Time-varying Coefficients Research Articles

The [formula omitted]th moment stability of stochastic functional differential equations with mixed time-varying delay and its applications

This paper investigates the pth moment stability with general decay rate for stochastic mixed time-varying delay functional differential equations with semi-Markov switching and Lévy noise (SMDFDEs-SMS-LN). By employing multiple degenerate Lyapunov functionals, the non-negative martingale convergence theorem, and functional Itô’s formula, a criterion for assessing the pth moment stability with general decay rate is derived. Auxiliary functions with multiple time-varying coefficients are introduced to regulate the diffusion operator, which enhances the system’s adaptability to high-order nonlinear coefficients. These conditions are applied to a stochastic mixed-delay neural network with semi-Markov switching and Lévy noise (SMDNN-SMS-LN) and a scalar non-autonomous stochastic mixed time-varying delay functional differential equations with semi-Markov switching and Lévy noise system with non-global Lipschitz conditions. Using vertex method and linear matrix inequalities, the pth moment stability criteria with general decay rate for the above systems are respectively derived. Finally, the effectiveness of the obtained results is validated through two numerical examples.

New order-dependent conditions to control a class of nonlinear real-order systems

In many application points of interest, controlling the responses of real-world applications that vary with time seems quite challenging and puzzling. A linear time-varying state feedback controller is widely known and can be useful to estimate bounds to the state control matrix in the design of many control systems. The issue of initialization of real-order control system enhancement remains a challenging issue in the systems analysis subject to random initial-time placed on a real number line. We address a new design of a class of real-order control systems that consists of separated linear and nonlinear terms affected by input functions to be controlled with an implemented time-varying linear state feedback controller. We utilize the fractional comparison method and under Lipschitz nonlinearity with a constant bounding matrix of time-varying coefficients of control systems to address new order-dependent conditions that provide local and global stabilization to controlled systems. Applications of results that include practical real-order single-machine-infinite-bus power systems have been illustrated to control the responses by the utility of theoretical conditions examined along with validation of numerical simulations. It is shown that the proposed controller is practically convenient and demonstrates the efficiency of measuring the performances of control systems.

Climate risk perception and oil financialization in China: Evidence from a time-varying Granger model

Financialization has introduced a new dimension to the oil market by incorporating various financial instruments and market participants. This study thus firstly measures the oil financialization by using daily data on Shanghai crude oil futures and Shanghai composite index. The results identify a lower level of oil financialization in China, implying that the oil market is independent on the stock market. Moreover, the exogenous events volatile the degree of oil financialization. We then estimate the impact of climate risk perception on oil financialization using a time-varying Granger model. Novel evidence finds negative and significant time-varying coefficients of climate risk perception on oil financialization, indicating that the effect of climate risk perception is non-constant over time. The empirical results reinforce the implications of the bounded rationality decision and signal theory.

Hyaluronic acid-based hydrogels as codelivery systems: The effect of intermolecular interactions investigated by HR-MAS and solid-state NMR Spectroscopy

Hydrogels based on hyaluronic acid and agarose-carbomer, due to their peculiar 3D architecture and biocompatibility, are promising candidates for pharmaceutical strategies based on the codelivery of drugs targeting different diseases. The successful development of these applications requires a precise understanding of drug-drug interactions and their effects on transport and release mechanisms. In this study, such an investigation is carried out on hydrogels loaded with ethosuximide and sodium salicylate at different concentrations. Intermolecular interactions and transport properties are characterized by means of High Resolution Magic Angle Spinning and solid-state Magic Angle Spinning NMR Spectroscopy.At variance with our previous findings on single-drug formulations, the two drugs exhibit closely similar diffusion patterns when co-loaded in the HA-based hydrogels, plausibly due to drug-drug intermolecular interactions. At the highest drug concentrations, where superdiffusion comes into play, we find a fraction of molecules with time-varying diffusion coefficients. A trapping-release mechanism is proposed to explain this observation, which also accounts for the role of drug-hydrogel interactions in drug diffusion motion. The effects of drug-drug interactions on release profiles are finally assessed by means of in vitro release experiments.

