Time-varying Properties Research Articles

Aberrant dynamic functional network connectivity in type 2 diabetes mellitus individuals

An increasing number of recent brain imaging studies are dedicated to understanding the neuro mechanism of cognitive impairment in type 2 diabetes mellitus (T2DM) individuals. In contrast to efforts to date that are limited to static functional connectivity, here we investigate abnormal connectivity in T2DM individuals by characterizing the time-varying properties of brain functional networks. Using group independent component analysis (GICA), sliding-window analysis, and k-means clustering, we extracted thirty-one intrinsic connectivity networks (ICNs) and estimated four recurring brain states. We observed significant group differences in fraction time (FT) and mean dwell time (MDT), and significant negative correlation between the Montreal Cognitive Assessment (MoCA) scores and FT/MDT. We found that in the T2DM group the inter- and intra-network connectivity decreases and increases respectively for the default mode network (DMN) and task-positive network (TPN). We also found alteration in the precuneus network (PCUN) and enhanced connectivity between the salience network (SN) and the TPN. Our study provides evidence of alterations of large-scale resting networks in T2DM individuals and shed light on the fundamental mechanisms of neurocognitive deficits in T2DM.

Contractile Adaptation of the Left Ventricle Post-myocardial Infarction: Predictions by Rodent-Specific Computational Modeling.

Myocardial infarction (MI) results in cardiac myocyte death and the formation of a fibrotic scar in the left ventricular free wall (LVFW). Following an acute MI, LVFW remodeling takes place consisting of several alterations in the structure and properties of cellular and extracellular components with a heterogeneous pattern across the LVFW. The normal function of the heart is strongly influenced by the passive and active biomechanical behavior of the LVFW, and progressive myocardial structural remodeling can have a detrimental effect on both diastolic and systolic functions of the LV leading to heart failure. Despite important advances in understanding LVFW passive remodeling in the setting of MI, heterogeneous remodeling in the LVFW active properties and its relationship to organ-level LV function remain understudied. To address these gaps, we developed high-fidelity finite-element (FE) rodent computational cardiac models (RCCMs) of MI using extensive datasets from MI rat hearts representing the heart remodeling from one-week (1-wk) to four-week (4-wk) post-MI timepoints. The rat-specific models (n = 2 for each timepoint) integrate detailed imaging data of the heart geometry, myocardial fiber architecture, and infarct zone determined using late gadolinium enhancement prior to terminal measurements. The computational models predicted a significantly higher level of active tension in remote myocardium in early post-MI hearts (1-wk post-MI) followed by a return to near the control level in late-stage MI (3- and 4-wk post-MI). The late-stage MI rats showed smaller myofiber ranges in the remote region and in-silico experiments using RCCMs suggested that the smaller fiber helicity is consistent with lower contractile forces needed to meet the measured ejection fractions in late-stage MI. In contrast, in-silico experiments predicted that collagen fiber transmural orientation in the infarct region has little influence on organ-level function. In addition, our MI RCCMs indicated that reduced and potentially positive circumferential strains in the infarct region at end-systole can be used to infer information about the time-varying properties of the infarct region. The detailed description of regional passive and active remodeling patterns can complement and enhance the traditional measures of LV anatomy and function that often lead to a gross and limited assessment of cardiac performance. The translation and implementation of our model in patient-specific organ-level simulations offer to advance the investigation of individualized prognosis and intervention for MI.

