in synthetic-aperture-radar (SAR) images takes account of effects of scheme used to sample raw SAR data, system noise, resolution of radar equipment, and speckle. Treatment of speckle incorporated into overall statistical treatment of speckle, system noise, and natural variations in texture. One computes speckle auto-correlation function from system transfer function that expresses effect of radar aperature and incorporates range and azimuth resolutions.

Magnetohydrodynamic stability of stochastically driven accretion flows.

PubMed

Nath, Sujit Kumar; Mukhopadhyay, Banibrata; Chattopadhyay, Amit K

2013-07-01

We investigate the evolution of magnetohydrodynamic (or hydromagnetic as coined by Chandrasekhar) perturbations in the presence of stochastic noise in rotating shear flows. The particular emphasis is the flows whose angular velocity decreases but specific angular momentum increases with increasing radial coordinate. Such flows, however, are Rayleigh stable but must be turbulent in order to explain astrophysical observed data and, hence, reveal a mismatch between the linear theory and observations and experiments. The mismatch seems to have been resolved, at least in certain regimes, in the presence of a weak magnetic field, revealing magnetorotational instability. The present work explores the effects of stochastic noise on such magnetohydrodynamic flows, in order to resolve the above mismatch generically for the hot flows. We essentially concentrate on a small section of such a flow which is nothing but a plane shear flow supplemented by the Coriolis effect, mimicking a small section of an astrophysical accretion disk around a compact object. It is found that such stochastically driven flows exhibit large temporal and spatial autocorrelations and cross-correlations of perturbation and, hence, large energy dissipations of perturbation, which generate instability. Interestingly, autocorrelations and cross-correlations appear independent of background angular velocity profiles, which are Rayleigh stable, indicating their universality. This work initiates our attempt to understand the evolution of three-dimensional hydromagnetic perturbations in rotating shear flows in the presence of stochastic noise.

Spatiotemporal patterns of severe fever with thrombocytopenia syndrome in China, 2011-2016.

PubMed

Sun, Jimin; Lu, Liang; Wu, Haixia; Yang, Jun; Liu, Keke; Liu, Qiyong

2018-05-01

Severe fever with thrombocytopenia syndrome (SFTS) is emerging and the number of SFTS cases have increased year by year in China. However, spatiotemporal patterns and trends of SFTS are less clear up to date. In order to explore spatiotemporal patterns and predict SFTS incidences, we analyzed temporal trends of SFTS using autoregressive integrated moving average (ARIMA) model, spatial patterns, and spatiotemporal clusters of SFTS cases at the county level based on SFTS data in China during 2011-2016. We determined the optimal time series model was ARIMA (2, 0, 1) × (0, 0, 1) 12 which fitted the SFTS cases reasonably well during the training process and forecast process. In the spatial clustering analysis, the global autocorrelation suggested that SFTS cases were not of random distribution. Local spatial autocorrelation analysis of SFTS identified foci mainly concentrated in Hubei Province, Henan Province, Anhui Province, Shandong Province, Liaoning Province, and Zhejiang Province. A most likely cluster including 21 counties in Henan Province and Hubei Province was observed in the central region of China from April 2015 to August 2016. Our results will provide a sound evidence base for future prevention and control programs of SFTS such as allocation of the health resources, surveillance in high-risk regions, health education, improvement of diagnosis and so on. Copyright © 2018 Elsevier GmbH. All rights reserved.

Instant Grainification: Real-Time Grain-Size Analysis from Digital Images in the Field

NASA Astrophysics Data System (ADS)

Rubin, D. M.; Chezar, H.

2007-12-01

Over the past few years, digital cameras and underwater microscopes have been developed to collect in-situ images of sand-sized bed sediment, and software has been developed to measure grain size from those digital images (Chezar and Rubin, 2004; Rubin, 2004; Rubin et al., 2006). Until now, all image processing and grain- size analysis was done back in the office where images were uploaded from cameras and processed on desktop computers. Computer hardware has become small and rugged enough to process images in the field, which for the first time allows real-time grain-size analysis of sand-sized bed sediment. We present such a system consisting of weatherproof tablet computer, open source image-processing software (autocorrelation code of Rubin, 2004, running under Octave and Cygwin), and digital camera with macro lens. Chezar, H., and Rubin, D., 2004, Underwater microscope system: U.S. Patent and Trademark Office, patent number 6,680,795, January 20, 2004. Rubin, D.M., 2004, A simple autocorrelation algorithm for determining grain size from digital images of sediment: Journal of Sedimentary Research, v. 74, p. 160-165. Rubin, D.M., Chezar, H., Harney, J.N., Topping, D.J., Melis, T.S., and Sherwood, C.R., 2006, Underwater microscope for measuring spatial and temporal changes in bed-sediment grain size: USGS Open-File Report 2006-1360.

Rolling element bearing fault diagnosis based on Over-Complete rational dilation wavelet transform and auto-correlation of analytic energy operator

NASA Astrophysics Data System (ADS)

Singh, Jaskaran; Darpe, A. K.; Singh, S. P.

2018-02-01

Local damage in rolling element bearings usually generates periodic impulses in vibration signals. The severity, repetition frequency and the fault excited resonance zone by these impulses are the key indicators for diagnosing bearing faults. In this paper, a methodology based on over complete rational dilation wavelet transform (ORDWT) is proposed, as it enjoys a good shift invariance. ORDWT offers flexibility in partitioning the frequency spectrum to generate a number of subbands (filters) with diverse bandwidths. The selection of the optimal filter that perfectly overlaps with the bearing fault excited resonance zone is based on the maximization of a proposed impulse detection measure "Temporal energy operated auto correlated kurtosis". The proposed indicator is robust and consistent in evaluating the impulsiveness of fault signals in presence of interfering vibration such as heavy background noise or sporadic shocks unrelated to the fault or normal operation. The structure of the proposed indicator enables it to be sensitive to fault severity. For enhanced fault classification, an autocorrelation of the energy time series of the signal filtered through the optimal subband is proposed. The application of the proposed methodology is validated on simulated and experimental data. The study shows that the performance of the proposed technique is more robust and consistent in comparison to the original fast kurtogram and wavelet kurtogram.

An investigation on thermal patterns in Iran based on spatial autocorrelation

NASA Astrophysics Data System (ADS)

Fallah Ghalhari, Gholamabbas; Dadashi Roudbari, Abbasali

2018-02-01

The present study aimed at investigating temporal-spatial patterns and monthly patterns of temperature in Iran using new spatial statistical methods such as cluster and outlier analysis, and hotspot analysis. To do so, climatic parameters, monthly average temperature of 122 synoptic stations, were assessed. Statistical analysis showed that January with 120.75% had the most fluctuation among the studied months. Global Moran's Index revealed that yearly changes of temperature in Iran followed a strong spatially clustered pattern. Findings showed that the biggest thermal cluster pattern in Iran, 0.975388, occurred in May. Cluster and outlier analyses showed that thermal homogeneity in Iran decreases in cold months, while it increases in warm months. This is due to the radiation angle and synoptic systems which strongly influence thermal order in Iran. The elevations, however, have the most notable part proved by Geographically weighted regression model. Iran's thermal analysis through hotspot showed that hot thermal patterns (very hot, hot, and semi-hot) were dominant in the South, covering an area of 33.5% (about 552,145.3 km2). Regions such as mountain foot and low lands lack any significant spatial autocorrelation, 25.2% covering about 415,345.1 km2. The last is the cold thermal area (very cold, cold, and semi-cold) with about 25.2% covering about 552,145.3 km2 of the whole area of Iran.

Spatio-temporal prediction of daily temperatures using time-series of MODIS LST images

NASA Astrophysics Data System (ADS)

Hengl, Tomislav; Heuvelink, Gerard B. M.; Perčec Tadić, Melita; Pebesma, Edzer J.

2012-01-01

A computational framework to generate daily temperature maps using time-series of publicly available MODIS MOD11A2 product Land Surface Temperature (LST) images (1 km resolution; 8-day composites) is illustrated using temperature measurements from the national network of meteorological stations (159) in Croatia. The input data set contains 57,282 ground measurements of daily temperature for the year 2008. Temperature was modeled as a function of latitude, longitude, distance from the sea, elevation, time, insolation, and the MODIS LST images. The original rasters were first converted to principal components to reduce noise and filter missing pixels in the LST images. The residual were next analyzed for spatio-temporal auto-correlation; sum-metric separable variograms were fitted to account for zonal and geometric space-time anisotropy. The final predictions were generated for time-slices of a 3D space-time cube, constructed in the R environment for statistical computing. The results show that the space-time regression model can explain a significant part of the variation in station-data (84%). MODIS LST 8-day (cloud-free) images are unbiased estimator of the daily temperature, but with relatively low precision (±4.1°C); however their added value is that they systematically improve detection of local changes in land surface temperature due to local meteorological conditions and/or active heat sources (urban areas, land cover classes). The results of 10-fold cross-validation show that use of spatio-temporal regression-kriging and incorporation of time-series of remote sensing images leads to significantly more accurate maps of temperature than if plain spatial techniques were used. The average (global) accuracy of mapping temperature was ±2.4°C. The regression-kriging explained 91% of variability in daily temperatures, compared to 44% for ordinary kriging. Further software advancement—interactive space-time variogram exploration and automated retrieval, resampling and filtering of MODIS images—are anticipated.

Violence: heightened brain attentional network response is selectively muted in Down syndrome.

PubMed

Anderson, Jeffrey S; Treiman, Scott M; Ferguson, Michael A; Nielsen, Jared A; Edgin, Jamie O; Dai, Li; Gerig, Guido; Korenberg, Julie R

2015-01-01

The ability to recognize and respond appropriately to threat is critical to survival, and the neural substrates subserving attention to threat may be probed using depictions of media violence. Whether neural responses to potential threat differ in Down syndrome is not known. We performed functional MRI scans of 15 adolescent and adult Down syndrome and 14 typically developing individuals, group matched by age and gender, during 50 min of passive cartoon viewing. Brain activation to auditory and visual features, violence, and presence of the protagonist and antagonist were compared across cartoon segments. fMRI signal from the brain's dorsal attention network was compared to thematic and violent events within the cartoons between Down syndrome and control samples. We found that in typical development, the brain's dorsal attention network was most active during violent scenes in the cartoons and that this was significantly and specifically reduced in Down syndrome. When the antagonist was on screen, there was significantly less activation in the left medial temporal lobe of individuals with Down syndrome. As scenes represented greater relative threat, the disparity between attentional brain activation in Down syndrome and control individuals increased. There was a reduction in the temporal autocorrelation of the dorsal attention network, consistent with a shortened attention span in Down syndrome. Individuals with Down syndrome exhibited significantly reduced activation in primary sensory cortices, and such perceptual impairments may constrain their ability to respond to more complex social cues such as violence. These findings may indicate a relative deficit in emotive perception of violence in Down syndrome, possibly mediated by impaired sensory perception and hypoactivation of medial temporal structures in response to threats, with relative preservation of activity in pro-social brain regions. These findings indicate that specific genetic differences associated with Down syndrome can modulate the brain's response to violence and other complex emotive ideas.

Spatiotemporal modelling of groundwater extraction in semi-arid central Queensland, Australia

NASA Astrophysics Data System (ADS)

Keir, Greg; Bulovic, Nevenka; McIntyre, Neil

2016-04-01

The semi-arid Surat Basin in central Queensland, Australia, forms part of the Great Artesian Basin, a groundwater resource of national significance. While this area relies heavily on groundwater supply bores to sustain agricultural industries and rural life in general, measurement of groundwater extraction rates is very limited. Consequently, regional groundwater extraction rates are not well known, which may have implications for regional numerical groundwater modelling. However, flows from a small number of bores are metered, and less precise anecdotal estimates of extraction are increasingly available. There is also an increasing number of other spatiotemporal datasets which may help predict extraction rates (e.g. rainfall, temperature, soils, stocking rates etc.). These can be used to construct spatial multivariate regression models to estimate extraction. The data exhibit complicated statistical features, such as zero-valued observations, non-Gaussianity, and non-stationarity, which limit the use of many classical estimation techniques, such as kriging. As well, water extraction histories may exhibit temporal autocorrelation. To account for these features, we employ a separable space-time model to predict bore extraction rates using the R-INLA package for computationally efficient Bayesian inference. A joint approach is used to model both the probability (using a binomial likelihood) and magnitude (using a gamma likelihood) of extraction. The correlation between extraction rates in space and time is modelled using a Gaussian Markov Random Field (GMRF) with a Matérn spatial covariance function which can evolve over time according to an autoregressive model. To reduce computational burden, we allow the GMRF to be evaluated at a relatively coarse temporal resolution, while still allowing predictions to be made at arbitrarily small time scales. We describe the process of model selection and inference using an information criterion approach, and present some preliminary results from the study area. We conclude by discussing issues related with upscaling of the modelling approach to the entire basin, including merging of extraction rate observations with different precision, temporal resolution, and even potentially different likelihoods.

Inferential Precision in Single-Case Time-Series Data Streams: How Well Does the EM Procedure Perform When Missing Observations Occur in Autocorrelated Data?

PubMed Central

Smith, Justin D.; Borckardt, Jeffrey J.; Nash, Michael R.

2013-01-01

The case-based time-series design is a viable methodology for treatment outcome research. However, the literature has not fully addressed the problem of missing observations with such autocorrelated data streams. Mainly, to what extent do missing observations compromise inference when observations are not independent? Do the available missing data replacement procedures preserve inferential integrity? Does the extent of autocorrelation matter? We use Monte Carlo simulation modeling of a single-subject intervention study to address these questions. We find power sensitivity to be within acceptable limits across four proportions of missing observations (10%, 20%, 30%, and 40%) when missing data are replaced using the Expectation-Maximization Algorithm, more commonly known as the EM Procedure (Dempster, Laird, & Rubin, 1977).This applies to data streams with lag-1 autocorrelation estimates under 0.80. As autocorrelation estimates approach 0.80, the replacement procedure yields an unacceptable power profile. The implications of these findings and directions for future research are discussed. PMID:22697454

Effects of autocorrelation upon LANDSAT classification accuracy. [Richmond, Virginia and Denver, Colorado

NASA Technical Reports Server (NTRS)

Craig, R. G. (Principal Investigator)

1983-01-01

Richmond, Virginia and Denver, Colorado were study sites in an effort to determine the effect of autocorrelation on the accuracy of a parallelopiped classifier of LANDSAT digital data. The autocorrelation was assumed to decay to insignificant levels when sampled at distances of at least ten pixels. Spectral themes developed using blocks of adjacent pixels, and using groups of pixels spaced at least 10 pixels apart were used. Effects of geometric distortions were minimized by using only pixels from the interiors of land cover sections. Accuracy was evaluated for three classes; agriculture, residential and "all other"; both type 1 and type 2 errors were evaluated by means of overall classification accuracy. All classes give comparable results. Accuracy is approximately the same in both techniques; however, the variance in accuracy is significantly higher using the themes developed from autocorrelated data. The vectors of mean spectral response were nearly identical regardless of sampling method used. The estimated variances were much larger when using autocorrelated pixels.

Negligible influence of spatial autocorrelation in the assessment of fire effects in a mixed conifer forest

USGS Publications Warehouse

van Mantgem, P.J.; Schwilk, D.W.

2009-01-01

Fire is an important feature of many forest ecosystems, although the quantification of its effects is compromised by the large scale at which fire occurs and its inherent unpredictability. A recurring problem is the use of subsamples collected within individual burns, potentially resulting in spatially autocorrelated data. Using subsamples from six different fires (and three unburned control areas) we show little evidence for strong spatial autocorrelation either before or after burning for eight measures of forest conditions (both fuels and vegetation). Additionally, including a term for spatially autocorrelated errors provided little improvement for simple linear models contrasting the effects of early versus late season burning. While the effects of spatial autocorrelation should always be examined, it may not always greatly influence assessments of fire effects. If high patch scale variability is common in Sierra Nevada mixed conifer forests, even following more than a century of fire exclusion, treatments designed to encourage further heterogeneity in forest conditions prior to the reintroduction of fire will likely be unnecessary.

Calling depths of baleen whales from single sensor data: development of an autocorrelation method using multipath localization.

PubMed

Valtierra, Robert D; Glynn Holt, R; Cholewiak, Danielle; Van Parijs, Sofie M

2013-09-01

Multipath localization techniques have not previously been applied to baleen whale vocalizations due to difficulties in application to tonal vocalizations. Here it is shown that an autocorrelation method coupled with the direct reflected time difference of arrival localization technique can successfully resolve location information. A derivation was made to model the autocorrelation of a direct signal and its overlapping reflections to illustrate that an autocorrelation may be used to extract reflection information from longer duration signals containing a frequency sweep, such as some calls produced by baleen whales. An analysis was performed to characterize the difference in behavior of the autocorrelation when applied to call types with varying parameters (sweep rate, call duration). The method's feasibility was tested using data from playback transmissions to localize an acoustic transducer at a known depth and location. The method was then used to estimate the depth and range of a single North Atlantic right whale (Eubalaena glacialis) and humpback whale (Megaptera novaeangliae) from two separate experiments.

Rapid and stable measurement of respiratory rate from Doppler radar signals using time domain autocorrelation model.

PubMed

Sun, Guanghao; Matsui, Takemi

2015-01-01

Noncontact measurement of respiratory rate using Doppler radar will play a vital role in future clinical practice. Doppler radar remotely monitors the tiny chest wall movements induced by respiration activity. The most competitive advantage of this technique is to allow users fully unconstrained with no biological electrode attachments. However, the Doppler radar, unlike other contact-type sensors, is easily affected by the random body movements. In this paper, we proposed a time domain autocorrelation model to process the radar signals for rapid and stable estimation of the respiratory rate. We tested the autocorrelation model on 8 subjects in laboratory, and compared the respiratory rates detected by noncontact radar with reference contact-type respiratory effort belt. Autocorrelation model showed the effects of reducing the random body movement noise added to Doppler radar's respiration signals. Moreover, the respiratory rate can be rapidly calculated from the first main peak in the autocorrelation waveform within 10 s.

Scilab software package for the study of dynamical systems

NASA Astrophysics Data System (ADS)

Bordeianu, C. C.; Beşliu, C.; Jipa, Al.; Felea, D.; Grossu, I. V.

2008-05-01

This work presents a new software package for the study of chaotic flows and maps. The codes were written using Scilab, a software package for numerical computations providing a powerful open computing environment for engineering and scientific applications. It was found that Scilab provides various functions for ordinary differential equation solving, Fast Fourier Transform, autocorrelation, and excellent 2D and 3D graphical capabilities. The chaotic behaviors of the nonlinear dynamics systems were analyzed using phase-space maps, autocorrelation functions, power spectra, Lyapunov exponents and Kolmogorov-Sinai entropy. Various well known examples are implemented, with the capability of the users inserting their own ODE. Program summaryProgram title: Chaos Catalogue identifier: AEAP_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEAP_v1_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.html No. of lines in distributed program, including test data, etc.: 885 No. of bytes in distributed program, including test data, etc.: 5925 Distribution format: tar.gz Programming language: Scilab 3.1.1 Computer: PC-compatible running Scilab on MS Windows or Linux Operating system: Windows XP, Linux RAM: below 100 Megabytes Classification: 6.2 Nature of problem: Any physical model containing linear or nonlinear ordinary differential equations (ODE). Solution method: Numerical solving of ordinary differential equations. The chaotic behavior of the nonlinear dynamical system is analyzed using Poincaré sections, phase-space maps, autocorrelation functions, power spectra, Lyapunov exponents and Kolmogorov-Sinai entropies. Restrictions: The package routines are normally able to handle ODE systems of high orders (up to order twelve and possibly higher), depending on the nature of the problem. Running time: 10 to 20 seconds for problems that do not involve Lyapunov exponents calculation; 60 to 1000 seconds for problems that involve high orders ODE and Lyapunov exponents calculation.

Early Warning Signals for Abrupt Change Raise False Alarm During Sea Ice Loss

NASA Astrophysics Data System (ADS)

Wagner, T. J. W.; Eisenman, I.

2015-12-01

Uncovering universal early warning signals for critical transitions has become a coveted goal in diverse scientific disciplines, ranging from climate science to financial mathematics. There has been a flurry of recent research proposing such signals, with increasing autocorrelation and increasing variance being among the most widely discussed candidates. A number of studies have suggested that increasing autocorrelation alone may suffice to signal an impending transition, although some others have questioned this. Here, we consider variance and autocorrelation in the context of sea ice loss in an idealized model of the global climate system. The model features no bifurcation, nor increased rate of retreat, as the ice disappears. Nonetheless, the autocorrelation of summer sea ice area is found to increase with diminishing sea ice cover in a global warming scenario. The variance, by contrast, decreases. A simple physical mechanism is proposed to explain the occurrence of increasing autocorrelation but not variance in the model when there is no approaching bifurcation. Additionally, a similar mechanism is shown to allow an increase in both indicators with no physically attainable bifurcation. This implies that relying on autocorrelation and variance as early warning signals can raise false alarms in the climate system, warning of "tipping points" that are not actually there.