Signed integer-valued autoregressive model with time-varying coefficients

An integer time series is a sequence of discrete integers arranged chronologically to represent a specific event or phenomenon. It may include negative integers, and its behavior can be influenced by variations in covariates. To address these complexities, this paper introduces a p-order signed integer-valued autoregressive model with time-varying coefficients. Considering the presence of zero-inflated phenomena, we specifically employ the zero-inflated Skellam distribution. To estimate the unknown parameters and nonparametric functions in our proposed model, we adopt a two-step iterative process that combines local linear estimation with either conditional least squares or conditional maximum likelihood. Through a comprehensive simulation study, we evaluate the performance of our estimation method and demonstrate the usefulness of the proposed model using a real-life dataset.

Association of obesity and metabolic syndrome with incident primary open angle glaucoma in the UK Biobank.

We sought to investigate the association between obesity, metabolic syndrome, and metabolic health with incident primary open-angle glaucoma (POAG). We included 103 249 UK Biobank participants without previously diagnosed glaucoma or glaucoma-related procedures at enrolment. The primary outcome was POAG identified from diagnostic coding via linked hospital inpatient and primary care data. We used multivariable Cox regression to evaluate the association of body mass index (BMI), and the interaction with metabolic syndrome (MetS) and a novel definition of metabolic health status with incident POAG. BMI was modelled as a time-varying coefficient. Multivariable analysis was adjusted for age, sex, ethnicity, intraocular pressure, spherical equivalent, polygenic risk score and stratified by the presence of primary care data. There were 647 events of incident POAG over 464 117 580 person-years and a mean follow-up of 12.6 years. At baseline (time = 0), each one unit increase in BMI was associated with a 9% lower hazard of incident glaucoma (HR 0.91, CI 0.86-0.97, p = 0.0066). Further, compared to a normal BMI range of 18.5-24 kg/m2, a BMI ≥30 kg/m2 was associated with a 65% relative hazard reduction (HR 0.35, CI 0.16-0.80, p = 0.012). There was no significant interaction between BMI and metabolic syndrome or metabolic health (all p > 0.05). The effect of BMI on the risk of incident POAG varied with time. Higher BMI was associated with a decreased risk of incident POAG in this large prospective cohort. There was no significant association with systemic metabolic health.

Bootstrap Inference for Linear Time-Varying Coefficient Models in Locally Stationary Time Series

Time-varying coefficient models can capture evolving relationships. However, constructing asymptotic confidence bands for coefficient curves in these models is challenging due to slow convergence rates and the presence of various nuisance parameters. A residual-based sieve bootstrap method has recently been proposed to address these issues. While it successfully produces confidence bands with accurate empirical coverage, its applicability is restricted to strictly stationary processes. We introduce a new bootstrap scheme, the local blockwise wild bootstrap (LBWB), that allows for locally stationary processes. The LBWB can replicate the distribution of the parameter estimates while automatically accounting for nuisance parameters. An extensive simulation study reveals the superior performance of the LBWB compared to various benchmark approaches. It also shows the potential applicability of the LBWB in broader scenarios, including time-varying cointegrating models. We then examine herding effects in the Chinese renewable energy market using the proposed methods. Our findings strongly support the presence of herding behaviors before 2016, aligning with earlier studies. However, contrary to previous research, we find no significant evidence of herding between around 2018 and 2021. Supplementary materials for this article are available online.

Fixed-time neurodynamic optimization approach with time-varying coefficients to variational inequality problems and applications

The article presents a novel fixed-time (FT) neurodynamic optimization approach featuring time-varying coefficients, tailored for variational inequality problems (VIPs). This method exhibits noteworthy properties, including FT convergence from any initial point, accelerated by the strategic selection of time-varying coefficients. Detailed upper bounds on the settling time for the time-varying neurodynamic approach are provided. Additionally, the article delves into the robustness of the neurodynamic approach against bounded noise disturbances. Three implementation ways by numerical discretization, analog circuits, and field-programmable gate array (FPGA) of the proposed FT neurodynamic optimization approach are demonstrated. Finally, two applications on Nash equilibrium seeking problems and image recovery are conducted to validate the practicability and superiority of the proposed time-varying neurodynamic approach.

