Lag Parameter Research Articles

A novel optimal temporal lag air2stream model using dynamic time series matching

ABSTRACT In order to solve the problem that the current air2stream model does not consider the lag change of water temperature, this paper proposes a new method that combines empirical mode decomposition, power spectral density and dynamic time warping algorithm to determine the optimal time lag parameters, and then constructs the air2stream with the optimal time lag (a2swtl). Finally, the effectiveness and stability of the new method and model were verified based on the measured data from 2020-2022 at three hydrological stations in the middle and lower reaches of the Yangtze River that are close to natural river conditions. The results show that the new method overcomes the problem of low accuracy in determining the time lag of the existing correlation coefficient method. Compared with the original model, a2swtl has better stability without introducing additional observation data, and the average prediction accuracy is improved by about 6.33%. 8.36% and 15.4%.

Short-term power load forecasting based on hybrid feature extraction and parallel BiLSTM network

Electricity is a vital resource for societal and economic activities. Accurate electricity load forecasting can effectively reduce costs and enhance energy efficiency. Nevertheless, current research has limitations in effectively extracting and utilizing load forecasting information. This encompasses both undiscovered latent features and models that have not effectively captured long-term dependencies. To address these issues, this paper proposes a novel network architecture, which utilizes a hybrid feature extraction strategy and integrates machine learning methods with deep learning techniques. This method employs a hybrid feature extraction strategy composed of gradient-boosted regression trees, Fisher Score, and mutual information to achieve comprehensive feature representation. The network architecture utilizes a multi-head convolutional neural network and a bi-directional long short-term memory (BiLSTM) with lag parameters. This architecture can simultaneously learn multiple levels and aspects of features, further enhancing the BiLSTM model’s ability to capture long-term dependencies. Through multiple experiments conducted on datasets from Maine, Singapore, New South Wales, Australia, and European countries, our method outperformed the comparison models in three out of the five datasets.

Forecasting the output of high-tech industry in China: A novel nonlinear grey time-delay multivariable model with variable lag parameters

Under the rapidly developing economy in China, accurate forecasting holds vital significance for policymaking and operational planning within the high-tech industry. However, the influencing factors affecting the output, accompanied by the time-delay effect, could be nonlinear, and uncertain. Thereby, this paper proposes a new nonlinear grey multivariable model with time-varying lag parameters. To be specific, the newly designed time delay function and power exponent are introduced, which can significantly enhance the adaptability and flexibility of the proposed method. The Grey Wolf Optimization algorithm is utilized to calculate the dynamic time lag parameters and power exponent to improve the prediction reliability. Furthermore, this new approach is applied to predict the high-tech industry’s output in China, Shanghai Municipality, and the Eastern Region, with due consideration given to the time-delay effect between input factors and outputs. To assess its efficacy, some leading models are selected for comparison to the proposed model. Furthermore, the utilization of Monte-Carlo simulation, the Probability Density Analysis, and the simulations are used to demonstrate the robustness and stability of this new method. The findings show that the proposed model is a feasible and applicable approach for prediction, exhibiting outstanding accuracy.

Synchronization transitions in adaptive Kuramoto-Sakaguchi oscillators with higher-order interactions.

Coupled oscillators models help us in understanding the origin of synchronization phenomenon prevalent in both natural and artificial systems. Here, we study the coupled Kuramoto oscillator model having phase lag and adaptation in higher-order interactions. We find that the type of transition to synchronization changes from the first-order to second-order through tiered synchronization depending on the adaptation parameters. Phase lag enables this transition at a lower exponent of the adaptation parameters. Moreover, an interplay between the adaptation and phase lag parameters eliminates tiered synchronization, facilitating a direct transition from the first to second-order. In the thermodynamic limit, the Ott-Antonsen approach accurately describes all stationary and (un)stable states, with analytical results matching those obtained from numerical simulations for finite system sizes.

Marshall–Olkin Bivariate Weibull Model with Modified Singularity (MOBW-μ): A Study of Its Properties and Correlation Structure

We propose the “Marshall–Olkin Bivariate Weibull Model with Modified Singularity MOBW-μ”, which focuses on bivariate distributions essential for reliability and survival analyses. Distributions such as the Marshall–Olkin bivariate exponential (MOBE) and the Marshall–Olkin bivariate Weibull (MOBW) are discussed. The MOBW-μ model is introduced, which incorporates a lag parameter μ in the singular part, and probabilistic properties such as the joint survival function, marginal density functions, and the bivariate hazard rate function are explored. In addition, aspects such as the correlation structure and survival copulation are addressed and we show that the correlation of the MOBW-μ is always lower than that of its copula, regardless of the parameters. The latter result implies that the MOBW-μ does not have the Lancaster’s phenomenon that explains that any nonlinear transformation of variables decreases the correlation in absolute value. The article concludes by presenting a robust theoretical framework applicable to various disciplines.