Evolution of dispersal in spatially and temporally variable environments: The importance of life cycles.

PubMed

Massol, François; Débarre, Florence

2015-07-01

Spatiotemporal variability of the environment is bound to affect the evolution of dispersal, and yet model predictions strongly differ on this particular effect. Recent studies on the evolution of local adaptation have shown that the life cycle chosen to model the selective effects of spatiotemporal variability of the environment is a critical factor determining evolutionary outcomes. Here, we investigate the effect of the order of events in the life cycle on the evolution of unconditional dispersal in a spatially heterogeneous, temporally varying landscape. Our results show that the occurrence of intermediate singular strategies and disruptive selection are conditioned by the temporal autocorrelation of the environment and by the life cycle. Life cycles with dispersal of adults versus dispersal of juveniles, local versus global density regulation, give radically different evolutionary outcomes that include selection for total philopatry, evolutionary bistability, selection for intermediate stable states, and evolutionary branching points. Our results highlight the importance of accounting for life-cycle specifics when predicting the effects of the environment on evolutionarily selected trait values, such as dispersal, as well as the need to check the robustness of model conclusions against modifications of the life cycle. © 2015 The Author(s). Evolution © 2015 The Society for the Study of Evolution.

An accessible method for implementing hierarchical models with spatio-temporal abundance data

USGS Publications Warehouse

Ross, Beth E.; Hooten, Melvin B.; Koons, David N.

2012-01-01

A common goal in ecology and wildlife management is to determine the causes of variation in population dynamics over long periods of time and across large spatial scales. Many assumptions must nevertheless be overcome to make appropriate inference about spatio-temporal variation in population dynamics, such as autocorrelation among data points, excess zeros, and observation error in count data. To address these issues, many scientists and statisticians have recommended the use of Bayesian hierarchical models. Unfortunately, hierarchical statistical models remain somewhat difficult to use because of the necessary quantitative background needed to implement them, or because of the computational demands of using Markov Chain Monte Carlo algorithms to estimate parameters. Fortunately, new tools have recently been developed that make it more feasible for wildlife biologists to fit sophisticated hierarchical Bayesian models (i.e., Integrated Nested Laplace Approximation, ‘INLA’). We present a case study using two important game species in North America, the lesser and greater scaup, to demonstrate how INLA can be used to estimate the parameters in a hierarchical model that decouples observation error from process variation, and accounts for unknown sources of excess zeros as well as spatial and temporal dependence in the data. Ultimately, our goal was to make unbiased inference about spatial variation in population trends over time.

The spatial and temporal domains of modern ecology.

PubMed

Estes, Lyndon; Elsen, Paul R; Treuer, Timothy; Ahmed, Labeeb; Caylor, Kelly; Chang, Jason; Choi, Jonathan J; Ellis, Erle C

2018-05-01

To understand ecological phenomena, it is necessary to observe their behaviour across multiple spatial and temporal scales. Since this need was first highlighted in the 1980s, technology has opened previously inaccessible scales to observation. To help to determine whether there have been corresponding changes in the scales observed by modern ecologists, we analysed the resolution, extent, interval and duration of observations (excluding experiments) in 348 studies that have been published between 2004 and 2014. We found that observational scales were generally narrow, because ecologists still primarily use conventional field techniques. In the spatial domain, most observations had resolutions ≤1 m 2 and extents ≤10,000 ha. In the temporal domain, most observations were either unreplicated or infrequently repeated (>1 month interval) and ≤1 year in duration. Compared with studies conducted before 2004, observational durations and resolutions appear largely unchanged, but intervals have become finer and extents larger. We also found a large gulf between the scales at which phenomena are actually observed and the scales those observations ostensibly represent, raising concerns about observational comprehensiveness. Furthermore, most studies did not clearly report scale, suggesting that it remains a minor concern. Ecologists can better understand the scales represented by observations by incorporating autocorrelation measures, while journals can promote attentiveness to scale by implementing scale-reporting standards.

The Azimuth Structure of Nuclear Collisions — I

NASA Astrophysics Data System (ADS)

Trainor, Thomas A.; Kettler, David T.

We describe azimuth structure commonly associated with elliptic and directed flow in the context of 2D angular autocorrelations for the purpose of precise separation of so-called nonflow (mainly minijets) from flow. We extend the Fourier-transform description of azimuth structure to include power spectra and autocorrelations related by the Wiener-Khintchine theorem. We analyze several examples of conventional flow analysis in that context and question the relevance of reaction plane estimation to flow analysis. We introduce the 2D angular autocorrelation with examples from data analysis and describe a simulation exercise which demonstrates precise separation of flow and nonflow using the 2D autocorrelation method. We show that an alternative correlation measure based on Pearson's normalized covariance provides a more intuitive measure of azimuth structure.

Monitoring autocorrelated process: A geometric Brownian motion process approach

NASA Astrophysics Data System (ADS)

Li, Lee Siaw; Djauhari, Maman A.

2013-09-01

Autocorrelated process control is common in today's modern industrial process control practice. The current practice of autocorrelated process control is to eliminate the autocorrelation by using an appropriate model such as Box-Jenkins models or other models and then to conduct process control operation based on the residuals. In this paper we show that many time series are governed by a geometric Brownian motion (GBM) process. Therefore, in this case, by using the properties of a GBM process, we only need an appropriate transformation and model the transformed data to come up with the condition needs in traditional process control. An industrial example of cocoa powder production process in a Malaysian company will be presented and discussed to illustrate the advantages of the GBM approach.

Image correlation and sampling study

NASA Technical Reports Server (NTRS)

Popp, D. J.; Mccormack, D. S.; Sedwick, J. L.

1972-01-01

The development of analytical approaches for solving image correlation and image sampling of multispectral data is discussed. Relevant multispectral image statistics which are applicable to image correlation and sampling are identified. The general image statistics include intensity mean, variance, amplitude histogram, power spectral density function, and autocorrelation function. The translation problem associated with digital image registration and the analytical means for comparing commonly used correlation techniques are considered. General expressions for determining the reconstruction error for specific image sampling strategies are developed.

Understanding the determinants of volatility clustering in terms of stationary Markovian processes

NASA Astrophysics Data System (ADS)

Miccichè, S.

2016-11-01

Volatility is a key variable in the modeling of financial markets. The most striking feature of volatility is that it is a long-range correlated stochastic variable, i.e. its autocorrelation function decays like a power-law τ-β for large time lags. In the present work we investigate the determinants of such feature, starting from the empirical observation that the exponent β of a certain stock's volatility is a linear function of the average correlation of such stock's volatility with all other volatilities. We propose a simple approach consisting in diagonalizing the cross-correlation matrix of volatilities and investigating whether or not the diagonalized volatilities still keep some of the original volatility stylized facts. As a result, the diagonalized volatilities result to share with the original volatilities either the power-law decay of the probability density function and the power-law decay of the autocorrelation function. This would indicate that volatility clustering is already present in the diagonalized un-correlated volatilities. We therefore present a parsimonious univariate model based on a non-linear Langevin equation that well reproduces these two stylized facts of volatility. The model helps us in understanding that the main source of volatility clustering, once volatilities have been diagonalized, is that the economic forces driving volatility can be modeled in terms of a Smoluchowski potential with logarithmic tails.

Linking snowflake microstructure to multi-frequency radar observations

NASA Astrophysics Data System (ADS)

Leinonen, J.; Moisseev, D.; Nousiainen, T.

2013-04-01

Spherical or spheroidal particle shape models are commonly used to calculate numerically the radar backscattering properties of aggregate snowflakes. A more complicated and computationally intensive approach is to use detailed models of snowflake structure together with numerical scattering models that can operate on arbitrary particle shapes. Recent studies have shown that there can be significant differences between the results of these approaches. In this paper, an analytical model, based on the Rayleigh-Gans scattering theory, is formulated to explain this discrepancy in terms of the effect of discrete ice crystals that constitute the snowflake. The ice crystals cause small-scale inhomogeneities whose effects can be understood through the density autocorrelation function of the particle mass, which the Rayleigh-Gans theory connects to the function that gives the radar reflectivity as a function of frequency. The derived model is a weighted sum of two Gaussian functions. A term that corresponds to the average shape of the particle, similar to that given by the spheroidal shape model, dominates at low frequencies. At high frequencies, that term vanishes and is gradually replaced by the effect of the ice crystal monomers. The autocorrelation-based description of snowflake microstructure appears to be sufficient for multi-frequency radar studies. The link between multi-frequency radar observations and the particle microstructure can thus be used to infer particle properties from the observations.

Narayanaswamy’s 1971 aging theory and material time

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dyre, Jeppe C., E-mail: dyre@ruc.dk

2015-09-21

The Bochkov-Kuzovlev nonlinear fluctuation-dissipation theorem is used to derive Narayanaswamy’s phenomenological theory of physical aging, in which this highly nonlinear phenomenon is described by a linear material-time convolution integral. A characteristic property of the Narayanaswamy aging description is material-time translational invariance, which is here taken as the basic assumption of the derivation. It is shown that only one possible definition of the material time obeys this invariance, namely, the square of the distance travelled from a configuration of the system far back in time. The paper concludes with suggestions for computer simulations that test for consequences of material-time translational invariance.more » One of these is the “unique-triangles property” according to which any three points on the system’s path form a triangle such that two side lengths determine the third; this is equivalent to the well-known triangular relation for time-autocorrelation functions of aging spin glasses [L. F. Cugliandolo and J. Kurchan, J. Phys. A: Math. Gen. 27, 5749 (1994)]. The unique-triangles property implies a simple geometric interpretation of out-of-equilibrium time-autocorrelation functions, which extends to aging a previously proposed framework for such functions in equilibrium [J. C. Dyre, e-print arXiv:cond-mat/9712222 (1997)].« less

First-principles calculation of entropy for liquid metals.

PubMed

Desjarlais, Michael P

2013-12-01

We demonstrate the accurate calculation of entropies and free energies for a variety of liquid metals using an extension of the two-phase thermodynamic (2PT) model based on a decomposition of the velocity autocorrelation function into gas-like (hard sphere) and solid-like (harmonic) subsystems. The hard sphere model for the gas-like component is shown to give systematically high entropies for liquid metals as a direct result of the unphysical Lorentzian high-frequency tail. Using a memory function framework we derive a generally applicable velocity autocorrelation and frequency spectrum for the diffusive component which recovers the low-frequency (long-time) behavior of the hard sphere model while providing for realistic short-time coherence and high-frequency tails to the spectrum. This approach provides a significant increase in the accuracy of the calculated entropies for liquid metals and is compared to ambient pressure data for liquid sodium, aluminum, gallium, tin, and iron. The use of this method for the determination of melt boundaries is demonstrated with a calculation of the high-pressure bcc melt boundary for sodium. With the significantly improved accuracy available with the memory function treatment for softer interatomic potentials, the 2PT model for entropy calculations should find broader application in high energy density science, warm dense matter, planetary science, geophysics, and material science.

First-principles calculation of entropy for liquid metals

NASA Astrophysics Data System (ADS)

Desjarlais, Michael P.

2013-12-01

We demonstrate the accurate calculation of entropies and free energies for a variety of liquid metals using an extension of the two-phase thermodynamic (2PT) model based on a decomposition of the velocity autocorrelation function into gas-like (hard sphere) and solid-like (harmonic) subsystems. The hard sphere model for the gas-like component is shown to give systematically high entropies for liquid metals as a direct result of the unphysical Lorentzian high-frequency tail. Using a memory function framework we derive a generally applicable velocity autocorrelation and frequency spectrum for the diffusive component which recovers the low-frequency (long-time) behavior of the hard sphere model while providing for realistic short-time coherence and high-frequency tails to the spectrum. This approach provides a significant increase in the accuracy of the calculated entropies for liquid metals and is compared to ambient pressure data for liquid sodium, aluminum, gallium, tin, and iron. The use of this method for the determination of melt boundaries is demonstrated with a calculation of the high-pressure bcc melt boundary for sodium. With the significantly improved accuracy available with the memory function treatment for softer interatomic potentials, the 2PT model for entropy calculations should find broader application in high energy density science, warm dense matter, planetary science, geophysics, and material science.

Dissipative particle dynamics study of velocity autocorrelation function and self-diffusion coefficient in terms of interaction potential strength

NASA Astrophysics Data System (ADS)

Zohravi, Elnaz; Shirani, Ebrahim; Pishevar, Ahmadreza; Karimpour, Hossein

2018-07-01

This research focuses on numerically investigating the self-diffusion coefficient and velocity autocorrelation function (VACF) of a dissipative particle dynamics (DPD) fluid as a function of the conservative interaction strength. Analytic solutions to VACF and self-diffusion coefficients in DPD were obtained by many researchers in some restricted cases including ideal gases, without the account of conservative force. As departure from the ideal gas conditions are accentuated with increasing the relative proportion of conservative force, it is anticipated that the VACF should gradually deviate from its normally expected exponentially decay. This trend is confirmed through numerical simulations and an expression in terms of the conservative force parameter, density and temperature is proposed for the self-diffusion coefficient. As it concerned the VACF, the equivalent Langevin equation describing Brownian motion of particles with a harmonic potential is adapted to the problem and reveals an exponentially decaying oscillatory pattern influenced by the conservative force parameter, dissipative parameter and temperature. Although the proposed model for obtaining the self-diffusion coefficient with consideration of the conservative force could not be verified due to computational complexities, nonetheless the Arrhenius dependency of the self-diffusion coefficient to temperature and pressure permits to certify our model over a definite range of DPD parameters.

Thermal noise in confined fluids.

PubMed

Sanghi, T; Aluru, N R

2014-11-07

In this work, we discuss a combined memory function equation (MFE) and generalized Langevin equation (GLE) approach (referred to as MFE/GLE formulation) to characterize thermal noise in confined fluids. Our study reveals that for fluids confined inside nanoscale geometries, the correlation time and the time decay of the autocorrelation function of the thermal noise are not significantly different across the confinement. We show that it is the strong cross-correlation of the mean force with the molecular velocity that gives rise to the spatial anisotropy in the velocity-autocorrelation function of the confined fluids. Further, we use the MFE/GLE formulation to extract the thermal force a fluid molecule experiences in a MD simulation. Noise extraction from MD simulation suggests that the frequency distribution of the thermal force is non-Gaussian. Also, the frequency distribution of the thermal force near the confining surface is found to be different in the direction parallel and perpendicular to the confinement. We also use the formulation to compute the noise correlation time of water confined inside a (6,6) carbon-nanotube (CNT). It is observed that inside the (6,6) CNT, in which water arranges itself in a highly concerted single-file arrangement, the correlation time of thermal noise is about an order of magnitude higher than that of bulk water.

Thermal noise in confined fluids

NASA Astrophysics Data System (ADS)

Sanghi, T.; Aluru, N. R.

2014-11-01

In this work, we discuss a combined memory function equation (MFE) and generalized Langevin equation (GLE) approach (referred to as MFE/GLE formulation) to characterize thermal noise in confined fluids. Our study reveals that for fluids confined inside nanoscale geometries, the correlation time and the time decay of the autocorrelation function of the thermal noise are not significantly different across the confinement. We show that it is the strong cross-correlation of the mean force with the molecular velocity that gives rise to the spatial anisotropy in the velocity-autocorrelation function of the confined fluids. Further, we use the MFE/GLE formulation to extract the thermal force a fluid molecule experiences in a MD simulation. Noise extraction from MD simulation suggests that the frequency distribution of the thermal force is non-Gaussian. Also, the frequency distribution of the thermal force near the confining surface is found to be different in the direction parallel and perpendicular to the confinement. We also use the formulation to compute the noise correlation time of water confined inside a (6,6) carbon-nanotube (CNT). It is observed that inside the (6,6) CNT, in which water arranges itself in a highly concerted single-file arrangement, the correlation time of thermal noise is about an order of magnitude higher than that of bulk water.

Zero Autocorrelation Waveforms: A Doppler Statistic and Multifunction Problems

DTIC Science & Technology

2006-01-01

by ANSI Std Z39-18 It is natural to refer to A as the ambiguity function of u, since in the usual setting on the real line R, the analogue ambiguity...Doppler statistic |Cu,uek(j)| is excellent and provable for detecting deodorized Doppler frequency shift [11] (see Fig. 2). Also, if one graphs only

Comprehensive comparisons of geodesic acoustic mode characteristics and dynamics between Tore Supra experiments and gyrokinetic simulations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Storelli, A., E-mail: alexandre.storelli@lpp.polytechnique.fr; Vermare, L.; Hennequin, P.

2015-06-15

In a dedicated collisionality scan in Tore Supra, the geodesic acoustic mode (GAM) is detected and identified with the Doppler backscattering technique. Observations are compared to the results of a simulation with the gyrokinetic code GYSELA. We found that the GAM frequency in experiments is lower than predicted by simulation and theory. Moreover, the disagreement is higher in the low collisionality scenario. Bursts of non harmonic GAM oscillations have been characterized with filtering techniques, such as the Hilbert-Huang transform. When comparing this dynamical behaviour between experiments and simulation, the probability density function of GAM amplitude and the burst autocorrelation timemore » are found to be remarkably similar. In the simulation, where the radial profile of GAM frequency is continuous, we observed a phenomenon of radial phase mixing of the GAM oscillations, which could influence the burst autocorrelation time.« less

Geary autocorrelation and DCCA coefficient: Application to predict apoptosis protein subcellular localization via PSSM

NASA Astrophysics Data System (ADS)

Liang, Yunyun; Liu, Sanyang; Zhang, Shengli

2017-02-01

Apoptosis is a fundamental process controlling normal tissue homeostasis by regulating a balance between cell proliferation and death. Predicting subcellular location of apoptosis proteins is very helpful for understanding its mechanism of programmed cell death. Prediction of apoptosis protein subcellular location is still a challenging and complicated task, and existing methods mainly based on protein primary sequences. In this paper, we propose a new position-specific scoring matrix (PSSM)-based model by using Geary autocorrelation function and detrended cross-correlation coefficient (DCCA coefficient). Then a 270-dimensional (270D) feature vector is constructed on three widely used datasets: ZD98, ZW225 and CL317, and support vector machine is adopted as classifier. The overall prediction accuracies are significantly improved by rigorous jackknife test. The results show that our model offers a reliable and effective PSSM-based tool for prediction of apoptosis protein subcellular localization.

Rainfall statistics, stationarity, and climate change.

PubMed

Sun, Fubao; Roderick, Michael L; Farquhar, Graham D

2018-03-06

There is a growing research interest in the detection of changes in hydrologic and climatic time series. Stationarity can be assessed using the autocorrelation function, but this is not yet common practice in hydrology and climate. Here, we use a global land-based gridded annual precipitation (hereafter P ) database (1940-2009) and find that the lag 1 autocorrelation coefficient is statistically significant at around 14% of the global land surface, implying nonstationary behavior (90% confidence). In contrast, around 76% of the global land surface shows little or no change, implying stationary behavior. We use these results to assess change in the observed P over the most recent decade of the database. We find that the changes for most (84%) grid boxes are within the plausible bounds of no significant change at the 90% CI. The results emphasize the importance of adequately accounting for natural variability when assessing change. Copyright © 2018 the Author(s). Published by PNAS.

Rainfall statistics, stationarity, and climate change

NASA Astrophysics Data System (ADS)

Sun, Fubao; Roderick, Michael L.; Farquhar, Graham D.

2018-03-01

There is a growing research interest in the detection of changes in hydrologic and climatic time series. Stationarity can be assessed using the autocorrelation function, but this is not yet common practice in hydrology and climate. Here, we use a global land-based gridded annual precipitation (hereafter P) database (1940–2009) and find that the lag 1 autocorrelation coefficient is statistically significant at around 14% of the global land surface, implying nonstationary behavior (90% confidence). In contrast, around 76% of the global land surface shows little or no change, implying stationary behavior. We use these results to assess change in the observed P over the most recent decade of the database. We find that the changes for most (84%) grid boxes are within the plausible bounds of no significant change at the 90% CI. The results emphasize the importance of adequately accounting for natural variability when assessing change.

First-passage problems: A probabilistic dynamic analysis for degraded structures

NASA Technical Reports Server (NTRS)

Shiao, Michael C.; Chamis, Christos C.

1990-01-01

Structures subjected to random excitations with uncertain system parameters degraded by surrounding environments (a random time history) are studied. Methods are developed to determine the statistics of dynamic responses, such as the time-varying mean, the standard deviation, the autocorrelation functions, and the joint probability density function of any response and its derivative. Moreover, the first-passage problems with deterministic and stationary/evolutionary random barriers are evaluated. The time-varying (joint) mean crossing rate and the probability density function of the first-passage time for various random barriers are derived.

Correlations and forecast of death tolls in the Syrian conflict.

