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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 selfand 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. [ABSTRACT FROM AUTHOR]

In real industrial scenarios, if the quality characteristics of a continuous or batch production process are monitored using Shewhart control charts, there could be a large number of false alarms about the process going out of control. This is because these control charts assume that the inherent noise of the monitored process is normally, independently and identically distributed, although the assumption of independence is not always correct for continuous and batch production processes. This paper presents three control chart pattern recognition systems where the inherent disturbance is assumed to be stationary. The systems use the first-order autoregressive (AR(1)), moving-average (MA(1)) and autoregressive moving-average (ARMA(1,1)) models. A special pattern generation scheme is adopted to ensure generality, randomness and comparability, as well as allowing the further categorisation of the studied patterns. Two different input representation techniques for the recognition systems were studied. These gave nearly the same performance for the MA(1) and ARMA(1,1) models, while the raw data yielded the highest accuracies when AR(1) was used. The effect of autocorrelation on the pattern recognition capabilities of the developed models was studied. It was observed that Normal and Upward Shift patterns were the most affected. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Linear regression models are commonly used for estimating ground PM2.5 concentrations, but the global spatial autocorrelation and local spatial heterogeneity of PM2.5 distribution are either ignored or only partially considered in commonly used models for estimating PM2.5 concentrations. Therefore, taking both global spatial autocorrelation and local spatial heterogeneity into consideration, a global-local regression (GLR) model is proposed for estimating ground PM2.5 concentrations in the Yangtze River Delta (YRD) and in the Beijing, Tianjin, Hebei (BTH) regions of China based on the aerosol optical depth data, meteorological data, remote sensing data, and pollution source data. Considering the global spatial autocorrelation, the GLR model extracts global factors by the eigenvector spatial filtering (ESF) method, and combines the fraction of them that passes further filtering with the geographically weighted regression (GWR) method to address the local spatial heterogeneity. Comprehensive results show that the GLR model outperforms the ordinary GWR and ESF models, and the GLR model has the best performance at the monthly, seasonal, and annual levels. The average adjusted R2 of the monthly GLR model in the YRD region (the BTH region) is 0.620 (0.853), which is 8.0% and 7.4% (6.8% and 7.0%) higher than that of the monthly ESF and GWR models, respectively. The average cross-validation root mean square error of the monthly GLR model is 7.024 μg/m3 in the YRD region, and 9.499 μg/m3 in the BTH region, which is lower than that of the ESF and GWR models. The GLR model can effectively address the spatial autocorrelation and spatial heterogeneity, and overcome the shortcoming of the ordinary GWR model that overfocuses on local features and the disadvantage of the poor local performance of the ordinary ESF model. Overall, the GLR model with good spatial and temporal applicability is a promising method for estimating PM2.5 concentrations. [ABSTRACT FROM AUTHOR]

Functional urban areas represent integrated urban contexts whose territories are economically interconnected. They, therefore, include a central city and all the municipalities that make up the commuting area for work reasons. The economic energies and settlement transformations that characterize these territories have been consolidated over time. The current geographic conformation, as defined today, does not provide information on each municipality's rank (role) in the overall functioning. In this perspective, the work presented examines the demographic and urban dynamics that have affected the FUA of Milan in the last 60 years and then evaluates the presence of possible homogeneous geographic clusters (hot and cold spots) through spatial correlation techniques. Statistic validation was performed through the ANOVA and subsequent posthoc analysis (Tukey–Kramer method). Results show a new configurational asset within the FUA of Milan, which could provide a new key to interpreting the territory, aimed at identifying homogeneous areas to adopt new and more effective forms of strategic planning. [ABSTRACT FROM AUTHOR]

Objectives：Outdoor advertisement can attract the attention of target user groups and increase brand influence, and site selection is the most important influencing factor for its effectiveness. Reasonable site selection plays a positive role in improving brand awareness and expanding the market. However, due to the difficulty in obtaining commercial data, previous studies on a macro and rough scales failed to conduct detailed analysis on the actual effects of site selection. Methods：We propose a framework to solve the above problems. Firstly, we extract feature sets from geographical attributes and commercial economic attributes and preprocess them using mathematical statistics and the geographical processing basic method. Then, by coupling of the in⁃store conversion rate with road network characteristics, point of interest (POI), and other multi⁃source spatial features, a random forest model is constructed to mine the correlation between them. Finally, the importance of each feature is quantified. Results：We choose Beijing as the study area, and build a prediction model for the in⁃store conversion rate of a home decoration brand based on this framework. The model shows a good performance (Standard R²=0.758). Then we obtain the spatial distribution and factors influencing the suitability of outdoor advertising for this home decoration brand in Beijing. The in⁃store conversion rate of the brand is high in the center and is low in the periphery of Beijing, which features the phenomena of strong spatial autocorrelation and high⁃value aggregation. Meanwhile, the in⁃store conversion rate has a significant correlation with social economy, commercial politics, and crowd activities. The location of continuous advertising exposure to the same group has a great influence on the in⁃store conversion rate. Conclusions：The fine mapping results of the model constructed in this study can quantitatively evaluate the advertising effect of each location and maximize the efficiency, which can provide a reference and theoretical basis for relevant studies on the outdoor advertisement or commercial site selection. [ABSTRACT FROM AUTHOR]

