Lag Dependence Research Articles

Bayesian Modeling of INGARCHX Models for Cellulitis Related to Meteorological Factors in Mahasarakham and Roi-Et HospitalsMahasarakham and Roi-Et Hospitals

The objective of this research is to develop integer-valued generalized autoregressive conditional heteroscedasticity (INGARCH) models to represent the weekly incidence of cellulitis cases in relation to two exogenous variables: seasonality and the weekly average of either relative humidity or maximum temperature. The key to the proposed model is its capacity to explain overdispersion and lag dependence. For predictions and model parameters, we employ the Bayesian Markov Chain Monte Carlo (MCMC) approach, as supported by both a simulation study and an empirical study. To assess different models, we apply the deviance information criterion (DIC) criterion to the weekly cellulitis case sample data with two independent variables. In addition, we offer a one-week prediction to help Mahasarakham and Roi-Et Hospitals manage the increasing volume of hospital admissions by estimating the weekly cellulitis case incidence rate.

Measuring the in-plane thermal diffusivity of anisotropic solids by lock-in infrared thermography: Laser-spot vs laser-line heating

A method is proposed to measure the in-plane thermal diffusivity of anisotropic solids along an arbitrary direction using laser-line lock-in thermography. In this method, the sample is heated by an intensity-modulated laser beam, which impinges on the sample surface with the shape of a narrow strip. The resulting temperature oscillations are recorded by a thermographic camera and a phase thermogram is obtained by lock-in processing the image sequence. The thermal diffusivity along the direction perpendicular to the laser line can be determined from the linear dependence of phase lag with the distance to the heating line using the slope method. Unlike laser-spot lock-in thermography, in which the slope method only provides the thermal diffusivity along the principal directions, in this configuration, the thermal diffusivity can be measured experimentally along any direction, just selecting the relative orientation of the sample with respect to the laser line. Moreover, this configuration allows averaging several parallel phase profiles leading to more reliable thermal diffusivity values. The experimental results are presented for a highly anisotropic material, namely, a unidirectional carbon fiber-reinforced polymer plate.

The Sloan Digital Sky Survey Reverberation Mapping Project: Investigation of Continuum Lag Dependence on Broad-line Contamination and Quasar Properties

This work studies the relationship between accretion-disk size and quasar properties, using a sample of 95 quasars from the Sloan Digital Sky Survey Reverberation Mapping Project with measured lags between the g and i photometric bands. Our sample includes disk lags that are both longer and shorter than predicted by the Shakura and Sunyaev model, requiring explanations that satisfy both cases. Although our quasars each have one lag measurement, we explore the wavelength-dependent effects of diffuse broad-line region (BLR) contamination through our sample’s broad redshift range, 0.1 < z < 1.2. We do not find significant evidence of variable diffuse Fe ii and Balmer nebular emission in the rms spectra, nor from Anderson–Darling tests of quasars in redshift ranges with and without diffuse nebular emission falling in the observed-frame filters. Contrary to previous work, we do not detect a significant correlation between the measured continuum and BLR lags in our luminous quasar sample, similarly suggesting that our continuum lags are not dominated by diffuse nebular emission. Similar to other studies, we find that quasars with larger-than-expected continuum lags have lower 3000 Å luminosities, and we additionally find longer continuum lags with lower X-ray luminosities and black hole masses. Our lack of evidence for diffuse BLR contribution to the lags indicates that the anticorrelation between continuum lag and luminosity is not likely to be due to the Baldwin effect. Instead, these anticorrelations favor models in which the continuum lag increases in lower-luminosity active galactic nuclei, including scenarios featuring magnetic coupling between the accretion disk and X-ray corona, and/or ripples or rims in the disk.

Spectropolarimetric investigation of magnetohydrodynamic wave modes in the photosphere: First results from PHI on board Solar Orbiter

Context.In November 2021, Solar Orbiter started its nominal mission phase. The remote-sensing instruments on board the spacecraft acquired scientific data during three observing windows surrounding the perihelion of the first orbit of this phase.Aims.The aim of the analysis is the detection of magnetohydrodynamic (MHD) wave modes in an active region by exploiting the capabilities of spectropolarimetric measurements.Mthods.The High Resolution Telescope (HRT) of the Polarimetric and Helioseismic Imager (SO/PHI) on board the Solar Orbiter acquired a high-cadence data set of an active region. This is studied in the paper. B-ωand phase-difference analyses are applied on line-of-sight velocity and circular polarization maps and other averaged quantities.Results.We find that several MHD modes at different frequencies are excited in all analysed structures. The leading sunspot shows a linear dependence of the phase lag on the angle between the magnetic field and the line of sight of the observer in its penumbra. The magnetic pore exhibits global resonances at several frequencies, which are also excited by different wave modes.Conclusions.The SO/PHI measurements clearly confirm the presence of magnetic and velocity oscillations that are compatible with one or more MHD wave modes in pores and a sunspot. Improvements in modelling are still necessary to interpret the relation between the fluctuations of different diagnostics.

