Value Of Time Delay Research Articles

Atmosphere air temperature forecasting using the honey badger optimization algorithm: on the warmest and coldest areas of the world

Precisely forecasting air temperature as a significant meteorological parameter has a critical role in environment quality management. Hence, this study employs a hybrid intelligent model for accurately monthly temperature forecasting for one to three times ahead in the hottest and coldest regions of the world. The hybrid model contains the artificial neural network (ANN) hybridized with the powerful hetaeristic Honey Badger Algorithm (HBA-ANN). The average mutual information (AMI) technique is employed to find the optimal time delay values for the temperature variable for different time horizons. Finally, the performance of the developed hybrid model is compared with the classical ANN and the Gene Expression Programming (GEP) using some statistical criteria, and the Taylor and scatter diagrams. Results indicated that in each time horizon, the HBA-ANN model with the lowest distance from observation points based on Taylor diagram, high values for NSE and R2, and low values for RMSE, MAE, and RSR outperformed the ANN and GEP models in both training and testing phases. Hence, using the Honey Badger Algorithm could increase the accuracy of the model. This model's precise performance supports the case for it to be employed to forecast other environmental parameters.

A new method for solving the mobile payment scheduling problem using harris hawks optimization algorithm during the COVID-19 pandemic

The Coronavirus Disease 2019 (COVID-19) epidemic is causing once-in-a-century upheavals in global civilization. Payment systems have advanced lately, from simple cash or credit card transactions to various forms of mobile payment systems. This transformation is occurred due to COVID 19 and shifts in the economy, the growth of social networks, technical advancements on the Internet, and the increased usage of mobile devices. Throughout COVID19, this article offers a unique approach to the payment scheduling issue, which seeks out a timetable that enhances the project's stakeholders' benefit. Both the sponsor and the contractor in a project want to have a strong payment plan on their own. To create an equal schedule between the sponsor and the development team, the timing of payments and the completion periods of project activities are decided concurrently. The Harris hawks optimization method is designed to tackle the problem because of its high NP-hardness. Harris hawks optimization is a novel meta-heuristic nature-inspired optimizer inspired by how Harris hawks hunt food in nature. By comparing the suggested Harris hawks optimization optimizer to existing nature-inspired methods, the efficacy of the suggested Harris hawks optimization optimizer is determined. The Harris hawks optimization algorithm appears to be highly promising based on the statistical findings and comparisons. The MATLAB simulator's simulation findings confirm the algorithm's superiority over earlier efforts regarding energy, cost, delay time, and net value.

Turing instability in a network-organized epidemic model with delay

In this paper, we show the impact of both network and time delays on Turing instability and demarcate the role of diffusion in the epidemic. The stability and bifurcation of equilibrium points are analyzed to reveal the epidemic state, which is the precondition of pattern formation. The network could lead to the transition from the endemic to the periodic outbreak via negative wavenumber, which provides a way to prevent significant harm or decrease the damage of the epidemic to humans by the delay, the connection rate, and the infection rate. Also, the threshold value of time delay is proportional to the minimum eigenvalue of the network matrix, which provides a way to control the periodic behavior. Finally, numerical simulations validate these analytical results and the mechanisms of frequent outbreaks.

The Day-to-Day Departure Time Choice of Heterogeneous Commuters Under an Anonymous Toll Charge for System Optimum

We consider a bottleneck system in a state of disequilibrium, in which commuters are heterogeneous in the values of travel time and schedule delays. Three representative dynamical systems are used to formulate the day-to-day departure time choice of commuters, and a Walrasian toll charge scheme is utilized to control the evolutionary process of traffic flows. The toll charge scheme is anonymous; that is, toll charges are identical for all user classes, and the scheme’s implementation does not need to resort to information on the values of travel time and schedule delays. We find that the system optimum (SO) flow pattern can be achieved at the stationary state under the implementation of the toll charge scheme. The equivalence between the stationary state and the SO, the existence of the toll charge for achieving the SO, and the convergence of day-to-day traffic flows to the SO under the implementation of the toll charge scheme are theoretically proved. The distributions of traffic flows and toll charges at the SO state during morning rush hour are analytically shown. The application of the toll charge scheme to a network with multiple trip origins is presented. The effectiveness of the toll charge scheme for achieving the SO is also examined by numerical analyses. Funding: This work was supported by the National Natural Science Foundation of China [Grants 72171007, 71890972/71890970, and 72021001]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2022.1191 .