Given observations from a stationary time series, permutation tests allow one to construct exactly level α tests under the null hypothesis of an i.i.d. (or, more generally, exchangeable) distribution. On the other hand, when the null hypothesis of interest is that the underlying process is an uncorrelated sequence, permutation tests are not necessarily level α, nor are they approximately level α in large samples. In addition, permutation tests may have large Type 3, or directional, errors, in which a two‐sided test rejects the null hypothesis and concludes that the first‐order autocorrelation is larger than 0, when in fact it is less than 0. In this article, under weak assumptions on the mixing coefficients and moments of the sequence, we provide a test procedure for which the asymptotic validity of the permutation test holds, while retaining the exact rejection probability α in finite samples when the observations are independent and identically distributed. A Monte Carlo simulation study, comparing the permutation test to other tests of autocorrelation, is also performed, along with an empirical example of application to financial data. [ABSTRACT FROM AUTHOR]

Mini-Sosie, which is convenient and environmentally friendly with high energy output, is gaining increasing attention as a seismic exploration source. Passive surface wave survey using ambient noise have become a powerful tool for exploration in urban areas due to the advantages of being efficient and non-destructive. A 2-D or pseudo-1D array that can attenuate phase velocity overestimation due to directional noise are the optimal choice, but such arrays are limited by the complex environment of the experimental site on the one hand and the need to ensure that the medium beneath the array meets the assumption of lateral isotropy on the other. However, the fully 1-D linear array as an alternative lacks the ability to suppress the directional effect of the noise source. In this study, we develop a novel approach for linear array analysis of passive surface waves combined with mini-Sosie technique, called LAPSS. We use the mini-Sosie technique with fixed-frequency impact in the in-line direction to provide the theoretical phase velocity values at several frequency points as a reference for the biased dispersion image obtained from the linear array analysis, so as to estimate the azimuth of the noise source and to perform the correction of the biased dispersion image. We present the detailed workflow of LAPSS and compare the performance of LAPSS with PLAS (which has proven to be superior to the frequency–wavenumber method, spatial autocorrelation method, refraction microtremor and multichannel analysis of passive surface waves) in achieving unbiased dispersion image with different noise source distributions through synthetic tests. The results show that the accuracy of the two methods is comparable, but LAPSS greatly improves the computational efficiency, has stronger generalizability and depends on weaker assumptions. Finally, a field experiment is conducted to verify the feasibility and effectiveness of LAPSS in obtaining unbiased dispersion images using a fully linear array. [ABSTRACT FROM AUTHOR]

The poultry production model is an efficiently intensified small-scale production model. One of the best ways to measure the efficiency of poultry production is the feed conversion ratio (FCR). This study investigated factors that influence the FCR while considering spatial autocorrelations. A random sample of 320 broiler chicken farmers in Musanze district, northern Rwanda was collected and the Spatial Error Model (SEM) was used for analysis. Results revealed a spatial autocorrelation and relationships between FCR and some factors. This study advances our understanding of sustainable intensification and contributes to the design of future investments and interventions in broiler chicken production. [ABSTRACT FROM AUTHOR]

When modeling growth curves, it should be considered that longitudinal data may show residual autocorrelation, and, if this characteristic is not considered, the results and inferences may be compromised. The Bayesian approach, which considers priori information about studied phenomenon has been shown to be efficient in estimating parameters. However, as it is generally not possible to obtain marginal distributions analytically, it is necessary to use some method, such as the weighted resampling method, to generate samples of these distributions and thus obtain an approximation. Among the advantages of this method, stand out the generation of independent samples and the fact that it is not necessary to evaluate convergence. In this context, the objective of this work research was: to present the Bayesian nonlinear modeling of the coffee tree height growth, irrigated and non-irrigated (NI), considering the residual autocorrelation and the nonlinear Logistic, Brody, von Bertalanffy and Richard models. Among the results, it was found that, for NI plants, the Deviance Information Criterion (DIC) and the Criterion of density Predictive Ordered (CPO), indicated that, among the evaluated models, the Logistic model is the one that best describes the height growth of the coffee tree over time. For irrigated plants, these same criteria indicated the Brody model. Thus, the growth of the non-irrigated and irrigated coffee tree followed different growth patterns, the height of the non-irrigated coffee tree showed sigmoidal growth with maximum growth rate at 726 days after planting and the irrigated coffee tree starts its development with high growth rates that gradually decrease over time. [ABSTRACT FROM AUTHOR]

Using elementary properties of primitive multiplicative characters we derive the autocorrelation of a unimodular $M$ -ary character-based sequence of length $pq$ , where $p$ and $q$ are distinct odd primes. Subsequently: (1) we identify three new sets of twin-prime quadriphase sequences with the largest autocorrelation sidelobe magnitude equal to $\sqrt {5}$ or 3, (2) we show that the smallest maximum autocorrelation sidelobe magnitude of an $M$ -ary character-based sequence, when $4|M$ , either decreases with $M$ and tends to 2, when $6\nmid M$ , or is equal to 2, when $6|M$ , and (3), we show that the smallest maximum autocorrelation sidelobe magnitude of a full-alphabet $M$ -ary character-based sequence, where $M>2$ , is greater or equal to $\sqrt {3}$ , and, in particular, is equal to $\sqrt {3}$ for the sextic phase sequence. [ABSTRACT FROM AUTHOR]

This paper proposes a model for realized variance that exploits information in realized quarticity. The realized variance and quarticity measures are both highly persistent and highly correlated with each other. The proposed model incorporates information from the observed realized quarticity process via autoregressive conditional variance dynamics. It exploits conditional dependence in higher order (fourth) moments in analogy to the class of GARCH models exploit conditional dependence in second moments. [ABSTRACT FROM AUTHOR]