PubMed

Fujita, Kazuki; Shinomoto, Shigeru; Rocha, Luis E C

2017-11-16

The Syrian armed conflict has been ongoing since 2011 and has already caused thousands of deaths. The analysis of death tolls helps to understand the dynamics of the conflict and to better allocate resources and aid to the affected areas. In this article, we use information on the daily number of deaths to study temporal and spatial correlations in the data, and exploit this information to forecast events of deaths. We found that the number of violent deaths per day in Syria varies more widely than that in England in which non-violent deaths dominate. We have identified strong positive auto-correlations in Syrian cities and non-trivial cross-correlations across some of them. The results indicate synchronization in the number of deaths at different times and locations, suggesting respectively that local attacks are followed by more attacks at subsequent days and that coordinated attacks may also take place across different locations. Thus the analysis of high temporal resolution data across multiple cities makes it possible to infer attack strategies, warn potential occurrence of future events, and hopefully avoid further deaths.

Fully Resolved Simulations of Particle-Bed-Turbulence Interactions in Oscillatory Flows

NASA Astrophysics Data System (ADS)

Apte, S.; Ghodke, C.

2017-12-01

Particle-resolved direct numerical simulations (DNS) are performed to investigate the behavior of an oscillatory flow field over a bed of closely packed fixed spherical particles for a range of Reynolds numbers in transitional and rough turbulent flow regime. Presence of roughness leads to a substantial modification of the underlying boundary layer mechanism resulting in increased bed shear stress, reduction in the near-bed anisotropy, modification of the near-bed sweep and ejection motions along with marked changes in turbulent energy transport mechanisms. Characterization of such resulting flow field is performed by studying statistical descriptions of the near-bed turbulence for different roughness parameters. A double-averaging technique is employed to reveal spatial inhomogeneities at the roughness scale that provide alternate paths of energy transport in the turbulent kinetic energy (TKE) budget. Spatio-temporal characteristics of unsteady particle forces by studying their spatial distribution, temporal auto-correlations, frequency spectra, cross-correlations with near-bed turbulent flow variables and intermittency intermittency in the forces using the concept of impulse are investigated in detail. These first principle simulations provide substantial insights into the modeling of incipient motion of sediments.

Very High-Frequency (VHF) ionospheric scintillation fading measurements at Lima, Peru

NASA Technical Reports Server (NTRS)

Blank, H. A.; Golden, T. S.

1972-01-01

During the spring equinox of 1970, scintillating signals at VHF (136.4 MHz) were observed at Lima, Peru. The transmission originated from ATS 3 and was observed through a pair of antennas spaced 1200 feet apart on an east-west baseline. The empirical data were digitized, reduced, and analyzed. The results include amplitude probability density and distribution functions, time autocorrelation functions, cross correlation functions for the spaced antennas, and appropriate spectral density functions. Results show estimates of the statistics of the ground diffraction pattern to gain insight into gross ionospheric irregularity size, and irregularity velocity in the antenna planes.

3D radiation belt diffusion model results using new empirical models of whistler chorus and hiss

NASA Astrophysics Data System (ADS)

Cunningham, G.; Chen, Y.; Henderson, M. G.; Reeves, G. D.; Tu, W.

2012-12-01

3D diffusion codes model the energization, radial transport, and pitch angle scattering due to wave-particle interactions. Diffusion codes are powerful but are limited by the lack of knowledge of the spatial & temporal distribution of waves that drive the interactions for a specific event. We present results from the 3D DREAM model using diffusion coefficients driven by new, activity-dependent, statistical models of chorus and hiss waves. Most 3D codes parameterize the diffusion coefficients or wave amplitudes as functions of magnetic activity indices like Kp, AE, or Dst. These functional representations produce the average value of the wave intensities for a given level of magnetic activity; however, the variability of the wave population at a given activity level is lost with such a representation. Our 3D code makes use of the full sample distributions contained in a set of empirical wave databases (one database for each wave type, including plasmaspheric hiss, lower and upper hand chorus) that were recently produced by our team using CRRES and THEMIS observations. The wave databases store the full probability distribution of observed wave intensity binned by AE, MLT, MLAT and L*. In this presentation, we show results that make use of the wave intensity sample probability distributions for lower-band and upper-band chorus by sampling the distributions stochastically during a representative CRRES-era storm. The sampling of the wave intensity probability distributions produces a collection of possible evolutions of the phase space density, which quantifies the uncertainty in the model predictions caused by the uncertainty of the chorus wave amplitudes for a specific event. A significant issue is the determination of an appropriate model for the spatio-temporal correlations of the wave intensities, since the diffusion coefficients are computed as spatio-temporal averages of the waves over MLT, MLAT and L*. The spatiotemporal correlations cannot be inferred from the wave databases. In this study we use a temporal correlation of ~1 hour for the sampled wave intensities that is informed by the observed autocorrelation in the AE index, a spatial correlation length of ~100 km in the two directions perpendicular to the magnetic field, and a spatial correlation length of 5000 km in the direction parallel to the magnetic field, according to the work of Santolik et al (2003), who used multi-spacecraft measurements from Cluster to quantify the correlation length scales for equatorial chorus . We find that, despite the small correlation length scale for chorus, there remains significant variability in the model outcomes driven by variability in the chorus wave intensities.

Vegetation Coverage and Impervious Surface Area Estimated Based on the Estarfm Model and Remote Sensing Monitoring

NASA Astrophysics Data System (ADS)

Hu, Rongming; Wang, Shu; Guo, Jiao; Guo, Liankun

2018-04-01

Impervious surface area and vegetation coverage are important biophysical indicators of urban surface features which can be derived from medium-resolution images. However, remote sensing data obtained by a single sensor are easily affected by many factors such as weather conditions, and the spatial and temporal resolution can not meet the needs for soil erosion estimation. Therefore, the integrated multi-source remote sensing data are needed to carry out high spatio-temporal resolution vegetation coverage estimation. Two spatial and temporal vegetation coverage data and impervious data were obtained from MODIS and Landsat 8 remote sensing images. Based on the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM), the vegetation coverage data of two scales were fused and the data of vegetation coverage fusion (ESTARFM FVC) and impervious layer with high spatiotemporal resolution (30 m, 8 day) were obtained. On this basis, the spatial variability of the seepage-free surface and the vegetation cover landscape in the study area was measured by means of statistics and spatial autocorrelation analysis. The results showed that: 1) ESTARFM FVC and impermeable surface have higher accuracy and can characterize the characteristics of the biophysical components covered by the earth's surface; 2) The average impervious surface proportion and the spatial configuration of each area are different, which are affected by natural conditions and urbanization. In the urban area of Xi'an, which has typical characteristics of spontaneous urbanization, landscapes are fragmented and have less spatial dependence.

Noise Characterization of Erbium-Doped Fiber Amplifiers and Avalanche Photodiodes in Optical Communication Systems.

NASA Astrophysics Data System (ADS)

Kahraman, Gokalp

We examine the performance of optical communication systems using erbium-doped fiber amplifiers (OFAs) and avalanche photodiodes (APDs) including nonlinear and transient effects in the former and transient effects in the latter. Transient effects become important as these amplifiers are operated at very high data rates. Nonlinear effects are important for high gain amplifiers. In most studies of noise in these devices, the temporal and nonlinear effects have been ignored. We present a quantum theory of noise in OFAs including the saturation of the atomic population inversion and the pump depletion. We study the quantum-statistical properties of pulse amplification. The generating function of the output photon number distribution (PND) is determined as a function of time during the course of the pulse with an arbitrary input PND assumed. Under stationary conditions, we determine the Kolmogorov equation obeyed by the PND. The PND at the output is determined for arbitrary input distributions. The effect of the counting time and the filter bandwidth used by the detection circuit is determined. We determine the gain, the noise figure, and the sensitivity of receivers using OFAs as preamplifiers, including the effect of backward amplified spontaneous emission (ASE). Backward ASE degrades the noise figure and the sensitivity by depleting the population inversion at the input side of the fiber and thus increasing the noise during signal amplification. We show that the sensitivity improves with the bit rate at low rates but degrades at high rates. We provide a stochastic model that describes the time dynamics in a double-carrier multiplication (DCM) APD. A discrete stochastic model for the electron/hole motion and multiplication is defined on a spatio-temporal lattice and used to derive recursive equations for the mean, the variance, and the autocorrelation of the impulse response as functions of time. The power spectral density of the photocurrent produced in response to a Poisson-distributed stream of photons of uniform rate is evaluated. A method is also developed for solving the coupled transport equations that describe the electron and hole currents in a DCM-APD of arbitrary structure.

Inferential precision in single-case time-series data streams: how well does the em procedure perform when missing observations occur in autocorrelated data?

PubMed

Smith, Justin D; Borckardt, Jeffrey J; Nash, Michael R

2012-09-01

The case-based time-series design is a viable methodology for treatment outcome research. However, the literature has not fully addressed the problem of missing observations with such autocorrelated data streams. Mainly, to what extent do missing observations compromise inference when observations are not independent? Do the available missing data replacement procedures preserve inferential integrity? Does the extent of autocorrelation matter? We use Monte Carlo simulation modeling of a single-subject intervention study to address these questions. We find power sensitivity to be within acceptable limits across four proportions of missing observations (10%, 20%, 30%, and 40%) when missing data are replaced using the Expectation-Maximization Algorithm, more commonly known as the EM Procedure (Dempster, Laird, & Rubin, 1977). This applies to data streams with lag-1 autocorrelation estimates under 0.80. As autocorrelation estimates approach 0.80, the replacement procedure yields an unacceptable power profile. The implications of these findings and directions for future research are discussed. Copyright © 2011. Published by Elsevier Ltd.

Field test comparison of an autocorrelation technique for determining grain size using a digital 'beachball' camera versus traditional methods

USGS Publications Warehouse

Barnard, P.L.; Rubin, D.M.; Harney, J.; Mustain, N.

2007-01-01

This extensive field test of an autocorrelation technique for determining grain size from digital images was conducted using a digital bed-sediment camera, or 'beachball' camera. Using 205 sediment samples and >1200 images from a variety of beaches on the west coast of the US, grain size ranging from sand to granules was measured from field samples using both the autocorrelation technique developed by Rubin [Rubin, D.M., 2004. A simple autocorrelation algorithm for determining grain size from digital images of sediment. Journal of Sedimentary Research, 74(1): 160-165.] and traditional methods (i.e. settling tube analysis, sieving, and point counts). To test the accuracy of the digital-image grain size algorithm, we compared results with manual point counts of an extensive image data set in the Santa Barbara littoral cell. Grain sizes calculated using the autocorrelation algorithm were highly correlated with the point counts of the same images (r2 = 0.93; n = 79) and had an error of only 1%. Comparisons of calculated grain sizes and grain sizes measured from grab samples demonstrated that the autocorrelation technique works well on high-energy dissipative beaches with well-sorted sediment such as in the Pacific Northwest (r2 ??? 0.92; n = 115). On less dissipative, more poorly sorted beaches such as Ocean Beach in San Francisco, results were not as good (r2 ??? 0.70; n = 67; within 3% accuracy). Because the algorithm works well compared with point counts of the same image, the poorer correlation with grab samples must be a result of actual spatial and vertical variability of sediment in the field; closer agreement between grain size in the images and grain size of grab samples can be achieved by increasing the sampling volume of the images (taking more images, distributed over a volume comparable to that of a grab sample). In all field tests the autocorrelation method was able to predict the mean and median grain size with ???96% accuracy, which is more than adequate for the majority of sedimentological applications, especially considering that the autocorrelation technique is estimated to be at least 100 times faster than traditional methods.

Statistical process control of mortality series in the Australian and New Zealand Intensive Care Society (ANZICS) adult patient database: implications of the data generating process.

PubMed

Moran, John L; Solomon, Patricia J

2013-05-24

Statistical process control (SPC), an industrial sphere initiative, has recently been applied in health care and public health surveillance. SPC methods assume independent observations and process autocorrelation has been associated with increase in false alarm frequency. Monthly mean raw mortality (at hospital discharge) time series, 1995-2009, at the individual Intensive Care unit (ICU) level, were generated from the Australia and New Zealand Intensive Care Society adult patient database. Evidence for series (i) autocorrelation and seasonality was demonstrated using (partial)-autocorrelation ((P)ACF) function displays and classical series decomposition and (ii) "in-control" status was sought using risk-adjusted (RA) exponentially weighted moving average (EWMA) control limits (3 sigma). Risk adjustment was achieved using a random coefficient (intercept as ICU site and slope as APACHE III score) logistic regression model, generating an expected mortality series. Application of time-series to an exemplar complete ICU series (1995-(end)2009) was via Box-Jenkins methodology: autoregressive moving average (ARMA) and (G)ARCH ((Generalised) Autoregressive Conditional Heteroscedasticity) models, the latter addressing volatility of the series variance. The overall data set, 1995-2009, consisted of 491324 records from 137 ICU sites; average raw mortality was 14.07%; average(SD) raw and expected mortalities ranged from 0.012(0.113) and 0.013(0.045) to 0.296(0.457) and 0.278(0.247) respectively. For the raw mortality series: 71 sites had continuous data for assessment up to or beyond lag40 and 35% had autocorrelation through to lag40; and of 36 sites with continuous data for ≥ 72 months, all demonstrated marked seasonality. Similar numbers and percentages were seen with the expected series. Out-of-control signalling was evident for the raw mortality series with respect to RA-EWMA control limits; a seasonal ARMA model, with GARCH effects, displayed white-noise residuals which were in-control with respect to EWMA control limits and one-step prediction error limits (3SE). The expected series was modelled with a multiplicative seasonal autoregressive model. The data generating process of monthly raw mortality series at the ICU level displayed autocorrelation, seasonality and volatility. False-positive signalling of the raw mortality series was evident with respect to RA-EWMA control limits. A time series approach using residual control charts resolved these issues.

Spatial patterns of native freshwater mussels in the Upper Mississippi River

USGS Publications Warehouse

Ries, Patricia R.; DeJager, Nathan R.; Zigler, Steven J.; Newton, Teresa

2016-01-01

Multiple physical and biological factors structure freshwater mussel communities in large rivers, and their distributions have been described as clumped or patchy. However, few surveys of mussel populations have been conducted over areas large enough and at resolutions fine enough to quantify spatial patterns in their distribution. We used global and local indicators of spatial autocorrelation (i.e., Moran’s I) to quantify spatial patterns of adult and juvenile (≤5 y of age) freshwater mussels across multiple scales based on survey data from 4 reaches (navigation pools 3, 5, 6, and 18) of the Upper Mississippi River, USA. Native mussel densities were sampled at a resolution of ∼300 m and across distances ranging from 21 to 37 km, making these some of the most spatially extensive surveys conducted in a large river. Patch density and the degree and scale of patchiness varied by river reach, age group, and the scale of analysis. In all 4 pools, some patches of adults overlapped patches of juveniles, suggesting spatial and temporal persistence of adequate habitat. In pools 3 and 5, patches of juveniles were found where there were few adults, suggesting recent emergence of positive structuring mechanisms. Last, in pools 3, 5, and 6, some patches of adults were found where there were few juveniles, suggesting that negative structuring mechanisms may have replaced positive ones, leading to a lack of localized recruitment. Our results suggest that: 1) the detection of patches of freshwater mussels requires a multiscaled approach, 2) insights into the spatial and temporal dynamics of structuring mechanisms can be gained by conducting independent analyses of adults and juveniles, and 3) maps of patch distributions can be used to guide restoration and management actions and identify areas where mussels are most likely to influence ecosystem function.

Time to burn: Modeling wildland arson as an autoregressive crime function

Treesearch

Jeffrey P. Prestemon; David T. Butry

2005-01-01

Six Poisson autoregressive models of order p [PAR(p)] of daily wildland arson ignition counts are estimated for five locations in Florida (1994-2001). In addition, a fixed effects time-series Poisson model of annual arson counts is estimated for all Florida counties (1995-2001). PAR(p) model estimates reveal highly significant arson ignition autocorrelation, lasting up...

Improved symbol rate identification method for on-off keying and advanced modulation format signals based on asynchronous delayed sampling

NASA Astrophysics Data System (ADS)

Cui, Sheng; Jin, Shang; Xia, Wenjuan; Ke, Changjian; Liu, Deming

2015-11-01

Symbol rate identification (SRI) based on asynchronous delayed sampling is accurate, cost-effective and robust to impairments. For on-off keying (OOK) signals the symbol rate can be derived from the periodicity of the second-order autocorrelation function (ACF2) of the delay tap samples. But it is found that when applied this method to advanced modulation format signals with auxiliary amplitude modulation (AAM), incorrect results may be produced because AAM has significant impact on ACF2 periodicity, which makes the symbol period harder or even unable to be correctly identified. In this paper it is demonstrated that for these signals the first order autocorrelation function (ACF1) has stronger periodicity and can be used to replace ACF2 to produce more accurate and robust results. Utilizing the characteristics of the ACFs, an improved SRI method is proposed to accommodate both OOK and advanced modulation formant signals in a transparent manner. Furthermore it is proposed that by minimizing the peak to average ratio (PAPR) of the delay tap samples with an additional tunable dispersion compensator (TDC) the limited dispersion tolerance can be expanded to desired values.

Diagnostic System Based on the Human AUDITORY-BRAIN Model for Measuring Environmental NOISE—AN Application to Railway Noise

NASA Astrophysics Data System (ADS)

SAKAI, H.; HOTEHAMA, T.; ANDO, Y.; PRODI, N.; POMPOLI, R.

2002-02-01

Measurements of railway noise were conducted by use of a diagnostic system of regional environmental noise. The system is based on the model of the human auditory-brain system. The model consists of the interplay of autocorrelators and an interaural crosscorrelator acting on the pressure signals arriving at the ear entrances, and takes into account the specialization of left and right human cerebral hemispheres. Different kinds of railway noise were measured through binaural microphones of a dummy head. To characterize the railway noise, physical factors, extracted from the autocorrelation functions (ACF) and interaural crosscorrelation function (IACF) of binaural signals, were used. The factors extracted from ACF were (1) energy represented at the origin of the delay, Φ (0), (2) effective duration of the envelope of the normalized ACF, τe, (3) the delay time of the first peak, τ1, and (4) its amplitude,ø1 . The factors extracted from IACF were (5) IACC, (6) interaural delay time at which the IACC is defined, τIACC, and (7) width of the IACF at the τIACC,WIACC . The factor Φ (0) can be represented as a geometrical mean of energies at both ears as listening level, LL.

Image correlation based method for the analysis of collagen fibers patterns

NASA Astrophysics Data System (ADS)

Rosa, Ramon G. T.; Pratavieira, Sebastião.; Kurachi, Cristina

2015-06-01

The collagen fibers are one of the most important structural proteins in skin, being responsible for its strength and flexibility. It is known that their properties, like fibers density, ordination and mean diameter can be affected by several skin conditions, what makes these properties a good parameter to be used on the diagnosis and evaluation of skin aging, cancer, healing, among other conditions. There is, however, a need for methods capable of analyzing quantitatively the organization patterns of these fibers. To address this need, we developed a method based on the autocorrelation function of the images that allows the construction of vector field plots of the fibers directions and does not require any kind of curve fitting or optimization. The analyzed images were obtained through Second Harmonic Generation Imaging Microscopy. This paper presents a concise review on the autocorrelation function and some of its applications to image processing, details the developed method and the results obtained through the analysis of hystopathological slides of landrace porcine skin. The method has high accuracy on the determination of the fibers direction and presents high performance. We look forward to perform further studies keeping track of different skin conditions over time.

Modulation transfer function of a fish-eye lens based on the sixth-order wave aberration theory.

PubMed

Jia, Han; Lu, Lijun; Cao, Yiqing

2018-01-10

A calculation program of the modulation transfer function (MTF) of a fish-eye lens is developed with the autocorrelation method, in which the sixth-order wave aberration theory of ultra-wide-angle optical systems is used to simulate the wave aberration distribution at the exit pupil of the optical systems. The autocorrelation integral is processed with the Gauss-Legendre integral, and the magnification chromatic aberration is discussed to calculate polychromatic MTF. The MTF calculation results of a given example are then compared with those previously obtained based on the fourth-order wave aberration theory of plane-symmetrical optical systems and with those from the Zemax program. The study shows that MTF based on the sixth-order wave aberration theory has satisfactory calculation accuracy even for a fish-eye lens with a large acceptance aperture. And the impacts of different types of aberrations on the MTF of a fish-eye lens are analyzed. Finally, we apply the self-adaptive and normalized real-coded genetic algorithm and the MTF developed in the paper to optimize the Nikon F/2.8 fish-eye lens; consequently, the optimized system shows better MTF performances than those of the original design.

Scattering images from autocorrelation functions of P-wave seismic velocity images: the case of Tenerife Island (Canary Islands, Spain)

NASA Astrophysics Data System (ADS)

García-Yeguas, A.; Sánchez-Alzola, A.; De Siena, L.; Prudencio, J.; Díaz-Moreno, A.; Ibáñez, J. M.

