Function Of Time Lag Research Articles

Development of narcissism across the life span: A meta-analytic review of longitudinal studies.

This meta-analytic review investigated the development of narcissism across the life span, by synthesizing the available longitudinal data on mean-level change and rank-order stability. Three factors of narcissism were examined: agentic, antagonistic, and neurotic narcissism. Analyses were based on data from 51 samples, including 37,247 participants. As effect size measures, we used the standardized mean change d per year and test-retest correlations that were corrected for attenuation due to measurement error. The results suggested that narcissism typically decreases from age 8 to 77 years (i.e., the observed age range), with aggregated changes of d = -0.28 for agentic narcissism, d = -0.41 for antagonistic narcissism, and d = -0.55 for neurotic narcissism. Rank-order stability of narcissism was high, with average values of .73 (agentic), .68 (antagonistic), and .60 (neurotic), based on an average time lag of 11.42 years. Rank-order stability did not vary as a function of age. However, rank-order stability declined as a function of time lag, asymptotically approaching values of .62 (agentic), .52 (antagonistic), and .33 (neurotic) across long time lags. Moderator analyses indicated that the findings on mean-level change and rank-order stability held across gender and birth cohort. The meta-analytic data set included mostly Western and White/European samples, pointing to the need of conducting more research with non-Western and ethnically diverse samples. In sum, the findings suggest that agentic, antagonistic, and neurotic narcissism show normative declines across the life span and that individual differences in these factors are moderately (neurotic) to highly (agentic, antagonistic) stable over time. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Rank-order stability of domain-specific self-esteem: A meta-analysis.

This meta-analysis examined the rank-order stability of domain-specific self-esteem by comprehensively synthesizing the available evidence in eight domains of self-esteem (i.e., academic, appearance, athletic, morality, romantic, social, mathematics, and verbal abilities). The analyses were based on longitudinal data from 118 independent samples, including 107,550 participants aged 4-24 years. The time lag between assessments ranged from 6 months to 20 years. As effect-size measure, we used test-retest correlations that were corrected for attenuation due to measurement error. The results suggested that individual differences in domain-specific self-esteem are relatively stable over time, with mean effect sizes ranging from .65 to .84 across domains. Rank-order stability systematically increased as a function of age, from low stability in early childhood to high stability in young adulthood. Moreover, rank-order stability systematically decreased as a function of time lag between assessments, asymptotically approaching medium-sized stabilities (ranging from .36 to .62 across domains) when the time lag became very long. Moderator analyses indicated that the findings held across differences with regard to gender and measure. In sum, the findings suggest that rank-order stability of domain-specific self-esteem is relatively high, even over long periods of time, indicating that the eight investigated facets of domain-specific self-esteem should be considered trait-like constructs. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Quantifying source contributions to ambient NH3 using Geo-AI with time lag and parcel tracking functions

Ambient ammonia (NH3) plays an important compound in forming particulate matters (PMs), and therefore, it is crucial to comprehend NH3′s properties in order to better reduce PMs. However, it is not easy to achieve this goal due to the limited range/real-time NH3 data monitored by the air quality stations. While there were other studies to predict NH3 and its source apportionment, this manuscript provides a novel method (i.e., GEO-AI)) to look into NH3 predictions and their contribution sources. This study represents a pioneering effort in the application of a novel geospatial-artificial intelligence (Geo-AI) base model with parcel tracking functions. This innovative approach seamlessly integrates various machine learning algorithms and geographic predictor variables to estimate NH3 concentrations, marking the first instance of such a comprehensive methodology. The Shapley additive explanation (SHAP) was used to further analyze source contribution of NH3 with domain knowledge. From 2016 to 2018, Taichung's hourly average NH3 values were predicted with total variance up to 96%. SHAP values revealed that waterbody, traffic and agriculture emissions were the most significant factors to affect NH3 concentrations in Taichung among all the characteristics. Our methodology is a vital first step for shaping future policies and regulations and is adaptable to regions with limited monitoring sites.