VLF/LF Lightning Location Based on LWPC and IRI Models: A Quantitative Study

The group velocity of lightning electromagnetic signals plays an important role in lightning location systems using the time difference of arrival (TDOA) method. Accurate estimation of group velocity is difficult due to the space- and time-varying properties of the Earth-ionosphere waveguide. Besides, the analytical solution of the group velocity is difficult to obtain from the classic mode theory, especially when higher-order modes, anisotropic geomagnetic background, diffuse ionosphere profile, and propagation path segmentation are all taken into consideration. To overcome these challenges, a novel numerical method is proposed in this paper to estimate the group velocity of the lightning signal during ionospheric quiet periods. The well-known Long Wavelength Propagation Capability (LWPC) code is used to model the propagation of VLF/LF radio waves. Since LWPC uses a simplified ionospheric model which is unable to describe the subtle variations of ionospheric parameters over time and space, the IRI-2016 model is incorporated into the numerical modeling process to provide more accurate ionosphere parameters. Experimental results of a VLF/LF lightning location network are demonstrated and analyzed to show the effectiveness of our method. The proposed method is also applicable when there is a sudden ionospheric disturbance as long as the parameters of the ionosphere are obtained in real time by remote sensing methods.

On demodulation of time-varying oscillations in process plant

Due to the presence of process fluctuation, transient impacts and noise, most of the oscillations in industrial control-loops evolve with time-varying properties. If significant variation is observed under a limited monitoring window, the existing regularity-based oscillation detectors may be invalid. To tackle this problem, this paper proposes to describe the oscillations as a kind of amplitude modulated and frequency modulated (AM–FM) signals, and designs corresponding scheme to extract and detect them. More specifically, our proposed method is featured by following steps: (i) Decompose the process output into several AM–FM modes using ensemble empirical mode decomposition; (ii) Demodulate each mode with Teager energy operator for extracting the corresponding instantaneous amplitude and frequency functions; (iii) Identify the oscillatory variables by developing a new statistical measure based on the deviation of the frequency variation. The applicability of the proposed method is validated through a set of simulations and representative chemical applications. Comparative studies also demonstrate that it outperforms existing time–frequency tools and typical regularity-based detectors.

Non-equilibrium statistical properties, path-dependent information geometry, and entropy relations in edge-localized modes in fusion plasmas

We investigate time-varying turbulence statistical properties of edge-localized modes (ELMs) in fusion plasmas. By utilizing a simplified stochastic model, we calculate a time-dependent probability density function and various entropy-related quantities such as entropy, entropy production, entropy flux, mutual information, and information flow and path-dependent information geometry. A thorough analysis is performed to elucidate the effects on ELM dynamics (evolution, suppression, mitigation, etc.) of different values of stochastic noise and different forms of a time-varying input power. Furthermore, the time-irreversibility and hysteresis are investigated through the employment of forward and back processes where a time-varying input power varies mirror-symmetrically in time. Among all the statistical quantities, the path-dependent information geometry is shown to be a robust diagnostic for quantifying hysteresis and self-regulation as well as for an early detection of subtle changes in ELM dynamics, for example, caused by a sudden change in the input power.

The behavior and determinants of illiquidity in the non-fungible tokens (NFTs) market

We provide the first study of time-varying and cross-sectional properties of liquidity in the nascent and rapidly growing non-fungible token (NFT) marketplace. Using transaction-level data, we document important weekday patterns in NFT liquidity. Despite the decentralized nature and infancy of the marketplace, we show the overall determinants of NFT liquidity have several parallels with highly developed financial markets. We then study how the arrival of new information impacts NFT marketplace liquidity. By reducing information frictions, NFT news increases NFT liquidity, especially when the media source tends to provide more specific or technical information about NFTs. Overall, our findings deepen our understanding of how liquidity impacts newly developed marketplaces.

Time-variant associations of ambulatory pulse rate with short-term neurologic functional outcomes following acute ischemic stroke.