2018-03-01

We present a P-wave scattering image of the volcanic structures under Tenerife Island using the autocorrelation functions of P-wave vertical velocity fluctuations. We have applied a cluster analysis to total quality factor attenuation ( {Q}_t^{-1} ) and scattering quality factor attenuation ( {Q}_{PSc}^{-1} ) images to interpret the structures in terms of intrinsic and scattering attenuation variations on a 2D plane, corresponding to a depth of 2000 m, and check the robustness of the scattering imaging. The results show that scattering patterns are similar to total attenuation patterns in the south of the island. There are two main areas where patterns differ: at Cañadas-Teide-Pico Viejo Complex, high total attenuation and average-to-low scattering values are observed. We interpret the difference as induced by intrinsic attenuation. In the Santiago Ridge Zone (SRZ) region, high scattering values correspond to average total attenuation. In our interpretation, the anomaly is induced by an extended scatterer, geometrically related to the surficial traces of Garachico and El Chinyero historical eruptions and the area of highest seismic activity during the 2004-2008 seismic crises.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Shumway, R.H.; McQuarrie, A.D.

Robust statistical approaches to the problem of discriminating between regional earthquakes and explosions are developed. We compare linear discriminant analysis using descriptive features like amplitude and spectral ratios with signal discrimination techniques using the original signal waveforms and spectral approximations to the log likelihood function. Robust information theoretic techniques are proposed and all methods are applied to 8 earthquakes and 8 mining explosions in Scandinavia and to an event from Novaya Zemlya of unknown origin. It is noted that signal discrimination approaches based on discrimination information and Renyi entropy perform better in the test sample than conventional methods based onmore » spectral ratios involving the P and S phases. Two techniques for identifying the ripple-firing pattern for typical mining explosions are proposed and shown to work well on simulated data and on several Scandinavian earthquakes and explosions. We use both cepstral analysis in the frequency domain and a time domain method based on the autocorrelation and partial autocorrelation functions. The proposed approach strips off underlying smooth spectral and seasonal spectral components corresponding to the echo pattern induced by two simple ripple-fired models. For two mining explosions, a pattern is identified whereas for two earthquakes, no pattern is evident.« less

Chemometric modeling of 5-Phenylthiophenecarboxylic acid derivatives as anti-rheumatic agents.

PubMed

Adhikari, Nilanjan; Jana, Dhritiman; Halder, Amit K; Mondal, Chanchal; Maiti, Milan K; Jha, Tarun

2012-09-01

Arthritis involves joint inflammation, synovial proliferation and damage of cartilage. Interleukin-1 undergoes acute and chronic inflammatory mechanisms of arthritis. Non-steroidal anti-inflammatory drugs can produce symptomatic relief but cannot act through mechanisms of arthritis. Diseases modifying anti-rheumatoid drugs reduce the symptoms of arthritis like decrease in pain and disability score, reduction of swollen joints, articular index and serum concentration of acute phage proteins. Recently, some literature references are obtained on molecular modeling of antirheumatic agents. We have tried chemometric modeling through 2D-QSAR studies on a dataset of fifty-one compounds out of which forty-four 5-Phenylthiophenecarboxylic acid derivatives have IL-1 inhibitory activity and forty-six 5-Phenylthiophenecarboxylic acid derivatives have %AIA suppressive activity. The work was done to find out the structural requirements of these anti-rheumatic agents. 2D QSAR models were generated by 2D and 3D descriptors by using multiple linear regression and partial least square method where IL-1 antagonism was considered as the biological activity parameter. Statistically significant models were developed on the training set developed by k-means cluster analysis. Sterimol parameters, electronic interaction at atom number 9, 2D autocorrelation descriptors, information content descriptor, average connectivity index chi-3, radial distribution function, Balaban 3D index and 3D-MoRSE descriptors were found to play crucial roles to modulate IL-1 inhibitory activity. 2D autocorrelation descriptors like Broto-Moreau autocorrelation of topological structure-lag 3 weighted by atomic van der Waals volumes, Geary autocorrelation-lag 7 associated with weighted atomic Sanderson electronegativities and 3D-MoRSE descriptors like 3D-MoRSE-signal 22 related to atomic van der Waals volumes, 3D-MoRSE-signal 28 related to atomic van der Waals volumes and 3D-MoRSE-signal 9 which was unweighted, were found to play important roles to model %AIA suppressive activity.

Dynamics of comb-of-comb-network polymers in random layered flows

NASA Astrophysics Data System (ADS)

Katyal, Divya; Kant, Rama

2016-12-01

We analyze the dynamics of comb-of-comb-network polymers in the presence of external random flows. The dynamics of such structures is evaluated through relevant physical quantities, viz., average square displacement (ASD) and the velocity autocorrelation function (VACF). We focus on comparing the dynamics of the comb-of-comb network with the linear polymer. The present work displays an anomalous diffusive behavior of this flexible network in the random layered flows. The effect of the polymer topology on the dynamics is analyzed by varying the number of generations and branch lengths in these networks. In addition, we investigate the influence of external flow on the dynamics by varying flow parameters, like the flow exponent α and flow strength Wα. Our analysis highlights two anomalous power-law regimes, viz., subdiffusive (intermediate-time polymer stretching and flow-induced diffusion) and superdiffusive (long-time flow-induced diffusion). The anomalous long-time dynamics is governed by the temporal exponent ν of ASD, viz., ν =2 -α /2 . Compared to a linear polymer, the comb-of-comb network shows a shorter crossover time (from the subdiffusive to superdiffusive regime) but a reduced magnitude of ASD. Our theory displays an anomalous VACF in the random layered flows that scales as t-α /2. We show that the network with greater total mass moves faster.

Listening level of music through headphones in train car noise environments.

PubMed

Shimokura, Ryota; Soeta, Yoshiharu

2012-09-01

Although portable music devices are useful for passing time on trains, exposure to music using headphones for long periods carries the risk of damaging hearing acuity. The aim of this study is to examine the listening level of music through headphones in the noisy environment of a train car. Eight subjects adjusted the volume to an optimum level (L(music)) in a simulated noisy train car environment. In Experiment I, the effects of noise level (L(train)) and type of train noise (rolling, squealing, impact, and resonance) were examined. Spectral and temporal characteristics were found to be different according to the train noise type. In Experiment II, the effects of L(train) and type of music (five vocal and five instrumental music) were examined. Each music type had a different pitch strength and spectral centroid, and each was evaluated by φ(1) and W(φ(0)), respectively. These were classified as factors of the autocorrelation function (ACF) of the music. Results showed that L(music) increased as L(train) increased in both experiments, while the type of music greatly influenced L(music). The type of train noise, however, only slightly influenced L(music). L(music) can be estimated using L(train) and the ACF factors φ(1) and W(φ(0)).

Role of density modulation in the spatially resolved dynamics of strongly confined liquids

DOE Office of Scientific and Technical Information (OSTI.GOV)

Saw, Shibu, E-mail: shibu.saw@sydney.edu.au; Dasgupta, Chandan, E-mail: cdgupta@physics.iisc.ernet.in

Confinement by walls usually produces a strong modulation in the density of dense liquids near the walls. Using molecular dynamics simulations, we examine the effects of the density modulation on the spatially resolved dynamics of a liquid confined between two parallel walls, using a resolution of a fraction of the interparticle distance in the liquid. The local dynamics is quantified by the relaxation time associated with the temporal autocorrelation function of the local density. We find that this local relaxation time varies in phase with the density modulation. The amplitude of the spatial modulation of the relaxation time can bemore » quite large, depending on the characteristics of the wall and thermodynamic parameters of the liquid. To disentangle the effects of confinement and density modulation on the spatially resolved dynamics, we compare the dynamics of a confined liquid with that of an unconfined one in which a similar density modulation is induced by an external potential. We find several differences indicating that density modulation alone cannot account for all the features seen in the spatially resolved dynamics of confined liquids. We also examine how the dynamics near a wall depends on the separation between the two walls and show that the features seen in our simulations persist in the limit of large wall separation.« less

Natural Time and Nowcasting Earthquakes: Are Large Global Earthquakes Temporally Clustered?

NASA Astrophysics Data System (ADS)

Luginbuhl, Molly; Rundle, John B.; Turcotte, Donald L.

2018-02-01

The objective of this paper is to analyze the temporal clustering of large global earthquakes with respect to natural time, or interevent count, as opposed to regular clock time. To do this, we use two techniques: (1) nowcasting, a new method of statistically classifying seismicity and seismic risk, and (2) time series analysis of interevent counts. We chose the sequences of M_{λ } ≥ 7.0 and M_{λ } ≥ 8.0 earthquakes from the global centroid moment tensor (CMT) catalog from 2004 to 2016 for analysis. A significant number of these earthquakes will be aftershocks of the largest events, but no satisfactory method of declustering the aftershocks in clock time is available. A major advantage of using natural time is that it eliminates the need for declustering aftershocks. The event count we utilize is the number of small earthquakes that occur between large earthquakes. The small earthquake magnitude is chosen to be as small as possible, such that the catalog is still complete based on the Gutenberg-Richter statistics. For the CMT catalog, starting in 2004, we found the completeness magnitude to be M_{σ } ≥ 5.1. For the nowcasting method, the cumulative probability distribution of these interevent counts is obtained. We quantify the distribution using the exponent, β, of the best fitting Weibull distribution; β = 1 for a random (exponential) distribution. We considered 197 earthquakes with M_{λ } ≥ 7.0 and found β = 0.83 ± 0.08. We considered 15 earthquakes with M_{λ } ≥ 8.0, but this number was considered too small to generate a meaningful distribution. For comparison, we generated synthetic catalogs of earthquakes that occur randomly with the Gutenberg-Richter frequency-magnitude statistics. We considered a synthetic catalog of 1.97 × 10^5 M_{λ } ≥ 7.0 earthquakes and found β = 0.99 ± 0.01. The random catalog converted to natural time was also random. We then generated 1.5 × 10^4 synthetic catalogs with 197 M_{λ } ≥ 7.0 in each catalog and found the statistical range of β values. The observed value of β = 0.83 for the CMT catalog corresponds to a p value of p=0.004 leading us to conclude that the interevent natural times in the CMT catalog are not random. For the time series analysis, we calculated the autocorrelation function for the sequence of natural time intervals between large global earthquakes and again compared with data from 1.5 × 10^4 synthetic catalogs of random data. In this case, the spread of autocorrelation values was much larger, so we concluded that this approach is insensitive to deviations from random behavior.

Typology of alcohol users based on longitudinal patterns of drinking.

PubMed

Harrington, Magdalena; Velicer, Wayne F; Ramsey, Susan

2014-03-01

Worldwide, alcohol is the most commonly used psychoactive substance. However, heterogeneity among alcohol users has been widely recognized. This paper presents a typology of alcohol users based on an implementation of idiographic methodology to examine longitudinal daily and cyclic (weekly) patterns of alcohol use at the individual level. A secondary data analysis was performed on the pre-intervention data from a large randomized control trial. A time series analysis was performed at the individual level, and a dynamic cluster analysis was employed to identify homogenous longitudinal patterns of drinking behavior at the group level. The analysis employed 180 daily observations of alcohol use in a sample of 177 alcohol users. The first order autocorrelations ranged from -.76 to .72, and seventh order autocorrelations ranged from -.27 to .79. Eight distinct profiles of alcohol users were identified, each characterized by a unique configuration of first and seventh autoregressive terms and longitudinal trajectories of alcohol use. External validity of the profiles confirmed the theoretical relevance of different patterns of alcohol use. Significant differences among the eight subtypes were found on gender, marital status, frequency of drug use, lifetime alcohol dependence, family history of alcohol use and the Short Index of Problems. Our findings demonstrate that individuals can have very different temporal patterns of drinking behavior. The daily and cyclic patterns of alcohol use may be important for designing tailored interventions for problem drinkers. Copyright © 2013 Elsevier Ltd. All rights reserved.

Typology of Alcohol Users Based on Longitudinal Patterns of Drinking

PubMed Central

Harrington, Magdalena; Velicer, Wayne F.; Ramsey, Susan

2014-01-01

Objective Worldwide, alcohol is the most commonly used psychoactive substance. However, heterogeneity among alcohol users has been widely recognized. This paper presents a typology of alcohol users based on an implementation of idiographic methodology to examine longitudinal daily and cyclic (weekly) patterns of alcohol use at the individual level. Method A secondary data analysis was performed on the pre-intervention data from a large randomized control trial. A time series analysis was performed at the individual level, and a dynamic cluster analysis was employed to identify homogenous longitudinal patterns of drinking behavior at the group level. The analysis employed 180 daily observations of alcohol use in a sample of 177 alcohol users. Results The first order autocorrelations ranged from −.76 to .72, and seventh order autocorrelations ranged from −.27 to .79. Eight distinct profiles of alcohol users were identified, each characterized by a unique configuration of first and seventh autoregressive terms and longitudinal trajectories of alcohol use. External validity of the profiles confirmed the theoretical relevance of different patterns of alcohol use. Significant differences among the eight subtypes were found on gender, marital status, frequency of drug use, lifetime alcohol dependence, family history of alcohol use and the Short Index of Problems. Conclusions Our findings demonstrate that individuals can have very different temporal patterns of drinking behavior. The daily and cyclic patterns of alcohol use may be important for designing tailored interventions for problem drinkers. PMID:24333036

Spatio-temporal pattern of sylvatic rabies in the Sultanate of Oman, 2006-2010.

PubMed

Hussain, Muhammad Hammad; Ward, Michael P; Body, Mohammed; Al-Rawahi, Abdulmajeed; Wadir, Ali Awlad; Al-Habsi, Saif; Saqib, Muhammad; Ahmed, Mohammed Sayed; Almaawali, Mahir Gharib

2013-07-01

Rabies was first reported in the Sultanate of Oman is 1990. We analysed passive surveillance data (444 samples) collected and reported between 2006 and 2010. During this period, between 45 and 75% of samples submitted from suspect animals were subsequently confirmed (fluorescent antibody test, histopathology and reverse transcription PCR) as rabies cases. Overall, 63% of submitted samples were confirmed as rabies cases. The spatial distribution of species-specific cases were similar (centred in north-central Oman with a northeast-southwest distribution), although fox cases had a wider distribution and an east-west orientation. Clustering of cases was detected using interpolation, local spatial autocorrelation and scan statistical analysis. Several local government areas (wilayats) in north-central Oman were identified where higher than expected numbers of laboratory-confirmed rabies cases were reported. For fox rabies, more clusters (local spatial autocorrelation analysis) and a larger clustered area (scan statistical analysis) were detected. In Oman, monthly reports of fox rabies cases were highly correlated (rSP>0.5) with reports of camel, cattle, sheep and goat rabies. The best-fitting ARIMA model included a seasonality component. Fox rabies cases reported 6 months previously best explained rabies reported cases in other animal species. Despite likely reporting bias, results suggest that rabies exists as a sylvatic cycle of transmission in Oman and an opportunity still exists to prevent establishment of dog-mediated rabies. Copyright © 2013 Elsevier B.V. All rights reserved.

Evaluating platelet aggregation dynamics from laser speckle fluctuations.

PubMed

Hajjarian, Zeinab; Tshikudi, Diane M; Nadkarni, Seemantini K

2017-07-01

Platelets are key to maintaining hemostasis and impaired platelet aggregation could lead to hemorrhage or thrombosis. We report a new approach that exploits laser speckle intensity fluctuations, emanated from a drop of platelet-rich-plasma (PRP), to profile aggregation. Speckle fluctuation rate is quantified by the speckle intensity autocorrelation, g 2 (t) , from which the aggregate size is deduced. We first apply this approach to evaluate polystyrene bead aggregation, triggered by salt. Next, we assess dose-dependent platelet aggregation and inhibition in human PRP spiked with adenosine diphosphate and clopidogrel. Additional spatio-temporal speckle analyses yield 2-dimensional maps of particle displacements to visualize platelet aggregate foci within minutes and quantify aggregation dynamics. These findings demonstrate the unique opportunity for assessing platelet health within minutes for diagnosing bleeding disorders and monitoring anti-platelet therapies.

Temporal measurement on and using pulses from spectrally narrowed emission in styrylpyridinium cyanine dye

NASA Astrophysics Data System (ADS)

Dharmadhikari, Aditya K.; Bhowmik, Achintya K.; Ahyi, Ayayi C.; Thakur, Mrinal

2001-11-01

Highly efficient spectrally narrowed emission (SNE) was observed in the solution of strylpyridinium cyanine dye (SPCD) pumped by fundamental and second harmonic of a picosecond Nd:YAG laser in two separate arrangements. A highly directional emission was observed in both the pumping arrangements without incorporating any mirrors. The pulse duration of the SNE was measured by background free SHG intensity autocorrelation technique. The measured duration of the pulses was 40 ps. These pulses, having a spectral linewidth of 10 nm (full width at half maximum), were used as a probe to measure the transient changes in the transmission in SPCD solution using a pump-probe setup. The transient optical transmission indicated a gain at the overlap and no gain was observed beyond a delay of 40 ps.

Simultaneous narrowband ultrasonic strain-flow imaging

NASA Astrophysics Data System (ADS)

Tsou, Jean K.; Mai, Jerome J.; Lupotti, Fermin A.; Insana, Michael F.

2004-04-01

We are summarizing new research aimed at forming spatially and temporally registered combinations of strain and color-flow images using echo data recorded from a commercial ultrasound system. Applications include diagnosis of vascular diseases and tumor malignancies. The challenge is to meet the diverse needs of each measurement. The approach is to first apply eigenfilters that separate echo components from moving tissues and blood flow, and then estimate blood velocity and tissue displacement from the filtered-IQ-signal phase modulations. At the cost of a lower acquisition frame rate, we find the autocorrelation strain estimator yields higher resolution strain estimate than the cross-correlator since estimates are made from ensembles at a single point in space. The technique is applied to in vivo carotid imaging, to demonstrate the sensitivity for strain-flow vascular imaging.

Spectral factorization of wavefields and wave operators

NASA Astrophysics Data System (ADS)

Rickett, James Edward

Spectral factorization is the problem of finding a minimum-phase function with a given power spectrum. Minimum phase functions have the property that they are causal with a causal (stable) inverse. In this thesis, I factor multidimensional systems into their minimum-phase components. Helical boundary conditions resolve any ambiguities over causality, allowing me to factor multi-dimensional systems with conventional one-dimensional spectral factorization algorithms. In the first part, I factor passive seismic wavefields recorded in two-dimensional spatial arrays. The result provides an estimate of the acoustic impulse response of the medium that has higher bandwidth than autocorrelation-derived estimates. Also, the function's minimum-phase nature mimics the physics of the system better than the zero-phase autocorrelation model. I demonstrate this on helioseismic data recorded by the satellite-based Michelson Doppler Imager (MDI) instrument, and shallow seismic data recorded at Long Beach, California. In the second part of this thesis, I take advantage of the stable-inverse property of minimum-phase functions to solve wave-equation partial differential equations. By factoring multi-dimensional finite-difference stencils into minimum-phase components, I can invert them efficiently, facilitating rapid implicit extrapolation without the azimuthal anisotropy that is observed with splitting approximations. The final part of this thesis describes how to calculate diagonal weighting functions that approximate the combined operation of seismic modeling and migration. These weighting functions capture the effects of irregular subsurface illumination, which can be the result of either the surface-recording geometry, or focusing and defocusing of the seismic wavefield as it propagates through the earth. Since they are diagonal, they can be easily both factored and inverted to compensate for uneven subsurface illumination in migrated images. Experimental results show that applying these weighting functions after migration leads to significantly improved estimates of seismic reflectivity.

Controlling for seasonal patterns and time varying confounders in time-series epidemiological models: a simulation study.

PubMed

Perrakis, Konstantinos; Gryparis, Alexandros; Schwartz, Joel; Le Tertre, Alain; Katsouyanni, Klea; Forastiere, Francesco; Stafoggia, Massimo; Samoli, Evangelia

2014-12-10

An important topic when estimating the effect of air pollutants on human health is choosing the best method to control for seasonal patterns and time varying confounders, such as temperature and humidity. Semi-parametric Poisson time-series models include smooth functions of calendar time and weather effects to control for potential confounders. Case-crossover (CC) approaches are considered efficient alternatives that control seasonal confounding by design and allow inclusion of smooth functions of weather confounders through their equivalent Poisson representations. We evaluate both methodological designs with respect to seasonal control and compare spline-based approaches, using natural splines and penalized splines, and two time-stratified CC approaches. For the spline-based methods, we consider fixed degrees of freedom, minimization of the partial autocorrelation function, and general cross-validation as smoothing criteria. Issues of model misspecification with respect to weather confounding are investigated under simulation scenarios, which allow quantifying omitted, misspecified, and irrelevant-variable bias. The simulations are based on fully parametric mechanisms designed to replicate two datasets with different mortality and atmospheric patterns. Overall, minimum partial autocorrelation function approaches provide more stable results for high mortality counts and strong seasonal trends, whereas natural splines with fixed degrees of freedom perform better for low mortality counts and weak seasonal trends followed by the time-season-stratified CC model, which performs equally well in terms of bias but yields higher standard errors. Copyright © 2014 John Wiley & Sons, Ltd.

