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We determine distributions and correlation properties of offshore wind speeds and wind speed increments by analyzing wind data sampled with a resolution of one second for 20 months at different heights above sea level in the North Sea. Distributions of horizontal wind speeds can be fitted to Weibull distributions with shape and scale parameters varying weakly with the vertical height separation. Kullback–Leibler divergences between distributions at different heights change with the squared logarithm of the height ratio. Cross-correlations between time derivatives of wind speeds are long-term anticorrelated, and the even parts of their correlation functions satisfy sum rules. Distributions of horizontal wind speed increments change from a tent-like shape to a Gaussian with rising increment lag. A surprising peak occurs in the left tail of the increment distributions for lags in a range 10 - 200 km after applying the Taylor’s hypothesis locally to transform time lags into distances. The peak is decisive in order to obtain an expected and observed linear scaling of third-order structure functions with distance. This suggests that it is an intrinsic feature of atmospheric turbulence. [ABSTRACT FROM AUTHOR]

A classical problem in coding theory addresses moments of the weight spectrum distribution. The results in this work are on the weight moments of individual codewords rather than the weight spectrum. The expectations of single and pairwise products of weights of nonzero words in a random binary linear code are given. We show that the covariance between the weights of any pair of distinct nonzero words is zero. Our main theorem has an application to sequence correlations problem. We prove that the sums of out of phase self correlations, as well as sums of cross-correlations, of nonzero words in a random binary linear code are equal to zero. [ABSTRACT FROM AUTHOR]

Taking six representative futures in the international energy and agricultural markets as the research objects, we use multifractal analysis methods to study the fluctuation characteristics, market risks and cross-correlations within and between these markets before and after the outbreak of the Russia–Ukraine conflict in this paper. The empirical results show that both the auto-correlations and cross-correlations have obvious multifractal features. It is confirmed that the multifractal strength and market risks of the international energy markets have weakened, while those of the international agricultural markets have enhanced after the Russia–Ukraine conflict broke out. In addition, the Russia–Ukraine conflict has intensified the strength of the multifractality and the degree of fluctuation complexity between these two classes of international markets. Further, the intrinsic multifractal natures of cross-correlations are tested, and the apparent and intrinsic multifractality before and after the conflict are revealed. Finally, some policy suggestions are put forward based on the empirical results. [ABSTRACT FROM AUTHOR]

Measuring the temperature changes of the deep ocean will be critical to understanding how the earth system will respond to climate change. In this work, we present a method for measuring the depth‐averaged, deep ocean temperature at local (∼3 km) spatial scales using passive estimates of acoustic propagation. These passive acoustic estimates of deep ocean temperature can be used with existing and future passive acoustic monitoring infrastructure to provide complimentary observations of the ocean to in situ measurements, and could be particularly useful in areas of poor ocean observation coverage. Using 8 years of ambient sound data, we demonstrate that the passive estimates agree with global ocean models and measurements by ARGO floats. The rms difference between the HYCOM ocean model is shown to be 0.13°C, and the rms difference between ARGO measurements is shown to be 0.086°C. Plain Language Summary: In this work, we demonstrate that the ambient sound in the ocean can be used to measure local, depth‐averaged, deep ocean temperature. Using 8 years of ambient sound, we compare the estimate of ocean temperature to a global ocean model and ocean temperature measurements. The observations of deep‐ocean temperature estimated from ambient sound are shown to be consistent with the ocean model and direct measurements. This technique could eventually be used as a method for observing the deep ocean and be used as data assimilation into global ocean models. Key Points: Using coherent ambient sound, surface reflection acoustic arrival times are estimated with a high accuracy (∼1 ms)These estimates of acoustic travel time are then used to estimate the depth‐averaged water temperature in the deep oceanIn this work, we demonstrate the feasibility of using passive acoustic monitoring infrastructure to observe deep ocean temperature [ABSTRACT FROM AUTHOR]

The purpose of this study was to investigate normative and age-related differences in trunk and pelvis kinematics and intersegmental coordination during sagittal plane flexion–extension. Trunk and pelvis kinematics were recorded while 76 participants performed a maximal range of motion task in the sagittal plane. Cross-correlation was calculated to determine the phase lag between adjacent segment motion, and coupling angles were calculated using vector coding and classified into one of 4 coordination patterns: in-phase, antiphase, superior, and inferior phase. A 2-way mixed-model multivariate analysis of variance was used to compare lumbar spine and pelvis angular kinematics, phase lags, and cross-correlation coefficients between groups. Young participants exhibited greater trunk range of motion compared with middle-aged participants. The lumbar spine and pelvis were predominantly rotating with minimum phase lag during flexion and extension movement for both age groups, and differences in coordination between the groups were seen during hyperextension and return to upright position. In conclusion, middle-aged adults displayed lower range of motion but maintained similar movement patterns to young adults, which could be attributed to protective mechanisms. Healthy lumbar and pelvis movement patterns are important to understand and need to be quantified as a baseline, which can be used to develop rehabilitation protocols for individuals with spinal ailments. [ABSTRACT FROM AUTHOR]

We investigate whether varying wavefield contributions are the likely cause to variation in microtremor horizontal-to-vertical spectral ratio (MHVSR) amplification shape between six sites in the relatively homogeneous geologic setting of Windsor, Ontario. We quantify the MHVSR shape in terms of peak broadness and its fitness using mathematical functions to identify potential wave type contributions. We develop a technique that uses particle motion plots of cross-correlated microtremor recordings to establish the dominant wave types contributing to the microtremor wavefield within three important frequency bandwidths (below, spanning, and above the fundamental peak frequency). We investigate the variability in the inverted shear-wave velocity (VS) depth profile by performing 21 MHVSR inversions with varying Rayleigh, Love, and body wave contributions. The impact to seismic site characterization is that the depth and VS of the resonator (half-space) layer are overestimated consistently by an average of 28% compared to the often-default-assumed Rayleigh ellipticity forward amplification model. Our study demonstrates the importance of correctly identifying wave type contributions of the microtremor wavefield for the proper estimation of VS depth profiles, especially to obtain correct thickness of the sediment layer and resonator VS and thereby the average VS of the upper 30 m (VS30). [ABSTRACT FROM AUTHOR]