Stability switch and Hopf bifurcations for a diffusive plankton system with nonlocal competition and toxic effect

<abstract><p>Since the distribution of plankton is always uneven, the nonlocal phytoplankton competition term indicates the spatial weighted mean of phytoplankton density, which is introduced into the plankton model with toxic substances effect to study the corresponding dynamic behavior. The stability of the positive equilibrium point and the existence of Hopf bifurcations are discussed by analysing the distribution of eigenvalues. The direction and stability of bifurcation periodic solution are researched based on an extended central manifold method and normal theory. Finally, spatially inhomogeneous oscillations are observed in the vicinity of the Hopf bifurcations. Through numerical simulation, we can observe that the system without nonlocal competition term only generates homogeneous periodic solution, and inhomogeneous periodic solution will produce only when both diffusion and nonlocal competition exist simultaneously. We can also see that when the toxin-producing rate of each phytoplankton is in an appropriate range, the system with nonlocal competition generates a stability switch with inhomogeneous periodic solution, when the value of time delay is in a certain interval, then Hopf bifurcations will appear, and with the increase of time delay, the quantity of plankton will eventually become stable.</p></abstract>

Hopf bifurcation in a two-species reaction-diffusion-advection competitive model with nonlocal delay

In this paper, we are concerned with a two-species reaction-diffusion-advection competitive model with nonlocal delay subject to the homogeneous Dirichlet boundary conditions. The existence of the spatially inhomogeneous steady state is studied by using the implicit function theorem. The stability of the inhomogeneous steady state and the associated Hopf bifurcation are investigated by analyzing a non-self-adjoint linear operator. Taking the time delay as the bifurcation parameter, we obtain the critical value of time delay for the Hopf bifurcation and the stability switch phenomenon.

The dynamics of a delayed ecological model with predator refuge and cannibalism

This study has contributed to understanding a delayed prey-predator system involving cannibalism. The system is assumed to use the Holling type II functional response to describe the consuming process and incorporates the predator's refuge against the cannibalism process. The characteristics of the solution are discussed. All potential equilibrium points have been identified. All equilibrium points' local stability analyses for all time delay values are investigated. The system exhibits a Hopf bifurcation at the coexistence equilibrium, which is further demonstrated. The center manifold and normal form theorems for functional differential equations are then used to establish the direction of Hopf bifurcation and the stability of the periodic solution. To demonstrate the key findings, various numerical simulations are then run.

Homology groups of embedded fractional Brownian motion.

A well-known class of nonstationary self-similar time series is the fractional Brownian motion (fBm) considered to model ubiquitous stochastic processes in nature. Due to noise and trends superimposed on data and even sample size and irregularity impacts, the well-known computational algorithm to compute the Hurst exponent (H) has encountered superior results. Motivated by this discrepancy, we examine the homology groups of high-dimensional point cloud data (PCD), a subset of the unit D-dimensional cube, constructed from synthetic fBm data as a pipeline to compute the H exponent. We compute topological measures for embedded PCD as a function of the associated Hurst exponent for different embedding dimensions, time delays, and amount of irregularity existing in the dataset in various scales. Our results show that for a regular synthetic fBm, the higher value of the embedding dimension leads to increasing the H dependency of topological measures based on zeroth and first homology groups. To achieve a reliable classification of fBm, we should consider the small value of time delay irrespective of the irregularity presented in the data. More interestingly, the value of the scale for which the PCD to be path connected and the postloopless regime scale are more robust concerning irregularity for distinguishing the fBm signal. Such robustness becomes less for the higher value of the embedding dimension. Finally, the associated Hurst exponents for our topological feature vector for the S&P500 were computed, and the results are consistent with the detrended fluctuation analysis method.

