Time-varying Complex Research Articles

Time-varying and tissue-dependent effects of adiposity on leptin levels: A Mendelian randomization study.

Findings from Mendelian randomization (MR) studies are conventionally interpreted as lifelong effects, which typically do not provide insight into the molecular mechanisms underlying the effect of an exposure on an outcome. In this study, we apply two recently developed MR approaches (known as 'lifecourse' and 'tissue-partitioned' MR) to investigate lifestage-specific effects and tissues of action in the relationship between adiposity and circulating leptin levels. Genetic instruments for childhood and adult adiposity were incorporated into a multivariable MR (MVMR) framework to estimate lifestage-specific effects on leptin levels measured during early life (mean age: 10 y) in the Avon Longitudinal Study of Parents and Children and in adulthood (mean age: 55 y) using summary-level data from the deCODE Health study. This was followed by partitioning body mass index (BMI) instruments into those whose effects are putatively mediated by gene expression in either subcutaneous adipose or brain tissues, followed by using MVMR to simultaneously estimate their separate effects on childhood and adult leptin levels. There was strong evidence that childhood adiposity has a direct effect on leptin levels at age 10 y in the lifecourse (β = 1.10 SD change in leptin levels, 95% CI = 0.90-1.30, p=6 × 10-28), whereas evidence of an indirect effect was found on adulthood leptin along the causal pathway involving adulthood body size (β = 0.74, 95% CI = 0.62-0.86, p=1 × 10-33). Tissue-partitioned MR analyses provided evidence to suggest that BMI exerts its effect on leptin levels during both childhood and adulthood via brain tissue-mediated pathways (β = 0.79, 95% CI = 0.22-1.36, p=6 × 10-3 and β = 0.51, 95% CI = 0.32-0.69, p=1 × 10-7, respectively). Our findings demonstrate the use of lifecourse MR to disentangle direct and indirect effects of early-life exposures on time-varying complex outcomes. Furthermore, by integrating tissue-specific data, we highlight the etiological importance of appetite regulation in the effect of adiposity on leptin levels. This work was supported by the Integrative Epidemiology Unit, which receives funding from the UK Medical Research Council and the University of Bristol (MC_UU_00011/1).

A unified approach of detecting phase transition in time-varying complex networks

Deciphering the non-trivial interactions and mechanisms driving the evolution of time-varying complex networks (TVCNs) plays a crucial role in designing optimal control strategies for such networks or enhancing their causal predictive capabilities. In this paper, we advance the science of TVCNs by providing a mathematical framework through which we can gauge how local changes within a complex weighted network affect its global properties. More precisely, we focus on unraveling unknown geometric properties of a network and determine its implications on detecting phase transitions within the dynamics of a TVCN. In this vein, we aim at elaborating a novel and unified approach that can be used to depict the relationship between local interactions in a complex network and its global kinetics. We propose a geometric-inspired framework to characterize the network’s state and detect a phase transition between different states, to infer the TVCN’s dynamics. A phase of a TVCN is determined by its Forman–Ricci curvature property. Numerical experiments show the usefulness of the proposed curvature formalism to detect the transition between phases within artificially generated networks. Furthermore, we demonstrate the effectiveness of the proposed framework in identifying the phase transition phenomena governing the training and learning processes of artificial neural networks. Moreover, we exploit this approach to investigate the phase transition phenomena in cellular re-programming by interpreting the dynamics of Hi-C matrices as TVCNs and observing singularity trends in the curvature network entropy. Finally, we demonstrate that this curvature formalism can detect a political change. Specifically, our framework can be applied to the US Senate data to detect a political change in the United States of America after the 1994 election, as discussed by political scientists.