Ambient noise tomography has become a popular method in the past two decades to image the crust and uppermost mantle structure. To date, broadband Rayleigh wave signals can be obtained from ambient noise, which can be utilized to study the earth's interior structure from the surface down to ∼200–300 km depths. However, it is hard to extract intermediate‐ and long‐period (>50 s) Love wave signals from ambient noise using conventional data processing techniques for ambient noise. Array‐based data processing techniques can enhance weak signals. In this study, we adopt a modified algorithm of the double‐beamforming method to extract broadband Love wave signals from ambient noise. We validate the accuracy of the dispersion curves measured from our method by comparing them to those measured from the conventional method. Then, we use a finite frequency ambient noise tomography method to construct broadband Love wave phase velocity maps across the contiguous USA. These phase velocity maps are consistent with those obtained from conventional methods at short periods (<40 s). Finally, we analyze the resolution of our double‐beamforming method based on checkerboard tests and find that the resolution of phase velocity maps based on our method is close to the aperture of the subarrays used in our double‐beamforming method. Plain Language Summary: Ambient noise tomography can be used to study the Earth's structure and has advantages over traditional teleseismic surface wave tomography. Broadband Rayleigh waves can be extracted from ambient noise data, but it is still challenging to extract intermediate‐ and long‐period Love wave signals from ambient noise data. Array‐based data processing techniques have the capability to enhance weak seismic signals. In this study, we adopt an array‐based method of double‐beamforming to improve the extraction of high‐quality broadband Love wave signals and demonstrate that these data can be used to image broadband Love wave phase velocity maps. Key Points: We adopt a modified double‐beamforming method to improve the extraction of broadband Love waves from ambient noiseWe obtain broadband Love waves at 10–120 s periods from ambient noise dataWe construct broadband Love wave phase velocity maps of the USA based on finite frequency ambient noise tomography [ABSTRACT FROM AUTHOR]

We have studied Rayleigh wave group velocities beneath the Aegean region of Türkiye using ambient noise tomography. Noise data were gathered from 43 broadband seismic stations belonging to three permanent broad-band arrays. The cross-correlation method was used to estimate empirical Green's functions. Group velocities of the fundamental mode Rayleigh waves were determined using multiple filter technique. We measured dispersion for each station pair in a period range of 2-12 s and computed maps of group velocity distribution using the fast marching surface tomography method. The group velocity maps correlate well with the geological and tectonic features of the region, displaying low velocities for the Quaternary alluvial basins, moderate velocities for Cretaceous rocks, and high velocities for the regions where gneiss and granitoid rocks outcrop. Low velocity anomalies may be associated with the grabens and horsts formed by faulting and to deep mantle flow. We detect potential offshore geothermal zones in Izmir and Candarli Bay. The information from broad-band networks improves the resolution of crustal surface wave velocity structures, particularly for shallow depths. This improvement will support the assessment of seismic hazard, as the shallow layers of the crust contribute significantly to seismic activity. [ABSTRACT FROM AUTHOR]

The activity of mitochondrial large-conductance voltage- and C a 2 + -activated K + channels (mitoBK) is regulated by a number of biochemical factors, including flavonoids. In particular, naringenin (Nar) and quercetin (Que) reached reasonable scientific attention due to their well-pronounced channel-activating effects. The open-reinforcing outcomes of Nar and Que on the mitoBK channel gating have been already reported. Nevertheless, the molecular picture of the corresponding channel–ligand interactions remains still to be revealed. In this work, we investigate the effects of the Nar and Que on the conformational dynamics of the mitoBK channel. In this aim, the cross-correlation-based analysis of the single-channel signals recorded by the patch-clamp method is performed. The obtained results in the form of phase space diagrams enable us to visually monitor the effects exerted by the considered flavonoids at the level of temporal characteristics of repetitive sequences of channel conformations. It turns out that the mitoBK channel activation by naringenin and quercetin does not lead to the change in the number of clusters within the phase space diagrams, which can be related to the constant number of available channel macroconformations regardless of the flavonoid administration. The localization and occupancy of the clusters of cross-correlated sequences suggest that mitoBK channel stimulation by flavonoids affects the relative stability of channel conformations and the kinetics of switching between them. For most clusters, greater net effects are observed in terms of quercetin administration in comparison with naringenin. It indicates stronger channel interaction with Que than Nar. [ABSTRACT FROM AUTHOR]

Abstract The elastodynamic half-space Green’s function has been the subject of research for more than a century since the Lamb’s classical solution. Here, we revisit the problem and present a new closed analytical solution, in frequency domain, based upon the Principle of Equipartition (EQP) of Energy. This principle asserts that the imaginary parts of the Green’s tensor components equal the average cross-correlations of the fields generated by the uniform incidence of P and S body waves and by Rayleigh surface waves with amplitudes weighted by partition factors. The real part of the Green’s function is the Hilbert transform of the imaginary part. We validate our results by comparing synthetic seismograms of well-known solutions for surface and buried forces and discuss the implications of this new solution. Constructing synthetic diffuse fields is a first step for identifying them in nature. Graphical Abstract

We have studied Rayleigh wave group velocities beneath the Aegean region of Türkiye using ambient noise tomography. Noise data were gathered from 43 broadband seismic stations belonging to three permanent broad-band arrays. The cross-correlation method was used to estimate empirical Green’s functions. Group velocities of the fundamental mode Rayleigh waves were determined using multiple filter technique. We measured dispersion for each station pair in a period range of 2–12 s and computed maps of group velocity distribution using the fast marching surface tomography method. The group velocity maps correlate well with the geological and tectonic features of the region, displaying low velocities for the Quaternary alluvial basins, moderate velocities for Cretaceous rocks, and high velocities for the regions where gneiss and granitoid rocks outcrop. Low velocity anomalies may be associated with the grabens and horsts formed by faulting and to deep mantle flow. We detect potential offshore geothermal zones in Izmir and Candarli Bay. The information from broad-band networks improves the resolution of crustal surface wave velocity structures, particularly for shallow depths. This improvement will support the assessment of seismic hazard, as the shallow layers of the crust contribute significantly to seismic activity.