Analysis of Transmitted and Received ECG Signal Based on Internet of Thing Using Web Browser and Server-Client HTML Protocol

Cardiovascular disease is one of the leading causes of death worldwide. One of the reasons for the large number of deaths from heart disease is the lack of regular cardiac monitoring. Electrocardiograph (ECG) is a method of monitoring heart conditions. There are still many ECG examinations in hospitals that are carried out directly in certain places and the results can only be seen at that time. Meanwhile, when negligence or carelessness occurs, it can endanger the patient due to delays in handling. One of the efforts that can be made to improve service to heart patients is by having ECG monitoring based on IoT (Internet of things). The purpose of this study is to analyze the ECG signals sent and received by IoT media so that they are useful for the diagnostic process. The contribution of this research is to know the shape of the ECG signal that is sent and received through IoT media. The procedure to achieve this goal is with the AD8232 sensor whose output will be processed through the microcontroller and displayed on computer and smartphones via IoT. From this research, the results obtained that the average value of lost data at BPM 60 and BPM 120 is quite good, namely 21.26% and 24.44%.hile the average time delay value at BPM 60 and BPM 120 is also quite good, namely 0.023 s and 0.03 s. So, it can be concluded that the sending of IoT-based ECG signals affects the form of signals sent and received. The results of this study are expected to be developed in further research with development in the form of adding leads or adding BPM parameters.

A Parametric МА-Algorithm for Gaussian Time Series Change Point Detection from Mathematical Expectation

The problem of online detection of a Gaussian time series change connected with a spontaneous abrupt change in its mathematical expectation is solved. For solving the problem, it is proposed to use a parametric controlling algorithm based on the Moving Average method or the MA- algorithm. It is noted that, despite the fact that this algorithm has been known almost since the first works in the field of statistical control, its properties, capabilities, and efficiency in comparison with other change detection algorithms are still (for a number of reasons) have been studied to a very poor extent. The purpose of this work is to thoroughly study the MA-algorithm’s characteristics with the aim to synthesize an optimal time series change detection procedure. The study was carried out using a simulation modeling method. The article presents the structure and element-wise description of the simulation experiment program, which fully replicates the MA-algorithm operation in the online mode. By using the developed program, it has been found that the conventional way of setting the control procedure deciding threshold h, the value of which should provide the preset value of the average time between false alarms, when an alarm signal about the appearance of a change is generated, although in reality the object remains in the "normal" state, is invalid. For a fixed series of the above-mentioned quantities, the correct values of the threshold h are obtained depending on the width of the moving averaging window N of the controlling MA-algorithm. Similarly, the values of the average delay time of producing an alarm signal when a change with the given fixed level  appears. Based on the data obtained, dependences of the control procedure efficiency indicator E on N for a set of different values of average time between false alarms and  have been found. It has been shown that for each such set there is a value of N at which the control procedure is most effective. This optimal procedure is compared with a similar procedure of the cumulative sum algorithm (a CUSUM algorithm), and it has been shown from the comparison results that the MA-algorithm is in general only slightly inferior to the CUSUM algorithm in efficiency, and in some cases even outperforms it.

New strong lensing modelling of SDSS J2222+2745 enhanced with VLT/MUSE spectroscopy

Context. SDSS J2222+2745, at z = 0.489, is one of the few currently known lens clusters with multiple images (six) of a background (z = 2.801) quasar with measured time delays between two image pairs (with a sub-percent relative error for the longer time delay). Systems of this kind can be exploited as alternative cosmological probes through high-precision and accurate strong lensing models. Aims. We present recent observations from the Multi Unit Spectroscopic Explorer (MUSE) on the Very Large Telescope (VLT) and new total mass models of the core of the galaxy cluster SDSS J2222+2745. Methods. We combine archival multi-band, high-resolution imaging from the Hubble Space Telescope (HST) with our VLT/MUSE spectroscopic data to securely identify 34 cluster members and 12 multiple images from 3 background sources. We also measure the stellar velocity dispersions of 13 cluster galaxies, down to HST F160W = 21 mag, enabling an independent estimate of the contribution of the sub-halo mass component to the lens total mass. By leveraging the new spectroscopic dataset, we build improved strong lensing models. Results. The projected total mass distribution of the lens cluster is best modelled with a single large-scale mass component, a galaxy-scale component, anchored by the VLT/MUSE kinematic information, and an external shear component. The best-fit strong lensing model yields a root mean square separation between the model-predicted and observed positions of the multiple images of 0″​​.29. When analysing the impact of systematic uncertainties, stemming from modelling assumptions and used observables, we find that the resulting projected total mass profile, the relative weight of the sub-halo mass component, and the critical lines are consistent, within the statistical uncertainties. The predicted magnification and time-delay values are, instead, more sensitive to the local details of the lens total mass distribution, and vary significantly among lens models that are similarly good at reproducing the observed multiple image positions. In particular, the model-predicted time delays can differ by a factor of up to ∼1.5. Conclusions. SDSS J2222+2745 is a promising lens cluster for cosmological applications. However, due to its complex morphology, the relatively low number of secure ‘point-like’ multiple images, and current model degeneracies, it becomes clear that additional information (from the observed surface brightness distribution of lensed sources and the measured time delays) needs to be included in the modelling for accurate and precise cosmological measurements. The full VLT/MUSE secure spectroscopic catalogue presented in this work is made publicly available.