Multi-membranes-based pumping flow of nanofluids: Application in thermofluidic system

Microscale heat transfer plays a crucial role in various smart thermal devices such as microchannel heat sinks, micro heat pipes, and thermosyphons, as well as in bio microfluidics applications where biological fluids are being transported via natural pumping mechanisms. Inspired by the importance of microscale heat transfer, a biothermal-based pumping flow model integrating membrane-based pumping mechanism and nanofluids is developed mathematically. A Ghost-Valve model for membrane pumping is adopted based on Yasser’s model. Conservation principles for mass, momentum, energy, and nanoparticle volume fraction are considered for the mathematical formulation. The main objectives of this bio-thermo-physical model are to provide insights into pressure gradients, velocity distributions, volumetric flow rates, shear stress, streamlines, isotherms, nanoparticle volume fraction, Nusselt number, and Sherwood number under the effects of key parameters. Additionally, entropy generation analysis is presented with variation of thermal Grashof number, basic density Grashof number, Brinkmann number, and relative temperature on system performance. An analytical approach has been employed to derive the solutions under low Reynolds number approximation, and results are computationally analyzed using MATLAB code. Our findings highlight the effectiveness of membrane-based pumps in controlling microfluidic flow and heat transfer within microchannels and it is reported that velocity field enhances in magnitude with increasing the time phase lag parameter. This model will also present a benchmark for various future models to be developed for the nanofluids flow with various combinations of nanoparticles and base fluids through various flow regimes to be utilized for various purposes of the thermal systems/energy systems and offering valuable insights for optimizing thermal-based pumping systems in microscale applications.

Modeling Spatial Regimes With Smooth Transitions

In this paper, we introduce a new way of modeling spatial regimes using smooth transitions. We propose an autoregressive spatial lag model where a logistic function captures structural variation in the spatial lag parameter. In the regime-switching spatial lag model with smooth transitions, the effect of the spatial neighbors depends on the transition variable that governs the regime switch. An LM test for detecting nonlinearity is derived, and a simulation study, where the properties of the test are investigated, is conducted. The test shows good power in relatively small samples with moderate deviation from linearity. An empirical application is included, where data on median house prices from the Boston area is used to explore the spatial dependence between census tracts. A smooth shift between regimes governed by the variable property tax rate is found. The smooth spatial lag model performs slightly better than the regular spatial lag model in terms of model fit and capturing spatial dependence.

Effect of thermal diffusivity on Rayleigh waves in human tissue

This work examines the nature of human tissue under the context of dual phase lag thermoelastic diffusion theory using the principle of propagation of Rayleigh wave along the stress free, thermally insulated/isothermal, and impermeable/isoconcentrated conditions. The governing equations for the tissue are formed by considering the third-order approximation of Taylor’s expansion for the modified Fourier’s law of heat conduction and modified Fick’s law of mass diffusion in terms of phase lag parameters. The irrational secular equations of Rayleigh waves are derived, which are then rationalized to extract their complex roots. The phase speeds and the attenuation coefficients are evaluated from these complex roots. The variation of these phase speeds and attenuation coefficients are plotted graphically against angular frequency for two significant modes. The behavioral patterns of displacement components, temperature, and concentration fields are computed and are expressed graphically. The paths of the particle motion are represented graphically. Some results of published articles are recovered from the present formulation to validate our results.