This study aims to assess the associations of functional outcomes following acute ischemic stroke (IS) with ambulatory pulse rate (PR) and characterize the time-variant properties of the associations. The prospective cohort consisted of 1831 patients who had ambulatory blood pressure (BP) and PR monitoring following acute IS, and neurologic status evaluated at discharge and 3-month follow-up. The neurologic disability was defined as modified Rankin Scale ≥3. Logistic regression and generalized penalized functional regression models were used to examine the associations of ambulatory BP and PR with neurologic disability. Adjusting for covariates, the neurologic disability at discharge and 3-month was associated with high average 24-h, daytime, and nocturnal PR (odds ratio, OR = 1.20-1.34; p < 0.05 for all), high standard derivation of nocturnal PR (OR = 1.19 and 1.32; p < 0.05 for both), and low nocturnal PR decline (OR = 0.76 and 0.76; p < 0.05 for both). The OR functions of ambulatory PR on neurologic disability were "W-shaped" from 0 a.m. to 12 p.m., with ORs >1 in the wee hours and at noon, and ORs <1 before dawn and at night. The ambulatory BP profiles were not associated with neurologic disability at discharge or 3-month. The ambulatory PR is associated with the risk of short-term neurologic disability of stroke patients, with four different phases in a 24-h cycle. Ambulatory PR monitoring, especially nocturnal PR monitoring, has significant clinical implications for the prevention of short-term neurologic disability in stroke inpatients.

IK-SPSA-Based Performance Optimization Strategy for Steam Generator Level Control System of Nuclear Power Plant

The steam generator (SG) is a critical component of the steam supply system in the nuclear power plant (NPP). Hence, it is necessary to control the SG level well to ensure the stable operation of the NPPs. However, its dynamic level response process has significant nonlinearity (such as the ‘swell and shrinks’ effect) and time-varying properties. As most of the SG level control systems (SGLCS) are constructed based on the Proportional-Integral-Derivative (PID) controllers with fixed parameters, the controller parameters should be optimized to improve the performance of the SGLCS. However, traditional parameters tuning methods are generally experience-based, cumbersome, and time-consuming, and it is difficult to obtain the optimal parameters. To address the challenge, this study adopts a knowledge-informed simultaneous perturbation stochastic approximation (IK-SPSA) based on adjacent iteration points information to improve the performance of the SGLCS. Rather than the traditional controller parameter tuning method, the IK-SPSA method optimizes the control system directly by using measurements of control performance. The method’s efficiency lies in the following aspects. Firstly, with the help of historical information during the optimization process, the IK-SPSA can dynamically sense the current status of the optimization process. Secondly, it can accomplish the iteration step size tuning adaptively according to the optimization process’s current status, reducing the optimization cost. Thirdly, it has the stochastic characteristic of simultaneous perturbation, which gives it high optimization efficiency to optimize high dimensional controller parameters. Fourthly, it incorporates an intelligent termination control mechanism to accomplish optimization progress control. This mechanism could terminate the optimization process intelligently through historical iterative process information, avoiding unnecessary iterations. The optimization method can improve the stability, safety, and economy of SGLCS. The simulation results demonstrated the effectiveness and efficiency of the method.

EVIboost for the Estimation of Extreme Value Index Under Heterogeneous Extremes

Modeling heterogeneity on heavy-tailed distributions under a regression framework is challenging, yet classical statistical methodologies usually place conditions on the distribution models to facilitate the learning procedure. However, these conditions will likely overlook the complex dependence structure between the heaviness of tails and the covariates. Moreover, data sparsity on tail regions makes the inference method less stable, leading to biased estimates for extreme-related quantities. This paper proposes a gradient boosting algorithm to estimate a functional extreme value index with heterogeneous extremes. Our proposed algorithm is a data-driven procedure capturing complex and dynamic structures in tail distributions. We also conduct extensive simulation studies to show the prediction accuracy of the proposed algorithm. In addition, we apply our method to a real-world data set to illustrate the state-dependent and time-varying properties of heavy-tail phenomena in the financial industry.

Shaping Impedances to Comply With Constrained Task Dynamics

Humans are capable of managing multiple tasks simultaneously. It is widely assumed that human motor control can be emulated by impedance control. To achieve human-like behavior, however, the impedance parameters of multiple tasks may vary during task execution. We propose an algorithm that shapes task impedance as a function of the robot’s time-varying inertial properties. These properties involve virtually constrained masses and virtually constrained inertias that counteract a task in order to comply with a given constraint. In this work, we not only detect task conflicts, but also show how to handle them. Our method is able to control kinematically redundant robots. We developed a damping-design method that does not interfere with our desired Cartesian task-space behavior. The control approach was verified in experiments on a real robot. We compared our impedance shaping method with two alternative control approaches: simple impedance superposition and nullspace projection. Our method preserved the passivity while improving the Cartesian task performance of an impedance controller. The method has computational advantages, beneficial to control robots with many degrees of freedom.