Roles of laser ellipticity in attoclocks

We study ionization of atoms in strong elliptically polarized laser fields numerically and analytically. We focus on the effects of laser ellipticity on the offset angle in the photoelectron momentum distribution. This angle is considered to encode the time information of tunneling ionization in attoclock experiments. The calculated offset angle increases with the decrease of ellipticity, but the momentum along the major axis of laser polarization related to this angle changes slowly, in agreement with experiments. With a Coulomb-included strong-field model, the scaling laws for the ellipticity dependence of this angle and relevant momentum components are obtained, and the ellipticity dependence of Coulomb-induced ionization time lag encoded in this angle is also addressed.

Discovery of the linear energy dependence of the spectral lag of X-ray bursts from SGR J1935+2154

ABSTRACTSpectral lag of the low-energy photons with respect to the high-energy ones is a common astrophysical phenomenon (such as gamma-ray bursts and the Crab Pulsar) and may serve as a key probe to the underlying radiation mechanism. However, spectral lag in keV range of the magnetar bursts has not been systematically studied yet. In this work, we perform a detailed spectral lag analysis with the Li et al.’s Cross-Correlation Function (Li-CCF) method for SGR J1935+2154 bursts observed by Insight-Hard X-ray Modulation Telescope (HXMT), Gravitational Wave High-energy Electromagnetic Counterpart All-sky Monitor (GECAM), and Fermi/Gamma-ray Burst Monitor (GBM) from 2014 July to 2022 January. We discover that the spectral lags of about 61 per cent (non-zero significance &amp;gt;1σ) bursts from SGR J1935+2154 are linearly dependent on the photon energy (E) with tlag(E) = α(E/keV) + C, which may be explained by a linear change of the temperature of the blackbody-emitting plasma with time. The distribution of the slope (α) approximately follows a Gaussian function with mean and standard deviation of 0.02 ms keV−1 (i.e. high-energy photons arrive earlier) and 0.02 ms keV−1, respectively. We also find that the distribution can be well fitted with three Gaussians with mean values of ∼−10.009, 0.013, and 0.039 ms keV−1, which may correspond to different origins of the bursts. These spectral lag features may have important implications on the magnetar bursts.

Bayesian Nonparametric Density Autoregression with Lag Selection

We develop a Bayesian nonparametric autoregressive model applied to flexibly estimate general transition densities exhibiting nonlinear lag dependence. Our approach is related to Bayesian density regression using Dirichlet process mixtures, with the Markovian likelihood defined through the conditional distribution obtained from the mixture. This results in a Bayesian nonparametric extension of a mixtures-of-experts model formulation. We address computational challenges to posterior sampling that arise from the Markovian structure in the likelihood. The base model is illustrated with synthetic data from a classical model for population dynamics, as well as a series of waiting times between eruptions of Old Faithful Geyser. We study inferences available through the base model before extending the methodology to include automatic relevance detection among a pre-specified set of lags. Inference for global and local lag selection is explored with additional simulation studies, and the methods are illustrated through analysis of an annual time series of pink salmon abundance in a stream in Alaska. We further explore and compare transition density estimation performance for alternative configurations of the proposed model. Supplementary materials are available online.

Temporal evolution and rigidity dependence of the solar modulation lag of Galactic cosmic rays

When traveling in the heliosphere, Galactic cosmic rays (GCRs) are subjected to the solar modulation effect, a quasiperiodical change of their intensity caused by the 11-year cycle of solar activity. Here we investigate the association of solar activity and cosmic radiation over five solar cycles, from 1965 to 2020, using a collection of multichannel data from neutron monitors, space missions, and solar observatories. In particular, we focus on the time lag between the monthly sunspot number and the GCR flux variations. We show that the modulation lag is subjected to a 22-year periodical variation, ranging from about 2 to 14 months and following the polarity cycle of the Sun's magnetic field. We also show that the lag is remarkably decreasing with increasing energy of the GCR particles. These results reflect the interplay of basic physics phenomena that cause the GCR modulation effect: the drift motion of charged particles in the interplanetary magnetic field, the latitudinal dependence of the solar wind, the energy dependence of their residence time in the heliosphere. Based on this interpretation, we end up with a global effective formula for the modulation lag and testable predictions for the flux evolution of cosmic particles and antiparticles over the solar cycle.