Multi-time scale dynamic operation optimization method for industrial park electricity-heat-gas integrated energy system considering demand elasticity

This study proposes a multi-time scale dynamic optimization (MSDO) method for ultra-short-term scheduling in industrial electricity-heat-gas integrated energy systems. The aim is to enhance the feasibility of scheduling schemes and to utilize flexibility potential. The MSDO method considers equipment response differences and demand fulfillment time disparities. The dynamic aspect is reflected in the supply-demand matching within the scheduling time window. A single-layer model with unified temporal granularity is constructed based on lag parameter identification. The study proposes various forms of energy demand elasticity, including steam networks and cumulative demand, and investigates the optimal minimum temporal granularity for scheduling. Case results demonstrate that the MSDO method effectively characterizes the multi-energy time lag of equipment. Compared with methods disregarding lag parameters, the MSDO method enhances total profit by ¥ 120711.42 and improves operation stability by 2.96%. The MSDO method outperforms baseline methods in cost and stability, accommodating a 160% increase in renewable energy growth. Under different elasticity spaces, the profit of the cumulative demand method exceeds that of the average method. The time lag of different equipment impacts scheduling profits. An optimal choice exists for the minimum temporal granularity to strike a balance between computational efficiency, economy, and feasibility.

A study of periodic solutions of several types of nonlinear models in biomathematics

Abstract Biomathematics is a cross-discipline formed by the interpenetration of mathematics with life sciences, biology, and other disciplines, and biomathematical models provide an effective tool for solving problems in the above application areas. Our aim in this paper is to combine mathematical analytical tools and numerical simulation methods to investigate the existence and steady state of periodic solutions in different nonlinear models. Time lags with both discrete and distributed characteristics are introduced into the Lotka-Volterra predator-feeder system, and based on the discussion of the central manifold theorem and canonical type theory, it is proved that the branching periodic solution exists when the discrete time lag parameter τ > τ 0. In the SEIRS infectious disease model with nonlinear incidence term and vertical transmission, the global stability of the disease-free equilibrium point and the local asymptotic stability of the endemic equilibrium point are analyzed through the computation and discussion of the fundamental regeneration number R 0 (p, q). A class of convergence-growth models with nonlinear sensitivity functions is studied, and the global boundedness of classical solutions and their conditions are demonstrated based on global dynamics. A mathematical generalization of the muscular vascular model is made by introducing a centralized parameter, the relationship between periodic solutions and chaotic phenomena is explored utilizing a systematic equivalence transformation, and the equation of the homoscedastic orbitals is deduced to be z 2 = x 2 ( A - 1 2 x 2 ) {z^2} = {x^2}\left( {A - {1 \over 2}{x^2}} \right) .

Implementation of a Robust PMAE Algorithm to Limit Large Gain Variation in a Fuel-Cell Integrated EV Charging Architecture

This paper proposes an adaptive error convergence technique to improve the charging dynamics of an electric vehicle (EV) charging system in a grid-integrated proton-exchange membrane fuel cell (PEMFC) architecture. The fuel cell is most often characterized by its slow dynamics during multiple load transients and preferably operated as an auxiliary energy source in EV charging architecture. The delayed response affects the overall charging dynamics with further controller phase lag and non-linear system parameters to induce a significant convergence error during reference command tracking. To effectively control multi-source integrated charging dynamics in an EV charging architecture, a projection-modified adaptive estimation (PMAE) algorithm is proposed in this paper. The control algorithm establishes a variable gain adaptation within a bounded error contour and updates the controller gains based on the tracking error dynamics gradient. The fuel cell, supporting other energy resources, provides a stronger charging resiliency with a duty-modified control technique at the EV charging station. To validate the effectiveness of the controller implementation, a grid-integrated multi-objective EV charging prototype is designed and developed for grid-to-vehicle (G2V) and vehicle-to-grid (V2G) at variable operating conditions.

Novel grey dynamic trend relational analysis models with different types of accumulation delay effects for time-delay systems

In order to analyze the unknown accumulation delay relationship between the given inputs and outputs, one basic model and four extended models are proposed to simulate the different accumulation delay processes. The basic model called grey dynamic trend relational analysis model calculates the similarity between two sequences based on their trends. On the basis of the basic model, four extended models are developed to simulate the different accumulation delay effects. Two types of the accumulation delay effects, called driven-type and yielded-type accumulation delay effects, are put forward for different time-delay relationships. Each type of the accumulation delay effects designs two weight distributions for analyzing the different time-delay processes. Two optimization models based on grid-search are given to determine the lag parameters in the four extended models. The pseudo-codes of intelligent algorithms are provided for the optimization problems. To explain how it can be employed in practice, the new approaches for the time-delay systems are applied to analyze the relationships between the air pollution and its driven factors and results in the Yangtze River Economic Belt. The comparing results show that the novel models can handle the time-delay systems with accumulation delay effects and can determine the delay processes.