Time-varying elastic wave mode conversion in vibrating elastic beams with subwavelength nonlinearity

Introduction of time-varying properties in a background medium can enable unconventional wave behavior. Furthermore, it is well-known that subjecting nonlinear materials to time-varying stress can be used for material characterization [Appl. Phys. Lett. 94, 011905 (2009)]. We propose a technique called Dynamic Asymmetric Transmission Measurement (DATM) that combines structural dynamic (SD) and ultrasonic (US) testing modalities in an elastic waveguide to detect and characterize changes in the global stress state of a structure that result in local time-varying stress conditions due to local nonlinearity. Asymmetric geometric features in beams and plates, paired with time-varying stress conditions, result in asymmetric mode conversion of guided US waves that depend on large-scale structural dynamics. The DATM technique is explored through finite element modeling, semi-analytical methods, and experiments and discussed in a structural health monitoring context. We also discuss the use of dynamic structures with engineered defects to introduce time-varying stress conditions for the purpose of manipulating elastic waves. [Sandia National Laboratories is a multimission laboratory managed and operated by the National Technology &amp; Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.]

Macroeconomic News and Stock–Bond Comovement

Abstract Covariances between aggregate stock returns and changes in bond yields change sign over time. Existing theories emphasize either time-varying properties of expected inflation or time-varying properties of real yields. Using revisions in survey forecasts as proxies for macroeconomic news, neither approach succeeds empirically. Inflation-centric models require much more news about expected future inflation than we observe from surveys. Real-centric models posit signs of covariances among macroeconomic news, changes in yields, and stock returns that do not match those in the data. In a nutshell, macroeconomic news appears to drive a substantial part of stock–bond comovement, but not in ways consistent with our theories.

Bayesian dynamic programming approach for tracking time-varying model properties in SHM

Structural health monitoring (SHM) quite often involves continually tracking the temporal variation of some properties of interest, where statistical information about variations under normal situation can be obtained and potential anomalies may be detected for further attention. Structural-related properties such as natural frequency and stiffness often need to be identified with a physics-based model that relates them to measured data. Conventional approaches empirically divide data into non-overlapping segments with equal lengths and identify the model parameters within each segment based on a time-invariant model. Potentially time-varying model properties are then investigated based on the variation of identified results from one data segment to another. Challenges do exist, e.g., how to choose segment length to balance modeling error and identification accuracy, how to set criterion for anomaly detection, etc., which should be addressed with proper extraction of probabilistic information from data. In this work, the SHM problem is formulated as a Bayesian model selection problem, where the data can be ‘partitioned’ in an arbitrary manner, whose optimal choice, and hence points of significant change, are determined together with the model inference process by maximizing the probabilistic evidence supported by data. An efficient algorithm based on dynamic programming is proposed to determine the optimal partitioning and associated piecewise-constant properties, which is otherwise computationally prohibitive. The methodology is applied to tracking modal properties, e.g., natural frequency and damping ratio, of structures using output-only ambient vibration data. It is investigated with synthetic data and then applied to field data of a tall building during a typhoon event.

An expert-knowledge-based algorithm for time-varying multi-objective coastal groundwater optimization

Groundwater optimization models coupling Non-dominated Sorting Genetic Algorithm II (NSGAII) with surrogate models have shown great success in recent years. However, most previous models adopted global optimization over time steps while ignoring the time-varying properties of optimization objectives, which may lead to an unrealistic strategy for specific months. Moreover, employing global optimization for time-varying optimization may result in computation inefficient. To address these issues, We developed EGN combining Expert knowledge, Graph Attention Networks and the NSGAII algorithm. Specifically, EGN defines a deterministic policy based on the encoded Expert knowledge to efficiently select the optimal strategy from the Pareto Front given by NSGAII. The framework significantly reduced the asymptotic complexity from O(NT) (global optimization) to O(N2). We evaluate EGN on a real-world dataset and the results show that EGN outperforms baselines and global optimization.