Urbanisation and ecosystem health in the Middle Reaches of the Yangtze River urban agglomerations, China: A U-curve relationship.

Rapid urbanisation in global urban agglomerations has caused serious disturbances to the structure, function, and health state of ecosystems. Investigating the driving mechanisms behind the impact of urbanisation level (UL) on ecosystem health index (EHI) is important for constructing ecological civilisation and developing superior urban agglomerations in China. However, no in-depth studies exist on these mechanisms in various urban agglomerations, which makes formulation and implementation of effective ecosystem management and control policies difficult. In this study, we estimated UL and EHI based on multisource data, and a set of spatial regression models were then used to analyse the driving mechanisms at global and local scales in the Middle Reaches of the Yangtze River urban agglomeration (MRYRUA) in China between 1995 and 2015. Our results demonstrated that EHIs in the MRYRUA were 0.627, 0.613, and 0.610 in 1995, 2005, and 2015, respectively, with 2.71% decreases during the study period. The EHI in the surrounding mountainous regions was considerably higher than that in the plains. There was a significant spatial dependence between the UL and EHI. Low UL and high EHI, high UL and low EHI, and low UL and low EHI were the dominant relationship types in the MRYRUA (25.61%, 11.83%, and 11.27%, respectively). A 10% increase in UL resulted in 1.79%, 2.50%, and 2.99% decrease in EHI for each reference year in the spatial error model with lag dependence model. A U-shaped relationship was identified between UL and EHI in different urban agglomerations and cities of different administrative levels. Therefore, the results of this study can provide a scientific basis for the formulation of macro-control policies and locally specific control policies for ecosystem protection in the MRYRUA.

Spatiotemporal Dynamics and Factors Driving the Distributions of Pine Wilt Disease-Damaged Forests in China

Many forests have suffered serious economic losses and ecological consequences of pine wilt disease (PWD) outbreaks. Climate change and human activities could accelerate the distribution of PWD, causing the exponential expansion of damaged forest areas in China. However, few studies have analyzed the spatiotemporal dynamics and the factors driving the distribution of PWD-damaged forests using continuous records of long-term damage, focusing on short-term environmental factors that influence multiple PWD outbreaks. We used a maximum entropy (MaxEnt) model that incorporated annual meteorological and human activity factors, as well as temporal dependence (the PWD distribution in the previous year), to determine the contributions of environmental factors to the annual distribution of PWD-damaged forests in the period 1982–2020. Overall, the MaxEnt showed good performance in modeling the PWD-damaged forest distributions between 1982 and 2020. Our results indicate that (i) the temporal lag dependence term for the presence/absence of PWD was the best predictor of the distribution of PWD-damaged forests; and (ii) Bio14 (precipitation in the driest month) was the most important meteorological factor for affecting the PWD-damaged forests. These results are essential to understanding the factors governing the distribution of PWD-damaged forests, which is important for forest management and pest control worldwide.

Intrinsic Mode Cross Correlation: A Novel Technique to Identify Scale-Dependent Lags Between Two Signals and Its Application to Ionospheric Science

In this work, we address the following question: can we use modern, cutting-edge techniques conceived for the analysis of nonlinear non-stationary signals to measure scale-wise lags? To this scope, we propose a novel technique, called intrinsic mode cross correlation (IMXC) method, which leverages on the decomposition of nonlinear non-stationary signals by the multivariate fast iterative filtering (MvFIF) technique and the computation of a scale-by-scale cross correlation. We evaluate this technique on artificial signals (whose ground truth is known) and plasma density data provided by the Langmuir probes onboard the Swarm satellites. We show that this technique allows indeed to reconstruct the lag dependence on the involved spatio/temporal scales for the artificial dataset (even in the presence of high levels of noise) and to estimate them in a real-life signal. This can pave the way to future uses of this technique in contexts in which the causation chain can be hidden in a complex, multiscale coupling of the investigated features.

