Time Skewness Research Articles

Impact of Geometrical Features on Solute Transport Behavior through Rough-Walled Rock Fractures

The solute transport in the fractured rock is dominated by a single fracture. The geometric characteristics of single rough-walled fractures considerably influence their solute transport behavior. According to the self-affinity of the rough fractures, the fractal model of single fractures is established based on the fractional Brownian motion and the successive random accumulation method. The Navier–Stokes equation and solute transport convective-dispersion equation are employed to analyze the effect of fractal dimension and standard deviation of aperture on the solute transport characteristics. The results show that the concentration front and streamline distribution are inhomogeneous, and the residence time distribution (RTD) curves have obvious tailing. For the larger fractal dimension and the standard deviation of aperture, the fracture surface becomes rougher, aperture distribution becomes more scattered, and the average flow velocity becomes slower. As a result, the average time of solute transport is a power function of the fractal dimension, while the time variance and the time skewness present a negative linear correlation with the fractal dimension. For the standard deviation of aperture, the average time exhibits a linearly decreasing trend, the time variance is increased by a power function, and the skewness is increased logarithmically.

Integrating Activity Recognition and Nursing Care Records

In this paper, we introduce a system of integrating activity recognition and collecting nursing care records at nursing care facilities as well as activity labels and sensors through smartphones, and describe experiments at a nursing care facility for 4 months. A system designed to be used even by staff not familiar with smartphones could collected enough number of data without losing but improving their workload for recording. For collected data, we revealed the nature of the collected data as for activities, care details, and timestamps, and considering them, we show a reference accuracy of recognition of nursing activity which is durable to time skewness, overlaps, and class imbalances. Moreover, we demonstrate the near future prediction to predict the next day's activities from the previous day's records which could be useful for proactive care management. The dataset collected is to be opened to the research community, and can be the utilized for activity recognition and data mining in care facilities.

Unscented Rauch–Tung–Streibel smoother‐based power system forecasting‐aided state estimator using hybrid measurements

A hybrid estimator to track the power system states considering fast-rate PMUs and slow-rate supervisory control and data acquisition (SCADA) system is proposed in this article. SCADA measurements arrive at the control centre with various time delays. The estimates obtained using these delayed measurements significantly differs from the actual power system states and is known as the time skew problem. The missing SCADA measurements, owing to their time skewness, are replaced by their predicted values which lead to reduced estimation accuracy. Optimal smoothing algorithms can be utilised to include dynamics in the future measurements to reduce the errors introduced by the time skew problem. Unscented Rauch–Tung–Streibel smoother is used in this study to handle the time skew problem. The performance of the proposed techniques in handling anomalous situations is also studied. The proposed estimator is validated under real-time environment using RTDS, a real-time simulation tool, to assess its applicability in the control centre.

Reducing the variability of inter-departure times of a single-server queueing system – The effects of skewness

A critical performance measure in serial production lines is the variability of inter-departure times of a single-server queue. Increasing upstream inter-departure time variability generates greater downstream variability, diminishing overall line performance. Theory suggests that the variability of inter-departure times of a single-server queue is reduced by decreasing the variance of inter-arrival and service times. This study investigates the effects of the skewness of inter-arrival and service time distributions on the variability of inter-departure times. Contrary to previous results suggesting that mean waiting times of a GI/G/1 queue can be reduced by increasing inter-arrival time skewness, this experimental study of a GI/G/1 queue with triangular inter-arrival and service times shows that the inter-departure time coefficient of variation is reduced through a combination of negative inter-arrival time skewness and positive service time skewness. These results also suggest that the absolute value of the negative autocorrelation between consecutive departures is reduced by the same combination of negative inter-arrival time skewness and positive service time skewness for low values of server’s utilization, while positive skewness for both inter-arrival and service times reduces this value for high values of server’s utilization. Finally, it was found that queue capacity constraints increase the coefficient of variation of inter-departure times, as has been previously suggested, as well as the skewness and the absolute correlation values of the inter-departure time distribution.

A Framework for Robust Hybrid State Estimation With Unknown Measurement Noise Statistics

In practical applications like power systems, the distribution of the measurement noise is usually unknown and frequently deviates from the assumed Gaussian model, yielding outliers. Under these conditions, the performances of the existing state estimators that rely on Gaussian assumption can deteriorate significantly. In addition, the sampling rates of measurements from supervisory control and data acquisition (SCADA) system and phasor measurement unit (PMU) are quite different, causing time skewness problem. In this paper, we propose a robust state estimation framework to address the unknown non-Gaussian noise and the measurement time skewness issue. In the framework, robust Mahalanbis distances are proposed to detect system abnormalities and assign appropriate weights to each chosen buffered PMU measurements. Those weights are further utilized by the Schweppe-type Huber generalized maximum-likelihood (SHGM) estimator to filter out non-Gaussian PMU measurement noise and help suppress outliers. In the meantime, the SHGM estimator is used to handle unknown noise of the received SCADA measurements, yielding another set of state estimates. We show that the state estimates provided by the SHGM estimator follow an asymptotical Gaussian distribution. This nice property allows us to obtain the optimal state estimates by resorting to the data fusion theory for the fusion of the estimation results from two independent SHGM estimators. Extensive simulation results carried out on the IEEE 14, 30 and 118-bus test systems demonstrate the effectiveness and robustness of the proposed method.