Estimation of correlation functions by stochastic approximation.

NASA Technical Reports Server (NTRS)

Habibi, A.; Wintz, P. A.

1972-01-01

Consideration of the autocorrelation function of a zero-mean stationary random process. The techniques are applicable to processes with nonzero mean provided the mean is estimated first and subtracted. Two recursive techniques are proposed, both of which are based on the method of stochastic approximation and assume a functional form for the correlation function that depends on a number of parameters that are recursively estimated from successive records. One technique uses a standard point estimator of the correlation function to provide estimates of the parameters that minimize the mean-square error between the point estimates and the parametric function. The other technique provides estimates of the parameters that maximize a likelihood function relating the parameters of the function to the random process. Examples are presented.

Autocorrelation of location estimates and the analysis of radiotracking data

USGS Publications Warehouse

Otis, D.L.; White, Gary C.

1999-01-01

The wildlife literature has been contradictory about the importance of autocorrelation in radiotracking data used for home range estimation and hypothesis tests of habitat selection. By definition, the concept of a home range involves autocorrelated movements, but estimates or hypothesis tests based on sampling designs that predefine a time frame of interest, and that generate representative samples of an animal's movement during this time frame, should not be affected by length of the sampling interval and autocorrelation. Intensive sampling of the individual's home range and habitat use during the time frame of the study leads to improved estimates for the individual, but use of location estimates as the sample unit to compare across animals is pseudoreplication. We therefore recommend against use of habitat selection analysis techniques that use locations instead of individuals as the sample unit. We offer a general outline for sampling designs for radiotracking studies.

Assessing the significance of global and local correlations under spatial autocorrelation: a nonparametric approach.

PubMed

Viladomat, Júlia; Mazumder, Rahul; McInturff, Alex; McCauley, Douglas J; Hastie, Trevor

2014-06-01

We propose a method to test the correlation of two random fields when they are both spatially autocorrelated. In this scenario, the assumption of independence for the pair of observations in the standard test does not hold, and as a result we reject in many cases where there is no effect (the precision of the null distribution is overestimated). Our method recovers the null distribution taking into account the autocorrelation. It uses Monte-Carlo methods, and focuses on permuting, and then smoothing and scaling one of the variables to destroy the correlation with the other, while maintaining at the same time the initial autocorrelation. With this simulation model, any test based on the independence of two (or more) random fields can be constructed. This research was motivated by a project in biodiversity and conservation in the Biology Department at Stanford University. © 2014, The International Biometric Society.

Method and apparatus for in-situ characterization of energy storage and energy conversion devices

DOEpatents

Christophersen, Jon P [Idaho Falls, ID; Motloch, Chester G [Idaho Falls, ID; Morrison, John L [Butte, MT; Albrecht, Weston [Layton, UT

2010-03-09

Disclosed are methods and apparatuses for determining an impedance of an energy-output device using a random noise stimulus applied to the energy-output device. A random noise signal is generated and converted to a random noise stimulus as a current source correlated to the random noise signal. A bias-reduced response of the energy-output device to the random noise stimulus is generated by comparing a voltage at the energy-output device terminal to an average voltage signal. The random noise stimulus and bias-reduced response may be periodically sampled to generate a time-varying current stimulus and a time-varying voltage response, which may be correlated to generate an autocorrelated stimulus, an autocorrelated response, and a cross-correlated response. Finally, the autocorrelated stimulus, the autocorrelated response, and the cross-correlated response may be combined to determine at least one of impedance amplitude, impedance phase, and complex impedance.

Lévy flights, autocorrelation, and slow convergence

NASA Astrophysics Data System (ADS)

Figueiredo, Annibal; Gleria, Iram; Matsushita, Raul; Da Silva, Sergio

2004-06-01

Previously we have put forward that the sluggish convergence of truncated Lévy flights to a Gaussian (Phys. Rev. Lett. 73 (1994) 2946) together with the scaling power laws in their probability of return to the origin (Nature 376 (1995) 46) can be explained by autocorrelation in data (Physica A 323 (2003) 601; Phys. Lett. A 315 (2003) 51). A purpose of this paper is to improve and enlarge the scope of such a result. The role of the autocorrelations in the convergence process as well as the problem of establishing the distance of a given distribution to the Gaussian are analyzed in greater detail. We show that whereas power laws in the second moment can still be explained by linear correlation of pairs, sluggish convergence can now emerge from nonlinear autocorrelations. Our approach is exemplified with data from the British pound-US dollar exchange rate.

Determination of design and operation parameters for upper atmospheric research instrumentation to yield optimum resolution with deconvolution, appendix 2

NASA Technical Reports Server (NTRS)

Ioup, George E.; Ioup, Juliette W.

1988-01-01

This thesis reviews the technique established to clear channels in the Power Spectral Estimate by applying linear combinations of well known window functions to the autocorrelation function. The need for windowing the auto correlation function is due to the fact that the true auto correlation is not generally used to obtain the Power Spectral Estimate. When applied, the windows serve to reduce the effect that modifies the auto correlation by truncating the data and possibly the autocorrelation has on the Power Spectral Estimate. It has been shown in previous work that a single channel has been cleared, allowing for the detection of a small peak in the presence of a large peak in the Power Spectral Estimate. The utility of this method is dependent on the robustness of it on different input situations. We extend the analysis in this paper, to include clearing up to three channels. We examine the relative positions of the spikes to each other and also the effect of taking different percentages of lags of the auto correlation in the Power Spectral Estimate. This method could have application wherever the Power Spectrum is used. An example of this is beam forming for source location, where a small target can be located next to a large target. Other possibilities extend into seismic data processing. As the method becomes more automated other applications may present themselves.

Coarse-grained modeling of polyethylene melts: Effect on dynamics

DOE PAGES

Peters, Brandon L.; Salerno, K. Michael; Agrawal, Anupriya; ...

2017-05-23

The distinctive viscoelastic behavior of polymers results from a coupled interplay of motion on multiple length and time scales. Capturing the broad time and length scales of polymer motion remains a challenge. Using polyethylene (PE) as a model macromolecule, we construct coarse-grained (CG) models of PE with three to six methyl groups per CG bead and probe two critical aspects of the technique: pressure corrections required after iterative Boltzmann inversion (IBI) to generate CG potentials that match the pressure of reference fully atomistic melt simulations and the transferability of CG potentials across temperatures. While IBI produces nonbonded pair potentials thatmore » give excellent agreement between the atomistic and CG pair correlation functions, the resulting pressure for the CG models is large compared with the pressure of the atomistic system. We find that correcting the potential to match the reference pressure leads to nonbonded interactions with much deeper minima and slightly smaller effective bead diameter. However, simulations with potentials generated by IBI and pressure-corrected IBI result in similar mean-square displacements (MSDs) and stress autocorrelation functions G( t) for PE melts. While the time rescaling factor required to match CG and atomistic models is the same for pressure- and non-pressure-corrected CG models, it strongly depends on temperature. Furthermore, transferability was investigated by comparing the MSDs and stress autocorrelation functions for potentials developed at different temperatures.« less

Coarse-grained modeling of polyethylene melts: Effect on dynamics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Peters, Brandon L.; Salerno, K. Michael; Agrawal, Anupriya

The distinctive viscoelastic behavior of polymers results from a coupled interplay of motion on multiple length and time scales. Capturing the broad time and length scales of polymer motion remains a challenge. Using polyethylene (PE) as a model macromolecule, we construct coarse-grained (CG) models of PE with three to six methyl groups per CG bead and probe two critical aspects of the technique: pressure corrections required after iterative Boltzmann inversion (IBI) to generate CG potentials that match the pressure of reference fully atomistic melt simulations and the transferability of CG potentials across temperatures. While IBI produces nonbonded pair potentials thatmore » give excellent agreement between the atomistic and CG pair correlation functions, the resulting pressure for the CG models is large compared with the pressure of the atomistic system. We find that correcting the potential to match the reference pressure leads to nonbonded interactions with much deeper minima and slightly smaller effective bead diameter. However, simulations with potentials generated by IBI and pressure-corrected IBI result in similar mean-square displacements (MSDs) and stress autocorrelation functions G( t) for PE melts. While the time rescaling factor required to match CG and atomistic models is the same for pressure- and non-pressure-corrected CG models, it strongly depends on temperature. Furthermore, transferability was investigated by comparing the MSDs and stress autocorrelation functions for potentials developed at different temperatures.« less

Using simulations and data to evaluate mean sensitivity (ζ) as a useful statistic in dendrochronology

Treesearch

Andrew G. Bunn; Esther Jansma; Mikko Korpela; Robert D. Westfall; James Baldwin

2013-01-01

Mean sensitivity (ζ) continues to be used in dendrochronology despite a literature that shows it to be of questionable value in describing the properties of a time series. We simulate first-order autoregressive models with known parameters and show that ζ is a function of variance and autocorrelation of a time series. We then use 500 random tree-ring...

Photon antibunching from a single lithographically defined InGaAs/GaAs quantum dot.

PubMed

Verma, V B; Stevens, Martin J; Silverman, K L; Dias, N L; Garg, A; Coleman, J J; Mirin, R P

2011-02-28

We demonstrate photon antibunching from a single lithographically defined quantum dot fabricated by electron beam lithography, wet chemical etching, and overgrowth of the barrier layers by metalorganic chemical vapor deposition. Measurement of the second-order autocorrelation function indicates g(2)(0)=0.395±0.030, below the 0.5 limit necessary for classification as a single photon source.

Hydration dynamics of a lipid membrane: Hydrogen bond networks and lipid-lipid associations

NASA Astrophysics Data System (ADS)

Srivastava, Abhinav; Debnath, Ananya

2018-03-01

Dynamics of hydration layers of a dimyristoylphosphatidylcholine (DMPC) bilayer are investigated using an all atom molecular dynamics simulation. Based upon the geometric criteria, continuously residing interface water molecules which form hydrogen bonds solely among themselves and then concertedly hydrogen bonded to carbonyl, phosphate, and glycerol head groups of DMPC are identified. The interface water hydrogen bonded to lipids shows slower relaxation rates for translational and rotational dynamics compared to that of the bulk water and is found to follow sub-diffusive and non-diffusive behaviors, respectively. The mean square displacements and the reorientational auto-correlation functions are slowest for the interfacial waters hydrogen bonded to the carbonyl oxygen since these are buried deep in the hydrophobic core among all interfacial water studied. The intermittent hydrogen bond auto-correlation functions are calculated, which allows breaking and reformations of the hydrogen bonds. The auto-correlation functions for interfacial hydrogen bonded networks develop humps during a transition from cage-like motion to eventual power law behavior of t-3/2. The asymptotic t-3/2 behavior indicates translational diffusion dictated dynamics during hydrogen bond breaking and formation irrespective of the nature of the chemical confinement. Employing reactive flux correlation analysis, the forward rate constant of hydrogen bond breaking and formation is calculated which is used to obtain Gibbs energy of activation of the hydrogen bond breaking. The relaxation rates of the networks buried in the hydrophobic core are slower than the networks near the lipid-water interface which is again slower than bulk due to the higher Gibbs energy of activation. Since hydrogen bond breakage follows a translational diffusion dictated mechanism, chemically confined hydrogen bond networks need an activation energy to diffuse through water depleted hydrophobic environments. Our calculations reveal that the slow relaxation rates of interfacial waters in the vicinity of lipids are originated from the chemical confinement of concerted hydrogen bond networks. The analysis suggests that the networks in the hydration layer of membranes dynamically facilitate the water mediated lipid-lipid associations which can provide insights on the thermodynamic stability of soft interfaces relevant to biological systems in the future.

Estimation of the characteristic parameters of the multilayered film model using the patterson differential function

DOE Office of Scientific and Technical Information (OSTI.GOV)

Astaf'ev, S. B., E-mail: webmaster@ns.crys.ras.ru; Shchedrin, B. M.; Yanusova, L. G.

The possibility of estimating the layered film structural parameters by constructing the autocorrelation function P{sub F}(z) (referred to as the Patterson differential function) for the derivative d{rho}/dz of electron density along the normal to the sample surface has been considered. An analytical expression P{sub F}(z) is presented for a multilayered film within the box model of the electron density profile. The possibilities of selecting structural information about layered films by analyzing the features of this function are demonstrated by model and real examples, in particular, by applying the method of shifted systems of peaks for the function P{sub F}(z).

Assessing spatial inequalities in accessing community pharmacies: a mixed geographically weighted approach.

PubMed

Domnich, Alexander; Arata, Lucia; Amicizia, Daniela; Signori, Alessio; Gasparini, Roberto; Panatto, Donatella

2016-11-16

Geographical accessibility is an important determinant for the utilisation of community pharmacies. The present study explored patterns of spatial accessibility with respect to pharmacies in Liguria, Italy, a region with particular geographical and demographic features. Municipal density of pharmacies was proxied as the number of pharmacies per capita and per km2, and spatial autocorrelation analysis was performed to identify spatial clusters. Both non-spatial and spatial models were constructed to predict the study outcome. Spatial autocorrelation analysis showed a highly significant clustered pattern in the density of pharmacies per capita (I=0.082) and per km2 (I=0.295). Potentially under-supplied areas were mostly located in the mountainous hinterland. Ordinary least-squares (OLS) regressions established a significant positive relationship between the density of pharmacies and income among municipalities located at high altitudes, while no such association was observed in lower-lying areas. However, residuals of the OLS models were spatially auto-correlated. The best-fitting mixed geographically weighted regression (GWR) models outperformed the corresponding OLS models. Pharmacies per capita were best predicted by two local predictors (altitude and proportion of immigrants) and two global ones (proportion of elderly residents and income), while the local terms population, mean altitude and rural status and the global term income functioned as independent variables predicting pharmacies per km2. The density of pharmacies in Liguria was found to be associated with both socio-economic and landscape factors. Mapping of mixed GWR results would be helpful to policy-makers.

Spatial and temporal analyses of citrus sudden death as a tool to generate hypotheses concerning its etiology.

PubMed

Bassanezi, Renato B; Bergamin Filho, Armando; Amorim, Lilian; Gimenes-Fernandes, Nelson; Gottwald, Tim R; Bové, Joseph M

2003-04-01

ABSTRACT Citrus sudden death (CSD), a new disease of unknown etiology that affects sweet orange grafted on Rangpur lime, was visually monitored for 14 months in 41 groves in Brazil. Ordinary runs analysis of CSD-symptomatic trees indicated a departure from randomness of symptomatic trees status among immediately adjacent trees mainly within rows. The binomial index of dispersion (D) and the intraclass correlation (k) for various quadrat sizes suggested aggregation of CSD-symptomatic trees for almost all plots within the quadrat sizes tested. Estimated parameters of the binary form of Taylor's power law provided an overall measure of aggregation of CSD-symptomatic trees for all quadrat sizes tested. Aggregation in each plot was dependent on disease incidence. Spatial autocorrelation analysis of proximity patterns suggested that aggregation often existed among quadrats of various sizes up to three lag distances; however, significant lag positions discontinuous from main proximity patterns were rare, indicating a lack of spatial association among discrete foci. Some asymmetry was also detected for some spatial autocorrelation proximity patterns, indicating that within-row versus across-row distributions are not necessarily equivalent. These results were interpreted to mean that the cause of the disease was most likely biotic and its dissemination was common within a local area of influence that extended to approximately six trees in all directions, including adjacent trees. Where asymmetry was indicated, this area of influence was somewhat elliptical. Longer-distance patterns were not detected within the confines of the plot sizes tested. Annual rates of CSD progress based on the Gompertz model ranged from 0.37 to 2.02. Numerous similarities were found between the spatial patterns of CSD and Citrus tristeza virus (CTV) described in the literature, both in the presence of the aphid vector, Toxoptera citricida. CSD differs from CTV in that symptoms occur in sweet orange grafted on Rangpur lime. Based on the symptoms of CSD and on its spatial and temporal patterns, our hypothesis is that CSD may be caused by a similar but undescribed pathogen such as a virus and probably vectored by insects such as aphids by similar spatial processes to those affecting CTV.

Bounds of memory strength for power-law series.

PubMed

Guo, Fangjian; Yang, Dan; Yang, Zimo; Zhao, Zhi-Dan; Zhou, Tao

2017-05-01

Many time series produced by complex systems are empirically found to follow power-law distributions with different exponents α. By permuting the independently drawn samples from a power-law distribution, we present nontrivial bounds on the memory strength (first-order autocorrelation) as a function of α, which are markedly different from the ordinary ±1 bounds for Gaussian or uniform distributions. When 1<α≤3, as α grows bigger, the upper bound increases from 0 to +1 while the lower bound remains 0; when α>3, the upper bound remains +1 while the lower bound descends below 0. Theoretical bounds agree well with numerical simulations. Based on the posts on Twitter, ratings of MovieLens, calling records of the mobile operator Orange, and the browsing behavior of Taobao, we find that empirical power-law-distributed data produced by human activities obey such constraints. The present findings explain some observed constraints in bursty time series and scale-free networks and challenge the validity of measures such as autocorrelation and assortativity coefficient in heterogeneous systems.

High-Responsivity Graphene-Boron Nitride Photodetector and Autocorrelator in a Silicon Photonic Integrated Circuit.

PubMed

Shiue, Ren-Jye; Gao, Yuanda; Wang, Yifei; Peng, Cheng; Robertson, Alexander D; Efetov, Dmitri K; Assefa, Solomon; Koppens, Frank H L; Hone, James; Englund, Dirk

2015-11-11

Graphene and other two-dimensional (2D) materials have emerged as promising materials for broadband and ultrafast photodetection and optical modulation. These optoelectronic capabilities can augment complementary metal-oxide-semiconductor (CMOS) devices for high-speed and low-power optical interconnects. Here, we demonstrate an on-chip ultrafast photodetector based on a two-dimensional heterostructure consisting of high-quality graphene encapsulated in hexagonal boron nitride. Coupled to the optical mode of a silicon waveguide, this 2D heterostructure-based photodetector exhibits a maximum responsivity of 0.36 A/W and high-speed operation with a 3 dB cutoff at 42 GHz. From photocurrent measurements as a function of the top-gate and source-drain voltages, we conclude that the photoresponse is consistent with hot electron mediated effects. At moderate peak powers above 50 mW, we observe a saturating photocurrent consistent with the mechanisms of electron-phonon supercollision cooling. This nonlinear photoresponse enables optical on-chip autocorrelation measurements with picosecond-scale timing resolution and exceptionally low peak powers.

Blind equalization with criterion with memory nonlinearity

NASA Astrophysics Data System (ADS)

Chen, Yuanjie; Nikias, Chrysostomos L.; Proakis, John G.

1992-06-01

Blind equalization methods usually combat the linear distortion caused by a nonideal channel via a transversal filter, without resorting to the a priori known training sequences. We introduce a new criterion with memory nonlinearity (CRIMNO) for the blind equalization problem. The basic idea of this criterion is to augment the Godard [or constant modulus algorithm (CMA)] cost function with additional terms that penalize the autocorrelations of the equalizer outputs. Several variations of the CRIMNO algorithms are derived, with the variations dependent on (1) whether the empirical averages or the single point estimates are used to approximate the expectations, (2) whether the recent or the delayed equalizer coefficients are used, and (3) whether the weights applied to the autocorrelation terms are fixed or are allowed to adapt. Simulation experiments show that the CRIMNO algorithm, and especially its adaptive weight version, exhibits faster convergence speed than the Godard (or CMA) algorithm. Extensions of the CRIMNO criterion to accommodate the case of correlated inputs to the channel are also presented.

Bounds of memory strength for power-law series

NASA Astrophysics Data System (ADS)

Guo, Fangjian; Yang, Dan; Yang, Zimo; Zhao, Zhi-Dan; Zhou, Tao

2017-05-01

Many time series produced by complex systems are empirically found to follow power-law distributions with different exponents α . By permuting the independently drawn samples from a power-law distribution, we present nontrivial bounds on the memory strength (first-order autocorrelation) as a function of α , which are markedly different from the ordinary ±1 bounds for Gaussian or uniform distributions. When 1 <α ≤3 , as α grows bigger, the upper bound increases from 0 to +1 while the lower bound remains 0; when α >3 , the upper bound remains +1 while the lower bound descends below 0. Theoretical bounds agree well with numerical simulations. Based on the posts on Twitter, ratings of MovieLens, calling records of the mobile operator Orange, and the browsing behavior of Taobao, we find that empirical power-law-distributed data produced by human activities obey such constraints. The present findings explain some observed constraints in bursty time series and scale-free networks and challenge the validity of measures such as autocorrelation and assortativity coefficient in heterogeneous systems.

Design of two-dimensional zero reference codes with cross-entropy method.