The ongoing COVID-19 shocked financial markets globally, including China's crude oil future market, which is the third-most traded crude oil futures after WTI and Brent. As China's first crude oil futures are accessible to foreign investors, the Shanghai crude oil futures (SC) have attracted significant interest since launch at the Shanghai International Energy Exchange. The impact of COVID-19 on the new crude oil futures is an important issue for investors and policy makers. Therefore, this paper studies the short-term influence of COVID-19 pandemic on SC via multifractal analysis. We compare the market efficiency of SC before and during the pandemic with the multifractal detrended fluctuation analysis and other commonly used random walk tests. Then, we generate shuffled and surrogate data to investigate the components of multifractal nature in SC. And we examine cross-correlations between SC returns and other financial assets returns as well as SC trading volume changes by the multifractal detrended cross-correlation analysis. The results show that market efficiency of SC and its cross-correlations with other assets increase significantly after the outbreak of COVID-19. Besides that, the sources of its multifractal nature have changed since the pandemic. The findings provide evidence for the short-term impacts of COVID-19 on SC. The results may have important implications for assets allocation, investment strategies and risk monitoring. [ABSTRACT FROM AUTHOR]

This document is a corrigendum for an article titled "Ambient noise tomography of the Aegean region of Türkiye from Rayleigh wave group velocity." The corrigendum acknowledges errors in the references of the published article, specifically incorrect publication years and missing authors. The correct references are provided in the corrigendum. The authors apologize for the errors and state that they do not affect the scientific conclusions of the article. The corrigendum also includes information about the authors and their affiliations. [Extracted from the article]

This study exploits multifractal cross-correlation analysis (MFCCA) to investigate the impact of the COVID-19 pandemic on the cross-correlations between gold and U.S. equity markets using 1-min high-frequency data from January 1, 2019, to December 29, 2020. The MFCCA method shows that the pandemic caused an increase of multifractality in cross-correlations between the two markets. Specifically, the cross-correlations of small fluctuations became more persistent while those of large fluctuations became less persistent, explaining the source of multifractality. The findings of this study carry significant implications for investors, academicians, and policymakers. For example, the increase of multifractality of cross-correlation means that the non-linear relationship between gold and U.S. equity returns prevails more during economic downturns. Therefore, academicians may resort to non-linear techniques to evaluate the relationship between gold and U.S. equity markets during the health pandemic. Moreover, investors can know the value of hedging benefits over different investment time horizons during the pandemic. Finally, policymakers can better assess the economic downturns (i.e., those caused by health pandemics) over the dynamics of cross-correlation between gold and equity markets to make sound financial policies. [ABSTRACT FROM AUTHOR]

In this study the cross-correlations between the cryptocurrency market represented by the two most liquid and highest-capitalized cryptocurrencies: bitcoin and ethereum, on the one side, and the instruments representing the traditional financial markets: stock indices, Forex, commodities, on the other side, are measured in the period: January 2020–October 2022. Our purpose is to address the question whether the cryptocurrency market still preserves its autonomy with respect to the traditional financial markets or it has already aligned with them in expense of its independence. We are motivated by the fact that some previous related studies gave mixed results. By calculating the q-dependent detrended cross-correlation coefficient based on the high frequency 10 s data in the rolling window, the dependence on various time scales, different fluctuation magnitudes, and different market periods are examined. There is a strong indication that the dynamics of the bitcoin and ethereum price changes since the March 2020 COVID-19 panic is no longer independent. Instead, it is related to the dynamics of the traditional financial markets, which is especially evident now in 2022, when the bitcoin and ethereum coupling to the US tech stocks is observed during the market bear phase. It is also worth emphasizing that the cryptocurrencies have begun to react to the economic data such as the Consumer Price Index readings in a similar way as traditional instruments. Such a spontaneous coupling of the so far independent degrees of freedom can be interpreted as a kind of phase transition that resembles the collective phenomena typical for the complex systems. Our results indicate that the cryptocurrencies cannot be considered as a safe haven for the financial investments. [ABSTRACT FROM AUTHOR]

This paper presents a transfer function time series forecast model for COVID-19 deaths using reported COVID-19 case positivity counts as the input series. We have used deaths and case counts data reported by the Center for Disease Control for the USA from July 24 to December 31, 2021. To demonstrate the effectiveness of the proposed transfer function methodology, we have compared some summary results of forecast errors of the fitted transfer function model to those of an adequate autoregressive integrated moving average model and observed that the transfer function model achieved better forecast results than the autoregressive integrated moving average model. Additionally, separate autoregressive integrated moving average models for COVID-19 cases and deaths are also reported. [ABSTRACT FROM AUTHOR]