Coastal erosion is a common phenomenon along the world's coasts. Studying it is complex because such studies must cover large portions of land, and it is necessary to understand the multiple processes that interact in each area, so it is important to recognize regional patterns that allow for defining representativeness in relation to the surrounding dynamics. Spatial statistics can be used in coastal geomorphology to identify and quantify trends in coastal morphodynamics. This study analyzes and interprets the spatio-temporal patterns present in the changes in a shoreline, that is, the processes of erosion and coastal sedimentation in the Pacific and the Colombian Caribbean. The results are derived from the detection of significant changes in the coastline via satellite images. For this study, the shoreline of Colombia was digitized for the years 1986 and 2016, thus obtaining changes in the shoreline at a medium temporal scale. The Global Moran's Index, Local Moran's Index and Getis–Ord Index were used to explain the spatial statistics. The Global I Moran values for the Pacific were I = 0.190, z = 31.063 and p = 0.01, and for the Caribbean I = 0.624, z = 74.545 and p = 0.01, which suggests good grouping in the Caribbean and very low grouping for the Pacific. The local indices (Moran's and Getis–Ord) allowed us to visualize and spatialize the significant points of coastal erosion and sedimentation. According to the results, three conceptual models are herein proposed that relate the indices with the geomorphological characteristics: (a) the greater the geomorphological heterogeneity, the greater the grouping; (b) the greater the geomorphological homogeneity, the lower the degree of clustering; (c) the greater the geomorphological complexity, the lower the degree of clustering. Finally, it is confirmed that coastal erosion and sedimentation processes predominate along low coasts. [ABSTRACT FROM AUTHOR]

The purpose of the present study was to estimate the parameters of the new stochastic Frontier model by nonlinear smooth transfer function structure with compound error. In the stochastic Frontier model, the compound error consists of two statistical components: model error and technical inefficiency, so that the technical inefficiency of the function is assumed to be autocorrelated. For the parametric part, the nonlinear smooth transfer function was used using the Taylor series, and for the non-parametric part, the moderating factor was used to estimate the model parameters. The research model was evaluated using data on the number of patients admitted to hospitals for Covid 19 disease. The result showed that the parameter estimates are consistent and have less error than conventional models. [ABSTRACT FROM AUTHOR]

Copyright of China Rural Water & Hydropower is the property of China Rural Water & Hydropower Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The variety of communication services and the growing number of different sensors with the appearance of IoT (Internet of Things) technology generate significantly different types of network traffic. This implies that the structure of network traffic will be heterogeneous, which requires deep analysis to find the internal features underlying the data. A common model for analyzing the processes of a multiservice network is a model based on time series. Numerous empirical data studies indicate that the packet intensity time series do not belong to the general aggregates of a normal distribution. The problem of predicting network traffic is still relevant due to managing information that flows into a heterogeneous network. In this work, the authors studied the time series for stationarity in order to select an appropriate forecasting model. A visual assessment of the series assumed non-stationarity. The Augmented Dickey-Fuller Test is applied, and the measured network traffic is predicted using the ARIMA (Auto-Regressive Integrated Moving Average) statistical method. Results were obtained using the Econometric Modeler Matlab (R2021b) application. The results of the autocorrelation function (ACF) and partial ACF are analyzed, with the help of which the ARIMA model is optimized. As a result of the study, a software algorithm for the ARIMA (0,2,1) model was developed. [ABSTRACT FROM AUTHOR]

How to provide a harmonious and fair competition environment for trade activities depends on the effective management of government departments and organizations. How to play a real protective role in trade activities requires the optimization of government functions and the enhancement of economic management capabilities. Therefore, timely forecasting changes in import and export trade and formulating targeted preferential policies are of great significance for evaluating the development of the national market economy, promoting the development of the national economy, and further enhancing the country's economic capacity. This chapter also establishes an early warning model of Shenzhen's import and export transactions based on GMDH network. On the basis of expounding the particle swarm optimization algorithm and GMDH algorithm, the optimization mode, method, and process of GMDH network based on particle swarm optimization are also expounded. First of all, in China's domestic macroeconomic environment, national policy environment indicators and foreign macroeconomic environment indicators, eight indexes related to international import and export transactions are selected, and the autocorrelation test and main component analysis of the indexes are carried out. Finally, the simulation results show that compared with a single GMDH network, the GMDH neural network after particle swarm optimization can get better prediction conclusions. The research results of this paper show that the GMDH network can more accurately predict the trend of Shenzhen's international import and export transactions because of its adaptive structure, creating a new way for international import and export transaction forecasting. [ABSTRACT FROM AUTHOR]

Based on nighttime light data and statistical data, this study calculated the level of urban–rural integration (URI) of Shandong province, researched spatial heterogeneity of URI levels by local spatial autocorrelation analysis, Geodetector, and geographically weighted regression, and analyzed its influencing factors and spatial heterogeneity. The results concluded that: (1) The spatial pattern of urban–rural integrated level is consistent with the level of regional economic development in Shandong province. The level of URI is higher along the Qingdao–Jinan railway and along the coast, whereas the level is lower in southwest Shandong and northwest Shandong. (2) The cities of Yantai and Weifang are High–High cluster areas of urban integration, and Jining is a Low–Low cluster area. The spatial agglomeration characteristics are not significant in other cities. (3) Among the main factors affecting URI, the explanatory power of the rural population with high school or technical secondary school education or above, the area of urban construction land, and the secondary and tertiary industry GDP to the spatial pattern of URI in Shandong province are 73.58%, 62.08%, and 58.66%, respectively. As the key factors, spatial heterogeneity, such as north–south differences, southwest-to-northeast differences, and east–west differences, is evident. [ABSTRACT FROM AUTHOR]