Comparison of Artificial Intelligence Control Strategies for a Peristaltically Pumped Low-Pressure Driven Membrane Process.

Peristaltic pumping is used in membrane applications where high and sterile sealing is required. However, control is difficult due to the pulsating pump characteristics and the time-varying properties of the system. In this work, three artificial intelligence control strategies (artificial neural networks (ANN), fuzzy logic expert systems, and fuzzy-integrated local models) were used to regulate transmembrane pressure and crossflow velocity in a microfiltration system under high fouling conditions. A pilot plant was used to obtain the necessary data to identify the AI models and to test the controllers. Humic acid was employed as a foulant, and cleaning-in-place with NaOH was used to restore the membrane state. Several starting operating points were studied and setpoint changes were performed to study the plant dynamics under different control strategies. The results showed that the control approaches were able to control the membrane system, but significant differences in the dynamics were observed. The ANN control was able to achieve the specifications but showed poor dynamics. Expert control was fast but showed problems in different working areas. Local models required less data than ANN, achieving high accuracy and robustness. Therefore, the technique to be used will depend on the available information and the application dynamics requirements.

Time-varying stability and vibration properties of a pyrotechnic ejection plate in narrow space

This work simulates the pyrotechnic launch process which is commonly seen in aerospace engineering, and the dynamic responses of the projectile during pyrotechnic ejection are analyzed by studying the aeroelastic stability and nonlinear vibration of a rectangular plate subjected to time-varying axial blast load in a narrow space. During the launch, the plate is accelerated under the explosion effect, which makes the plate and airflow in narrow space a complicated time-varying fluid structure interaction system. The change of the plate velocity is derived from the momentum theorem. Considering the Von Karman nonlinear thin plate theory and the linear potential flow theory, the governing equation of the pyrotechnic ejection plate in narrow space is established. In the process of solving the equation, the Hamilton’s principle and the assumed mode method are employed. By applying the generalized eigenvalue method to linear time-varying system, the variations of the natural frequencies of the plate during pyrotechnic ejection are studied, from which the stability mechanism of the system is obtained. For nonlinear vibration of the system, the displacement time responses of the plate during pyrotechnic ejection are discussed by employing the Runge-Kutta method. From the results, the plate loses stability rapidly when the explosion occurs, and then with the increase of the velocity of the plate, the plate exhibits the flutter instability. In the process of pyrotechnic ejection, the amplitude jumping phenomenon of the plate is observed. Besides, the explosion strengthen, explosion position, slit height and aspect ratio of the plate have significant effects on the stability and displacement jumping amplitude of the pyrotechnic ejection plate.

Research on stock volatility risk and investor sentiment contagion from the perspective of multi-layer dynamic network

In the post-epidemic period, the international economic structure has been readjusted, with risks contagious across financial and economic systems. This paper primarily uses the high-frequency TENET network and the Granger-causality network to describe the interconnectedness between the tail risk of stock volatility and investor sentiment, then the two-layer network is constructed by the generalized variance decomposition method to examine the inter-layer connectedness. Based on the two-layer network, the heterogeneity frequency response of network connectedness and dynamic network structure are further analyzed from the perspective of frequency domain. The study found that the tail risk of high-frequency stock volatility displays industry heterogeneity and time-varying property, and investor sentiment contagion network provides information transmission medium for stock risk. The double-layer network study found that stock volatility in consumer goods industry exhibits higher risk spillover to investor sentiment. The diversified financial industry, real estate industry and energy industry in the two-layer network are systemically important industries. In addition, the study of the frequency domain dynamic network found that the connectedness volatility in the short-term risk network of stock volatility was significantly higher than that of the investor sentiment network, and the short-term risk spillover effect of the network played a leading role in the total risk spillover. The research conclusions provide reference for preventing systemic risks from the perspective of systemically important industries and cyclical fluctuations.