Response-spectrum-based design method for gain-scheduling control of active base-isolated structures with nonlinear viscous dampers

The response-spectrum-based design method has been developed for linear active base-isolation to eliminate numerical simulations and trial-and-error testing. On the other hand, gain-scheduling is an effective control method for nonlinear systems and provides a way to apply linear system design methods. In this paper, we extend the response-spectrum-based design method to incorporate the gain-scheduling control strategy for active base-isolated structures with nonlinear viscous dampers (NVDs), aiming to effectively control these structures and simplify the design process. The gain scheduling of the controller is achieved based on the linear time-varying viscous coefficient of NVDs. The active base-isolated structure with NVDs is described as a pseudo linear parameter varying model. The stability of the system is proven and analyzed using a common Lyapunov function. Moreover, this paper proposes a control-force spectrum for estimating the maximum required control force without numerical simulations. Numerical examples are used to validate the effectiveness of the proposed spectra. Finally, we also detail the design process and verify the design method through a design example. The results show that this method directly determines the design parameters from the spectra and satisfies the design criteria of response and control force without numerical simulations, offering a promising design approach for active control of buildings with nonlinear energy dissipating devices.

Cox Regression Model with Time-varying Coefficients Applied to Survival Estimation of Heart Failure

This study focuses on survival analysis of heart failure patients. All the patients, who are over 40 years old, have NYHA classes III or IV stages of heart failure. Cox regression model with time-varying coefficients is applied to deal with these data, which is updated in 2015. Kaplan Meier curves are plotted to screen out categorical variables that violates PH assumptions. As for continuous variables violating PH assumption, Schoenfeld residual plot is drawn to realize time stratification. Therefore, for each time period, cox proportional hazard model can be fit to estimate mortality and find out the key factors promoting or inhibiting death event of heart failure. As a result, age, blood pressure, serum creatinine, and ejection fraction are the significant features of hazard of death led by heart failure, which is validated by Wald test, Likelihood ratio test, and Log-rank test. Discrimination ability is also characterized by ROC curves and AUC finally.

Fuel constrained sustainable water-gas-heat-power generation expansion planning of isolated microgrid

On account of slowly reducing of fossil-fuel, profitable utilization of available fossil-fuel to produce electric power is a major concern for electric power producers. Here, short-period fuel constrained power, heat, natural gas and clean water augmentation planning (FCPHGWAP) of isolated microgrid considering carbon capture is presented. FCPHGWAP finds out the most economical and consistent augmentation plan to meet the prophesied power, heat, natural gas and clean water demand over a short-term time horizon whilst satisfying several technical as well as societal constraints. An archetypical system having extant diesel generators (DGs), PVT panel, wind turbine generator (WG), micro-cogeneration unit (MCU), battery energy storage system (BESS), plug-in electric vehicles (PEVs), thermal energy storage system, natural gas storage unit, carbon capture unit (CCU), electrolyzer, hydrogen storage unit and aspirant PVT panel, WGs, MCUs and PEVs is taken into consideration. Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients and grey wolf optimization are used to solve this FCPHGWAP problem. It is observed that net present value having fuel constraints is less than that of without fuel constraints.

Viscoelastic study of cement and emulsified asphalt mortar under temperature variations based on a novel variable-order fractional Maxwell model

Cement and emulsified asphalt (CA) mortar is a critical viscoelastic component of ballastless track systems, exhibiting pronounced temperature sensitivity. However, accurately predicting the mechanical responses of CA mortar due to temperature fluctuations remains a challenge. This work proposes a novel variable-order fractional Maxwell (VOFM) model to investigate the viscoelastic properties of CA mortar under temperature variations. The VOFM model is numerically solved using a time-varying coefficient ladder (TVL) model. The VOFM model is validated by comparing with both numerical integration methods and experimental data. The basic properties of the VOFM model, the viscoelastic responses of CA mortar under varying temperatures and alternating stress are systematically investigated. The results show that the mechanical properties of VOFM model agree with the FM model when time-dependent parameters of the VOFM model correspond to that of the FM model. Moreover, high temperatures have a promoting effect on the deformation of CA mortar under both alternating and constant stress conditions. The peak amplitude of CA mortar strain under practical varying temperature and stress loading correlates with the trend of temperature changes.