Human cognition is fundamentally noisy. While routinely regarded as a nuisance in experimental investigation, the few studies investigating properties of cognitive noise have found surprising structure. A first line of research has shown that inter-response-time distributions are heavy-tailed. That is, response times between subsequent trials usually change only a small amount, but with occasional large changes. A second, separate, line of research has found that participants' estimates and response times both exhibit long-range autocorrelations (i.e., 1/f noise). Thus, each judgment and response time not only depends on its immediate predecessor but also on many previous responses. These two lines of research use different tasks and have distinct theoretical explanations: models that account for heavy-tailed response times do not predict 1/f autocorrelations and vice versa. Here, we find that 1/f noise and heavy-tailed response distributions co-occur in both types of tasks. We also show that a statistical sampling algorithm, developed to deal with patchy environments, generates both heavy-tailed distributions and 1/f noise, suggesting that cognitive noise may be a functional adaptation to dealing with a complex world. Author summary: Human behavior is inherently noisy, but this noise is surprisingly structured. Moment-by-moment fluctuations in responses are usually small but also occasionally quite large, mirroring a pattern seen in animal foraging. Separate work using different tasks has shown that response fluctuations do not depend just on recent responses, but also on a long history of past responses. In two experiments using very different tasks, we found that these two features co-occur. We show that a particular kind of algorithm, developed in computer science and statistics to approximate answers to difficult probabilistic problems, exhibits both these features as well, suggesting that noise is functionally important. [ABSTRACT FROM AUTHOR]

Ideally, a patient's response to medication can be monitored by measuring changes in performance of some activity. In observational studies, however, any detected association between treatment ("on-medication" vs "off-medication") and the outcome (performance in the activity) might be due to confounders. In particular, causal inferences at the personalized level are especially vulnerable to confounding effects that arise in a cyclic fashion. For quick acting medications, effects can be confounded by circadian rhythms and daily routines. Using the time-of-the-day as a surrogate for these confounders and the performance measurements as captured on a smartphone, we propose a personalized statistical approach to disentangle putative treatment and "time-of-the-day" effects, that leverages conditional independence relations spanned by causal graphical models involving the treatment, time-of-the-day, and outcome variables. Our approach is based on conditional independence tests implemented via standard and temporal linear regression models. Using synthetic data, we investigate when and how residual autocorrelation can affect the standard tests, and how time series modeling (namely, ARIMA and robust regression via HAC covariance matrix estimators) can remedy these issues. In particular, our simulations illustrate that when patients perform their activities in a paired fashion, positive autocorrelation can lead to conservative results for the standard regression approach (i.e., lead to deflated true positive detection), whereas negative autocorrelation can lead to anticonservative behavior (i.e., lead to inflated false positive detection). The adoption of time series methods, on the other hand, leads to well controlled type I error rates. We illustrate the application of our methodology with data from a Parkinson's disease mobile health study. [ABSTRACT FROM AUTHOR]

Microtremor array measurements, and passive surface wave methods in general, have been increasingly used to non-invasively estimate shear-wave velocity structures for various purposes. The methods estimate dispersion curves and invert them for retrieving S-wave velocity profiles. This paper summarizes principles, limitations, data collection, and processing methods. It intends to enable students and practitioners to understand the principles needed to plan a microtremor array investigation, record and process the data, and evaluate the quality of investigation result. The paper focuses on the spatial autocorrelation processing method among microtremor array processing methods because of its relatively simple calculation and stable applicability. Highlights: 1. A summary of fundamental principles of calculating phase velocity from ambient noise 2. General recommendations for MAM data collection and processing using SPAC methods 3. A discussion of limitations and uncertainties in the methods [ABSTRACT FROM AUTHOR]

The concept of local variance is used in the literature on image processing, and, to a lesser extent, in spatial analyses of local heterogeneity. Ord and Getis' local statistic of heterogeneity (LOSH) is used to test the null hypothesis that the local variance is no different from the variance for the entire study area. LOSH is a ratio of two variances, and it is shown here to also be closely related to the Geary statistic, which measures spatial autocorrelation. In this article, the Brown-Forsythe statistic is proposed as a way to test a similar null hypothesis—namely, that the local spatial variance is no different from that observed elsewhere in the study area. Rejection of the null in favor of significant homogeneity can be interpreted as an indicator of local positive spatial autocorrelation. The merits of the proposed test are illustrated via simulations of both null and alternative hypotheses, and the statistic is used to find local areas of homogeneous values in the classic spatial dataset on wheat yields in Rothamsted, England. [ABSTRACT FROM AUTHOR]

The uncertainty rock mechanical parameters (i.e., deformation and strength parameters) is an important factor in the safety estimation and support design of underground engineering. Ignoring this uncertainty could allow potential risks to the structure. To address this challenge, this paper develops and verifies a Bayesian approach for a rock's mechanical parameters estimation by integrating limited site data and prior knowledge, and the integrated knowledge is then transformed into a large number of equivalent samples of the rock's parameters. The experimental data of marble from triaxial compression tests are first used to verify this method, and the results show that this method can effectively estimate the distribution of the marble's deformation and strength parameters under the condition of small samples. Further, the probability distribution of rock mass parameters is obtained with the help of Hoek–Brown criterion. Then, the random field of the rock mass with a large cavern is constructed according to the obtained parameter distribution, the influence of different autocorrelation distances is discussed, and the excavation-induced deformation's statistical analysis is carried out. Finally, the failure of the surrounding rock is characterized probabilistically, which can be a reference to the reliability design of rock support in underground engineering. [ABSTRACT FROM AUTHOR]