Modeling the Time Variability of Accreting Compact Sources

We present model light curves for accreting black hole candidates (BHCs) based on a recently proposed model for their spectro-temporal properties. According to this model, the observed light curves and aperiodic variability of BHCs are due to a series of soft photon injections at random (Poisson) intervals near the compact object and their reprocessing into hard radiation in an extended but nonuniform hot plasma corona surrounding the compact object. We argue that the majority of the timing characteristics of these light curves are due to the stochastic nature of the Comptonization process in the extended corona, whose properties, most notably its radial density dependence, are imprinted in them. We compute the corresponding power spectral densities (PSD), autocorrelation functions, time skewness of the light curves, and time lags between the light curves of the sources at different photon energies and compare our results to observation. Our model light curves compare well with observations, providing good fits to their overall morphology, as manifest by the autocorrelation and skewness functions. The lags and PSDs of the model light curves are also in good agreement with those observed (the model can even accommodate the presence of quasi-periodic oscillations). Finally, while most of the variability power resides at timescales approximately a few seconds, at the same time, the model allows also for shots of a few milliseconds in duration, in accordance with observation. We suggest that refinements of this type of model along with spectral and phase lag information can be used to probe the structure of this class of high-energy sources.

Modeling the time variability of black hole candidates.

We present model light curves for accreting black hole candidates (BHC) based on a recently developed model of these sources. According to this model, the observed light curves and aperiodic variability of BHC are due to a series of soft photon injections at random (Poisson) intervals and the stochastic nature of the Comptonization process in converting these soft photons to the observed high energy radiation. The additional assumption of our model is that the Comptonization process takes place in an extended but non-uniform hot plasma corona surrounding the compact object. We compute the corresponding power spectral densities (PSD), autocorrelation functions, time skewness of the light curves, and time lags between the light curves of the sources at different photon energies and compare our results to observation. Our model reproduces the observed light curves well, in that it provides good fits to their overall morphology (as manifest by the autocorrelation and time skewness) and also to their PSDs and time lags, by producing most of the variability power at time scales approximately > a few seconds, while at the same time allowing for shots of a few msec in duration, in accordance with observation. We suggest that refinement of this type of model along with spectral and phase lag information can be used to probe the structure of this class of high energy sources.

Intelligence and brain myelination: A hypothesis

Many observations concerning intelligence could be explained if much variance in intelligence reflects myelination differences. More intelligent brains show faster nerve conduction, less glucose utilization in positron emission tomography, faster reaction times, faster inspection times, faster speeds in general, greater circumference and volume, smaller standard deviation in reaction times, greater variability in EEG measures, shorter white matter T 2 relaxation times, and higher gray-white matter contrast with magnetic resonance imaging. Also explainable are peculiarities of the increased reaction times and standard deviations with number of choices and complexity, reaction time skewness, the shorter latencies in evoked potentials, shorter latencies to the P300 wave, the high glial to neuron ratio in Einstein's brain, less glucose utilization per unit volume in large brains, certain results related to lipids, essential fatty acids, and cholesterol in adults and premature babies, and the survival of genes for lower intelligence. Children's improved performance with maturation might result from myelination. The slowing of response times with age, the decline in intelligence, and increased T 1 relaxation times could be explained. Differential myelination in the mouse brain might be able to explain the heterosis observed for myelination, brain size, caudal nerve conduction velocity, and maze performance observed.

A Data-Analytic Look at Skewness and Elongation in Common-Stock-Return Distributions

This article explores the nature of skewness and elongation in daily common-stock-return distributions of individual firms using estimates of g (for skewness) and h (for elongation) obtained from Tukey's g and h distributions. Both parametric and nonparametric (bootstrap) estimates of standard errors of the g estimates are computed and compared. Daily return distributions are first examined cross-sectionally over a large sample of firms. The estimates of the skewness parameter exhibit variation across individual firms, but some general trends are evident across industry groups and firm sizes. Return distributions typically seem to be more elongated than the Gaussian distribution. From a time series perspective, both skewness and elongation are persistent in the return distributions of individual firms and vary over a finite range. First-order autocorrelation coefficients of monthly g and h estimates are large and suggest a certain degree of predictability.

Short-term X-ray variability of the globular cluster source 4U 1820 - 30 (NGC 6624)

Analytical techniques for improved identification of the temporal and spectral variability properties of globular cluster and galactic bulge X-ray sources are described in terms of their application to a large set of observations of the source 4U 1820 - 30 in the globular cluster NGC 6624. The autocorrelation function, cross-correlations, time skewness function, erratic periodicities, and pulse trains are examined. The results are discussed in terms of current models with particular emphasis on recent accretion disk models. It is concluded that the analyzed observations provide the first evidence for shot-noise variability in a globular cluster X-ray source.