On Jump Discontinuities in Internal Forces of Flexible Structures Carrying Moving Subsystems

AbstractCombined systems, which are flexible structures carrying moving subsystems, are seen in various applications. Due to structure–subsystem interactions, the structure in a combined system encounters jump discontinuities in its internal forces (such as the bending moment and shear force of a beam). Accurate estimation of such jump discontinuities is important to the performance, safety, and longevity of a combined system. Because of the time-varying nature and complexity of structure–subsystem interactions, conventional series solution methods experience slow convergence, and the Gibbs phenomenon in computation and the improved series expansion methods are limited to certain proportionally damped continua under moving forces and moving oscillators. In this paper, a novel modified series expansion method (MSEM) is proposed to resolve the aforementioned issues with the existing series solution methods. Through the introduction of a jump influence function, the proposed method produces fast-convergent series solutions and accurately predicts the jump discontinuities without the Gibbs phenomenon. The MSEM is applicable to structures with nonproportional damping and subject to arbitrary boundary conditions, and it can easily manage general M-DOF moving subsystems having multiple contact points with a supporting structure. As an important result of this investigation, a mathematical proof of the convergence of the MSEM-based solutions is given for the first time. Additionally, two numerical examples are presented to demonstrate the accuracy, efficiency, and versatility of the proposed MSEM in modeling and analysis of combined systems.

Set-Membership Filtering for Time-Varying Complex Networks with Randomly Varying Nonlinear Coupling Structure

Set-Membership Filtering for Time-Varying Complex Networks with Randomly Varying Nonlinear Coupling Structure

Trading Risk Spillover Mechanism of Rare Earth in China: New Perspective Based on Time-Varying Connectedness Approach

Our research contributes a new point of view on China’s rare earth dynamic risk spillover measurement; this was performed by combining complex network and multivariate nonlinear Granger causality to construct the time-varying connectedness complex network and analyze the formation mechanism using the impulse response. First, our empirical research found that for the dynamic characteristics of China’s rare earth market, due to instability, uncertainty, and geopolitical decisions, disruption can be captured well by the TVP-VAR-SV model. Second, except for praseodymium, oxides are all risk takers and are more affected by the impact of other assets, which means that the composite index and catalysts are main sources of risk spillovers in China’s rare earth trading complex network system. Third, from the perspective of macroeconomic variables, there are significant multivariate nonlinear impacts on the total connectedness index of China’s rare earth market, and they exhibit asymmetric shock characteristics. These findings indicate that the overall linkage of the risk contagion in China’s rare earth trading market is strong. Strengthening the interconnections among the rare earth assets is of important practical significance. Empirical results also provide policy recommendations for establishing trading risk protection measures under macro-prudential supervision. Especially for investors and regulators, rare earth oxides are important assets for risk mitigation. When rare earth systemic trading risk occur, the allocation of oxide rare earth assets can hedge part of the trading risk.

A predefined-time and anti-noise varying-parameter ZNN model for solving time-varying complex Stein equations

A predefined-time and anti-noise varying-parameter zeroing neural network (PTAN-VPZNN) is designed to resolve time-varying complex Stein equations in this paper. Differing from the existing ZNNs, the merits of the proposed PTAN-VPZNN include: 1) a varying parameter that improves ZNN model’s convergence speed, which is more compatible with characteristics of the actual hardware parameter; 2) a noise-tolerant activation function which enables the PTAN-VPZNN model to solve Stein equations under noisy environments. Thence, the PTAN-VPZNN model has better convergence performance and noise immunity ability. Moreover, the predefined-time convergence of the PTAN-VPZNN is presented and the robustness of the PTAN-VPZNN is analyzed under constant noise, through rigorous theoretical derivations. Numerical studies demonstrate that the performance of the PTAN-VPZNN is better than the existing ZNNs including a linear ZNN (LZNN), a nonlinear ZNN (NLZNN), a finite-time convergent ZNN (FTCZNN) and a predefined-time convergent ZNN (PTCZNN), when solving Stein equations with or without noise involved. Finally, the PTAN-VPZNN is applied to a mobile manipulator for completing a path-tracking task, showing its potential application in robot control.