Remaining Oil Distribution and Development Strategy for Offshore Unconsolidated Sandstone Reservoir at Ultrahigh Water-Cut Stage

Due to the influence of long-term waterflooding, the reservoir physical properties and percolation characteristics tend to change greatly in offshore unconsolidated sandstone reservoirs at ultrahigh water-cut stage, which can affect the remaining oil distribution. Remaining oil characterization and proper development strategy-making are of vital importance to achieve high-efficiency development of mature reservoirs. The present numerical simulation method is difficult to apply in reservoir development due to the problems of noncontinuous characterization and low computational efficiency. Based on the extended function of commercial numerical simulator, the time-varying equivalent numerical simulation method of reservoir physical properties was established, and the research of numerical simulation of X offshore oilfield with 350,000 effective grids was completed. The results show that the time-varying reservoir properties have a significant impact on the distribution of remaining oil in ultrahigh water-cut reservoir. Compared with the conventional numerical simulation, the remaining oil at the top of main thick reservoir in X oilfield has increased by 18.5% and the remaining oil in the low-permeability zone at the edge of the nonmain reservoir has increased by 27.3%. The data of coring well and the implementation effect of measures in the X oilfield are consistent with the recognition of numerical simulation, which proves the rationality of numerical simulation results. The new method is based on a mature commercial numerical simulator, which is easy to operate and has reliable results.

Online structural damage state identification of concrete arch dams under dynamic loads using a recursive TVARX approach

Concrete arch dams have been widely constructed worldwide, and many of these dams are located in areas with high seismic activity. However, strong seismic loading poses a severe threat to concrete arch dams, and dams have the risk of suffering damage and the possibility of an ensuing dam break. In past decades, seismic and structural health monitoring has undergone rapid growth in the context of arch dams to track the operation state of arch dams under strong seismic loading. In fact, arch dams with damage caused by seismic loading normally exhibit nonlinear dynamic behaviors and time-variable dynamic properties. To effectively identify the structural damage state development of arch dams under seismic loading, an online damage identification method using a novel recursive time-variable autoregressive with exogenous (TVARX) input model was presented in this paper. To improve the tracking capability for the time-variable coefficients of the TVARX model, an adaptive adjustment algorithm of variable weighting factors was incorporated into the recursive least-squares method. In the proposed method, the predicted model residual, a damage index named the fit ratio (FR), the difference between fit ratios (DF), and a damage index based on the change in ARX model parameters (CH) were proposed to identify the presence of damage, damage area, and relative damage extent for arch dams. Eventually, the proposed approach was used for the online damage state identification of arch dams in a numerical simulation example and a shaking table test, and the identification results demonstrated the effectiveness of the proposed approach.

The Analysis of Causality and Risk Spillover between Crude Oil and China's Agricultural Futures.

This paper aims to apply the time-varying Granger causality test (TVGC) and the DY Spillover Index (Diebold and Yilmaz, 2012) to measure the Granger causality and dynamic risk spillover effects of the international crude oil futures market on China’s agricultural commodity futures market from the perspectives of return and volatility spillovers. Empirical evidence relating to the TVGC test suggests the existence of unidirectional Granger causality between crude oil futures and agricultural product futures. This relationship shows a strong time-varying property, in particular for sudden or extreme events such as financial crises and natural disasters. On the other hand, the volatility spillover in crude oil and agricultural product futures markets responds asymmetrically and bidirectionally according to the result of the DY Spillover index, and the periodicity of total volatility spillover correlates closely with the occurrence of global economic events, which indicates that the spillover effect between crude oil and agricultural commodity futures markets will be exacerbated in turbulent financial and economic times. Such findings are expected to help in formulating policy recommendations, portfolio design, and risk-management decisions.