A construction of binary sequences with period 4N and optimal autocorrelation magnitude has been investigated based on sampling and interleaving technique. We determine the exact value of the linear complexity of the constructed sequences according to the deep relationship among the characteristic polynomials, and show it is 2N + 2. Moreover, we determine the 2-adic complexity of these sequences by the autocorrelation function, and show it can attain the maximum value. Results show that such sequences can resist both the Berlekamp-Massey attack and the Rational Approximation Algorithm, in addition are good for communication. [ABSTRACT FROM AUTHOR]

A class of binary sequences with period 2p is constructed using generalized cyclotomic classes, and their linear complexity, minimal polynomial over F q as well as 2-adic complexity are determined using Gauss period and group ring theory. The results show that the linear complexity of these sequences attains the maximum when p ≡ ± 1 (mod 8) and is equal to p+1 when p ≡ ± 3 (mod 8) over extension field. Moreover, the 2-adic complexity of these sequences is maximum. According to Berlekamp–Massey(B–M) algorithm and the rational approximation algorithm (RAA), these sequences have quite good cryptographic properties in the aspect of linear complexity and 2-adic complexity. [ABSTRACT FROM AUTHOR]

Snow density estimates as a function of depth are used for understanding climate processes, evaluating water accumulation trends in polar regions, and estimating glacier mass balances. The common and interpretable physically derived differential equation models for snow density are piecewise linear as a function of depth (on a transformed scale); thus, they can fail to capture important data features. Moreover, the differential equation parameters show strong spatial autocorrelation. To address these issues, we allow the parameters of the physical model, including random change points over depth, to vary spatially. We also develop a framework for functionally smoothing the physically motivated model. To preserve inference on the interpretable physical model, we project the smoothing function into the physical model's spatially varying null space. The proposed spatially and functionally smoothed snow density model better fits the data while preserving inference on physical parameters. Using this model, we find significant spatial variation in the parameters that govern snow densification. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Chongqing University of Posts & Telecommunications (Natural Science Edition) is the property of Chongqing University of Posts & Telecommunications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Speech separation is a hot topic in multi-speaker speech recognition. The long-term autocorrelation of speech signal sequences is an essential task for speech separation. The keys are effective intra-autocorrelation learning for the speaker's speech, modelling the local (intra-blocks) and global (intra- and inter- blocks) dependence features of the speech sequence, with the real-time separation of as few parameters as possible. In this paper, the local and global dependence features of speech sequence information are extracted by utilizing different transformer structures. A forward adaptive module of channel and space autocorrelation is proposed to give the separated model good channel adaptability (channel adaptive modeling) and space adaptability (space adaptive modeling). In addition, at the back end of the separation model, a speaker enhancement module is considered to further enhance or suppress the speech of different speakers by taking advantage of the mutual suppression characteristics of each source signal. Experiments show that the scale-invariant signal-to-noise ratio improvement (SI-SNRi) of the proposed separation network on the public corpus WSJ0-2mix achieves better separation performance compared with the baseline models. The proposed method can provide a solution for speech separation and speech recognition in multi-speaker scenarios. [ABSTRACT FROM AUTHOR]

Network latency is a crucial factor affecting the quality of communications networks due to the irregularity of vehicular traffic. To address the problem of performance degradation or instability caused by latency in vehicular networks, this paper proposes a time delay prediction algorithm, in which digital twin technology is employed to obtain a large quantity of actual time delay data for vehicular networks and to verify autocorrelation. Subsequently, to meet the prediction conditions of the ARMA time series model, two neural networks, i.e., Radial basis function (RBF) and Elman networks, were employed to construct a time delay prediction model. The experimental results show that the average relative error of the RBF is 7.6%, whereas that of the Elman-NN is 14.2%. This indicates that the RBF has a better prediction performance, and a better real-time performance than the Elman-NN. [ABSTRACT FROM AUTHOR]

The estimation of non-stationary random medium parameters of petrophysical parameters is the key to the application of random medium theory in fine seismic exploration. We proposed a method for estimating non-stationary random medium parameters of petrophysical parameters using seismic data. Based on the linear petrophysical model, the relationship between seismic data and porosity, clay volume, and water saturation in the random medium was described, and the principle and method of estimating the autocorrelation parameters of the petrophysical parameter random medium were introduced in this study. Subsequently, the specific steps of applying the power spectrum method, for parameter estimation in non-stationary random media with petrophysical parameters, were explained. The feasibility and correctness of the method were verified through the estimation test of the two-dimensional theoretical model. Eventually, the estimation test of non-stationary random medium parameters of petrophysical parameters was carried out by field seismic data, and the results indicated that the non-stationary random medium parameters can better portray the information of subsurface medium petrophysical parameters. The method can provide a reference for the construction of a priori information on petrophysical parameters, and it can also provide a theoretical basis for the in-depth application of random medium theory to practical data. [ABSTRACT FROM AUTHOR]