Scale and Time Dependence of Alfvénicity in the Solar Wind as Observed by the Parker Solar Probe

Alfvénicity is a well-known property, common in the solar wind, characterized by a high correlation between magnetic and velocity fluctuations. Data from the Parker Solar Probe (PSP) enable the study of this property closer to the Sun than ever before, as well as in the sub-Alfvénic solar wind. We consider scale-dependent measures of Alfvénicity based on second-order functions of the magnetic and velocity increments as a function of time lag, including the normalized cross helicity σ c and residual energy σ r . Scale-dependent Alfvénicity is strongest for lags near the correlation scale and increases when moving closer to the Sun. We find that σ r typically remains close to the maximally negative value compatible with σ c . We did not observe significant changes in measures of Alfvénicity between sub-Alfvénic and super-Alfvénic wind. During most times, the solar wind was highly Alfvénic; however, lower Alfvénicity was observed when PSP approached the heliospheric current sheet or other magnetic structures with sudden changes in the radial magnetic field, non-unidirectional strahl electron pitch angle distributions, and strong electron density contrasts. These results are consistent with a picture in which Alfvénic fluctuations generated near the photosphere transport outward, forming highly Alfvénic states in the young solar wind, and subsequent interactions with large-scale structures and gradients lead to weaker Alfvénicity, as commonly observed at larger heliocentric distances.

Bedload transport fluctuations, flow conditions, and disequilibrium ratio at the Swiss Erlenbach stream: results from 27 years of high-resolution temporal measurements

Abstract. Based on measurements with the Swiss plate geophone system with a 1 min temporal resolution, bedload transport fluctuations were analysed as a function of the flow and transport conditions in the Swiss Erlenbach stream. The study confirms a finding from an earlier event-based analysis of the same bedload transport data, which showed that the disequilibrium ratio of measured to calculated transport rate (disequilibrium condition) influences the sediment transport behaviour. To analyse the transport conditions, the following elements were examined to characterise bedload transport fluctuations: (i) the autocorrelation coefficient of bedload transport rates as a function of lag time (memory effect), (ii) the critical discharge at the start and end of a transport event, (iii) the variability in the bedload transport rates, and (iv) a hysteresis index as a measure of the strength of bedload transport during the rising and falling limb of the hydrograph. This study underlines that above-average disequilibrium conditions, which are associated with a larger sediment availability on the streambed, generally have a stronger effect on subsequent transport conditions than below-average disequilibrium conditions, which are associated with comparatively less sediment availability on the streambed. The findings highlight the important roles of the sediment availability on the streambed, the disequilibrium ratio, and the hydraulic forcing in view of a better understanding of the bedload transport fluctuations in a steep mountain stream.

Particle Image micro-Rheology (PIR) using displacement probability density function

We present a novel approach to perform passive microrheology. A method to measure the rheological properties of fluids from the Brownian motion of suspended particles. Rheological properties are found from the particles' mean square displacements (MSDs) as a function of measurement time lag. Current state-of-the-art approaches find the MSD by tracking multiple particles' trajectories. However, particle tracking approaches face many limitations, including low accuracy and high computational cost, and they are only applicable to low particle seeding densities. Here, we present a novel method, termed particle image rheometry (PIR), for estimating the particle ensemble MSD from the temporal evolution of the probability density function of the displacement as a function of measurement time lag. First, the probability density function (PDF) of the particle displacements for each time lag is found using a generalized ensemble image cross-correlation approach that eliminates the need for particle tracking. Then, PDFs are used to calculate the MSD from which the complex viscosity of the solution is measured. We evaluate the performance of PIR using synthetic datasets and show that it can achieve an error of less than 1% in passive microrheology measurements, which corresponds to a twofold lower error than existing methods. Finally, we compare the measured complex viscosity from PIR with bulk rheometry for a polymeric solution and show agreement between the two measurements.

Scalable Variational Inference for Low-Rank Spatiotemporal Receptive Fields.

An important problem in systems neuroscience is to characterize how a neuron integrates sensory inputs across space and time. The linear receptive field provides a mathematical characterization of this weighting function and is commonly used to quantify neural response properties and classify cell types. However, estimating receptive fields is difficult in settings with limited data and correlated or high-dimensional stimuli. To overcome these difficulties, we propose a hierarchical model designed to flexibly parameterize low-rank receptive fields. The model includes gaussian process priors over spatial and temporal components of the receptive field, encouraging smoothness in space and time. We also propose a new temporal prior, temporal relevance determination, which imposes a variable degree of smoothness as a function of time lag. We derive a scalable algorithm for variational Bayesian inference for both spatial and temporal receptive field components and hyperparameters. The resulting estimator scales to high-dimensional settings in which full-rank maximum likelihood or a posteriori estimates are intractable. We evaluate our approach on neural data from rat retina and primate cortex and show that it substantially outperforms a variety of existing estimators. Our modeling approach will have useful extensions to a variety of other high-dimensional inference problems with smooth or low-rank structure.