PubMed

Chen, Jung-Chieh; Wen, Chao-Kai

2010-06-20

We present a cross-entropy (CE)-based method for the design of optimum two-dimensional (2D) zero reference codes (ZRCs) in order to generate a zero reference signal for a grating measurement system and achieve absolute position, a coordinate origin, or a machine home position. In the absence of diffraction effects, the 2D ZRC design problem is known as the autocorrelation approximation. Based on the properties of the autocorrelation function, the design of the 2D ZRC is first formulated as a particular combination optimization problem. The CE method is then applied to search for an optimal 2D ZRC and thus obtain the desirable zero reference signal. Computer simulation results indicate that there are 15.38% and 14.29% reductions in the second maxima value for the 16x16 grating system with n(1)=64 and the 100x100 grating system with n(1)=300, respectively, where n(1) is the number of transparent pixels, compared with those of the conventional genetic algorithm.

Autocorrelation analysis for the unbiased determination of power-law exponents in single-quantum-dot blinking.

PubMed

Houel, Julien; Doan, Quang T; Cajgfinger, Thomas; Ledoux, Gilles; Amans, David; Aubret, Antoine; Dominjon, Agnès; Ferriol, Sylvain; Barbier, Rémi; Nasilowski, Michel; Lhuillier, Emmanuel; Dubertret, Benoît; Dujardin, Christophe; Kulzer, Florian

2015-01-27

We present an unbiased and robust analysis method for power-law blinking statistics in the photoluminescence of single nanoemitters, allowing us to extract both the bright- and dark-state power-law exponents from the emitters' intensity autocorrelation functions. As opposed to the widely used threshold method, our technique therefore does not require discriminating the emission levels of bright and dark states in the experimental intensity timetraces. We rely on the simultaneous recording of 450 emission timetraces of single CdSe/CdS core/shell quantum dots at a frame rate of 250 Hz with single photon sensitivity. Under these conditions, our approach can determine ON and OFF power-law exponents with a precision of 3% from a comparison to numerical simulations, even for shot-noise-dominated emission signals with an average intensity below 1 photon per frame and per quantum dot. These capabilities pave the way for the unbiased, threshold-free determination of blinking power-law exponents at the microsecond time scale.

Real time correlation function in a single phase space integral beyond the linearized semiclassical initial value representation.

PubMed

Liu, Jian; Miller, William H

2007-06-21

It is shown how quantum mechanical time correlation functions [defined, e.g., in Eq. (1.1)] can be expressed, without approximation, in the same form as the linearized approximation of the semiclassical initial value representation (LSC-IVR), or classical Wigner model, for the correlation function [cf. Eq. (2.1)], i.e., as a phase space average (over initial conditions for trajectories) of the Wigner functions corresponding to the two operators. The difference is that the trajectories involved in the LSC-IVR evolve classically, i.e., according to the classical equations of motion, while in the exact theory they evolve according to generalized equations of motion that are derived here. Approximations to the exact equations of motion are then introduced to achieve practical methods that are applicable to complex (i.e., large) molecular systems. Four such methods are proposed in the paper--the full Wigner dynamics (full WD) and the second order WD based on "Wigner trajectories" [H. W. Lee and M. D. Scully, J. Chem. Phys. 77, 4604 (1982)] and the full Donoso-Martens dynamics (full DMD) and the second order DMD based on "Donoso-Martens trajectories" [A. Donoso and C. C. Martens, Phys. Rev. Lett. 8722, 223202 (2001)]--all of which can be viewed as generalizations of the original LSC-IVR method. Numerical tests of the four versions of this new approach are made for two anharmonic model problems, and for each the momentum autocorrelation function (i.e., operators linear in coordinate or momentum operators) and the force autocorrelation function (nonlinear operators) have been calculated. These four new approximate treatments are indeed seen to be significant improvements to the original LSC-IVR approximation.

DS/LPI autocorrelation detection in noise plus random-tone interference. [Direct Sequence Low-Probabilty of Intercept

NASA Technical Reports Server (NTRS)

Hinedi, S.; Polydoros, A.

1988-01-01

The authors present and analyze a frequency-noncoherent two-lag autocorrelation statistic for the wideband detection of random BPSK signals in noise-plus-random-multitone interference. It is shown that this detector is quite robust to the presence or absence of interference and its specific parameter values, contrary to the case of an energy detector. The rule assumes knowledge of the data rate and the active scenario under H0. It is concluded that the real-time autocorrelation domain and its samples (lags) are a viable approach for detecting random signals in dense environments.

Path integral Liouville dynamics: Applications to infrared spectra of OH, water, ammonia, and methane

DOE Office of Scientific and Technical Information (OSTI.GOV)

Liu, Jian, E-mail: jianliupku@pku.edu.cn; State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing 100871; Zhang, Zhijun

Path integral Liouville dynamics (PILD) is applied to vibrational dynamics of several simple but representative realistic molecular systems (OH, water, ammonia, and methane). The dipole-derivative autocorrelation function is employed to obtain the infrared spectrum as a function of temperature and isotopic substitution. Comparison to the exact vibrational frequency shows that PILD produces a reasonably accurate peak position with a relatively small full width at half maximum. PILD offers a potentially useful trajectory-based quantum dynamics approach to compute vibrational spectra of molecular systems.

Random packing of regular polygons and star polygons on a flat two-dimensional surface.

PubMed

Cieśla, Michał; Barbasz, Jakub

2014-08-01

Random packing of unoriented regular polygons and star polygons on a two-dimensional flat continuous surface is studied numerically using random sequential adsorption algorithm. Obtained results are analyzed to determine the saturated random packing ratio as well as its density autocorrelation function. Additionally, the kinetics of packing growth and available surface function are measured. In general, stars give lower packing ratios than polygons, but when the number of vertexes is large enough, both shapes approach disks and, therefore, properties of their packing reproduce already known results for disks.

Meteorological factors for PM10 concentration levels in Northern Spain

NASA Astrophysics Data System (ADS)

Santurtún, Ana; Mínguez, Roberto; Villar-Fernández, Alejandro; González Hidalgo, Juan Carlos; Zarrabeitia, María Teresa

2013-04-01

Atmospheric particulate matter (PM) is made up of a mixture of solid and aqueous species which enter the atmosphere by anthropogenic and natural pathways. The levels and composition of ambient air PM depend on the climatology and on the geography (topography, soil cover, proximity to arid zones or to the coast) of a given region. Spain has particular difficulties in achieving compliance with the limit values established by the European Union (based on recommendations from the World Health Organization) for particulate matter on the order of 10 micrometers of diameter or less (PM10), but not only antropogenical emissions are responsible for this: some studies show that PM10 concentrations originating from these kinds of sources are similar to what is found in other European countries, while some of the geographical features of the Iberian Peninsula (such as African mineral dust intrusion, soil aridity or rainfall) are proven to be a factor for higher PM concentrations. This work aims to describe PM10 concentration levels in Cantabria (Northern Spain) and their relationship with the following meteorological variables: rainfall, solar radiation, temperature, barometric pressure and wind speed. Data consists of daily series obtained from hourly data records for the 2000-2010 period, of PM10 concentrations from 4 different urban-background stations, and daily series of the meteorological variables provided by Spanish National Meteorology Agency. The method used for establishing the relationships between these variables consists of several steps: i) fitting a non-stationary probability density function for each variable accounting for long-term trends, seasonality during the year and possible seasonality during the week to distinguish between work and weekend days, ii) using the marginal distribution function obtained, transform the time series of historical values of each variable into a normalized Gaussian time series. This step allows using consistently time series models, iii) fitting of a times series model (Autoregressive moving average, ARMA) to the transformed historical values in order to eliminate the temporal autocorrelation structure of each stochastic process, obtaining a white noise for each variable, and finally, iv) the calculation of cross correlations between white noises at different time lags. These cross correlations allow characterization of the true correlation between signals, avoiding the problems induced by data scaling or autocorrelations inherent to each signal. Results provide the relationship and possible contribution to PM10 concentration levels associated with each meteorological variable. This information can be used to improve PM10 concentration levels forecasting using existing meteorological forecasts.

All-Fiber, Directly Chirped Laser Source for Chirped-Pulse-Amplification

NASA Astrophysics Data System (ADS)

Xin, Ran

Chirped-pulse-amplification (CPA) technology is widely used to produce ultra-short optical pulses (sub picosecond to femtoseconds) with high pulse energy. A chirped pulse laser source with flexible dispersion control is highly desirable as a CPA seed. This thesis presents an all-fiber, directly chirped laser source (DCLS) that produces nanosecond, linearly-chirped laser pulses at 1053 nm for seeding high energy CPA systems. DCLS produces a frequency chirp on an optical pulse through direct temporal phase modulation. DCLS provides programmable control for the temporal phase of the pulse, high pulse energy and diffraction-limited beam performance, which are beneficial for CPA systems. The DCLS concept is first described. Its key enabling technologies are identified and their experimental demonstration is presented. These include high-precision temporal phase control using an arbitrary waveform generator, multi-pass phase modulation to achieve high modulation depth, regenerative amplification in a fiber ring cavity and a negative feedback system that controls the amplifier cavity dynamics. A few technical challenges that arise from the multi-pass architecture are described and their solutions are presented, such as polarization management and gain-spectrum engineering in the DCLS fiber cavity. A DCLS has been built and its integration into a high energy OPCPA system is demonstrated. DCLS produces a 1-ns chirped pulse with a 3-nm bandwidth. The temporal phase and group delay dispersion on the DCLS output pulse is measured using temporal interferometry. The measured temporal phase has an ˜1000 rad amplitude and is close to a quadratic shape. The chirped pulse is amplified from 0.9 nJ to 76 mJ in an OPCPA system. The amplified pulse is compressed to close to its Fourier transform limit, producing an intensity autocorrelation trace with a 1.5-ps width. Direct compressed-pulse duration control by adjusting the phase modulation drive amplitude is demonstrated. Limitation to pulse compression is investigated using numerical simulation.

Influence of pedestrian age and gender on spatial and temporal distribution of pedestrian crashes.

PubMed

Toran Pour, Alireza; Moridpour, Sara; Tay, Richard; Rajabifard, Abbas

2018-01-02

Every year, about 1.24 million people are killed in traffic crashes worldwide and more than 22% of these deaths are pedestrians. Therefore, pedestrian safety has become a significant traffic safety issue worldwide. In order to develop effective and targeted safety programs, the location- and time-specific influences on vehicle-pedestrian crashes must be assessed. The main purpose of this research is to explore the influence of pedestrian age and gender on the temporal and spatial distribution of vehicle-pedestrian crashes to identify the hotspots and hot times. Data for all vehicle-pedestrian crashes on public roadways in the Melbourne metropolitan area from 2004 to 2013 are used in this research. Spatial autocorrelation is applied in examining the vehicle-pedestrian crashes in geographic information systems (GIS) to identify any dependency between time and location of these crashes. Spider plots and kernel density estimation (KDE) are then used to determine the temporal and spatial patterns of vehicle-pedestrian crashes for different age groups and genders. Temporal analysis shows that pedestrian age has a significant influence on the temporal distribution of vehicle-pedestrian crashes. Furthermore, men and women have different crash patterns. In addition, results of the spatial analysis shows that areas with high risk of vehicle-pedestrian crashes can vary during different times of the day for different age groups and genders. For example, for those between ages 18 and 65, most vehicle-pedestrian crashes occur in the central business district (CBD) during the day, but between 7:00 p.m. and 6:00 a.m., crashes among this age group occur mostly around hotels, clubs, and bars. This research reveals that temporal and spatial distributions of vehicle-pedestrian crashes vary for different pedestrian age groups and genders. Therefore, specific safety measures should be in place during high crash times at different locations for different age groups and genders to increase the effectiveness of the countermeasures in preventing and reducing vehicle-pedestrian crashes.

Does red noise increase or decrease extinction risk? Single extreme events versus series of unfavorable conditions.

PubMed

Schwager, Monika; Johst, Karin; Jeltsch, Florian

2006-06-01

Recent theoretical studies have shown contrasting effects of temporal correlation of environmental fluctuations (red noise) on the risk of population extinction. It is still debated whether and under which conditions red noise increases or decreases extinction risk compared with uncorrelated (white) noise. Here, we explain the opposing effects by introducing two features of red noise time series. On the one hand, positive autocorrelation increases the probability of series of poor environmental conditions, implying increasing extinction risk. On the other hand, for a given time period, the probability of at least one extremely bad year ("catastrophe") is reduced compared with white noise, implying decreasing extinction risk. Which of these two features determines extinction risk depends on the strength of environmental fluctuations and the sensitivity of population dynamics to these fluctuations. If extreme (catastrophic) events can occur (strong noise) or sensitivity is high (overcompensatory density dependence), then temporal correlation decreases extinction risk; otherwise, it increases it. Thus, our results provide a simple explanation for the contrasting previous findings and are a crucial step toward a general understanding of the effect of noise color on extinction risk.

An open-population hierarchical distance sampling model

USGS Publications Warehouse

Sollmann, Rachel; Beth Gardner,; Richard B Chandler,; Royle, J. Andrew; T Scott Sillett,

2015-01-01

Modeling population dynamics while accounting for imperfect detection is essential to monitoring programs. Distance sampling allows estimating population size while accounting for imperfect detection, but existing methods do not allow for direct estimation of demographic parameters. We develop a model that uses temporal correlation in abundance arising from underlying population dynamics to estimate demographic parameters from repeated distance sampling surveys. Using a simulation study motivated by designing a monitoring program for island scrub-jays (Aphelocoma insularis), we investigated the power of this model to detect population trends. We generated temporally autocorrelated abundance and distance sampling data over six surveys, using population rates of change of 0.95 and 0.90. We fit the data generating Markovian model and a mis-specified model with a log-linear time effect on abundance, and derived post hoc trend estimates from a model estimating abundance for each survey separately. We performed these analyses for varying number of survey points. Power to detect population changes was consistently greater under the Markov model than under the alternatives, particularly for reduced numbers of survey points. The model can readily be extended to more complex demographic processes than considered in our simulations. This novel framework can be widely adopted for wildlife population monitoring.

An open-population hierarchical distance sampling model.

PubMed

Sollmann, Rahel; Gardner, Beth; Chandler, Richard B; Royle, J Andrew; Sillett, T Scott

2015-02-01

Modeling population dynamics while accounting for imperfect detection is essential to monitoring programs. Distance sampling allows estimating population size while accounting for imperfect detection, but existing methods do not allow for estimation of demographic parameters. We develop a model that uses temporal correlation in abundance arising from underlying population dynamics to estimate demographic parameters from repeated distance sampling surveys. Using a simulation study motivated by designing a monitoring program for Island Scrub-Jays (Aphelocoma insularis), we investigated the power of this model to detect population trends. We generated temporally autocorrelated abundance and distance sampling data over six surveys, using population rates of change of 0.95 and 0.90. We fit the data generating Markovian model and a mis-specified model with a log-linear time effect on abundance, and derived post hoc trend estimates from a model estimating abundance for each survey separately. We performed these analyses for varying numbers of survey points. Power to detect population changes was consistently greater under the Markov model than under the alternatives, particularly for reduced numbers of survey points. The model can readily be extended to more complex demographic processes than considered in our simulations. This novel framework can be widely adopted for wildlife population monitoring.

Estimation of Random Medium Parameters from 2D Post-Stack Seismic Data and Its Application in Seismic Inversion

NASA Astrophysics Data System (ADS)

Yang, X.; Zhu, P.; Gu, Y.; Xu, Z.

2015-12-01

Small scale heterogeneities of subsurface medium can be characterized conveniently and effectively using a few simple random medium parameters (RMP), such as autocorrelation length, angle and roughness factor, etc. The estimation of these parameters is significant in both oil reservoir prediction and metallic mine exploration. Poor accuracy and low stability existed in current estimation approaches limit the application of random medium theory in seismic exploration. This study focuses on improving the accuracy and stability of RMP estimation from post-stacked seismic data and its application in the seismic inversion. Experiment and theory analysis indicate that, although the autocorrelation of random medium is related to those of corresponding post-stacked seismic data, the relationship is obviously affected by the seismic dominant frequency, the autocorrelation length, roughness factor and so on. Also the error of calculation of autocorrelation in the case of finite and discrete model decreases the accuracy. In order to improve the precision of estimation of RMP, we design two improved approaches. Firstly, we apply region growing algorithm, which often used in image processing, to reduce the influence of noise in the autocorrelation calculated by the power spectrum method. Secondly, the orientation of autocorrelation is used as a new constraint in the estimation algorithm. The numerical experiments proved that it is feasible. In addition, in post-stack seismic inversion of random medium, the estimated RMP may be used to constrain inverse procedure and to construct the initial model. The experiment results indicate that taking inversed model as random medium and using relatively accurate estimated RMP to construct initial model can get better inversion result, which contained more details conformed to the actual underground medium.

Evaluating platelet aggregation dynamics from laser speckle fluctuations

PubMed Central

Hajjarian, Zeinab; Tshikudi, Diane M.; Nadkarni, Seemantini K.

2017-01-01

Platelets are key to maintaining hemostasis and impaired platelet aggregation could lead to hemorrhage or thrombosis. We report a new approach that exploits laser speckle intensity fluctuations, emanated from a drop of platelet-rich-plasma (PRP), to profile aggregation. Speckle fluctuation rate is quantified by the speckle intensity autocorrelation, g2(t), from which the aggregate size is deduced. We first apply this approach to evaluate polystyrene bead aggregation, triggered by salt. Next, we assess dose-dependent platelet aggregation and inhibition in human PRP spiked with adenosine diphosphate and clopidogrel. Additional spatio-temporal speckle analyses yield 2-dimensional maps of particle displacements to visualize platelet aggregate foci within minutes and quantify aggregation dynamics. These findings demonstrate the unique opportunity for assessing platelet health within minutes for diagnosing bleeding disorders and monitoring anti-platelet therapies. PMID:28717586

Temporal Quantum Correlations in Inelastic Light Scattering from Water.

PubMed

Kasperczyk, Mark; de Aguiar Júnior, Filomeno S; Rabelo, Cassiano; Saraiva, Andre; Santos, Marcelo F; Novotny, Lukas; Jorio, Ado

2016-12-09

Water is one of the most prevalent chemicals on our planet, an integral part of both our environment and our existence as a species. Yet it is also rich in anomalous behaviors. Here we reveal that water is a novel-yet ubiquitous-source for quantum correlated photon pairs at ambient conditions. The photon pairs are produced through Raman scattering, and the correlations arise from the shared quantum of a vibrational mode between the Stokes and anti-Stokes scattering events. We confirm the nonclassical nature of the produced photon pairs by showing that the cross-correlation and autocorrelations of the signals violate a Cauchy-Schwarz inequality by over 5 orders of magnitude. The unprecedented degree of violating the inequality in pure water, as well as the well-defined polarization properties of the photon pairs, points to its usefulness in quantum information.

Quantitative single-molecule imaging by confocal laser scanning microscopy.

PubMed

Vukojevic, Vladana; Heidkamp, Marcus; Ming, Yu; Johansson, Björn; Terenius, Lars; Rigler, Rudolf

2008-11-25

A new approach to quantitative single-molecule imaging by confocal laser scanning microscopy (CLSM) is presented. It relies on fluorescence intensity distribution to analyze the molecular occurrence statistics captured by digital imaging and enables direct determination of the number of fluorescent molecules and their diffusion rates without resorting to temporal or spatial autocorrelation analyses. Digital images of fluorescent molecules were recorded by using fast scanning and avalanche photodiode detectors. In this way the signal-to-background ratio was significantly improved, enabling direct quantitative imaging by CLSM. The potential of the proposed approach is demonstrated by using standard solutions of fluorescent dyes, fluorescently labeled DNA molecules, quantum dots, and the Enhanced Green Fluorescent Protein in solution and in live cells. The method was verified by using fluorescence correlation spectroscopy. The relevance for biological applications, in particular, for live cell imaging, is discussed.

Stochastic generators of multi-site daily temperature: comparison of performances in various applications

NASA Astrophysics Data System (ADS)

Evin, Guillaume; Favre, Anne-Catherine; Hingray, Benoit

2018-02-01

We present a multi-site stochastic model for the generation of average daily temperature, which includes a flexible parametric distribution and a multivariate autoregressive process. Different versions of this model are applied to a set of 26 stations located in Switzerland. The importance of specific statistical characteristics of the model (seasonality, marginal distributions of standardized temperature, spatial and temporal dependence) is discussed. In particular, the proposed marginal distribution is shown to improve the reproduction of extreme temperatures (minima and maxima). We also demonstrate that the frequency and duration of cold spells and heat waves are dramatically underestimated when the autocorrelation of temperature is not taken into account in the model. An adequate representation of these characteristics can be crucial depending on the field of application, and we discuss potential implications in different contexts (agriculture, forestry, hydrology, human health).