Home range analysis is a standard and fundamental concept in ecology used to describe animal space use over their lifetimes. Connecting home range sizes with animal characteristics, location, and habitat can be used to inform conservation decisions. Reptiles are frequently lacking robust estimates of space use, particularly reptiles in tropical regions. Here we analyse a publicly available dataset, collected by the authors of this study, describing the movements of Critically Endangered Elongated tortoises Indotestudo elongata. The tortoise data included the locations of 17 tortoises (12 females, 5 males) collected on average once every three days for an average duration of 353.76 SE ± 33.10 days. We use these data to estimate the home range of Elongated tortoise, and explore how tortoise size and sex influences home range size. To mitigate issues resulting from low effective sample sizes and low temporal resolution of the data, we used a modern home range estimation method - Autocorrelated Kernel Density Estimators (AKDE). We found 14 of 17 individuals appear to be occupying a stable home range (using variograms to determine range residency). The average AKDE home range for all 14 individuals with range residency was 44.81 ± 10.44 ha. Bayesian Regression Models suggest comparable size estimates between male and female home ranges, despite males being physically larger than females in both mass and carapace length. These AKDE home range estimates have the added utility of being more comparable with other studies, less susceptible to errors from a suboptimal tracking regime, and are well positioned for inclusion in future meta-analyses. [ABSTRACT FROM AUTHOR]

A method is proposed to reconstruct the 3D molecular structure from micrographs collected at just one sample tilt angle in the random conical tilt scheme in cryo‐electron microscopy. The method uses autocorrelation analysis on the micrographs to estimate features of the molecule which are invariant under certain nuisance parameters such as the positions of molecular projections in the micrographs. This enables the molecular structure to be reconstructed directly from micrographs, completely circumventing the need for particle picking. Reconstructions are demonstrated with simulated data and the effect of the missing‐cone region is investigated. These results show promise to reduce the size limit for single‐particle reconstruction in cryo‐electron microscopy. [ABSTRACT FROM AUTHOR]

Destructive aftershocks such as the Mw 7.2 Van earthquake on October 23, 2011, and the Hoy (Iran) earthquake with Mw 5.9 on February 23, 2020, occurred in the province of Van and its surroundings. In earthquake studies, the issue of examining the distribution and homogeneity of earthquake incidences with Geographic Information Systems (GIS) based via spatial autocorrelation techniques is frequently investigated. Van province and its surroundings are among the areas with high earthquake risk due to its location on the East Anatolian Compressive Tectonic Block. The aim of this study is to analyze the spatial patterns of earthquakes with magnitude Mw 4 and above that occurred in the province of Van and its surroundings during the instrumental period and to determine to cluster. Spatial cluster analyses play an important role in examining the distribution of seismicity. The data used in the study have been taken from the database system of the Earthquake Department of the Republic of Turkey Ministry of Interior Disaster and Emergency Management Presidency. Moran's I and Getis-Ord Gi methods from spatial autocorrelation techniques were preferred on the earthquake data set to be used in this research. It has aimed to determine the dangerous areas by testing the earthquake distributions in clustered regions via spatial autocorrelation techniques. [ABSTRACT FROM AUTHOR]

The spatial information has been widely utilized in Spectral Mixture Analysis (SMA) studies over the years. However, the spatial autocorrelation, as a prerequisite of these studies, was seldomly examined. In the complex urban systems, it remains largely unknown whether and how land cover spectra at different locations are spatially autocorrelated and to which extent spatial autocorrelation can explain the great spectral variability. This study proposed an SMA method with Spatial Endmember Modeling (SEMSMA) to improve the fraction estimation of major urban land cover types. SEMSMA used the Incremental Spatial Autocorrelation (ISA) analysis to quantify spatial autocorrelation patterns in broadband reflectance. With the patterns, SEMSMA predicted endmember signatures that incorporated both spatial (location-oriented) and non-spatial spectral variability via the Restricted Maximum Likelihood-Empirical Best Linear Unbiased Predictor (REML-EBLUP) and Monte Carlo sampling. The spectral variabilities were then addressed in pixel unmixing by an iterative mixture analysis method. Results of ISA demonstrated diverse spatial autocorrelation patterns in reflectance of urban land covers. These patterns can be related to the pathways that spatial processes acted on land cover properties. The predicted endmember signatures explained most of the spectral variability for all land cover types. Subpixel fraction estimates showed that SEMSMA outperformed multiple endmember SMA (MESMA) and spatially adaptive SMA (SASMA), based on the root mean square error (0.04–0.15), the mean absolute error (0.01–0.09), and the Systematic Error (SE) (−80.00–0.01). The low SE of SEMSMA revealed unbiased estimation of land cover fractions. Results of this study underscored the necessity of spatial autocorrelation examination and the advantage of rigorous spatial endmember modeling when incorporating spatial information into SMA. [ABSTRACT FROM AUTHOR]