Metrics to relate COVID-19 wastewater data to clinical testing dynamics

Wastewater surveillance has emerged as a useful tool in the public health response to the COVID-19 pandemic. While wastewater surveillance has been applied at various scales to monitor population-level COVID-19 dynamics, there is a need for quantitative metrics to interpret wastewater data in the context of public health trends. 24-hour composite wastewater samples were collected from March 2020 through May 2021 from a Massachusetts wastewater treatment plant and SARS-CoV-2 RNA concentrations were measured using RT-qPCR. The relationship between wastewater copy numbers of SARS-CoV-2 gene fragments and COVID-19 clinical cases and deaths varies over time. We demonstrate the utility of three new metrics to monitor changes in COVID-19 epidemiology: (1) the ratio between wastewater copy numbers of SARS-CoV-2 gene fragments and clinical cases (WC ratio), (2) the time lag between wastewater and clinical reporting, and (3) a transfer function between the wastewater and clinical case curves. The WC ratio increases after key events, providing insight into the balance between disease spread and public health response. Time lag and transfer function analysis showed that wastewater data preceded clinically reported cases in the first wave of the pandemic but did not serve as a leading indicator in the second wave, likely due to increased testing capacity, which allows for more timely case detection and reporting. These three metrics could help further integrate wastewater surveillance into the public health response to the COVID-19 pandemic and future pandemics.

Probing Physical Properties of the Cellular Membrane in Senescent Cells by Fluorescence Imaging.

Cellular senescence is the irreversible cell cycle arrest in response to various types of stress. Although the plasma membrane and its composition are significantly affected by cellular senescence, detailed studies on the physical properties of the plasma membrane have shown inconclusive results. In this study, we utilized both ensemble and single-molecule fluorescence imaging to investigate how membrane properties, such as fluidity, hydrophobicity, and ganglioside GM1 level are affected by cellular senescence. The diffusion coefficient of lipid probes, as well as the type of diffusion determined by an exponent α, which is the slope of the log-log plot of mean squared displacement as a function of time lag, were analyzed. We found that the number of molecules with a lower diffusion coefficient increased as cells became senescent. The changes in the population with a lower diffusion coefficient, observed after methyl-β-cyclodextrin treatment, and the increase in ceramide levels, detected using a ceramide-specific antibody, suggest that ceramide-rich lipid rafts were enhanced in senescent cells. Our results emphasize the importance of membrane properties in cellular senescence and might serve as a base for in-depth studies to determine how such domains facilitate the signaling pathway specific to cellular senescence.

Determining the Temporal and Spatial Coherence of Plasmaspheric Hiss Waves in the Magnetosphere

AbstractPlasmaspheric hiss is one of the most important plasma waves in the Earth's magnetosphere to contribute to radiation belt dynamics by pitch‐angle scattering energetic electrons via wave‐particle interactions. There is growing evidence that the temporal and spatial variability of wave‐particle interactions are important factors in the construction of diffusion‐based models of the radiation belts. Hiss amplitudes are thought to be coherent across large distances and on long timescales inside the plasmapause, which means that hiss can act on radiation belt electrons throughout their drift trajectories for many hours. In this study, we investigate both the spatial and temporal coherence of plasmaspheric hiss between the two Van Allen Probes from November 2012 to July 2019. We find ∼3,264 events where we can determine the correlation of wave amplitudes as a function of both spatial distance and time lag in order to study the spatial and temporal coherence of plasmaspheric hiss. The statistical results show that both the spatial and temporal correlation of plasmaspheric hiss decrease with increasing L‐shell, and become incoherent at L > ∼4.5. Inside of L = ∼4.5, we find that hiss is coherent to within a spatial extent of up to ∼1,500 km and a time lag up to ∼10 min. We find that the spatial and temporal coherence of plasmaspheric hiss does not depend strongly on the geomagnetic index (AL*) or magnetic local time. We discuss the ramifications of our results with relevance to understanding the global characteristics of plasmaspheric hiss waves and their role in radiation belt dynamics.