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Aims: To estimate whether the use of wagering inducements has a significant impact on the gambling behaviors of on‐line gamblers and describe this temporal relation under naturalistic conditions. Design This longitudinal observational study is part of the second stage of the Screening for Excessive Gambling Behaviors on the Internet (EDEIN) research program. Setting: Gambling tracking data from the French national on‐line gambling authority (poker, horse race betting and sports betting) and from the French national lottery operator (lotteries and scratch games). Participants: A total of 9306 gamblers who played poker, horse race or sports betting and 5682 gamblers who played lotteries and scratch games completed an on‐line survey. The gender ratio was largely male (between 87.1% and 92.9% for poker, horse race betting and sports betting, and equal to 65.1% for lotteries). Median age ranged from 35 (sports betting) to 53 (horse race betting and lotteries). Measurements The survey used the Problem Gambling Severity Index (PGSI) to determine the status of the gamblers (at‐risk or not). Gambling tracking data included weekly gambling intensity (wagers, deposits), gambling frequency (number of gambling days), proxies of at‐risk gambling behaviors (chasing and breadth of involvement) and use of wagering inducements. Findings The use of wagering inducements was associated with an increase of gambling intensity [β between −0.06 (−0.08; –0.05) and 0.57 (0.54; 0.60)], gambling frequency [β between 0.12 (0.10; 0.18) and 0.29 (0.28; 0.31)] and at‐risk gambling behaviors [odds ratio between 1.32 (1.16; 1.50) and 4.82 (4.61; 5.05)] at the same week of their use. This effect was stronger for at‐risk gambling behaviors and at‐risk gamblers. Conclusions: Wagering inducements may represent a risk factor for developing or exacerbating gambling problems. [ABSTRACT FROM AUTHOR]

This paper explores the COVID-19 influences on the cross-correlation between the movie market and the financial market. The nonlinear cross-correlations between movie box office data and Google search volumes of financial terms such as Dow Jones Industrial Average (DJIA), NASDAQ and PMI are investigated based on multifractal detrended cross-correlation analysis (MF-DCCA). The empirical results show there are nonlinear cross-correlations between movie market and financial market. Metrics such as Hurst exponents, singular exponents and multifractal spectrum demonstrate that the cross-correlation between movie market and financial market is persistent, and the cross-correlation in long term is more stable than that in short term. In the COVID-19 period, the multifractal features of cross-correlation become stronger implying that COVID-19 enhanced the complexity between the movie industry and the financial market. Furthermore, through the rolling window analysis, the Hurst exponent dynamic trends indicate that COVID-19 has a clear influence on the cross-correlation between movie market and financial market. [ABSTRACT FROM AUTHOR]

With the rapid development of economic globalization, the stock markets in China and the US are increasingly linked. The fluctuation features and cross-correlations of the two countries' markets have attracted extensive attention from market investors and researchers. In this paper, the fractal analysis methods including multifractal asymmetric detrended cross-correlation analysis (MF-ADCCA) and coupled detrended cross-correlation analysis (CDCCA) are applied to explore the volatilities of CSI300 and SP500 sector stock indexes as well as the cross-correlations and coupling cross-correlations between the two corresponding sector stock indexes. The results show that the auto-correlations, cross-correlations and coupling cross-correlations have multifractal fluctuation characteristics, and that the cross-correlations are asymmetric. Additionally, the coupling cross-correlation strengths are distinct due to the different influence of long-range correlations and fat-tailed distribution. Further, the co-movement between China and the US sector stock markets is susceptible to external market factors such as major economic events and national policies. [ABSTRACT FROM AUTHOR]

In this contribution, a vector-autoregressive (VAR) process with multivariate t-distributed random deviations is incorporated into the Gauss-Helmert model (GHM), resulting in an innovative adjustment model. This model is versatile since it allows for a wide range of functional models, unknown forms of auto- and cross-correlations, and outlier patterns. Subsequently, a computationally convenient iteratively reweighted least squares method based on an expectation maximization algorithm is derived in order to estimate the parameters of the functional model, the unknown coefficients of the VAR process, the cofactor matrix, and the degree of freedom of the t-distribution. The proposed method is validated in terms of its estimation bias and convergence behavior by means of a Monte Carlo simulation based on a GHM of a circle in two dimensions. The methodology is applied in two different fields of application within engineering geodesy: In the first scenario, the offset and linear drift of a noisy accelerometer are estimated based on a Gauss-Markov model with VAR and multivariate t-distributed errors, as a special case of the proposed GHM. In the second scenario real laser tracker measurements with outliers are adjusted to estimate the parameters of a sphere employing the proposed GHM with VAR and multivariate t-distributed errors. For both scenarios the estimated parameters of the fitted VAR model and multivariate t-distribution are analyzed for evidence of auto- or cross-correlations and deviation from a normal distribution regarding the measurement noise. [ABSTRACT FROM AUTHOR]