Fractional Gaussian noise models the time series with long-range dependence; when the Hurst index H(1/2, 1), it has positive correlation reflecting a persistent autocorrelation structure. This paper studies the numerical method for solving the stochastic fractional diffusion equation driven by fractional Gaussian noise. Using the operator theoretical approach, we present the regularity estimate of the mild solution and the fully discrete scheme with finite element approximation in space and backward Euler convolution quadrature in time. The O(H-pα) convergence rate in time in space are obtained, showing the relationship between the regularity of noise and convergence rates, where p is a parameter to measure the regularity of noise and α (0, 1). Finally, numerical experiments are performed to support the theoretical results. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Emerging Economies & Policy is the property of JOEEP: Journal of Emerging Economies & Policy and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Acta Scientiarum Naturalium Universitatis Sunyatseni / Zhongshan Daxue Xuebao is the property of Sun-Yat-Sen University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Dual control volume grand canonical molecular dynamics is used to perform the first calculation of fluid-fluid interfacial mobilities. The mobility is calculated from one-dimensional random walks of the interface by relating the diffusion coefficient to the interfacial mobility. Three different calculation methods are employed: one using the interfacial position variance as a function of time, one using the mean-squared interfacial displacement, and one using the time-autocorrelation of the interfacial velocity. The mobility is calculated for two liquid-liquid interfaces and one liquid-vapor interface to examine the robustness of the methods. Excellent agreement between the three calculation methods is shown for all the three interfaces, indicating that any of them could be used to calculate the interfacial mobility. [ABSTRACT FROM AUTHOR]

A highly pure and stable single-photon source is prepared that comprises a well-designed pillar array, in which each pillar contains only a few InAs quantum dots. A nano-pillar in this array is in direct contact with a fiber end surface and cooled in a liquid-He bath. Auto-correlation measurements show that this source provides an average g(2)(0) value of 0.0174 in the measured excitationpower range. This photon source and fiber coupling are quite rigid against external disturbances such as cooling-heating cycles and vibration, with long-term stability. [ABSTRACT FROM AUTHOR]

Vibrationally resolved electronic absorption spectra including the effect of vibrational pre-excitation are computed in order to interpret and predict vibronic transitions that are probed in the Vibrationally Promoted Electronic Resonance (VIPER) experiment [L. J. G. W. van Wilderen et al., Angew. Chem., Int. Ed. 53, 2667 (2014)]. To this end, we employ time-independent and time-dependent methods based on the evaluation of Franck-Condon overlap integrals and Fourier transformation of timedomain wavepacket autocorrelation functions, respectively. The time-independent approach uses a generalized version of the FC-classes method [F. Santoro et al., J. Chem. Phys. 126, 084509 (2007)]. In the time-dependent approach, autocorrelation functions are obtained by wavepacket propagation and by the evaluation of analytic expressions, within the harmonic approximation including Duschinsky rotation effects. For several medium-sized polyatomic systems, it is shown that selective pre-excitation of particular vibrational modes leads to a redshift of the low-frequency edge of the electronic absorption spectrum, which is a prerequisite for the VIPER experiment. This effect is typically most pronounced upon excitation of modes that are significantly displaced during the electronic transition, such as ring distortion modes within an aromatic π-system. Theoretical predictions as to which modes show the strongest VIPER effect are found to be in excellent agreement with experiment. [ABSTRACT FROM AUTHOR]

On-line detection of chatter is one of the key techniques to avoid the harmful effects caused by chatter in grinding process. The key to chatter detection is to capture reliable chatter features and thresholds. To achieve this, it is important to make clear and extract the essential characteristics of the grinding chatter signal, which has not yet been well studied. In this paper, we are going to investigate the essential characteristics of the grinding chatter signal and propose a new approach for on-line detection of grinding chatter. The proposed approach for on-line detection of grinding chatter is based on minimum entropy deconvolution and autocorrelation function, in which the minimum entropy deconvolution is employed to deconvolve the effect of transmission path, and further to restore the essential characteristics of the chatter signals. To eliminate the interference of the non-periodic impulse signals in the measured vibration signals, an autocorrelation function is introduced. Kurtosis is employed to indicate chatter according to the changes of the processed signal. The validity of the proposed method is demonstrated through the measured vibration signals obtained from grinding processes and the presented chatter detection index is independent from the grinding conditions with excellent detection accuracy and permissible computational efficiency. This demonstrates the effectiveness of proposed method in on-line implementation. [ABSTRACT FROM AUTHOR]

In this article we develop an asymptotic theory for sample tail autocorrelations of time series data that can exhibit serial dependence in both tail and non-tail regions. Unlike with the traditional autocorrelation function, the study of tail autocorrelations requires a double asymptotic scheme to capture the tail phenomena, and our results do not impose any restrictions on the dependence structure in non-tail regions and allow processes that are not necessarily strongly mixing. The newly developed asymptotic theory reveals a previously undiscovered phase transition phenomenon, where the asymptotic behaviour of sample tail autocorrelations, including their convergence rate, can transition from one phase to another as the lag index moves past the point beyond which serial tail dependence vanishes. The phase transition discovery fills a gap in existing research on tail autocorrelations and can be used to construct the lines of significance, in analogy to the traditional autocorrelation plot, when visualizing sample tail autocorrelations to assess the existence of serial tail dependence or to identify the maximal lag of tail dependence. [ABSTRACT FROM AUTHOR]

In inverse synthetic-aperture radar (ISAR) imaging, it is essential to deal with the Doppler ambiguity of group targets with complex maneuvers in order to avoid the bias of target position towards the actual value. Simultaneously, migration through resolution cell (MTRC) under the Doppler ambiguity is unable to be compensated for as a preprocessing. Traditional ISAR imaging methods for maneuvering targets, however, are undesirable to handle the severe deformation and defocusing in the imaging results induced by the Doppler ambiguity and MTRC. In this paper, we propose a novel and effective ISAR imaging method to improve the imaging quality by removing the Doppler ambiguity and compensating for the MTRC. Specifically, we first model the echo as a multi-component cubic phase signal (m-CPS) and design a high-order instantaneous autocorrelation function–generalized scaled Fourier transform (HIAF–GSCFT) to process the echo. This is to estimate the rotational parameters without MTRC compensation. Then, a maximum weighted contrast algorithm is used to remove the Doppler ambiguity, followed by reconstructing the echo. Compared with the existing method, the proposed method can accurately estimate the rotational parameters under the existing MTRCs and achieves a high-quality ISAR image for group targets, with complex maneuvers without Doppler ambiguity. Experiments of simulated and measured datasets validate its effectiveness and robustness for single target and group targets. [ABSTRACT FROM AUTHOR]