Quantitative analysis of hidden particles diffusing behind a scattering layer using speckle correlation.

Speckle-correlation imaging is a family of methods that makes use of the "memory effect" to image objects hidden behind visually opaque layers. Here, we show that a correlation analysis can be applied to quantitative imaging of an ensemble of dynamic fluorescent beads diffusing on a 2D surface. We use an epi-fluorescence microscope where both the illumination and detection light patterns are speckled, due to light scattering by a thin disordered layer. The spatio-temporal cross-correlation of the detection speckle pattern is calculated as a function of lag time and spatial shift and is used to determine the diffusion constant and number of fluorescent particles in the sample without requiring any phase retrieval procedure. It is worth to note that the "memory effect" range is not required to extend beyond a distance of few speckle grains, thus making our method potentially useful for nearly arbitrary values of the thickness of the scattering layer.

Sustainable Timber Trade: A Study on Discrepancies in Chinese Logs and Lumber Trade Statistics

Discrepancies in trade statistics can be normal or benign and attributed to a wide variety of unintentional factors, or in some instances within the timber products sector, such discrepancies can be associated with “systemic” factors that distort trade statistics, including (i) measurement and shipment issues, (ii) misreporting of product volumes, (iii) misclassification of timber product types, and (iv) government regulations concerned about trade. This study measured trade discrepancies in logs and lumber trade statistics for China and its trading partner countries from 2002 to 2018 using a time-lagged function, based on the customs data available from Global Trade Information Services (GTIS), with the aim of exploring a more nuanced understanding of trade discrepancies and their “systemic” factors. The results showed that the range of overall discrepancies in logs and lumber trade statistics shrunk over time, from [−0.069, 1.207] in 2002–2007 to [−0.120, 0.408] in 2013–2018. The larger trade flows of logs and lumber from Russia, New Zealand, and the U.S. (each above 10% of total China’s import) showed small trade statistics discrepancy ratios, which were less than ± 0.06. However, trade discrepancies still remained large at the disaggregated level, and significant differences of trade discrepancies between tropical and non-tropical countries. The range of trade discrepancies in hardwood logs increased from 2002 to 2018 and appeared to be attributed to misclassification and misreporting in tropical countries such as Indonesia, the Philippines, Thailand, and Ghana. However, these countries’ trade flows are becoming relatively minor over time. Government policies are suggested to play an important role in influencing both the occurrence and resolution of trade discrepancies.

A New Type of Jet in a Polar Limb of the Solar Coronal Hole

Abstract A new type of chromospheric jet in a polar limb of a coronal hole is discovered in the Ca ii filtergram of the Solar Optical Telescope on board the Hinode. We identify 30 jets in a filtered Ca ii movie with a duration of 53 minutes. The average speed at their maximum heights is found to be 132 ± 44 km s−1 ranging from 57 to 264 km s−1 along the propagation direction. The average lifetime is 20 ± 6 ranging from 11 to 36 s. The speed and lifetime of the jets are located at end-tails of those parameters determined for type II spicules, hence implying a new type of jets. To confirm whether these jets are different from conventional spicules, we construct a time–height image averaged over a horizontal region of 1″, and calculate lagged cross-correlations of intensity profiles at each height with the intensity at 2 Mm. From this, we obtain a cross-correlation map as a function of lag and height. We find that the correlation curve as a function of lag time is well fitted into three different Gaussian functions whose standard deviations of the lag time are 193, 42, and 17 s. The corresponding propagation speeds are calculated to be 9 km s−1, 67 km s−1, and 121 km s−1, respectively. The kinematic properties of the former two components seem to correspond to the 3-minute oscillations and type II spicules, while the latter component to the jets is addressed in this study.