Ripple-Based Analysis of Asymmetrical Subharmonic Instability in Grid-Connected Inverters

The unexpected subharmonic oscillation with an evident asymmetric feature is one of the most salient dynamical behaviors in the grid-connected inverter, which will bring disaster to normal system operation. In view of time-varying equilibrium points and complex dynamical response of the system, this asymmetrical subharmonic oscillation is difficult to predict through traditional analysis methods. In this paper, a ripple-based current model is proposed to describe the special subharmonic oscillation of single-phase grid-connected DC–AC inverter with the One-Cycle Compensation (OCC) method. Based on the proposed model, the influence of dynamic response and the time-varying equilibrium points on the stability of the system are comprehensively analyzed, and the internal mechanism of asymmetric subharmonic oscillation is revealed. Furthermore, the critical points of the subharmonic oscillation at the beginning and the end of the whole circuit cycle are predicted. Particularly, a global stable operation boundary is identified from the ripple-based analysis to optimize circuit design. Finally, circuit experiments are performed to verify these numerical and theoretical results.

High sensitivity tracking of free-moving targets in time-varying complex backgrounds

Tracking of low-visibility targets in complex backgrounds is an important research field, where existing methods struggle to image low-visibility targets with irregular changes in moving direction and speed. Move contrast imaging can greatly improve the sensitivity of target tracking, which has achieved important applications in the field of X-ray imaging, including high-resolution imaging to microvessels in living rats with the help of contrast agents, <i>in-situ</i> dynamic observation of ion migration and redox reactions during electrochemical reactions, and water refilling along vessels in willow branch without resorting to agents. However, all these applications are limited to imaging with fixed trajectories or monotonous backgrounds. In principle, move contrast imaging is based on the frequency spectral characteristics of the time-domain grayscale signal and is highly sensitive to moving components, which is wavelength-independent. This paper extends the move contrast imaging to the visible light waveband for tracking free-moving targets in time-varying complex backgrounds. To meet the need for tracking imaging of free-moving targets in complex backgrounds, we develop a move contrast imaging (MCI) method based on continuous wavelet transform (CWT) and Hilbert-Huang transform (HHT) with high discriminatory capability for non-stationary signals. Selecting birds in the sky and forest for the tracking imaging, the irregular grayscale changes caused by natural light intensity in the wild field and random swaying of tree leaves result in complex imaging backgrounds. The tracing results of low-visibility free-moving targets show that FT-MCI method, CWT-MCI method and HHT-MCI method can improve the target tracing imaging sensitivity by 179.9 times, 175.8 times and 214.6 times compared with temporal subtraction imaging, respectively. The results of tracking imaging of free-moving targets in complex backgrounds show that compared with the FT-MCI method and CWT-MCI method, the HHT-MCI method can further effectively suppress the influence of background noise on tracking imaging of targets of interest, thus achieving high sensitivity imaging of free-moving targets in time-varying complex backgrounds. Combining the phase diagram of FT-MCI and the imaging parameters, we can further show the motion direction, the motion speed or the distance from the observation point. Therefore, the HHT-MCI imaging method developed in this paper is expected to provide a novel method for tracking free-moving targets in time-varying complex backgrounds.

A novel method for chloride-induced corrosion analysis incorporating consistent ionic diffusivity and concrete resistivity

Corrosion is one of the most serious durability concerns for steel reinforced structures exposed to coastal and offshore environments. In engineering practice, the corrosion process of steel reinforcements is closely influenced by the electrical resistivity of cover material. Misinterpretations of this property would substantially result in unreliable corrosion assessment. In this paper, an electrochemical model coupling ionic transportation with steel corrosion for the chloride-induced corrosion analysis is newly established. Through the proposed method, the explicit evaluation of the material resistivity is avoided. Instead, the resistivity is reflected by the ionic diffusivity under the effect of consistent ionic transportation and electric current during the corrosion process. To solve the highly nonlinear numerical system, a finite element-based solution algorithm is also presented in detail. The proposed model is first compared with the reported method in both uniform and nonuniform corrosion scenarios. While both methods yield identical results in a uniform case, the major advantage of the present model is reflected in the modelling of nonuniform corrosion. By further validating against reported experiments, the proposed method is of demonstrated capability and practical values to assess chloride-induced corrosion under time-variant service conditions and complex reinforcement configurations.