Roles of Rossby Waves, Rossby–Gravity Waves, and Gravity Waves Generated in the Middle Atmosphere for Interhemispheric Coupling

AbstractIt has often been reported that warming at high latitudes in the Southern Hemisphere (SH) summer mesosphere and lower thermosphere (MLT) appears during Arctic sudden stratospheric warming (SSW) events. This phenomenon, which is called “interhemispheric coupling” (IHC), has been thought to occur because of the modulation of mesospheric meridional circulation driven by forcing of gravity waves (GWs) originating in the troposphere. However, quasi-two-day waves (QTDWs) develop during SSWs and result in strong wave forcing in the SH mesosphere. Thus, this study revisits IHC following Arctic SSWs from the viewpoint of wave forcing, not only by GWs and Rossby waves (RWs) originating in the troposphere but also by GWs, RWs, and Rossby–gravity waves generated in situ in the middle atmosphere, and elucidates the causes of warm anomalies in the SH MLT region. During SSWs, westward wind anomaly forms because of cold equatorial stratosphere, GW forcing is then modulated, and barotropic/baroclinic and shear instabilities are strengthened in the SH mesosphere. These instabilities generate QTDWs and GWs, respectively, which cause significant anomalous westward wave forcing, forming a warm anomaly in the SH MLT region. The intraseasonal variation in QTDW activity can explain seasonal dependence of the time lag in IHC. Moreover, it is revealed that the cold equatorial stratosphere is formed by middle-atmosphere Hadley circulation, which is strengthened by wave forcing associated with stationary RW breaking leading to SSWs. The IHC mechanism revealed in this study indicates that waves generated in the middle atmosphere contribute significantly to the meridional circulation, especially during SSWs.

Gamma-ray blazar variability: new statistical methods of time-flux distributions

ABSTRACT Variable γ-ray emission from blazars, one of the most powerful classes of astronomical sources featuring relativistic jets, is a widely discussed topic. In this work, we present the results of a variability study of a sample of 20 blazars using γ-ray (0.1–300 GeV) observations from Fermi/LAT telescope. Using maximum likelihood estimation (MLE) methods, we find that the probability density functions that best describe the γ-ray blazar flux distributions use the stable distribution family, which generalizes the Gaussian distribution. The results suggest that the average behaviour of the γ-ray flux variability over this period can be characterized by log-stable distributions. For most of the sample sources, this estimate leads to standard lognormal distribution (α = 2). However, a few sources clearly display heavy tail distributions (MLE leads to α &amp;lt; 2), suggesting underlying multiplicative processes of infinite variance. Furthermore, the light curves were analysed by employing novel non-stationarity and autocorrelation analyses. The former analysis allowed us to quantitatively evaluate non-stationarity in each source – finding the forgetting rate (corresponding to decay time) maximizing the log-likelihood for the modelled evolution of the probability density functions. Additionally, evaluation of local variability allows us to detect local anomalies, suggesting a transient nature of some of the statistical properties of the light curves. With the autocorrelation analysis, we examined the lag dependence of the statistical behaviour of all the {(yt, yt + l)} points, described by various mixed moments, allowing us to quantitatively evaluate multiple characteristic time scales and implying possible hidden periodic processes.

Autoregressive density modeling with the Gaussian process mixture transition distribution

We develop a mixture model for transition density approximation, together with soft model selection, in the presence of noisy and heterogeneous nonlinear dynamics. Our model builds on the Gaussian mixture transition distribution (MTD) model for continuous state spaces, extending component means with nonlinear functions that are modeled using Gaussian process (GP) priors. The resulting model flexibly captures nonlinear and heterogeneous lag dependence when several mixture components are active, identifies low‐order nonlinear dependence while inferring relevant lags when few components are active, and averages over multiple and competing single‐lag models to quantify/propagate uncertainty. Sparsity‐inducing priors on the mixture weights aid in selecting a subset of active lags. The hierarchical model specification follows conventions for both GP regression and MTD models, admitting a convenient Gibbs sampling scheme for posterior inference. We demonstrate properties of the proposed model with two simulated and two real time series, emphasizing approximation of lag‐dependent transition densities and model selection. In most cases, the model decisively recovers important features. The proposed model provides a simple, yet flexible framework that preserves useful and distinguishing characteristics of the MTD model class.

Measurement of thermal diffusivity and evaluation of fiber condition of discontinuous fiber CFRP

This paper proposes a new method to evaluate the degree of resin impregnating into carbon fiber in carbon fiber-reinforced plastic (CFRP) before molding based on the measurement of thermal diffusivity distribution in the thickness direction. The sample’s surface is periodically line-heated at an arbitrary frequency and the phase lag distribution is measured by a lock-in thermography. The thermal diffusivity distribution in the thickness direction is obtained from the frequency dependence of the phase lag. The degree of resin impregnating into carbon fiber in CFRP is evaluated by analysing the range of the measured thermal diffusivity distribution. First, a reference material with known thermal diffusivity is used to confirm the validity of the thermal diffusivity measurements. Measurement results are within 10% range relative to the reference value. The proposed method is then applied to the CFRP before molding process. Two types of CFRPs are measured with different degrees of resin impregnating into carbon fiber: impregnated CFRP and non-impregnated CFRP. Results show that the value of the thermal diffusivities for the impregnated CFRP is mainly distributed between those of the resin and the carbon fiber in the radial direction. On the other hand, thermal diffusivity values for the non-impregnated CFRP are lower than those of the resin, suggesting the presence of many voids in the specimen. This is consistent with the material structure of CFRP in each degree of resin impregnating into carbon fiber. Results of this study demonstrate the validity of the proposed method for evaluating the degree of resin impregnating into carbon fiber in CFRP before molding.