Modern financial markets are characterized by a rapid flow of information, a vast number of participants having diversified investment horizons, and multiple feedback mechanisms, which collectively lead to the emergence of complex phenomena, for example speculative bubbles or crashes. As such, they are considered as one of the most complex systems known. Numerous studies have illuminated stylized facts, also called complexity characteristics, which are observed across the vast majority of financial markets. These include the so-called "fat tails" of the returns distribution, volatility clustering, the "long memory", strong stochasticity alongside non-linear correlations, persistence, and the effects resembling fractality and even multifractality. The striking development of the cryptocurrency market over the last few years – from being entirely peripheral to capitalizing at the level of an intermediate-size stock exchange – provides a unique opportunity to observe its evolution in a short period. The availability of high-frequency data allows conducting advanced statistical analysis of fluctuations on cryptocurrency exchanges right from their birth up to the present day. This opens a window that allows quantifying the evolutionary changes in the complexity characteristics which accompany market emergence and maturation. The purpose of the present review, then, is to examine the properties of the cryptocurrency market and the associated phenomena. The aim is to clarify to what extent, after such an impetuous development, the characteristics of the complexity of exchange rates on the cryptocurrency market have become similar to traditional and mature markets, such as stocks, bonds, commodities or currencies. The review introduces the history of cryptocurrencies, offering a description of the blockchain technology behind them. Differences between cryptocurrencies and the exchanges on which they are traded have been consistently shown. The central part of the review surveys the analysis of cryptocurrency price changes on various platforms. The statistical properties of the fluctuations in the cryptocurrency market have been compared to the traditional markets. With the help of the latest statistical physics methods, namely, the multifractal cross-correlation analysis and the q -dependent detrended cross-correlation coefficient, the non-linear correlations and multiscale characteristics of the cryptocurrency market are analyzed. In the last part of this paper, through applying matrix and network formalisms, the co-evolution of the correlation structure among the 100 cryptocurrencies having the largest capitalization is retraced. The detailed topology of cryptocurrency network on the Binance platform from bitcoin perspective is also considered. Finally, an interesting observation on the Covid-19 pandemic impact on the cryptocurrency market is presented and discussed: recently we have witnessed a "phase transition" of the cryptocurrencies from being a hedge opportunity for the investors fleeing the traditional markets to become a part of the global market that is substantially coupled to the traditional financial instruments like the currencies, stocks, and commodities. The main contribution is an extensive demonstration that, fueled by the increased transaction frequency, turnover, and the number of participants, structural self-organization in the cryptocurrency markets has caused the same to attain complexity characteristics that are nearly indistinguishable from the Forex market at the level of individual time-series. However, the cross-correlations between the exchange rates on cryptocurrency platforms differ from it. The cryptocurrency market is less synchronized and the information flows more slowly, which results in more frequent arbitrage opportunities. The methodology used in the review allows the latter to be detected, and lead–lag relationships to be discovered. Hypothetically, the methods for describing correlations and hierarchical relationships between exchange rates presented in this review could be used to construct investment portfolios and reduce exposure to risk. A new investment asset class appears to be dawning, wherein the bitcoin assumes the role of the natural base currency to trade. [ABSTRACT FROM AUTHOR]

Strongly coupled atmosphere–ocean data assimilation offers the ability to improve information exchange across the modelled air‐sea interface by enabling observations in one domain to have a direct influence on the analysis in the other. For incremental 4D‐Var assimilation a strongly coupled approach enables both domains to be updated at the beginning of the assimilation window, whether they are observed or not, and is hence more likely to produce consistent initial model states. This is made possible by the explicit inclusion of cross‐domain forecast error covariance information in the coupled forecast error covariance matrix. In this study we use an idealised 1D single‐column coupled atmosphere–ocean model to examine the extent to which explicit cross‐domain forecast error covariances play a role in shaping the coupled analysis increments compared to those implicitly generated in the inner‐loop of the incremental formulation of the 4D‐Var algorithm. This is done via a set of single‐observation experiments with and without initial cross‐domain forecast error covariances prescribed. Using single observations allows us to obtain explicit expressions for the atmosphere and ocean analysis updates, separating out the individual effects of the explicitly prescribed and implicitly generated cross‐domain covariances. Our experiments show that when only one domain is observed, including explicit cross‐domain error covariances allows more consistent adjustment of the unobserved domain. Neglecting the cross‐domain terms and relying solely on the covariances implicitly generated by the coupled tangent linear and adjoint models restricts the ability of the covariance matrix to impose balance between the two domains. In this case the coupling is essentially one‐way; the update to the observed domain is independent of the unobserved domain and so is likely to produce atmosphere and ocean updates that are inconsistent with one another. As we show, this has important consequences for the balance of the coupled analysis state. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the relationship between unexpected information from postings and news, and the unexpected information is measured by the residual of regressions of trading volume on numbers of news or postings. We mainly find that (i) There are significant positive contemporaneous correlations between the unexpected information coming from postings and different kinds of news; the correlation between the unexpected information coming from postings and new media news is stronger than that between the unexpected information coming from postings and mass media news; (ii) The unexpected information coming from postings could cause the unexpected information coming from news, but only the unexpected information coming from the mass media news could cause that coming from postings; (iii) There are persistent power-law cross-correlations between the unexpected information coming from postings and that coming from mass media news and new media news. The cross-correlation between the unexpected information coming from postings and new media news is more persistent than the one between the unexpected information coming from postings and mass media news. The cross-correlations are all more stable in long term than in short term. We attribute our findings above to the dissemination speed of the information on the Internet. [ABSTRACT FROM AUTHOR]

The iteratively reweighted least-squares approach to self-tuning robust adjustment of parameters in linear regression models with autoregressive (AR) and t-distributed random errors, previously established in Kargoll et al. (in J Geod 92(3):271–297, 2018. 10.1007/s00190-017-1062-6), is extended to multivariate approaches. Multivariate models are used to describe the behavior of multiple observables measured contemporaneously. The proposed approaches allow for the modeling of both auto- and cross-correlations through a vector-autoregressive (VAR) process, where the components of the white-noise input vector are modeled at every time instance either as stochastically independent t-distributed (herein called “stochastic model A”) or as multivariate t-distributed random variables (herein called “stochastic model B”). Both stochastic models are complementary in the sense that the former allows for group-specific degrees of freedom (df) of the t-distributions (thus, sensor-component-specific tail or outlier characteristics) but not for correlations within each white-noise vector, whereas the latter allows for such correlations but not for different dfs. Within the observation equations, nonlinear (differentiable) regression models are generally allowed for. Two different generalized expectation maximization (GEM) algorithms are derived to estimate the regression model parameters jointly with the VAR coefficients, the variance components (in case of stochastic model A) or the cofactor matrix (for stochastic model B), and the df(s). To enable the validation of the fitted VAR model and the selection of the best model order, the multivariate portmanteau test and Akaike’s information criterion are applied. The performance of the algorithms and of the white noise test is evaluated by means of Monte Carlo simulations. Furthermore, the suitability of one of the proposed models and the corresponding GEM algorithm is investigated within a case study involving the multivariate modeling and adjustment of time-series data at four GPS stations in the EUREF Permanent Network (EPN). [ABSTRACT FROM AUTHOR]