Deformation transients of confined droplets within interacting electric and magnetic field environment

A theoretical exploration and an analytical model for the electro-magneto-hydrodynamics (EMHD) of leaky dielectric liquid droplets suspended in an immiscible confined fluid domain have been presented. The analytical solution for the system, under small deformation approximation in the creeping flow regime, has been put forward. The study of droplet deformation suggests that its temporal evolution is exponential and dependent on the electric and magnetic field interaction. Furthermore, the direction of the applied magnetic field relative to the electric field determines whether the magnetic force opposes or facilitates the interfacial net electrical force caused by the electric field. Validation of the proposed model at the asymptotic limits of vanishing magnetic field shows that the model accurately reduces to the case of the transient electrohydrodynamic model. We also propose a magnetic discriminating function (ϕM) to quantify the steady-state droplet deformation in the presence of interacting electric and magnetic fields. The change of droplets from a spherical shape to prolate and oblate spheroids corresponds to ϕM> 0 and < 0 regimes, respectively. It is shown that a substantial augmentation in the deformation parameter and the associated EMHD circulation within and around the droplet is achieved when aided by a low-magnitude magnetic field. The analysis also reveals the deformation lag and specific critical parameters that aid or suppress this lag behavior, discussed in terms of relevant non-dimensional parameters.

The Effect of Nonlocal Scale Value and Phase Lags on Thermoelastic Waves in a Multilayered LEMV/CFRP Composite Cylinder

In this study, the effect of nonlocal scale value and two phase lags on the free vibration of generalized thermoelastic multilayered LEMV (Linear Elastic Material with Voids)/CFRP (Carbon Fiber Reinforced Polymer) composite cylinder is studied using nonlocal form of linear theory of elasticity. The governing equation of motion is established in longitudinal axis and variable separation model is used to transform the governing equations into a system of differential equations. To investigate vibration analysis from frequency equations, the stress free boundary conditions are adopted at the inner, outer and interface boundaries. The graphical representation of the numerically calculated results for frequency shift, natural frequency, and thermoelastic damping is presented. A special care has been taken to inspect the effect of nonlocal parameter on the aforementioned quantities. The results suggest that the nonlocal scale and the phase lag parameters alter the vibration characteristics of composite cylinders significantly.

An MA-MRR model for transaction-level analysis of high-frequency trading processes

The transaction-level analysis of security price changes by Madhavan, Richardson, and Roomans (1997, hereafter MRR) is a useful framework for financial analysis. The first-order Markov property of trading indicator variables is a critical assumption in the MRR model, which contradicts the information lag empirically demonstrated in high-frequency trading processes. In this study, a nonparametric test is employed, which shows that the Markov property of the trading indicator variables is rejected on most trading days. Based on the spread decomposed structure, an MA-MRR model was proposed with a moving average structure adopted to absorb the information lag as an extension. The empirical results show that the information lag plays an important role in measuring the adverse selection risk parameter and that the difference in this parameter between the original and the extension is significant. Furthermore, our analysis suggests that the information lag parameter is a useful measure of the average speed at which information is incorporated into prices.

Uncertainties in Markov State Models of Small Proteins.

Markov state models are widely used to describe and analyze protein dynamics based on molecular dynamics simulations, specifically to extract functionally relevant characteristic time scales and motions. Particularly for larger biomolecules such as proteins, however, insufficient sampling is a notorious concern and often the source of large uncertainties that are difficult to quantify. Furthermore, there are several other sources of uncertainty, such as choice of the number of Markov states and lag time, choice and parameters of dimension reduction preprocessing step, and uncertainty due to the limited number of observed transitions; the latter is often estimated via a Bayesian approach. Here, we quantified and ranked all of these uncertainties for four small globular test proteins. We found that the largest uncertainty is due to insufficient sampling and initially increases with the total trajectory length T up to a critical tipping point, after which it decreases as , thus providing guidelines for how much sampling is required for given accuracy. We also found that single long trajectories yielded better sampling accuracy than many shorter trajectories starting from the same structure. In comparison, the remaining sources of the above uncertainties are generally smaller by a factor of about 5, rendering them less of a concern but certainly not negligible. Importantly, the Bayes uncertainty, commonly used as the only uncertainty estimate, captures only a relatively small part of the true uncertainty, which is thus often drastically underestimated.