Simultaneous State and Unknown Input Estimation for Complex Networks With Redundant Channels Under Dynamic Event-Triggered Mechanisms.

This article addresses the simultaneous state and unknown input estimation problem for a class of discrete time-varying complex networks (CNs) under redundant channels and dynamic event-triggered mechanisms (ETMs). The redundant channels, modeled by an array of mutually independent Bernoulli distributed stochastic variables, are exploited to enhance transmission reliability. For energy-saving purposes, a dynamic event-triggered transmission scheme is enforced to ensure that every sensor node sends its measurement to the corresponding estimator only when a certain condition holds. The primary objective of the investigation carried out is to construct a recursive estimator for both the state and the unknown input such that certain upper bounds on the estimation error covariances are first guaranteed and then minimized at each time instant in the presence of dynamic event-triggered strategies and redundant channels. By solving two series of recursive difference equations, the desired estimator gains are computed. Finally, an illustrative example is presented to show the usefulness of the developed estimator design method.

Multivariate time-varying complex signal processing framework and its application in rotating machinery rotor-bearing system

Affected by multi-field coupling factors, the vibration response of rotating machinery similar to the hydro-generator unit often exhibits strong time-varying frequency components, which makes rotor fault detection more challenging. The fusion analysis of the vibration signals of multiple bearing sections of the rotor has been proved to be a very effective method for rotor vibration fault diagnosis. However, how to more accurately and synchronously extract the instantaneous features of rotor non-stationary vibration signals associated with multiple sections has been unresolved. To this end, a framework for multivariate time-varying complex signal decomposition of the rotor-bearing system (RBS) is proposed, namely multivariate complex nonlinear chirp mode decomposition. First, the decomposition of multivariate time-varying complex signals is realized by two-stage processing. Second, instantaneous orbit features (IOFs) are obtained through the proposed framework. Finally, a three-dimensional instantaneous orbit map reflecting the time-varying process is constructed through the IOFs. The framework not only realizes the decomposition of the multi-channel time-varying complex signals of the rotor but also simultaneously extracts the instantaneous features of the multi-channel signals. In addition, it also realizes the description of the instantaneous vibration state of the RBS in the non-stationary process (such as startup and shutdown). Simulation experiments show that the framework is superior to other multi-channel signal processing methods in processing time-varying complex signals. The results based on field-measured signals show that the framework can guide the real-time analysis of the signals generated by rotating machinery, which improves the intuition of condition monitoring.

Aberrant dynamic minimal spanning tree parameters within default mode network in patients with autism spectrum disorder.

The altered functional connectivity (FC) level and its temporal characteristics within certain cortical networks, such as the default mode network (DMN), could provide a possible explanatory framework for Autism spectrum disorder (ASD). In the current study, we hypothesized that the topographical organization along with its temporal dynamics of the autistic brain measured by temporal mean and variance of complex network measures, respectively, were significantly altered, which may further explain the autistic symptom severity in patients with ASD. To validate these hypotheses, the precise FCs between DMN regions at each time point were calculated using the resting-state functional magnetic resonance imaging (fMRI) datasets from the Autism Brain Imaging Data Exchange (ABIDE) project. Then, the minimal spanning tree (MST) technique was applied to construct a time-varying complex network of DMN. By analyzing the temporal mean and variance of MST parameters and their relationship with autistic symptom severity, we found that in persons with ASD, the information exchange efficiencies between cortical regions within DMN were significantly lower and more volatile compared with those in typical developing participants. Moreover, these alterations within DMN were closely associated with the autistic symptom severity of the ASD group.