We derive group velocity maps for crustal Rayleigh waves across Costa Rica and corresponding 3-D shear-wave velocity structure from ambient noise cross-correlations between 56 seismic broadband stations. The daily inter-station cross-correlations for the period 2010–2015 of 56 seismic broadband stations are stacked and analysed to warrant a robust extraction of empirical Green's functions which then are used to measure fundamental mode Rayleigh wave group velocities. Rayleigh wave dispersion curves show consistent patterns within the different geological domains in Costa Rica. Dispersion curves were evaluated in the microseism band from 5- to 17-s period and inverted for group velocity maps using iterative nonlinear travel time tomography. The group velocities at each grid point were inverted for 1-D profiles using a non-linear simulated annealing approach, and transformed into the 3-D velocity structure. The final tomographic model shows clearly the main velocity anomalies associated with tectonic and volcanic activity in Costa Rica. Three localized negative velocity anomalies are seen at all periods (5–17 s) consistent with deep-routed crustal-scale magmatic systems located beneath the Rincón de la Vieja-Miravalles, Arenal-Poás and Turrialba-Irazú volcanic systems, that showed activity over the last 100 years. High velocities can be found beneath the Talamanca arc in southeastern Costa Rica, where active volcanism stopped in the late Miocene. Significant along-strike variations in the morphology of the subducting Cocos plate are imaged consistently by velocity variations in the forearc. [ABSTRACT FROM AUTHOR]

The analysis of linkages between financial markets has been a promising subject of study after globalization. Main consequence of these linkages is on transmission of the crisis from one country to another. Therefore, identifying and modeling of those linkages are important issues in the analysis of financial markets. According to studies the cross-correlations present nonlinear behavior and in general the well-known methods fail to predict such correlations. In this paper, we aim to show the existence of nonlinear cross-correlations between Turkish Stock Exchange and major developed country indices. For this purpose, we use the Multifractal Detrending Moving-Average Cross-correlation Analysis (MF-X-DMA) which is designed for detecting long-range power-law correlations. In the analysis we use the daily financial return and volatility series of Turkish stock market index BIST100 and developed market indexes which are S&P500, DAX30, FTSE100 for a period of 11 years between 01/01/2007-01/01/2018. The results show the existence of multifractality and long-range power-law cross-correlations. • MF-X- DMA is implemented. • Cross-correlations between Turkish Market and Developed Markets are investigated. • Cross-correlations show multifractal nature. • Cross-correlations between volatilities exhibit power-law, long-range dependence. [ABSTRACT FROM AUTHOR]

Abstract The study analyses the impact of central bank activities carried out as a result of the crisis in 2008 on cross-correlations observed on stocks exchanges. Using the power law classification scheme (PLCS), histograms of the correlation strength for five groups of entities from different regions of the world were examined. Each of the groups contained 16 quotes selected among the companies listed on the following indexes: All Ordinaries, CAC 40, DAX, DJI, FTSE 100, Hang Seng, Nikkei 225. The analysis was carried out by comparing the correlation strengths histograms for the selected periods before and after 2008 year. Significant differences between the group of markets on which central banks' intervention took place from those where such intervention was not carried out were pointed. In particular, the cross-correlations among companies in the USA and European markets were compared with the situation on the Australian market. The Japanese and Singapore markets can be considered as the special cases where no direct intervention took place but due to the strong relationship with European and USA economy the cross-correlations have been disturbed. Highlights • It was showed that the political intervention influenced cross-correlation among companies. • The analysis of cross-correlations among companies before and after the financial crisis in 2008. • Application of Power Law Classification Scheme (PLCS) to the analysis of central bank influence on the stock markets cross-correlations. [ABSTRACT FROM AUTHOR]

This article investigates the cross-correlations between WTI crude oil prices and fear gauges using cross-correlation statistic test and multifractal detrended cross-correlation analysis. The results show that the cross-correlations between crude oil prices and three different kinds of fear gauges are multifractal. By finding the 'crossover', we separate the three pairs of time series into the short term and long term, and find that cross-correlations of small fluctuations are persistent in the short and long terms, cross-correlations of large fluctuations are strongly anti-persistent in the short and long terms. The relationship is useful to profit in future markets. [ABSTRACT FROM AUTHOR]

Abstract With the empirical mode decomposition, we investigate the dynamic fluctuations of cross-correlations in multi-time scale. In this approach, the time series of price returns in financial markets is decomposed into a small number of intrinsic mode functions, and the corresponding time series of phases can be obtained with the Hilbert transform. Through the empirical mode decomposition, we uncover that the cross-correlations between stocks change significantly for different time scales and exhibit singular behaviors during a financial crisis. With the phase correlation analysis, we observe that the cross-correlations between market indices vary also for different time scales. Dividing the total time interval into two sub-intervals by the year 2007, we detect that the market indices are much more correlated after 2007. Highlights • The dynamic fluctuations of cross-correlations in multi-time scale are investigated. • Special attention is put on the dynamics before, around and after a financial crisis. • We study the cross-correlations between indices with the phase correlation analysis. • The cross-correlations between indices also vary for different time scales. [ABSTRACT FROM AUTHOR]