Detection of cardiac arrhythmia patterns in ECG through H × C plane.

The aim of this study is to formulate a new methodology based upon informational tools to detect patients with cardiac arrhythmias. As it is known, sudden death is the consequence of a final arrhythmia, and here lies the relevance of the efforts aimed at the early detection of arrhythmias. The information content in the time series from an electrocardiogram (ECG) signal is conveyed in the form of a probability distribution function, to compute the permutation entropy proposed by Bandt and Pompe. This selection was made seeking its remarkable conceptual simplicity, computational speed, and robustness to noise. In this work, two well-known databases were used, one containing normal sinus rhythms and another one containing arrhythmias, both from the MIT medical databank. For different values of embedding time delay τ, normalized permutation entropy and statistical complexity measure are computed to finally represent them on the horizontal and vertical axes, respectively, which define the causal plane H×C. To improve the results obtained in previous works, a feature set composed by these two magnitudes is built to train the following supervised machine learning algorithms: random forest (RF), support vector machine (SVM), and k nearest neighbors (kNN). To evaluate the performance of each classification technique, a 10-fold cross-validation scheme repeated 10 times was implemented. Finally, to select the best model, three quality parameters were computed, namely, accuracy, the area under the receiver operative characteristic (ROC) curve (AUC), and the F1-score. The results obtained show that the best classification model to detect the ECG coming from arrhythmic patients is RF. The values of the quality parameters were at the same levels reported in the available literature using a larger data set, thus supporting this proposal that uses a very small-sized feature space to train the model later used to classify. Summarizing, the attained results show the possibility to discriminate both groups of patients, with normal sinus rhythm or arrhythmic ECG, showing a promising efficiency in the definition of new markers for the detection of cardiovascular pathologies.

Modeling the transmission dynamics of a time-delayed epidemic model with saturated treatment rate

In this paper, an attempt has been made to explore a new delayed epidemiological model assuming that the disease is transmitted among the susceptible population and possessing nonlinear incidence function along with a saturated treatment rate. Due attention is paid to the positivity and boundedness of the solutions and the bifurcation of the dynamical system as well. Basic reproduction number is being calculated, and considering the latent period as a bifurcation parameter, it has been examined that a Hopf-bifurcation occurs near the endemic equilibrium point while the parameter attains critical values. We have also discussed the stability and direction of Hopf-bifurcation near the endemic equilibrium point, the global stability analysis and the optimal control theory. We conclude that the system reveals chaotic dynamics through a specific time-delay value. Numerical simulations are being performed in order to explain the accuracy and effectiveness of the acquired theoretical results.