Adaptive Fault-Tolerant Control of Mobile Robots with Fractional-Order Exponential Super-Twisting Sliding Mode

Industrial mobile robots easily experience actuator loss of some effectiveness and additive bias faults due to the working scenarios, resulting in unexpected performance degradation. This article proposes a novel adaptive fault-tolerant control (FTC) strategy for nonholonomic mobile robot systems subject to simultaneous actuator lock-in-place (LIP) and partial loss-of-effectiveness (LOE) faults. First, a nominal fractional-order sliding mode controller based on the designed exponential super-twisting reaching law is investigated to reduce the reaching phase time and eliminate the chattering. To address the time-varying LIP faults and uncertainties, a novel barrier function (BF)-based gain is explored to assist the super-twisting law. An estimator is designed to estimate the lower bound of the time-varying partial LOE fault coefficients, thus without requiring the boundary information of faults that is commonly requested in traditional FTC schemes. Combined with the nominal controller clubbed with BF and estimator-based LOE fault compensation term, the fault-tolerant controller is finally constructed. The proposed FTC scheme achieves fast convergence and the sliding variables can be confined in a predetermined neighborhood of the sliding manifold under actuator faults. The results show that the proposed controller has superior tracking performance under faulty conditions compared with other state-of-the-art adaptive FTC approaches.

Event-triggered bipartite consensus to heterogeneous multiagent systems under DoS attacks: A fully distributed method

This paper studies event-triggered bipartite output consensus problem of heterogeneous multiagent systems under denial-of-service (DoS) attacks. A novel dynamic event-triggered scheme (DETS) is proposed, which, by introducing an extra dynamic function with time-varying coefficients into triggering conditions, can guarantee strictly positive minimum inter-event intervals no matter DoS attacks occur or not. An event-based resilient compensator with adaptive coupling coefficients is then designed to estimate leader's state, and a hybrid model with jump dynamics is constructed that can incorporate the estimation error, DETS, and DoS attacks, and is useful for convergence analysis. Then, a fully distributed observer-based control protocol is designed to regulate the bipartite output consensus. The main advantages of the proposed method include: 1) global information is not needed to implement the event-based control protocol; 2) strictly positive inter-event intervals are guaranteed even under DoS attacks. Finally, a numerical example is presented to testify the main results.

Nonlinear performance of aerospace face gear-planetary gear transmission system under multi-factors including the effect of temperature

Face gear-planetary gear transmission system combines the advantages of both planetary and face gears. And has enormous advantages in the aerospace field. The meshing of gear teeth generates heat through friction, resulting in thermal deformation and affects nonlinear dynamic characteristics. Exploring the temperature effect on the nonlinear dynamics of the transmission system of face gear-planetary gear is of great significance. Considering the temperature variations effect and time-varying meshing stiffness, tooth side clearance as well as meshing error, a nonlinear dynamic model of the face gear-planetary gear system is established. The nonlinear dynamic characteristics of the system were described by maximum Lyapunov exponent diagram, phase diagram, bifurcation diagram et al. The findings indicate that the effect of temperature variations on the gear system is related to the values of the time-varying mesh stiffness coefficient and mesh damping ratio. When the mesh damping ratio is small, the influence of temperature changes on the bifurcation characteristics of the system is more obvious. As the time-varying mesh stiffness value increases, the temperature rise gradually stabilizes in the range of 15°C to 42°C; Under certain temperature changes, as the meshing damping ratio and time-varying meshing stiffness coefficient increase, the face gear planetary gear system gradually tends to stabilize.