Indications for a potential synchronization between the phase evolution of the Madden–Julian oscillation and the solar 27-day cycle

Abstract. The Madden–Julian oscillation (MJO) is a major source of intraseasonal variability in the troposphere. Recently, studies have indicated that also the solar 27-day variability could cause variability in the troposphere. Furthermore, it has been indicated that both sources could be linked, and particularly that the occurrence of strong MJO events could be modulated by the solar 27-day cycle. In this paper, we analyze whether the temporal evolution of the MJO phases could also be linked to the solar 27-day cycle. We basically count the occurrences of particular MJO phases as a function of time lag after the solar 27-day extrema in about 38 years of MJO data. Furthermore, we develop a quantification approach to measure the strength of such a possible relationship and use this to compare the behavior for different atmospheric conditions and different datasets, among others. The significance of the results is estimated based on different variants of the Monte Carlo approach, which are also compared. We find indications for a synchronization between the MJO phase evolution and the solar 27-day cycle, which are most notable under certain conditions: MJO events with a strength greater than 0.5, during the easterly phase of the quasi-biennial oscillation, and during boreal winter. The MJO appears to cycle through its eight phases within two solar 27-day cycles. The phase relation between the MJO and the solar variation appears to be such that the MJO predominantly transitions from phase 8 to 1 or from phase 4 and 5 during the solar 27-day minimum. These results strongly depend on the MJO index used such that the synchronization is most clearly seen when using univariate indices like the OLR-based MJO index (OMI) in the analysis but can hardly be seen with multivariate indices like the real-time multivariate MJO index (RMM). One possible explanation could be that the synchronization pattern is encoded particularly in the underlying outgoing longwave radiation (OLR) data. A weaker dependence of the results on the underlying solar proxy is also observed but not further investigated. Although we think that these initial indications are already worth noting, we do not claim to unambiguously prove this relationship in the present study, neither in a statistical nor in a causal sense. Instead, we challenge these initial findings ourselves in detail by varying underlying datasets and methods and critically discuss resulting open questions to lay a solid foundation for further research.

Temporal similarity metrics for latent network reconstruction: The role of time-lag decay

When investigating the spreading of a piece of information or the diffusion of an innovation, we often lack information on the underlying propagation network. Reconstructing the hidden propagation paths based on the observed diffusion process is a challenging problem which has recently attracted attention from diverse research fields. To address this reconstruction problem, based on static similarity metrics commonly used in the link prediction literature, we introduce new node-node temporal similarity metrics. The new metrics take as input the time-series of multiple independent spreading processes, based on the hypothesis that two nodes are more likely to be connected if they were often infected at similar points in time. This hypothesis is implemented by introducing a time-lag function which penalizes distant infection times. We find that the choice of this time-lag function strongly affects the metrics’ reconstruction accuracy, depending on the network’s clustering coefficient, and we provide an extensive comparative analysis of static and temporal similarity metrics for network reconstruction. Our findings shed new light on the notion of similarity between pairs of nodes in complex networks.

Green's Function and Eigenvalue Representation of Time Lag for Absorptive Permeation Across a Heterogeneous Membrane

The time‐lag problem is treated for absorptive penetration across a heterogeneous membrane, where both the diffusivity D(x) and the partition coefficient K(x) depend on the coordinate x (0 ≦ x ≦ h), with 0 and h being the coordinates of the upstream and downstream faces, respectively. A new concept of time‐lag distribution is introduced, and the first (time) moment and the second (time) moment over this distribution are also difined and treated in the Lapalce domain in conjuction with the Green's function G(x,y), and eigenvalues associated with the time‐independent diffusion equation subject to the absorbing boundary condition at both ends of the membrane. Our central results include and , where λi is the ith eigenvalue of the aforementioned diffusion equation. The merits of these new resprentations and comparison with the treatments of Frisch or Eyring are also discussed.

Statistics of the two-point cross-covariance function of solar oscillations

Context: The cross-covariance of solar oscillations observed at pairs of points on the solar surface is a fundamental ingredient in time-distance helioseismology. Wave travel times are extracted from the cross-covariance function and are used to infer the physical conditions in the solar interior. Aims: Understanding the statistics of the two-point cross-covariance function is a necessary step towards optimizing the measurement of travel times. Methods: By modeling stochastic solar oscillations, we evaluate the variance of the cross-covariance function as function of time-lag and distance between the two points. Results: We show that the variance of the cross-covariance is independent of both time-lag and distance in the far field, i.e., when they are large compared to the coherence scales of the solar oscillations. Conclusions: The constant noise level for the cross-covariance means that the signal-to-noise ratio for the cross-covariance is proportional to the amplitude of the expectation value of the cross-covariance. This observation is important for planning data analysis efforts.