Bifurkasi Hopf Pada Model Siklus Bisnis Investment Saving-Liquidity Money dengan Waktu Tunda

The economy is not always good, meaning it experiences ups and downs. This situation requires forecasting. One of which is the Investment Saving-Liquidity Money business cycle model, which was first introduced by Kalecki in 1935 with the assumption that profits will be saved to be used as initial investment capital, thus causing a delay in the investment process (delay time). The addition of time delay in the system of differential equations causes a change in stability at the equilibrium point so that bifurcation occurs. Another modification given to the model is the use of time delays. The model analyzed in this study is a non-linear IS-LM business cycle model with time delay. Delay time is required for the gross product, capital, and interest rate to be used as an investment. This model was observed in two cases. First Case is a model without time delay. The second case is a model with a time delay. In this case, the critical value of delay is obtained. Hopf bifurcation occurs when the value of delay time is equal to the critical value of delay and satisfies the transversality condition. Observations on the model simulation are carried out by varying the delay time value. When the Hopf bifurcation occurs, the graph on the solution plane shows a constant oscillatory movement. If the value of delay time given is less than the critical value of delay, the controlled system solution goes to a balanced condition. Then when the delay time value is greater than the critical value of delay, the system solution continues to fluctuate, causing an unstable system condition.

Comparative investigation on combustion characteristics of ADN-based liquid propellants in inert gas and oxidizing gas atmospheres with resistive ignition method

An experimental investigation was performed to study combustion characteristics of ADN based liquid propellant droplet with resistive ignition method. The experiment was conducted in Ar atmosphere and different oxidizing gas atmospheres under an on-load voltage range of 190–230 V; the oxidizing gases included N2O, NO and O2. The influence of the atmosphere, on-load voltage, droplet volume on the ignition energy, ignition delay time, burning duration, and droplet equivalent diameter development during ignition and combustion was studied. ADN based liquid propellant showed feasibility to combustion by the resistive ignition method in the inert gas atmosphere. The ignition energy accounted for more than 80 % of the total energy during the ignition and combustion process. With the increase of the droplet volume, the value of the ignition delay time and burning duration increased. The atmosphere had a significant influence on the burning duration and droplet diameter, but had little influence on the ignition delay time. Under our experiment conditions, the N2O and O2 atmospheres improved the combustion process, while, the NO atmosphere inhibited the oxidation of methanol.

Stochastic delay differential equations: Analysis and simulation studies

Stochastic delay differential equations play an important role in modelling scientific and engineering systems. In this paper the partial differential equation satisfying the time evolution of the probability density function of the state variable governed by the stochastic delay differential equation (SDDE) with additive white noise and coloured noise perturbation is obtained under small time delay and small correlation time approximation using path integral formalism. This partial differential equation reduces to a Fokker–Planck equation for particular linear and non-linear SDDE. Fokker–Planck equation is then solved with reflecting boundary conditions to get the analytical solutions for Stationary Probability Density Function (SPDF). The analytical solutions for SPDF is compared with the SPDF obtained using simulation. Further the Mean First Passage Time (MFPT) of the bistable system is calculated for various values of time delay and noise strength and compared with that of the simulation. The MFPT for the time delayed system for the case of cubic potential driven by Gaussian and Levy noise as well as asymmetric bistable potential driven by a noise and periodic driving force has been investigated.

Hopf bifurcation of the model with terms of two time-delays and delay-dependent parameter based on the theory of crossing curves.

A three-layer prey-predator model with two time-delays and one delay-dependent parameter is established in this paper. To begin, the paper calculates the conditions for each population in the model to maintain the quantity stable and Hopf bifurcation when τ = τ = 0, τ = 0 , τ ≠ 0, τ is in the stable interval, and τ > 0. The crossing curves, their type, and the direction of the crossing curves are then obtained using the crossing curve method, which is composed of the threshold values of the dynamic behavior change on the two time-delays plane when τ , τ > 0. The real data from the forage grass-Ochotona curzoniae-Buteo hemilasius food chain is used to conduct an empirical study of the model. When τ , τ > 0, the feasible region of the crossing curves is open-ended, and the model's crossing curves on the ( τ , τ ) plane are truncated. This indicates that the model's threshold distribution of dynamic behavior change is a regular curve made of several curves. The simulation using the time-delay value on the crossing curves shows that the model produces different dynamic behaviors such as stability, bifurcation, and chaos depending on the time-delay value on both sides of the curves. The critical values of dynamic behavior change are time-delay values on the crossing curves. The empirical study shows that increasing Ochotona curzoniae's environmental capacity can easily cause Hopf bifurcation of the system. At this time, the number of each population in the Plateau ecosystem constantly fluctuates, and Ochotona curzoniae is vulnerable to extinction.