Abstract The passive surface wave method, as a valid supplement to the active survey method for the deeper imaging, has gained growing attentions from geophysicists and civil engineering communities, particularly in urban applications. The multi-channel analysis of passive surface waves (MAPS), as a newly developed method, combines seismic interferometry and multi-channel analysis of surface waves and shows high potential in extracting high-frequency surface-wave energy in urban environment. The unknown source-distribution of ambient noise and the complicated anthropogenic environment, however, make it difficult to obtain high-quality dispersion images of passive surface waves. We proposed a data selection technique in time domain for selective stacking of cross-correlations and applied it to improve the MAPS method. The proposed technique sorts the time segments with the defined acausal-to-causal ratio factor, and automatically detects the optimum time segments with the highest signal-to-noise ratio (SNR). Three real-world applications have demonstrated the feasibility of the proposed data selection technique. The results indicated that our method is able to preserve the time segments with coherent signals, and reject the time segments polluted by non-stationary noise sources and/or spatial aliasing. Compared with the conventional MAPS method, the improved one can broaden interested frequency band of surface waves and deblur the measured image of dispersion energy. Finally, we discussed the influence of velocity window parameter for SNR calculation on the performance of the proposed technique, and suggested that the velocity window should be appropriately wide to guarantee the accuracy of surface-wave imaging. Highlights • We propose a temporal data selection technique for selective stacking of cross-correlations. • We apply the proposed technique to improve the multi-channel analysis of passive surface waves method. • Real-world applications demonstrate the feasibility of the proposed technique. [ABSTRACT FROM AUTHOR]

Within the paper, the problem of globalisation during financial crises is analysed. The research is based on the Forex exchange rates. In the analysis, the power law classification scheme (PLCS) is used. The study shows that during crises cross-correlations increase resulting in significant growth of cliques, and also the ranks of nodes on the converging time series network are growing. This suggests that the crises expose the globalisation processes, which can be verified by the proposed analysis.

Abstract Background Environmental factors play a major role in transmission of malaria given their relationship to both the development and survival of the mosquito and parasite. The associations between environmental factors and malaria can be used to inform the development of early warning systems for increases in malaria burden. The objective of this study was to assess temporal relationships between rainfall, temperature and vegetation with malaria morbidity across three different transmission settings in Uganda. Methods Temporal relationships between environmental factors (weekly total rainfall, mean day time temperature and enhanced vegetation index series) and malaria morbidity (weekly malaria case count data and test positivity rate series) over the period January 2010–May 2013 in three sites located in varying malaria transmission settings in Uganda was explored using cross-correlation with pre-whitening. Sites included Kamwezi (low transmission), Kasambya (moderate transmission) and Nagongera (high transmission). Results Nagongera received the most rain (30.6 mm) and experienced, on average, the highest daytime temperatures (29.8 °C) per week. In the study period, weekly TPR and number of malaria cases were highest at Kasambya and lowest at Kamwezi. The largest cross-correlation coefficients between environmental factors and malaria morbidity for each site was 0.27 for Kamwezi (rainfall and cases), 0.21 for Kasambya (vegetation and TPR), and −0.27 for Nagongera (daytime temperature and TPR). Temporal associations between environmental factors (rainfall, temperature and vegetation) with malaria morbidity (number of malaria cases and TPR) varied by transmission setting. Longer time lags were observed at Kamwezi and Kasambya compared to Nagongera in the relationship between rainfall and number of malaria cases. Comparable time lags were observed at Kasambya and Nagongera in the relationship between temperature and malaria morbidity. Temporal analysis of vegetation with malaria morbidity revealed longer lags at Kasambya compared to those observed at the other two sites. Conclusions This study showed that temporal associations between environmental factors with malaria morbidity vary by transmission setting in Uganda. This suggests the need to incorporate local transmission differences when developing malaria early warning systems that have environmental predictors in Uganda. This will result in development of more accurate early warning systems, which are a prerequisite for effective malaria control in such a setting.

The article presents detailed geostatistical analysis of spatial distribution of lead and zinc concentration in water, suspension and bottom sediments of large, urban lake exposed to intensive anthropogenic pressure within a large city. Systematic chemical measurements were performed at eleven cross-sections located along Czerniakowskie Lake, the largest lake in Warsaw, the capital of Poland. During the summer, the lake is used as a public bathing area, therefore, to better evaluate human impacts, field measurements were carried out in high-use seasons. It was found that the spatial distributions of aqueous lead and zinc differ during the summer and autumn. In summer several Pb and Zn hot-spots were observed, while during autumn spatial distributions of Pb and Zn were rather homogenous throughout the entire lake. Large seasonal differences in spatial distributions of Pb and Zn were found in bottom sediments. Autumn concentrations of both heavy metals were ten times higher in comparison with summer values.

The multifractal properties of six acknowledged agro-meteorological parameters, such as reference evapotranspiration (ET0), wind speed (U), incoming solar radiation (SR), air temperature (T), air pressure (P), and relative air humidity (RH) of five stations in California, USA were examined. The investigation of multifractality of datasets from stations with differing terrain conditions using the Multifractal Detrended Fluctuation Analysis (MFDFA) showed the existence of a long-term persistence and multifractality irrespective of the location. The scaling exponents of SR and T time series are found to be higher for stations with higher altitudes. Subsequently, this study proposed using the novel multifractal cross correlation (MFCCA) method to examine the multiscale-multifractal correlations properties between ET0 and other investigated variables. The MFCCA could successfully capture the scale dependent association of different variables and the dynamics in the nature of their associations from weekly to inter-annual time scales. The multifractal exponents of P and U are consistently lower than the exponents of ET0, irrespective of station location. This study found that joint scaling exponent was nearly the average of scaling exponents of individual series in different pairs of variables. Additionally, the α-values of joint multifractal spectrum were lower than the α values of both of the individual spectra, validating two universal properties in the MFCCA studies for agro-meteorological time series. The temporal evolution of cross-correlation determined by the MFCCA successfully captured the dynamics in the nature of associations in the P-ET0 link.