This article regards the dependency structure of wind power and load time series at each moment as the conditional probability of a high-dimensional discrete stochastic process. A dynamic Bayesian network (DBN) model is proposed to calculate the joint probability distribution of high-dimensional stochastic processes, which can completely describe the potential dependency structure of wind power and load at each time. The DBN model is based on a data-driven approach, using Bayesian information criteria (BICs) as the scoring function, and using a greedy search algorithm to determine the initial network and transmission network DBN structure. Maximum likelihood estimation (MLE) is used to estimate the parameters of the initial network and the transmission network. The DBN model is established from these two networks, and the joint probability distribution of the stochastic process is obtained. Sequential Monte Carlo (SMC) sampling is performed on the DBN model to generate the dependent simulation sequence of wind power and load. Using probability density, mean value, maximum output fluctuation, and autocorrelation and cross-correlation functions as evaluation indicators, the statistical characteristics of the historical sequence, simulation sequence based on the DBN model, and simulation sequence based on the hidden Markov model (HMM) are comprehensively compared and analyzed to verify the effectiveness of the DBN model. [ABSTRACT FROM AUTHOR]

Rough surfaces such as metal additive manufactured surfaces are quite challenging for measurement. Artifacts caused by irregular and difficult-to-measure geometries are inevitable. Removing all the artifacts would cause a portion of surface information to be missing. Different from previous works, the postprocessing in this paper includes an additional step to eliminate artifacts based on autocorrelation functions of particular subimages instead of simply removing them. This increases the accuracy with respect to surface roughness and provides a more comprehensive view on the topography. In addition, a dome shape LED array ring light is proposed to provide all-round lighting due to the high degree of irregularity of workpiece surfaces. The experimental results obtained from FVM are validated and compared with the given roughness values of a Rubert Microsurf 329 comparator test panel as well as measurement results of a metal additive workpiece by a confocal microscope. [ABSTRACT FROM AUTHOR]

BACKGROUND: Developing a model that adequately explains pest population dynamics based on weather‐related parameters is fundamentally important for proper pest management. Autocorrelation with past occurrences should be considered when modeling the relationship between the time series of pest occurrence data and meteorological factors; however, few attempts have been made to model these factors simultaneously. In this study, we constructed an autoregressive integrated moving average with exogenous variables (ARIMAX) model to represent the occurrence of the common cutworm, Spodoptera litura (F.) (Lepidoptera: Noctuidae), a major moth pest species in Asia, using the trap catch data of S. litura recorded approximately every 5 days. The multiple meteorological measurements taken over several past periods before S. litura occurrence were included as explanatory variables to evaluate their lag effects on future occurrences. RESULTS: It was suggested that temperature had the most important effect on S. litura occurrences among other meteorological factors (i.e., humidity, wind speed, and precipitation). Especially, higher temperatures during the larval/egg stage seemed to presage a higher moth abundance. When the model was fitted using independent data that were not used for calibrating the model, the model was able to capture trends in increases in the scale of occurrence, particularly after July, when the occurrence rapidly increased. CONCLUSION: Past temperature condition, particularly during the larval and egg states, is suggested to be highly important for predicting future S. litura occurrences. The ARIMAX model proposed here will allow preventive measures to be taken, effectively safeguarding food resources against pest outbreaks. © 2022 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

This article defines the Autoregressive Fractional Unit Root Integrated Moving Average (ARFURIMA) model for modelling ILM time series with fractional difference value in the interval of 1 < d < 2. The performance of the ARFURIMA model is examined through a Monte Carlo simulation. Also, some applications were presented using the energy series, bitcoin exchange rates and some financial data to compare the performance of the ARFURIMA and the Semiparametric Fractional Autoregressive Moving Average (SEMIFARMA) models. Findings showed that the ARFURIMA outperformed the SEMIFARMA model. The study's conclusion provides another perspective in analysing large time series data for modelling and forecasting, and the findings suggest that the ARFURIMA model should be applied if the studied data show a type of ILM process with a degree of fractional difference in the interval of 1 < d < 2. [ABSTRACT FROM AUTHOR]

This study intends to test the presence of β-convergence in the global Environmental Performance (EP). For this purpose, spatial Green Solow model is used as the theoretical framework of the study. Data of 88 developed and developing countries for the periods 1978–2017 is used. The present study utilizes ecological footprint (EF) as a comprehensive Environmental Performance Indicator (EPI). For data analysis, spatial econometric techniques have been used. To explore the spatial dependence of EP, Moran's I statistic was used. For regression analysis, this study has made the use of Spatial Durbin Model (SDM). Findings of the study indicate that there is positive spatial autocorrelation in the EP of the countries which means countries with similar EP are clustered together. Results of SDM confirm the existence of β-convergence in the global EP. Physical capital was found to degrade environment while break-even investment (BEI) was found to improve it. [ABSTRACT FROM AUTHOR]