Joint Estimation for Time Delay and Direction of Arrival in Reconfigurable Intelligent Surface with OFDM.

Recently, the joint estimation for time delay (TD) and direction of arrival (DOA) has suffered from the high complexity of processing multi-dimensional signal models and the ineffectiveness of correlated/coherent signals. In order to improve this situation, a joint estimation method using orthogonal frequency division multiplexing (OFDM) and a uniform planar array composed of reconfigurable intelligent surface (RIS) is proposed. First, the time-domain coding function of the RIS is combined with the multi-carrier characteristic of the OFDM signal to construct the coded channel frequency response in tensor form. Then, the coded channel frequency response covariance matrix is decomposed by CANDECOMP/PARAFAC (CPD) to separate the signal subspaces of TD and DOA. Finally, we perform a one-dimensional (1D) spectral search for TD values and a two-dimensional (2D) spectral search for DOA values. Compared to previous efforts, this algorithm not only enhances the adaptability of coherent signals, but also greatly decreases the complexity. Simulation results indicate the robustness and effectiveness for the proposed algorithm in independent, coherent, and mixed multipath environments and low signal-to-noise ratio (SNR) conditions.

New Orbit Determination Method for GEO Satellites Based on BeiDou Short-Message Communication Ranging

The radio determination service system (RDSS), a navigation and positioning system independently developed by China, features services such as short-message communication, position reporting, and international search and rescue. The L-band pseudo-range and phase data are the primary data sources in precise orbit determination (POD) for geostationary Earth orbit (GEO) satellite in the BeiDou system, especially in the orbit manoeuvre period. These data are the only data sources in the POD for GEOs. However, when the pseudo-range and phase data is abnormal due to unforeseen reasons, such as satellite hardware failure or monitoring receiver abnormalities, the data abnormality leads to orbit determination abnormalities or even failures for GEOs, then the service performance and availability of the RDSS system are greatly degraded. Therefore, a new POD method for GEOs based on BeiDou short-message communication ranging data has gained research attention to improve the service reliability of the BeiDou navigation satellite system (BDS)-3, realising the deep integration of communication and navigation services of the BDS. This problem has not been addressed so far. Therefore, in this study, a new POD method for GEO satellites is investigated using high-precision satellite laser ranging (SLR) data and RDSS data. The SLR data are used as the benchmark to calibrate the time delay value of RDSS equipment, and RDSS data are only used in the orbit determination process by fixing the corrected RDSS time delay value, and the satellite orbit parameters and dynamic parameters are solved. Experimental analysis is conducted using the measured SLR and RDSS data of the BDS, and the orbit accuracy in this paper is evaluated by the precise ephemeris of the Multi-GNSS pilot project (MGEX) and SLR data. The results show that the orbit accuracy in the orbital arc and the 2-h orbital prediction arc for GEOs are 6.01 m and 6.99 m, respectively, compared with the ephemeris of MGEX, and the short-arc orbit accuracy after 4 h of manoeuvring is 11.11 m. The orbit accuracy in the radial component by SLR data is 0.54 m. The required orbit accuracy for GEO satellites in the RDSS service of the BDS-3 is 15 m. The orbit accuracy achieved in this paper is superior to that of this technical index. This method expands the application field of the RDSS data and greatly enriches the POD method for GEOs. It can be adopted as a backup technology for the POD method for GEOs based on RNSS data, significantly improving the service reliability of the BeiDou RDSS service.

A novel model on nonlocal thermoelastic rotating porous medium with memory-dependent derivative

PurposeIn the present article, the three-phase-lag (3PHL) model and the Green-Naghdi theory of types II, III with memory-dependent derivative is used to study the effect of rotation on a nonlocal porous thermoelastic medium.Design/methodology/approachIn this study normal mode analysis is used to obtain analytical expressions of the physical quantities. The numerical results are given and presented graphically when mechanical force is applied.FindingsThe model is illustrated in the context of the Green-Naghdi theory of types II, III and the three-phase lags model. Expressions for the physical quantities are solved by using the normal mode analysis and represented graphically.Originality/valueComparisons are made with the results predicted in the absence and presence of the rotation as well as a nonlocal parameter. Also, the comparisons are made with the results of the 3PHL model for different values of time delay.