The lead–lag relationship between Chinese mainland and Hong Kong stock markets

With the integration of international financial markets, the links between stock markets have become closer. Firstly, based on one-minute high-frequency returns, this paper applies the thermal optimal path (TOP) method to examine the lead–lag dependence between CSI 300 index and HSI index from 2016 to 2020. Secondly, regression analysis and correlation test are applied to cross-verify the results of TOP method. Finally, the robustness is tested through analysis with different T values and various market conditions as well as the replacing of proxy variable. The empirical results show that Hong Kong stock leads Chinese mainland stock for about one minute, and this leading effect is magnified when the stock markets fall. The experimental result is robust and has passed consistency test. This research is of great significance that not only guides investors, but also provides empirical evidence and effective information for policy makers.

Travel distance and land use: a generalized Box–Cox model with conditional spatial lag dependence

ABSTRACT Disentangling individual contributions to travel distance continues to be an active field of research, with advances in econometrics being germane. In this study, we focus on several econometric techniques, some of which have been applied in isolation in past studies, but the combination of which has yet to be applied to the problem. We apply a series of statistical tests using the method of artificial regression to test the joint effects of functional form and spatial autocorrelation on model fit. An empirical application is made to the Greater Toronto Area using a large-scale travel survey. Several results found in past studies are refuted through the use of advanced econometric methods, including the previous finding of a positive correlation between travel distance and density in the study region. Model results are validated using bootstrapped sampling and local indicators of spatial association.

Parameter estimation in spatial econometric models with non-random missing data

ABSTRACT This study examines the problem of parameter estimation in spatial econometric/social interaction models with non-random missing outcome data. First, we construct a sample selection model considering spatial lag (autoregressive) dependence. Then, we suggest a parameter estimation method for this model by slightly modifying the Bayesian Markov chain Monte Carlo algorithm proposed in an existing study. A simple illustration indicates that the proposed parameter estimation method performs well overall if the spatial autocorrelation is moderate (spatial parameter equals 0.5 or less), even under a relatively high missing data ratio (around 40%).

Amplitude and phase of higher harmonic of magnetic nanoparticles’ magnetization under low frequency magnetic field

The rotational relaxations exist in magnetic nanoparticles (MNPs) exposed to AC excitation magnetic field, including Brownian rotational relaxation and Neel rotational relaxation. The magnetization of MNPs exposed in AC magnetic field may be affected by rotational relaxation (Brownian relaxation play a dominated role when frequency is less than 1 kHz), and phase lag of magnetization harmonics are existed. In this paper, we employ a Fokker-Planck Equation, which describe accurately AC magnetization dynamic (dominated by Brownian rotational relaxation), analyze the difference of AC magnetization based on Fokker-Planck Equation and Langevin Function, study magnetization harmonic and phase lag dependence on Brownian rotational relaxation, impose a simple, empirical harmonic model. The simulation and experiment results show that the harmonic model can describe accurately AC magnetization when the Brownian rotational relaxation is dominated in MNPs. It is expected to be useful for improving MPI and MNPT.

Unveiling the Temporal Properties of MAXI J1820+070 through AstroSat Observations

Abstract We present here the results of the first broadband simultaneous spectral and temporal studies of the newly detected black hole binary MAXI J1820+070 as seen by Soft X-ray Telescope and Large Area X-ray Proportional Counter (LAXPC) on board AstroSat. The observed combined spectra in the energy range 0.7–80 keV were well modeled using disk blackbody emission, thermal Comptonization, and a reflection component. The spectral analysis revealed that the source was in its hard spectral state (Γ = 1.61) with a cool disk (kT in = 0.22 keV). We report the energy dependent time-lag and root mean squared (rms) variability at different frequencies in the energy range 3–80 keV using LAXPC data. We also modeled the flux variability using a single-zone stochastic propagation model to quantify the observed energy dependence of time lag and fractional rms variability, and then compared the results with that of Cygnus X-1. Additionally, we confirm the detection of a quasi-periodic oscillation with the centroid frequency at 47.7 mHz.