Statistical process control of mortality series in the Australian and New Zealand Intensive Care Society (ANZICS) adult patient database: implications of the data generating process

PubMed Central

2013-01-01

Background Statistical process control (SPC), an industrial sphere initiative, has recently been applied in health care and public health surveillance. SPC methods assume independent observations and process autocorrelation has been associated with increase in false alarm frequency. Methods Monthly mean raw mortality (at hospital discharge) time series, 1995–2009, at the individual Intensive Care unit (ICU) level, were generated from the Australia and New Zealand Intensive Care Society adult patient database. Evidence for series (i) autocorrelation and seasonality was demonstrated using (partial)-autocorrelation ((P)ACF) function displays and classical series decomposition and (ii) “in-control” status was sought using risk-adjusted (RA) exponentially weighted moving average (EWMA) control limits (3 sigma). Risk adjustment was achieved using a random coefficient (intercept as ICU site and slope as APACHE III score) logistic regression model, generating an expected mortality series. Application of time-series to an exemplar complete ICU series (1995-(end)2009) was via Box-Jenkins methodology: autoregressive moving average (ARMA) and (G)ARCH ((Generalised) Autoregressive Conditional Heteroscedasticity) models, the latter addressing volatility of the series variance. Results The overall data set, 1995-2009, consisted of 491324 records from 137 ICU sites; average raw mortality was 14.07%; average(SD) raw and expected mortalities ranged from 0.012(0.113) and 0.013(0.045) to 0.296(0.457) and 0.278(0.247) respectively. For the raw mortality series: 71 sites had continuous data for assessment up to or beyond lag40 and 35% had autocorrelation through to lag40; and of 36 sites with continuous data for ≥ 72 months, all demonstrated marked seasonality. Similar numbers and percentages were seen with the expected series. Out-of-control signalling was evident for the raw mortality series with respect to RA-EWMA control limits; a seasonal ARMA model, with GARCH effects, displayed white-noise residuals which were in-control with respect to EWMA control limits and one-step prediction error limits (3SE). The expected series was modelled with a multiplicative seasonal autoregressive model. Conclusions The data generating process of monthly raw mortality series at the ICU level displayed autocorrelation, seasonality and volatility. False-positive signalling of the raw mortality series was evident with respect to RA-EWMA control limits. A time series approach using residual control charts resolved these issues. PMID:23705957

Counting the peaks in the excitation function for precompound processes

NASA Astrophysics Data System (ADS)

Bonetti, R.; Hussein, M. S.; Mello, P. A.

1983-08-01

The "counting of maxima" method of Brink and Stephen, conventionally used for the extraction of the correlation width of statistical (compound nucleus) reactions, is generalized to include precompound processes as well. It is found that this method supplies an important independent check of the results obtained from autocorrelation studies. An application is made to the reaction 25Mg(3He,p). NUCLEAR REACTIONS Statistical multistep compound processes discussed.

Communication: Symmetrical quasi-classical analysis of linear optical spectroscopy

NASA Astrophysics Data System (ADS)

Provazza, Justin; Coker, David F.

2018-05-01

The symmetrical quasi-classical approach for propagation of a many degree of freedom density matrix is explored in the context of computing linear spectra. Calculations on a simple two state model for which exact results are available suggest that the approach gives a qualitative description of peak positions, relative amplitudes, and line broadening. Short time details in the computed dipole autocorrelation function result in exaggerated tails in the spectrum.

Random sequential adsorption of cubes

NASA Astrophysics Data System (ADS)

Cieśla, Michał; Kubala, Piotr

2018-01-01

Random packings built of cubes are studied numerically using a random sequential adsorption algorithm. To compare the obtained results with previous reports, three different models of cube orientation sampling were used. Also, three different cube-cube intersection algorithms were tested to find the most efficient one. The study focuses on the mean saturated packing fraction as well as kinetics of packing growth. Microstructural properties of packings were analyzed using density autocorrelation function.

Ozone data and mission sampling analysis

NASA Technical Reports Server (NTRS)

Robbins, J. L.

1980-01-01

A methodology was developed to analyze discrete data obtained from the global distribution of ozone. Statistical analysis techniques were applied to describe the distribution of data variance in terms of empirical orthogonal functions and components of spherical harmonic models. The effects of uneven data distribution and missing data were considered. Data fill based on the autocorrelation structure of the data is described. Computer coding of the analysis techniques is included.

Effects of calcium leaching on diffusion properties of hardened and altered cement pastes

NASA Astrophysics Data System (ADS)

Kurumisawa, Kiyofumi; Haga, Kazuko; Hayashi, Daisuke; Owada, Hitoshi

2017-06-01

It is very important to predict alterations in the concrete used for fabricating disposal containers for radioactive waste. Therefore, it is necessary to understand the alteration of cementitious materials caused by calcium leaching when they are in contact with ground water in the long term. To evaluate the long-term transport characteristics of cementitious materials, the microstructural behavior of these materials should be considered. However, many predictive models of transport characteristics focus on the pore structure, while only few such models consider both, the spatial distribution of calcium silicate hydrate (C-S-H), portlandite, and the pore spaces. This study focused on the spatial distribution of these cement phases. The auto-correlation function of each phase of cementitious materials was calculated from two-dimensional backscattered electron imaging, and the three-dimensional spatial image of the cementitious material was produced using these auto-correlation functions. An attempt was made to estimate the diffusion coefficient of chloride from the three-dimensional spatial image. The estimated diffusion coefficient of the altered sample from the three-dimensional spatial image was found to be comparable to the measured value. This demonstrated that it is possible to predict the diffusion coefficient of the altered cement paste by using the proposed model.

Green-Kubo relation for viscosity tested using experimental data for a two-dimensional dusty plasma

NASA Astrophysics Data System (ADS)

Feng, Yan; Goree, J.; Liu, Bin; Cohen, E. G. D.

2011-10-01

The theoretical Green-Kubo relation for viscosity is tested using experimentally obtained data. In a dusty plasma experiment, micron-sized dust particles are introduced into a partially ionized argon plasma, where they become negatively charged. They are electrically levitated to form a single-layer Wigner crystal, which is subsequently melted using laser heating. In the liquid phase, these dust particles experience interparticle electric repulsion, laser heating, and friction from the ambient neutral argon gas, and they can be considered to be in a nonequilibrium steady state. Direct measurements of the positions and velocities of individual dust particles are then used to obtain a time series for an off-diagonal element of the stress tensor and its time autocorrelation function. This calculation also requires the interparticle potential, which was not measured experimentally but was obtained using a Debye-Hückel-type model with experimentally determined parameters. Integrating the autocorrelation function over time yields the viscosity for shearing motion among dust particles. The viscosity so obtained is found to agree with results from a previous experiment using a hydrodynamical Navier-Stokes equation. This comparison serves as a test of the Green-Kubo relation for viscosity. Our result is also compared to the predictions of several simulations.

Floquet spin states in graphene under ac-driven spin-orbit interaction

NASA Astrophysics Data System (ADS)

López, A.; Sun, Z. Z.; Schliemann, J.

2012-05-01

We study the role of periodically driven time-dependent Rashba spin-orbit coupling (RSOC) on a monolayer graphene sample. After recasting the originally 4×4 system of dynamical equations as two time-reversal related two-level problems, the quasienergy spectrum and the related dynamics are investigated via various techniques and approximations. In the static case, the system is gapped at the Dirac point. The rotating wave approximation (RWA) applied to the driven system unphysically preserves this feature, while the Magnus-Floquet approach as well as a numerically exact evaluation of the Floquet equation show that this gap is dynamically closed. In addition, a sizable oscillating pattern of the out-of-plane spin polarization is found in the driven case for states that are completely unpolarized in the static limit. Evaluation of the autocorrelation function shows that the original uniform interference pattern corresponding to time-independent RSOC gets distorted. The resulting structure can be qualitatively explained as a consequence of the transitions induced by the ac driving among the static eigenstates, i.e., these transitions modulate the relative phases that add up to give the quantum revivals of the autocorrelation function. Contrary to the static case, in the driven scenario, quantum revivals (suppressions) are correlated to spin-up (down) phases.

Molecular dynamic simulations of N2-broadened methane line shapes and comparison with experiments

NASA Astrophysics Data System (ADS)

Le, Tuong; Doménech, José-Luis; Lepère, Muriel; Tran, Ha

2017-03-01

Absorption spectra of methane transitions broadened by nitrogen have been calculated for the first time using classical molecular dynamic simulations. For that, the time evolution of the auto-correlation function of the dipole moment vector, assumed along a C-H axis, was computed using an accurate site-site intermolecular potential for CH4-N2. Quaternion coordinates were used to treat the rotation of the molecules. A requantization procedure was applied to the classical rotation and spectra were then derived as the Fourier-Laplace transform of the auto-correlation function. These computed spectra were compared with experimental ones recorded with a tunable diode laser and a difference-frequency laser spectrometer. Specifically, nine isolated methane lines broadened by nitrogen, belonging to various vibrational bands and having rotational quantum numbers J from 0 to 9, were measured at room temperature and at several pressures from 20 to 945 mbar. Comparisons between measured and calculated spectra were made through their fits using the Voigt profile. The results show that ab initio calculated spectra reproduce with very high fidelity non-Voigt effects on the measurements and that classical molecular dynamic simulations can be used to predict spectral shapes of isolated lines of methane perturbed by nitrogen.

Correlation time and diffusion coefficient imaging: application to a granular flow system.

PubMed

Caprihan, A; Seymour, J D

2000-05-01

A parametric method for spatially resolved measurements for velocity autocorrelation functions, R(u)(tau) = , expressed as a sum of exponentials, is presented. The method is applied to a granular flow system of 2-mm oil-filled spheres rotated in a half-filled horizontal cylinder, which is an Ornstein-Uhlenbeck process with velocity autocorrelation function R(u)(tau) = e(- ||tau ||/tau(c)), where tau(c) is the correlation time and D = tau(c) is the diffusion coefficient. The pulsed-field-gradient NMR method consists of applying three different gradient pulse sequences of varying motion sensitivity to distinguish the range of correlation times present for particle motion. Time-dependent apparent diffusion coefficients are measured for these three sequences and tau(c) and D are then calculated from the apparent diffusion coefficient images. For the cylinder rotation rate of 2.3 rad/s, the axial diffusion coefficient at the top center of the free surface was 5.5 x 10(-6) m(2)/s, the correlation time was 3 ms, and the velocity fluctuation or granular temperature was 1.8 x 10(-3) m(2)/s(2). This method is also applicable to study transport in systems involving turbulence and porous media flows. Copyright 2000 Academic Press.

Lithium ion dynamics in Li2S+GeS2+GeO2 glasses studied using (7)Li NMR field-cycling relaxometry and line-shape analysis.

PubMed

Gabriel, Jan; Petrov, Oleg V; Kim, Youngsik; Martin, Steve W; Vogel, Michael

2015-09-01

We use (7)Li NMR to study the ionic jump motion in ternary 0.5Li2S+0.5[(1-x)GeS2+xGeO2] glassy lithium ion conductors. Exploring the "mixed glass former effect" in this system led to the assumption of a homogeneous and random variation of diffusion barriers in this system. We exploit that combining traditional line-shape analysis with novel field-cycling relaxometry, it is possible to measure the spectral density of the ionic jump motion in broad frequency and temperature ranges and, thus, to determine the distribution of activation energies. Two models are employed to parameterize the (7)Li NMR data, namely, the multi-exponential autocorrelation function model and the power-law waiting times model. Careful evaluation of both of these models indicates a broadly inhomogeneous energy landscape for both the single (x=0.0) and the mixed (x=0.1) network former glasses. The multi-exponential autocorrelation function model can be well described by a Gaussian distribution of activation barriers. Applicability of the methods used and their sensitivity to microscopic details of ionic motion are discussed. Copyright © 2015 Elsevier Inc. All rights reserved.

Quantifying the impact of current and future concentrations of air pollutants on respiratory disease risk in England.

PubMed

Pannullo, Francesca; Lee, Duncan; Neal, Lucy; Dalvi, Mohit; Agnew, Paul; O'Connor, Fiona M; Mukhopadhyay, Sabyasachi; Sahu, Sujit; Sarran, Christophe

2017-03-27

Estimating the long-term health impact of air pollution in a spatio-temporal ecological study requires representative concentrations of air pollutants to be constructed for each geographical unit and time period. Averaging concentrations in space and time is commonly carried out, but little is known about how robust the estimated health effects are to different aggregation functions. A second under researched question is what impact air pollution is likely to have in the future. We conducted a study for England between 2007 and 2011, investigating the relationship between respiratory hospital admissions and different pollutants: nitrogen dioxide (NO 2 ); ozone (O 3 ); particulate matter, the latter including particles with an aerodynamic diameter less than 2.5 micrometers (PM 2.5 ), and less than 10 micrometers (PM 10 ); and sulphur dioxide (SO 2 ). Bayesian Poisson regression models accounting for localised spatio-temporal autocorrelation were used to estimate the relative risks (RRs) of pollution on disease risk, and for each pollutant four representative concentrations were constructed using combinations of spatial and temporal averages and maximums. The estimated RRs were then used to make projections of the numbers of likely respiratory hospital admissions in the 2050s attributable to air pollution, based on emission projections from a number of Representative Concentration Pathways (RCP). NO 2 exhibited the largest association with respiratory hospital admissions out of the pollutants considered, with estimated increased risks of between 0.9 and 1.6% for a one standard deviation increase in concentrations. In the future the projected numbers of respiratory hospital admissions attributable to NO 2 in the 2050s are lower than present day rates under 3 Representative Concentration Pathways (RCPs): 2.6, 6.0, and 8.5, which is due to projected reductions in future NO 2 emissions and concentrations. NO 2 concentrations exhibit consistent substantial present-day health effects regardless of how a representative concentration is constructed in space and time. Thus as concentrations are predicted to remain above limits set by European Union Legislation until the 2030s in parts of urban England, it will remain a substantial health risk for some time.

Effect of the spatial autocorrelation of empty sites on the evolution of cooperation

NASA Astrophysics Data System (ADS)

Zhang, Hui; Wang, Li; Hou, Dongshuang

2016-02-01

An evolutionary game model is constructed to investigate the spatial autocorrelation of empty sites on the evolution of cooperation. Each individual is assumed to imitate the strategy of the one who scores the highest in its neighborhood including itself. Simulation results illustrate that the evolutionary dynamics based on the Prisoner's Dilemma game (PD) depends severely on the initial conditions, while the Snowdrift game (SD) is hardly affected by that. A high degree of autocorrelation of empty sites is beneficial for the evolution of cooperation in the PD, whereas it shows diversification effects depending on the parameter of temptation to defect in the SD. Moreover, for the repeated game with three strategies, 'always defect' (ALLD), 'tit-for-tat' (TFT), and 'always cooperate' (ALLC), simulations reveal that an amazing evolutionary diversity appears for varying of parameters of the temptation to defect and the probability of playing in the next round of the game. The spatial autocorrelation of empty sites can have profound effects on evolutionary dynamics (equilibrium and oscillation) and spatial distribution.

Dynamical Correlation In Some Liquid Alkaline Earth Metals Near Melting

NASA Astrophysics Data System (ADS)

Thakore, B. Y.; Suthar, P. H.; Khambholja, S. G.; Gajjar, P. N.; Jani, A. R.

2010-12-01

The study of dynamical variables: velocity autocorrelation function (VACF) and power spectrum of liquid alkaline earth metals (Ca, Sr, and Ba) have been presented based on the static harmonic well approximation. The effective interatomic potential for liquid metals is computed using our well recognized model potential with the exchange correlation functions due to Hartree, Taylor, Ichimaru and Utsumi, Farid et al. and Sarkar et al. It is observed that the VACF computed using Sarkar et al. gives the good agreement with available molecular dynamics simulation (MD) results [Phys Rev. B 62, 14818 (2000)]. The shoulder of the power spectrum depends upon the type of local field correlation function used.

Non-Markovian properties and multiscale hidden Markovian network buried in single molecule time series

NASA Astrophysics Data System (ADS)

Sultana, Tahmina; Takagi, Hiroaki; Morimatsu, Miki; Teramoto, Hiroshi; Li, Chun-Biu; Sako, Yasushi; Komatsuzaki, Tamiki

2013-12-01

We present a novel scheme to extract a multiscale state space network (SSN) from single-molecule time series. The multiscale SSN is a type of hidden Markov model that takes into account both multiple states buried in the measurement and memory effects in the process of the observable whenever they exist. Most biological systems function in a nonstationary manner across multiple timescales. Combined with a recently established nonlinear time series analysis based on information theory, a simple scheme is proposed to deal with the properties of multiscale and nonstationarity for a discrete time series. We derived an explicit analytical expression of the autocorrelation function in terms of the SSN. To demonstrate the potential of our scheme, we investigated single-molecule time series of dissociation and association kinetics between epidermal growth factor receptor (EGFR) on the plasma membrane and its adaptor protein Ash/Grb2 (Grb2) in an in vitro reconstituted system. We found that our formula successfully reproduces their autocorrelation function for a wide range of timescales (up to 3 s), and the underlying SSNs change their topographical structure as a function of the timescale; while the corresponding SSN is simple at the short timescale (0.033-0.1 s), the SSN at the longer timescales (0.1 s to ˜3 s) becomes rather complex in order to capture multiscale nonstationary kinetics emerging at longer timescales. It is also found that visiting the unbound form of the EGFR-Grb2 system approximately resets all information of history or memory of the process.

Commercial counterboard for 10 ns software correlator for photon and fluorescence correlation spectroscopy.

PubMed

Molteni, Matteo; Ferri, Fabio

2016-11-01

A 10 ns time resolution, multi-tau software correlator, capable of computing simultaneous autocorrelation (A-A, B-B) and cross (A-B) correlation functions at count rates up to ∼10 MHz, with no data loss, has been developed in LabVIEW and C++ by using the National Instrument timer/counterboard (NI PCIe-6612) and a fast Personal Computer (PC) (Intel Core i7-4790 Processor 3.60 GHz ). The correlator works by using two algorithms: for large lag times (τ ≳ 1 μs), a classical time-mode scheme, based on the measure of the number of pulses per time interval, is used; differently, for τ ≲ 1 μs a photon-mode (PM) scheme is adopted and the correlation function is retrieved from the sequence of the photon arrival times. Single auto- and cross-correlation functions can be processed online in full real time up to count rates of ∼1.8 MHz and ∼1.2 MHz, respectively. Two autocorrelation (A-A, B-B) and a cross correlation (A-B) functions can be simultaneously processed in full real time only up to count rates of ∼750 kHz. At higher count rates, the online processing takes place in a delayed modality, but with no data loss. When tested with simulated correlation data and latex spheres solutions, the overall performances of the correlator appear to be comparable with those of commercial hardware correlators, but with several nontrivial advantages related to its flexibility, low cost, and easy adaptability to future developments of PC and data acquisition technology.

Commercial counterboard for 10 ns software correlator for photon and fluorescence correlation spectroscopy

NASA Astrophysics Data System (ADS)

Molteni, Matteo; Ferri, Fabio

2016-11-01

A 10 ns time resolution, multi-tau software correlator, capable of computing simultaneous autocorrelation (A-A, B-B) and cross (A-B) correlation functions at count rates up to ˜10 MHz, with no data loss, has been developed in LabVIEW and C++ by using the National Instrument timer/counterboard (NI PCIe-6612) and a fast Personal Computer (PC) (Intel Core i7-4790 Processor 3.60 GHz ). The correlator works by using two algorithms: for large lag times (τ ≳ 1 μs), a classical time-mode scheme, based on the measure of the number of pulses per time interval, is used; differently, for τ ≲ 1 μs a photon-mode (PM) scheme is adopted and the correlation function is retrieved from the sequence of the photon arrival times. Single auto- and cross-correlation functions can be processed online in full real time up to count rates of ˜1.8 MHz and ˜1.2 MHz, respectively. Two autocorrelation (A-A, B-B) and a cross correlation (A-B) functions can be simultaneously processed in full real time only up to count rates of ˜750 kHz. At higher count rates, the online processing takes place in a delayed modality, but with no data loss. When tested with simulated correlation data and latex spheres solutions, the overall performances of the correlator appear to be comparable with those of commercial hardware correlators, but with several nontrivial advantages related to its flexibility, low cost, and easy adaptability to future developments of PC and data acquisition technology.

Analysis of extreme rainfall events using attributes control charts in temporal rainfall processes

NASA Astrophysics Data System (ADS)

Villeta, María; Valencia, Jose Luis; Saá-Requejo, Antonio; María Tarquis, Ana

2015-04-01

The impacts of most intense rainfall events on agriculture and insurance industry can be very severe. This research focuses in the analysis of extreme rainfall events throughout the use of attributes control charts, which constitutes a usual tool in Statistical Process Control (SPC) but unusual in climate studios. Here, series of daily precipitations for the years 1931-2009 within a Spanish region are analyzed, based on a new type of attributes control chart that takes into account the autocorrelation between the extreme rainfall events. The aim is to conclude if there exist or not evidence of a change in the extreme rainfall model of the considered series. After adjusting seasonally the precipitation series and considering the data of the first 30 years, a frequency-based criterion allowed fixing specification limits in order to discriminate between extreme observed rainfall days and normal observed rainfall days. The autocorrelation amongst maximum precipitation is taken into account by a New Binomial Markov Extended Process obtained for each rainfall series. These modelling of the extreme rainfall processes provide a way to generate the attributes control charts for the annual fraction of rainfall extreme days. The extreme rainfall processes along the rest of the years under study can then be monitored by such attributes control charts. The results of the application of this methodology show evidence of change in the model of extreme rainfall events in some of the analyzed precipitation series. This suggests that the attributes control charts proposed for the analysis of the most intense precipitation events will be of practical interest to agriculture and insurance sectors in next future.