Analytical solution for the skip trajectory of hypersonic vehicles with time-varying aerodynamic coefficients

Abstract An analytical method is proposed for calculating the altitude and flight path angle of hypersonic vehicles’ skip trajectories under the influence of arbitrary time-varying aerodynamic coefficients. First, a longitudinal plane dynamics equation is established based on the inertial Earth model and the specific kinetic energy is introduced as independent variable to simplify the derivation. Subsequently, the solution is obtained through the perturbation method, where the zero-order analytical solution is based on existing literature, and the first-order linear time-varying system is decoupled through matrix linear transformation. Finally, by comparing numerical integration and analytical solutions with constant aerodynamic coefficients, the precision of this method in dealing with time-varying aerodynamic coefficients is verified, demonstrating its advantages over analytical solutions with constant aerodynamic coefficients.

A Flexible Adaptive Lasso Cox Frailty Model Based on the Full Likelihood.

In this work, a method to regularize Cox frailty models is proposed that accommodates time-varying covariates and time-varying coefficients and is based on the full likelihood instead of the partial likelihood. A particular advantage of this framework is that the baseline hazard can be explicitly modeled in a smooth, semiparametric way, for example, via P-splines. Regularization for variable selection is performed via a lasso penalty and via group lasso for categorical variables while a second penalty regularizes wiggliness of smooth estimates of time-varying coefficients and the baseline hazard. Additionally, adaptive weights are included to stabilize the estimation. The method is implemented in the R function coxlasso, which is now integrated into the package PenCoxFrail, and will be compared to other packages for regularized Coxregression.

Tests for time-varying coefficient spatial autoregressive panel data model with fixed effects

Tests for time-varying coefficient spatial autoregressive panel data model with fixed effects

Can stronger governance and institutional quality drive growth through inward foreign direct investments in BRICS nations?

Background: This research focusses on the significance of institutional quality (INSQ) and governance in shaping the relationship between foreign direct investments (FDI) and growth. It specifically examines the BRICS nations (Brazil, Russia, India, China and South Africa) because of their economic significance in the global economy.Aim: This study aims to analyse the impact of INSQ and governance on sustainable growth, with a particular focus on its effects through the channels of FDI in the BRICS countries.Setting: Annual panel data from the Organisation for Economic Cooperation and Development (OECD) statistics, the United Nations Conference on Trade and Development (UNCTAD) statistics and the World Bank indicators spanning two decades (2000–2022) are used to analyse BRICS nations.Methods: The research study employed the Bayesian time-varying coefficient vector autoregression (BTVC-VAR) model to achieve the objective of the study.Results: The findings indicate that there is no long-term relationship between FDI, INSQ and economic growth in the BRICS countries. However, there is a noticeable co-movement among these variables in the short run.Conclusion: Given the obtained results, the policymakers should prioritise efforts to strengthen institutional capacity in the short term while focussing on the Sustainable Development Goals (SDG-8 and SDG-16).Contribution: The existing studies have assumed static economic, social and political conditions, potentially failing to accurately depict the complexities of an actual economy. This study offers methodological innovation by employing Bayesian time-varying coefficient vector autoregression (BTVC-VAR), enabling coefficients to adapt to evolving economic conditions over time. This effectively captures the dynamic nature of variables and provides reliable estimates.

Estimation of temperature field for blade battery based on frequency domain heat generation model

Sinusoidal pulse excitation is widely used in the pre-heating process of commercial lithium batteries due to its fast heating rate and almost no capacity degradation under long cycles. For the emerging blade cell, its temperature distribution gradient is significant under the Alternating Current (AC) pulse, and it isn't easy to realize the all-around monitoring of its temperature by the lumped parameter model. So this paper proposes a Joint Kalman Filter (JKF) based on a distributed heat source, and it can realize the joint estimation of tab temperature and the time-varying thermal coefficient. Firstly, the impedance at different temperatures is analyzed by Electrochemical Impedance Spectroscopy (EIS), and the distributed heat model in the frequency domain is constructed. Then, the thermal resistance grid is used to divide the nodes, and the heat transfer equation is formed by using the multi-point temperatures and time-varying heat coefficients to estimate the temperature field. Simulation results show that the method can track the temperature field of the blade cell and realize the temperature estimation with a maximum error of less than 1.5 ℃ in a wide domain range, which is expected to realize the online temperature management and has great application potential in the design of the thermal management system.