Scintillation of partially coherent light in time-varying complex media.

We present a theory for wave scintillation in the situation of a time-dependent partially coherent source and a time-dependent randomly heterogeneous medium. Our objective is to understand how the scintillation index of the measured intensity depends on the source and medium parameters. We deduce from an asymptotic analysis of the random wave equation a general form of the scintillation index, and we evaluate this in various scaling regimes. The scintillation index is a fundamental quantity that is used to analyze and optimize imaging and communication schemes. Our results are useful to quantify the scintillation index under realistic propagation scenarios and to address such optimization challenges.

Joint state and fault estimation for nonlinear complex networks with mixed time-delays and uncertain inner coupling: non-fragile recursive method

In this paper, the non-fragile joint state and fault estimation problem is investigated for a class of nonlinear time-varying complex networks (NTVCNs) with uncertain inner coupling and mixed time-delays. Compared with the constant inner coupling strength in the existing literature, the inner coupling strength is permitted to vary within certain intervals. A new non-fragile model is adopted to describe the parameter perturbations of the estimator gain matrix which is described by zero-mean multiplicative noises. The attention of this paper is focussed on the design of a locally optimal estimation method, which can estimate both the state and the fault at the same time. Then, by reasonably designing the estimator gain matrix, the minimized upper bound of the state estimation error covariance matrix (SEECM) can be obtained. In addition, the boundedness analysis is taken into account, and a sufficient condition is provided to ensure the boundedness of the upper bound of the SEECM by using the mathematical induction. Lastly, a simulation example is provided to testify the feasibility of the joint state and fault estimation scheme.

Distributed Ellipsoidal Intersection Fusion Estimation for Multi-Sensor Complex Systems.

This paper investigates the problem of distributed ellipsoidal intersection (DEI) fusion estimation for linear time-varying multi-sensor complex systems with unknown input disturbances and measurement data transmission delays. For the problem with external unknown input disturbance signals, a non-informative prior distribution is used to model the problem. A set of independent random variables obeying Bernoulli distribution is also used to describe the situation of measurement data transmission delay caused by network channel congestion, and appropriate buffer areas are added at the link nodes to retrieve the delayed transmission data values. For multi-sensor systems with complex situations, a minimum mean square error (MMSE) local estimator is designed in a Bayesian framework based on the maximum a posteriori (MAP) estimation criterion. In order to deal with the unknown correlations among the local estimators and to select the fusion estimator with lower computational complexity, the fusion estimator is designed using ellipsoidal intersection (EI) fusion technique, and the consistency of the estimator is demonstrated. In this paper, the difference between DEI fusion and distributed covariance intersection (DCI) fusion and centralized fusion estimation is analyzed by a numerical example, and the superiority of the DEI fusion method is demonstrated.

An Arctan-Type Varying-Parameter ZNN for Solving Time-Varying Complex Sylvester Equations in Finite Time

Zeroing neural network (ZNN) is an effective neural solution to time-varying problems, including time-varying complex Sylvester equations. Generally, a ZNN model involves a convergence design parameter (CDP) that influences its convergence rate. In traditional fixed-parameter ZNNs (FP-ZNNs), the CDPs are set to be constant, which is not realistic since the CDPs are actually time-varying in practical hardware environments. By considering this fact, varying-parameter ZNNs (VP-ZNNs) with time-varying CDPs have been researched in the literature. Although these VP-ZNNs have been demonstrated to deliver superior convergence as compared with FP-ZNNs, they have one drawback, that is, their CDPs usually keep increasing with time, meaning that the CDPs tend to be infinity large with time progresses. Evidently, infinity large CDPs are unacceptable in practice. Moreover, computing resources will be wasted by growing the CDPs with time after the VP-ZNNs become convergent. To tackle the above issues, this article, for the first time, proposes an arctan-type VP-ZNN (ATVP-ZNN) with finite-time convergence for solving time-varying complex Sylvester equations. The ATVP-ZNN is able to adjust its CDPs that finally converge to be constant when the ATVP-ZNN becomes convergent in finite time. In theory, the finite-time convergence of the ATVP-ZNN and the upper bound of the CDPs are mathematically analyzed. Numerical studies are comparatively performed with the superior convergence of the ATVP-ZNN substantiated.