Diapause and maintenance of facultative sexual reproductive strategies

PubMed Central

Lehtonen, Jussi

2016-01-01

Facultative sex combines sexual and asexual reproduction in the same individual (or clone) and allows for a large diversity of life-history patterns regarding the timing, frequency and intensity of sexual episodes. In addition, other life-history traits such as a diapause stage may become linked to sex. Here, we develop a matrix modelling framework for addressing the cost of sex in facultative sexuals, in constant, periodic and stochastically fluctuating environments. The model is parametrized using life-history data from Brachionus calyciflorus, a facultative sexual rotifer in which sex and diapause are linked. Sexual propensity was an important driver of costs in constant environments, in which high costs (always > onefold, and sometimes > twofold) indicated that asexuals should outcompete facultative sexuals. By contrast, stochastic environments with high temporal autocorrelation favoured facultative sex over obligate asex, in particular, if the penalty to fecundity in ‘bad’ environments was large. In such environments, obligate asexuals were constrained by their life cycle length (i.e. time from birth to last reproductive adult age class), which determined an upper limit to the number of consecutive bad periods they could tolerate. Nevertheless, when facultative asexuals with different sexual propensities competed simultaneously against each other and asex, the lowest sex propensity was the most successful in stochastic environments with positive autocorrelation. Our results suggest that a highly specific mechanism (i.e. diapause linked to sex) can alone stabilize facultative sex in these animals, and protect it from invasion of both asexual and pure sexual strategies. This article is part of the themed issue ‘Weird sex: the underappreciated diversity of sexual reproduction’. PMID:27619700

High-latitude tree growth and satellite vegetation indices: Correlations and trends in Russia and Canada (1982-2008)

NASA Astrophysics Data System (ADS)

Berner, Logan T.; Beck, Pieter S. A.; Bunn, Andrew G.; Lloyd, Andrea H.; Goetz, Scott J.

2011-03-01

Vegetation in northern high latitudes affects regional and global climate through energy partitioning and carbon storage. Spaceborne observations of vegetation, largely based on the normalized difference vegetation index (NDVI), suggest decreased productivity during recent decades in many regions of the Eurasian and North American boreal forests. To improve interpretation of NDVI trends over forest regions, we examined the relationship between NDVI from the advanced very high resolution radiometers and tree ring width measurements, a proxy of tree productivity. We collected tree core samples from spruce, pine, and larch at 22 sites in northeast Russia and northwest Canada. Annual growth rings were measured and used to generate site-level ring width index (RWI) chronologies. Correlation analysis was used to assess the association between RWI and summer NDVI from 1982 to 2008, while linear regression was used to examine trends in both measurements. The correlation between NDVI and RWI was highly variable across sites, though consistently positive (r = 0.43, SD = 0.19, n = 27). We observed significant temporal autocorrelation in both NDVI and RWI measurements at sites with evergreen conifers (spruce and pine), though weak autocorrelation at sites with deciduous conifers (larch). No sites exhibited a positive trend in both NDVI and RWI, although five sites showed negative trends in both measurements. While there are technological and physiological limitations to this approach, these findings demonstrate a positive association between NDVI and tree ring measurements, as well as the importance of considering lagged effects when modeling vegetation productivity using satellite data.

Estimating temporal trend in the presence of spatial complexity: A Bayesian hierarchical model for a wetland plant population undergoing restoration

USGS Publications Warehouse

Rodhouse, T.J.; Irvine, K.M.; Vierling, K.T.; Vierling, L.A.

2011-01-01

Monitoring programs that evaluate restoration and inform adaptive management are important for addressing environmental degradation. These efforts may be well served by spatially explicit hierarchical approaches to modeling because of unavoidable spatial structure inherited from past land use patterns and other factors. We developed Bayesian hierarchical models to estimate trends from annual density counts observed in a spatially structured wetland forb (Camassia quamash [camas]) population following the cessation of grazing and mowing on the study area, and in a separate reference population of camas. The restoration site was bisected by roads and drainage ditches, resulting in distinct subpopulations ("zones") with different land use histories. We modeled this spatial structure by fitting zone-specific intercepts and slopes. We allowed spatial covariance parameters in the model to vary by zone, as in stratified kriging, accommodating anisotropy and improving computation and biological interpretation. Trend estimates provided evidence of a positive effect of passive restoration, and the strength of evidence was influenced by the amount of spatial structure in the model. Allowing trends to vary among zones and accounting for topographic heterogeneity increased precision of trend estimates. Accounting for spatial autocorrelation shifted parameter coefficients in ways that varied among zones depending on strength of statistical shrinkage, autocorrelation and topographic heterogeneity-a phenomenon not widely described. Spatially explicit estimates of trend from hierarchical models will generally be more useful to land managers than pooled regional estimates and provide more realistic assessments of uncertainty. The ability to grapple with historical contingency is an appealing benefit of this approach.

Comparison of two fractal interpolation methods

NASA Astrophysics Data System (ADS)

Fu, Yang; Zheng, Zeyu; Xiao, Rui; Shi, Haibo

2017-03-01

As a tool for studying complex shapes and structures in nature, fractal theory plays a critical role in revealing the organizational structure of the complex phenomenon. Numerous fractal interpolation methods have been proposed over the past few decades, but they differ substantially in the form features and statistical properties. In this study, we simulated one- and two-dimensional fractal surfaces by using the midpoint displacement method and the Weierstrass-Mandelbrot fractal function method, and observed great differences between the two methods in the statistical characteristics and autocorrelation features. From the aspect of form features, the simulations of the midpoint displacement method showed a relatively flat surface which appears to have peaks with different height as the fractal dimension increases. While the simulations of the Weierstrass-Mandelbrot fractal function method showed a rough surface which appears to have dense and highly similar peaks as the fractal dimension increases. From the aspect of statistical properties, the peak heights from the Weierstrass-Mandelbrot simulations are greater than those of the middle point displacement method with the same fractal dimension, and the variances are approximately two times larger. When the fractal dimension equals to 1.2, 1.4, 1.6, and 1.8, the skewness is positive with the midpoint displacement method and the peaks are all convex, but for the Weierstrass-Mandelbrot fractal function method the skewness is both positive and negative with values fluctuating in the vicinity of zero. The kurtosis is less than one with the midpoint displacement method, and generally less than that of the Weierstrass-Mandelbrot fractal function method. The autocorrelation analysis indicated that the simulation of the midpoint displacement method is not periodic with prominent randomness, which is suitable for simulating aperiodic surface. While the simulation of the Weierstrass-Mandelbrot fractal function method has strong periodicity, which is suitable for simulating periodic surface.

MD simulations of the formation of stable clusters in mixtures of alkaline salts and imidazolium-based ionic liquids.

PubMed

Méndez-Morales, Trinidad; Carrete, Jesús; Bouzón-Capelo, Silvia; Pérez-Rodríguez, Martín; Cabeza, Óscar; Gallego, Luis J; Varela, Luis M

2013-03-21

Structural and dynamical properties of room-temperature ionic liquids containing the cation 1-butyl-3-methylimidazolium ([BMIM](+)) and three different anions (hexafluorophosphate, [PF6](-), tetrafluoroborate, [BF4](-), and bis(trifluoromethylsulfonyl)imide, [NTf2](-)) doped with several molar fractions of lithium salts with a common anion at 298.15 K and 1 atm were investigated by means of molecular dynamics simulations. The effect of the size of the salt cation was also analyzed by comparing these results with those for mixtures of [BMIM][PF6] with NaPF6. Lithium/sodium solvation and ionic mobilities were analyzed via the study of radial distribution functions, coordination numbers, cage autocorrelation functions, mean-square displacements (including the analysis of both ballistic and diffusive regimes), self-diffusion coefficients of all the ionic species, velocity and current autocorrelation functions, and ionic conductivity in all the ionic liquid/salt systems. We found that lithium and sodium cations are strongly coordinated in two different positions with the anion present in the mixture. Moreover, [Li](+) and [Na](+) cations were found to form bonded-like, long-lived aggregates with the anions in their first solvation shell, which act as very stable kinetic entities within which a marked rattling motion of salt ions takes place. With very long MD simulation runs, this phenomenon is proved to be on the basis of the decrease of self-diffusion coefficients and ionic conductivities previously reported in experimental and computational results.

Time series analyses of hydrological parameter variations and their correlations at a coastal area in Busan, South Korea

NASA Astrophysics Data System (ADS)

Chung, Sang Yong; Senapathi, Venkatramanan; Sekar, Selvam; Kim, Tae Hyung

2018-02-01

Monitoring and time-series analysis of the hydrological parameters electrical conductivity (EC), water pressure, precipitation and tide were carried out, to understand the characteristics of the parameter variations and their correlations at a coastal area in Busan, South Korea. The monitoring data were collected at a sharp interface between freshwater and saline water at the depth of 25 m below ground. Two well-logging profiles showed that seawater intrusion has largely expanded (progressed inland), and has greatly affected the groundwater quality in a coastal aquifer of tuffaceous sedimentary rock over a 9-year period. According to the time series analyses, the periodograms of the hydrological parameters present very similar trends to the power spectral densities (PSD) of the hydrological parameters. Autocorrelation functions (ACF) and partial autocorrelation functions (PACF) of the hydrological parameters were produced to evaluate their self-correlations. The ACFs of all hydrologic parameters showed very good correlation over the entire time lag, but the PACF revealed that the correlations were good only at time lag 1. Crosscorrelation functions (CCF) were used to evaluate the correlations between the hydrological parameters and the characteristics of seawater intrusion in the coastal aquifer system. The CCFs showed that EC had a close relationship with water pressure and precipitation rather than tide. The CCFs of water pressure with tide and precipitation were in inverse proportion, and the CCF of water pressure with precipitation was larger than that with tide.

The Generalized Multilevel Facets Model for Longitudinal Data

ERIC Educational Resources Information Center

Hung, Lai-Fa; Wang, Wen-Chung

2012-01-01

In the human sciences, ability tests or psychological inventories are often repeatedly conducted to measure growth. Standard item response models do not take into account possible autocorrelation in longitudinal data. In this study, the authors propose an item response model to account for autocorrelation. The proposed three-level model consists…

Exploring Online Learning Data Using Fractal Dimensions. Research Report. ETS RR-17-15

ERIC Educational Resources Information Center

Guo, Hongwen

2017-01-01

Data collected from online learning and tutoring systems for individual students showed strong autocorrelation or dependence because of content connection, knowledge-based dependency, or persistence of learning behavior. When the response data show little dependence or negative autocorrelations for individual students, it is suspected that…

VizieR Online Data Catalog: Molecular clumps in W51 giant molecular cloud (Parsons+, 2012)

NASA Astrophysics Data System (ADS)

Parsons, H.; Thompson, M. A.; Clark, J. S.; Chrysostomou, A.

2013-04-01

The W51 GMC was mapped using the Heterodyne Array Receiver Programme (HARP) receiver with the back-end digital autocorrelator spectrometer Auto-Correlation Spectral Imaging System (ACSIS) on the James Clerk Maxwell Telescope (JCMT). Data were taken in 2008 May. (2 data files).

Spatial Autocorrelation And Autoregressive Models In Ecology

Treesearch

Jeremy W. Lichstein; Theodore R. Simons; Susan A. Shriner; Kathleen E. Franzreb

2003-01-01

Abstract. Recognition and analysis of spatial autocorrelation has defined a new paradigm in ecology. Attention to spatial pattern can lead to insights that would have been otherwise overlooked, while ignoring space may lead to false conclusions about ecological relationships. We used Gaussian spatial autoregressive models, fit with widely available...

Measurement and data processing approach for detecting anisotropic spatial statistics of the turbulence-induced index of refraction fluctuations in the upper atmosphere.

PubMed

Havens, Timothy C; Roggemann, Michael C; Schulz, Timothy J; Brown, Wade W; Beyer, Jeff T; Otten, L John

2002-05-20

We discuss a method of data reduction and analysis that has been developed for a novel experiment to detect anisotropic turbulence in the tropopause and to measure the spatial statistics of these flows. The experimental concept is to make measurements of temperature at 15 points on a hexagonal grid for altitudes from 12,000 to 18,000 m while suspended from a balloon performing a controlled descent. From the temperature data, we estimate the index of refraction and study the spatial statistics of the turbulence-induced index of refraction fluctuations. We present and evaluate the performance of a processing approach to estimate the parameters of an anisotropic model for the spatial power spectrum of the turbulence-induced index of refraction fluctuations. A Gaussian correlation model and a least-squares optimization routine are used to estimate the parameters of the model from the measurements. In addition, we implemented a quick-look algorithm to have a computationally nonintensive way of viewing the autocorrelation function of the index fluctuations. The autocorrelation of the index of refraction fluctuations is binned and interpolated onto a uniform grid from the sparse points that exist in our experiment. This allows the autocorrelation to be viewed with a three-dimensional plot to determine whether anisotropy exists in a specific data slab. Simulation results presented here show that, in the presence of the anticipated levels of measurement noise, the least-squares estimation technique allows turbulence parameters to be estimated with low rms error.

Using Predictions Based on Geostatistics to Monitor Trends in Aspergillus flavus Strain Composition.

PubMed

Orum, T V; Bigelow, D M; Cotty, P J; Nelson, M R

1999-09-01

ABSTRACT Aspergillus flavus is a soil-inhabiting fungus that frequently produces aflatoxins, potent carcinogens, in cottonseed and other seed crops. A. flavus S strain isolates, characterized on the basis of sclerotial morphology, are highly toxigenic. Spatial and temporal characteristics of the percentage of the A. flavus isolates that are S strain (S strain incidence) were used to predict patterns across areas of more than 30 km(2). Spatial autocorrelation in S strain incidence in Yuma County, AZ, was shown to extend beyond field boundaries to adjacent fields. Variograms revealed both short-range (2 to 6 km) and long-range (20 to 30 km) spatial structure in S strain incidence. S strain incidence at 36 locations sampled in July 1997 was predicted with a high correlation between expected and observed values (R = 0.85, P = 0.0001) by kriging data from July 1995 and July 1996. S strain incidence at locations sampled in October 1997 and March 1998 was markedly less than predicted by kriging data from the same months in prior years. Temporal analysis of four locations repeatedly sampled from April 1995 through July 1998 also indicated a major reduction in S strain incidence in the Texas Hill area after July 1997. Surface maps generated by kriging point data indicated a similarity in the spatial pattern of S strain incidence among all sampling dates despite temporal changes in the overall S strain incidence. Geostatistics provided useful descriptions of variability in S strain incidence over space and time.

Linking Dynamic Habitat Selection with Wading Bird Foraging Distributions across Resource Gradients

PubMed Central

Beerens, James M.; Noonburg, Erik G.; Gawlik, Dale E.

2015-01-01

Species distribution models (SDM) link species occurrence with a suite of environmental predictors and provide an estimate of habitat quality when the variable set captures the biological requirements of the species. SDMs are inherently more complex when they include components of a species’ ecology such as conspecific attraction and behavioral flexibility to exploit resources that vary across time and space. Wading birds are highly mobile, demonstrate flexible habitat selection, and respond quickly to changes in habitat quality; thus serving as important indicator species for wetland systems. We developed a spatio-temporal, multi-SDM framework using Great Egret (Ardea alba), White Ibis (Eudocimus albus), and Wood Stork (Mycteria Americana) distributions over a decadal gradient of environmental conditions to predict species-specific abundance across space and locations used on the landscape over time. In models of temporal dynamics, species demonstrated conditional preferences for resources based on resource levels linked to differing temporal scales. Wading bird abundance was highest when prey production from optimal periods of inundation was concentrated in shallow depths. Similar responses were observed in models predicting locations used over time, accounting for spatial autocorrelation. Species clustered in response to differing habitat conditions, indicating that social attraction can co-vary with foraging strategy, water-level changes, and habitat quality. This modeling framework can be applied to evaluate the multi-annual resource pulses occurring in real-time, climate change scenarios, or restorative hydrological regimes by tracking changing seasonal and annual distribution and abundance of high quality foraging patches. PMID:26107386

Effect of transmission intensity on hotspots and micro-epidemiology of malaria in sub-Saharan Africa.

PubMed

Mogeni, Polycarp; Omedo, Irene; Nyundo, Christopher; Kamau, Alice; Noor, Abdisalan; Bejon, Philip

2017-06-30

Malaria transmission intensity is heterogeneous, complicating the implementation of malaria control interventions. We provide a description of the spatial micro-epidemiology of symptomatic malaria and asymptomatic parasitaemia in multiple sites. We assembled data from 19 studies conducted between 1996 and 2015 in seven countries of sub-Saharan Africa with homestead-level geospatial data. Data from each site were used to quantify spatial autocorrelation and examine the temporal stability of hotspots. Parameters from these analyses were examined to identify trends over varying transmission intensity. Significant hotspots of malaria transmission were observed in most years and sites. The risk ratios of malaria within hotspots were highest at low malaria positive fractions (MPFs) and decreased with increasing MPF (p < 0.001). However, statistical significance of hotspots was lowest at extremely low and extremely high MPFs, with a peak in statistical significance at an MPF of ~0.3. In four sites with longitudinal data we noted temporal instability and variable negative correlations between MPF and average age of symptomatic malaria across all sites, suggesting varying degrees of temporal stability. We observed geographical micro-variation in malaria transmission at sites with a variety of transmission intensities across sub-Saharan Africa. Hotspots are marked at lower transmission intensity, but it becomes difficult to show statistical significance when cases are sparse at very low transmission intensity. Given the predictability with which hotspots occur as transmission intensity falls, malaria control programmes should have a low threshold for responding to apparent clustering of cases.

Spatio-Temporal Trends and Risk Factors for Shigella from 2001 to 2011 in Jiangsu Province, People's Republic of China

PubMed Central

Bao, Changjun; Hu, Jianli; Liu, Wendong; Liang, Qi; Wu, Ying; Norris, Jessie; Peng, Zhihang; Yu, Rongbin; Shen, Hongbing; Chen, Feng

2014-01-01

Objective This study aimed to describe the spatial and temporal trends of Shigella incidence rates in Jiangsu Province, People's Republic of China. It also intended to explore complex risk modes facilitating Shigella transmission. Methods County-level incidence rates were obtained for analysis using geographic information system (GIS) tools. Trend surface and incidence maps were established to describe geographic distributions. Spatio-temporal cluster analysis and autocorrelation analysis were used for detecting clusters. Based on the number of monthly Shigella cases, an autoregressive integrated moving average (ARIMA) model successfully established a time series model. A spatial correlation analysis and a case-control study were conducted to identify risk factors contributing to Shigella transmissions. Results The far southwestern and northwestern areas of Jiangsu were the most infected. A cluster was detected in southwestern Jiangsu (LLR = 11674.74, P<0.001). The time series model was established as ARIMA (1, 12, 0), which predicted well for cases from August to December, 2011. Highways and water sources potentially caused spatial variation in Shigella development in Jiangsu. The case-control study confirmed not washing hands before dinner (OR = 3.64) and not having access to a safe water source (OR = 2.04) as the main causes of Shigella in Jiangsu Province. Conclusion Improvement of sanitation and hygiene should be strengthened in economically developed counties, while access to a safe water supply in impoverished areas should be increased at the same time. PMID:24416167

Linking dynamic habitat selection with wading bird foraging distributions across resource gradients

USGS Publications Warehouse

Beerens, James M.; Noonberg, Erik G.; Gawlik, Dale E.

2015-01-01

Species distribution models (SDM) link species occurrence with a suite of environmental predictors and provide an estimate of habitat quality when the variable set captures the biological requirements of the species. SDMs are inherently more complex when they include components of a species' ecology such as conspecific attraction and behavioral flexibility to exploit resources that vary across time and space. Wading birds are highly mobile, demonstrate flexible habitat selection, and respond quickly to changes in habitat quality; thus serving as important indicator species for wetland systems. We developed a spatio-temporal, multi-SDM framework using Great Egret (Ardea alba), White Ibis (Eudocimus albus), and Wood Stork (Mycteria Americana) distributions over a decadal gradient of environmental conditions to predict species-specific abundance across space and locations used on the landscape over time. In models of temporal dynamics, species demonstrated conditional preferences for resources based on resource levels linked to differing temporal scales. Wading bird abundance was highest when prey production from optimal periods of inundation was concentrated in shallow depths. Similar responses were observed in models predicting locations used over time, accounting for spatial autocorrelation. Species clustered in response to differing habitat conditions, indicating that social attraction can co-vary with foraging strategy, water-level changes, and habitat quality. This modeling framework can be applied to evaluate the multi-annual resource pulses occurring in real-time, climate change scenarios, or restorative hydrological regimes by tracking changing seasonal and annual distribution and abundance of high quality foraging patches.