Optimization of Short-Lag Spatial Coherence Imaging Method

The computing performance optimization of the Short-Lag Spatial Coherence (SLSC) method applied to ultrasound data processing is presented. The method is based on the theory that signals from adjacent receivers are correlated, drawing on a simplified conclusion of the van Cittert-Zernike theorem. It has been proven that it can be successfully used in ultrasound data reconstruction with despeckling. Former works have shown that the SLSC method in its original form has two main drawbacks: time-consuming processing and low contrast in the area near the transceivers. In this study, we introduce a method that allows to overcome both of these drawbacks. The presented approach removes the dependency on distance (the “lag” parameter value) between signals used to calculate correlations. The approach has been tested by comparing results obtained with the original SLSC algorithm on data acquired from tissue phantoms. The modified method proposed here leads to constant complexity, thus execution time is independent of the lag parameter value, instead of the linear complexity. The presented approach increases computation speed over 10 times in comparison to the base SLSC algorithm for a typical lag parameter value. The approach also improves the output image quality in shallow areas and does not decrease quality in deeper areas.

Effect of Nonlocal Elasticity and Phase Lags on the Magneto Thermoelastic Waves in a Composite Cylinder with Hall Current

In this study, the effect of nonlocal scale value and two phases lags on the free vibration of generalized magneto thermoelastic multilayered LEMV (Linear Elastic Material with Voids)/CFRP (Carbon Fiber Reinforced Polymer) composite cylinder is studied using nonlocal form of linear theory of elasticity. The governing equation of motion is established in longitudinal axis and variable separation model is used to transform the governing equations into a system of differential equations. To investigate vibration analysis from frequency equations, the stress free boundary conditions are adopted at the inner, outer and interface boundaries. The graphical representation of the numerically calculated results for frequency shift, natural frequency, and thermoelastic damping is presented. A special care has been taken to inspect the effect of nonlocal parameter on the aforementioned quantities. The results suggest that the nonlocal scale and the phase lag parameters alter the vibration characteristics of composite cylinders significantly.

Nonlinear Vibration Analysis of the Coupled Gear-Rotor-Bearing Transmission System for a New Energy Vehicle

Considering the effects of time-varying meshing stiffness, time-varying support stiffness, transmission errors, tooth side clearance and bearing clearance, a nonlinear dynamics model of the coupled gear-rotor-bearing transmission system of a new energy vehicle is constructed. Firstly, the fourth-order Runge–Kutta integral method is used to solve the differential equations of the system dynamics, and the time-varying meshing force diagram, time history diagram, phase diagram, FFT spectrum diagram, Poincaré map and bifurcation diagram of the system are obtained to study the influence of the external load excitation frequency on the dynamics characteristics of the system. In addition, the multiscale method is used to analyze the main resonance characteristics of the system and to determine the main resonance stability conditions of the system. The effect of time lag control parameters and external load excitation frequency on the main resonance of the system is analyzed by numerical methods. The results show that the gear-rotor-bearing coupled transmission system of the new energy vehicle has obviously nonlinear characteristics, avoiding the system instability interval reasonable selection of external load excitation frequency, meshing damping, time lag parameters and load fluctuations, which can be used to improve the stability of the transmission system of the new energy vehicle.

Chimera states in multiplex networks: Chameleon-like across-layer synchronization.

Different across-layer synchronization types of chimera states in multilayer networks have been discovered recently. We investigate possible relations between them, for example, if the onset of some synchronization type implies the onset of some other type. For this purpose, we use a two-layer network with multiplex inter-layer coupling. Each layer consists of a ring of non-locally coupled phase oscillators. While oscillators in each layer are identical, the layers are made non-identical by introducing mismatches in the oscillators' mean frequencies and phase lag parameters of the intra-layer coupling. We use different metrics to quantify the degree of various across-layer synchronization types. These include phase-locking between individual interacting oscillators, amplitude and phase synchronization between the order parameters of each layer, generalized synchronization between the driver and response layer, and the alignment of the incoherent oscillator groups' position on the two rings. For positive phase lag parameter mismatches, we get a cascaded onset of synchronization upon a gradual increase of the inter-layer coupling strength. For example, the two order parameters show phase synchronization before any of the interacting oscillator pairs does. For negative mismatches, most synchronization types have their onset in a narrow range of the coupling strength. Weaker couplings can destabilize chimera states in the response layer toward an almost fully coherent or fully incoherent motion. Finally, in the absence of a phase lag mismatch, sufficient coupling turns the response dynamics into a replica of the driver dynamics with the phases of all oscillators shifted by a constant lag.