State estimation of nonlinear time-varying complex networks with time-varying sensor delay for unknown noise distributions

The state estimation for nonlinear time-varying complex networks is a common problem, especially for the systems with time-varying sensor delay. This paper presents an artificial immune strategy based on particle filtering algorithm to estimate the states. Firstly, considering the nonlinear time-varying complex networks with time-varying sensor delay, this paper gives a theoretical proof that particle filter can be used for the state estimation. On this basis, this paper adopts the artificial immune algorithm to solve the problem which is particle impoverishment in particle algorithm. This paper gives the convergence of the proposed algorithm theoretically. Furthermore, for nonlinear time-varying complex networks with unknown noise distributions, this paper gives an improved algorithm which uses an improved kernel density estimation algorithm which has window width varying with probability density to estimate noise distributions. Simulation results demonstrate the effectiveness of the scheme, especially when nonlinear time-varying complex networks have time-varying sensor delay and noise distributions are unknown.

Dynamic event-triggered resilient state estimation for time-varying complex networks with Markovian switching topologies

This paper addresses a resilient state estimation problem for an array of nonlinear complex networks with switching topologies under the dynamic event-triggered mechanism (ETM). To reduce the unnecessary data delivery, the dynamic ETM is introduced to schedule the data delivery from sensors to estimators. The model of the switched complex networks is established by adopting a Markov chain which is better to reflect the characteristics of practical complex networks. A set of novel estimators is obtained by using the properties of Kronecker product combining with the Lyapunov–Krasovskii method, and some easy-to-check conditions are derived such that the dynamics of state estimation error satisfies the prescribed H∞ performance index. In addition, the parameters of the designed resilient state estimators can be acquired by solving a series of convex optimization problems. In the end, a simulation example is given to demonstrate the validity of the proposed theoretical results in this paper.

Solution] Prepare your video for streaming with Segue

We identify new opportunities in video streaming, involving the joint consideration of offline video chunking and on-line rate adaptation. Due to a video’s complexity varyingover time, certain parts are more likely to cause performanceimpairments during playback with a particular rate adaptationalgorithm. To address such an issue, we propose Segue ,which carefully uses variable-length video segments, and augment specific segments with additional bitrate tracks. The keynovelty of our approach is in making such decisions basedon the video’s time-varying complexity and the expected rateadaptation behavior over time. We propose and implementseveral methods for such adaptation-aware chunking. Ourresults show that Segue substantially reduces rebufferingand quality fluctuations, while maintaining video quality delivered; Segue improves QoE by 9% on average, and by 22%in low-bandwidth conditions. Finally, we view our problemframing as a first step in a new thread on algorithmic anddesign innovation in video streaming, and leave the readerwith several interesting open questions.

Jumping Risk Communities in the Energy Industry: An Empirical Analysis Based on Time-Varying Complex Networks

This paper uses the 5-five-minute high-frequency data of energy-listed companies in China's A-share market to extract the jump of energy stock prices and build a dynamic stock price jump complex network. Then, we analyze the clustering effect of the complex network. The research shows that the energy stock price jump is an important part of stock price volatility, and the complex network of energy stock jump risk has obvious time-varying characteristics. However, the infection problem of stock price jump risks needs specific analysis. China's coal industry has an important influence on the development of China's energy industry. According to the clustering analysis results of the network community, the clustering effect of the network community has time-varying characteristics. After October 2017, the clustering effect of the jumping risk of the coal industry and the new energy industry is obvious. The risk contagion within the new energy industry community is a key point for the development of the new energy industry.