Biofilm, a colony forming cooperative response of microorganisms under environmental stress, is a major concern for food safety, water safety and drug resistance. Most current works focus on controlling biofilm growth by targeting single genes. Here, the authors investigated transcriptome-wide expressions of the yeast Saccharomyces cerevisiae biofilm in wildtype, and six previously identified biofilm regulating overexpression strains. Using statistical distributions for low expression filter (TPM > 5), Pearson auto- and cross-correlations reveal a strong transcriptome-wide invariance among all genotypes. The 50 highly expressed genes, however, differ significantly between the genotypes. Principal components analysis shows the global similarity between most overexpression strains. Thus, though single overexpression strains may show significant favourable local and acute expression changes (short-range disorder), the almost unperturbed global and collective structure between the genotypes indicate gradual adaptive response converging to original stable biofilm states (long-range order). Hierarchical clustering and gene ontology show 11 groups of local (e.g. mitochondria processes, amine and nucleotide metabolic processes) and 6 groups of global (e.g. transcription, translation and cell cycle) processes for all genotypes. The overall data indicate that there is a strong global regulatory structure that keeps the overall biofilm stable in all investigated strains.

Abstract We present a lattice calculation on the cross-correlations of conserved charges (baryon number, electric charge and strangeness) near the transition temperature. We extrapolate to small baryo-chemical potentials, and thus we cover typical STAR energies. We confront our finding to the latest STAR date set on cross-correlations. In this work we present continuum lattice results with resolution up to N t = 16. [ABSTRACT FROM AUTHOR]

Abstract We investigate intrinsic correlations between Brazilian energy (ethanol) and food market (sugar) by applying recently introduced method Detrended partial cross correlation analysis (DPCCA) on returns and volatility series of sugar, ethanol and crude oil prices. The results show that intrinsic cross-correlations between ethanol and sugar are positive and increase with time scale for both returns and volatility, indicating that the diversion of sugar cane for biofuel production (due to the variations in ethanol prices), affects more sugar prices than the price movements in external energy market. Ethanol/oil volatility series and sugar/oil return series do not show correlations for small scales, while at larger scales intrinsic correlations become negative. The intrinsic correlations in oil/ethanol return series and oil/sugar volatility series show similar behavior as corresponding ethanol/sugar series, indicating the existence of strong correlations between sugar and ethanol price variations. Highlights • We study cross-correlations between energy and food markets. • Strongest correlations are found for ethanol and sugar return and volatility. • Correlations of crude oil with sugar and ethanol are negative at larger scales. [ABSTRACT FROM AUTHOR]

In this paper, we quantify the cross-correlations between Google Trends and Bitcoin market. By employing the Multifractal Detrended Cross-correlation Analysis (MF-DCCA) method, we find that the change of Google Trends (CGT) and Bitcoin market, i.e., returns and changes of volume, are overall significantly cross-correlated based on the cross-correlation test. In particular, the empirical results show that: (1) there exist power-law cross-correlations between CGT and Bitcoin returns as well as CGT and changes of volume; (2) the cross-correlations between CGT and returns have a higher degree of multifractal in the long-term and weak multifractal in the short-term, while the cross-correlations between CGT and change of volume show the opposite trend. (3) with the rolling window analysis, we further find that there is a decrease trend for the cross-correlations between CGT and Bitcoin returns over time, and the cross-correlations scaling exponents are less than 0.5, which indicate that they are both anti-persistent cross-correlated. [ABSTRACT FROM AUTHOR]

Using multifractal detrended cross-correlation analysis (MF-DCCA), this research investigates the dynamic relationship between the RMB exchange index and the liquidity of the Shanghai and Shenzhen stock market during the period from 22 June 2010 to 17 February 2017. As suggested by our empirical results, the well-known efficient market hypothesis by no means explains the dynamics between the RMB index and stock market liquidity. Indeed, the RMB index and stock market liquidity are cross-correlated, and the cross-correlations exhibit multifractal features. Further, cross-correlations between the RMB index and the liquidity of the Shanghai and Shenzhen stock market demonstrate strong and positive persistence. In particular, the positive persistence of cross-correlations is strengthened in the presence of a tightening monetary policy. [ABSTRACT FROM AUTHOR]

In this paper, we explore the cross-correlations between two commonly-employed online sentiment proxies, i.e., the Financial and Economic Attitudes Revealed by Search (FEARS) from Google Trends and Daily Happiness Sentiment (DHS) from Twitter, with the methodology of MF-DCCA. The empirical results mainly show that: firstly, there exists power-law cross-correlation between the FEARS and DHS and the cross-correlation between them perform multifractality; secondly, the degree of multifractality in short term is significantly smaller than that in long term indicating a more stable cross-correlation in short term; finally, with the rolling window analysis, we further find that the evolution of the cross-correlations between FEARS and DHS is erratic. [ABSTRACT FROM AUTHOR]

Abstract Using stock market returns of two stock exchanges in China, this paper employs MF-DCCA to investigate the non-linear cross-correlation between individual investor sentimentand Chinese stock market return. We find that there exists a power-law cross-correlation between individual investor sentiment and Chinese stock market return, and the cross-correlations are significantly multifractal. In addition, the cross-correlation between individual investor sentiment and Shenzhen Component Index (SZSE) return is more anti-persistent than that between individual investor sentiment and Shanghai Composite Index (SSEC) return, implying that individual investor sentiment has a stronger impact on small stocks. Besides, long-range correlations, fat-tailed distribution and extreme value all contribute to the multifractality for cross-correlation between individual investor sentiment and SSEC return, while long-range correlation is the main source of multifractality for the cross-correlation between individual investor sentiment and SZSE return. Highlights • Cross-correlation between investor sentiment and stock return is investigated. • Yu'ebao Sentiment Index and Shenzhen stock market return is more anti-persistent. • The sources of multifractal features are investigated. [ABSTRACT FROM AUTHOR]