A Geostatistical Framework for Quantifying the Temporal Evolution and Predictability of Rainfall Fields

Abstract A geostatistical framework for quantifying the temporal evolution and predictability of rainfall fields for time lags between 5 min and 3 h is proposed. The method is based on the computation of experimental space–time variogram maps of radar reflectivity fields. Two new metrics for quantifying temporal innovation and predictability based on minimum semivariance values at different time lags are proposed. The method is applied to high-resolution composite radar reflectivity maps over the United States to study the evolution of 25 convective and 25 stratiform events during the warm season of 2014. Results show that the temporal innovation can be modeled as the sum of two exponential functions of time lag, with approximately 50% of the total innovation occurring over the first 60 min. The median predictable scales for convective events are on the order of 1.6 km at 5 min, 5 km at 15 min, and 12.7 km at 1 h. Furthermore, the optimal time lag for predicting future innovation, taking into account measurement uncertainty and natural variability, appears to be between 30 and 60 min.

Dynamic correlation of serum C-reactive protein (CRP) levels with serum prostate-specific antigen (PSA) levels in patients with prostate cancer (PCa) on androgen deprivation therapy (ADT).

181 Background: CRP, a marker of inflammation, has been associated with disease extent and prognosis in urologic cancers (Huang J, et al. Mol Aspects Med, 2015). In this study we wanted to see whether CRP is dynamically correlated with PSA in patients with PCa when they were on ADT. Methods: 83 patients on ADT were included in this analysis. Their CRP and PSA were recorded longitudinally and the range of length of follow up time is from 3 to 47 (median 16) months. All patients had at least 3 pairs of CRP and PSA measurements available. Statistical analysis was conducted using Dynamic Correlation (Dubin JA, Muller H-G. J Am Stat Assoc, 2005) package ‘dynCorr’ in R 3.1.2, and SAS Version 9.4. Significance levels were set at 0.05. We calculated dynamical correlation as function of time lag between CRP and PSA measurements along with 95% bootstrap confidence intervals and p value. Results: See below table. Conclusions: We found that at lag = 30 days (CRP leads PSA by 30 days), the correlation is significant at level of < 0.05, which means any increase or decrease of CRP at a given time point will lead to about same change in PSA 30 days later. Though these findings must be verified in a larger setting, our results support, in the setting of ADT use, the role of CRP as an early marker of PCa progression and treatment response. [Table: see text]

Using the Cross-Correlation Function to Evaluate the Quality of Eddy-Covariance Data

A cross-correlation test is proposed for evaluating the quality of 30-min eddy-covariance data. Cross-correlation as a function of time lag is computed for vertical velocity paired with temperature, humidity, and carbon dioxide concentration. High quality data have a dominant peak at zero time lag and approach zero within a time lag of 20 s. Poor quality data have erratic cross-correlation functions, which indicates that the eddy flux may no longer represent the energy and mass exchange between the atmospheric surface layer and the canopy, and such data should be rejected in post-data analyses. Eddy-covariance data over grassland in July 2004 are used to evaluate the proposed test. The results show that 17, 29, and 36 % of the available data should be rejected because of poor quality measurements of sensible heat, latent heat, and \(\hbox {CO}_{2}\) fluxes, respectively. The rejected data mainly occurred on calm nights and day/night transitions when the atmospheric surface layer became stable or neutrally stratified. We found no friction velocity (\(u_*\)) threshold below which all data should be rejected, a test that many other studies have implemented for rejecting questionable data. We instead found that some data with low \(u_*\) were reliable, whereas other data with higher \(u_*\) were not. The poor quality measurements collected under less than ideal conditions were replaced by using the mean diurnal variation gap-filling method. The correction for poor quality data shifted the daily average \(\hbox {CO}_{2}\) flux by +0.34 g C m\(^{-2}\) day\(^{-1}\). After applying the quality-control test, the eddy \(\hbox {CO}_{2}\) fluxes did not display a clear dependence on \(u_*\). The results suggest that the cross-correlation test is a potentially valuable step in evaluating the quality of eddy-covariance data.