Delay Components Research Articles

Input-to-state stability of stochastic Markovian jump genetic regulatory networks

The development of gene circuits in logic modules that start enormous output distributions with low signal-to-noise ratios is a difficult problem in engineering. As a result, the gene model depicts the transcription and translation of a single gene produced in the modification of noise in isolated logic modules. Our goal is to construct such networks with all types of connectivity. Further, the impacts of noise on further complex genetic networks have been investigated using stochastic gene models. Using this information as a foundation, our research investigates the input-to-state stability investigation for stochastic Markovian jump genetic regulatory networks with time-varying delay components. The goal of this article is to develop genetic networks with temporal delays, which are crucial for genetic regulation because slow biochemical processes like gene transcription and translation need time to occur. Additionally, the Markovian chain is essential for demonstrating how a system shifts from one mode to another with known transition probabilities. In the stochastic case, some complex systems with random disturbance will occur. Due to this significance the genetic regulatory network with stochastic case is applied to identify the complex behaviour among genes and proteins of the micro perspective. By establishing the Lyapunov functional with Ito’s and Dynkin’s formula, new stability conditions are derived and which is effectively solved by MATLAB toolbox. The efficiency of the suggested technique is demonstrated using a numerical example.

Sensitivity of Shipborne GNSS Estimates to Processing Modeling Based on Simulated Dataset.

The atmospheric water vapor is commonly monitored from ground Global Navigation Satellite System (GNSS) measurements, by retrieving the tropospheric delay under the Zenith Wet Delay (ZWD) component, linked to the water vapor content in the atmosphere. In recent years, the GNSS ZWD retrieval has been performed on shipborne antennas to gather more atmospheric data above the oceans for climatology and meteorology study purposes. However, when analyzing GNSS data acquired by a moving antenna, it is more complex to decorrelate the height of the antenna and the ZWD during the Precise Point Positioning (PPP) processing. Therefore, the observation modeling and processing parametrization must be tuned. This study addresses the impact of modeling on the estimation of height and ZWD from the simulation of shipborne GNSS measurements. The GNSS simulation is based on an authors-designed simulator presented in this article. We tested different processing models (elevation cut-off angle, elevation weighting function, and random walk of ZWD) and simulation configurations (the constellations used, the sampling of measurements, the location of the antenna, etc.). According to our results, we recommend processing shipborne GNSS measurements with 3° of cut-off angle, elevation weighting function square root of sine, and an average of 5 mm·h-1/2 of random walk on ZWD, the latter being specifically adapted to mid-latitudes but which could be extended to other areas. This processing modeling will be applied in further studies to monitor the distribution of water vapor above the oceans from systematic analysis of shipborne GNSS measurements.

ELoran Propagation Delay Prediction Model Based on a BP Neural Network for a Complex Meteorological Environment.

The core of eLoran ground-based timing navigation systems is the accurate measurement of groundwave propagation delay. However, meteorological changes will disturb the conductive characteristic factors along the groundwave propagation path, especially for a complex terrestrial propagation environment, and may even lead to microsecond-level propagation delay fluctuation, seriously affecting the timing accuracy of the system. Aiming at this problem, this paper proposes a propagation delay prediction model based on a Back-Propagation neural network (BPNN) for a complex meteorological environment, which realizes the function of directly mapping propagation delay fluctuation through meteorological factors. First, the theoretical influence of meteorological factors on each component of propagation delay is analyzed based on calculation parameters. Then, through the correlation analysis of the measured data, the complex relationship between the seven main meteorological factors and the propagation delay, as well as their regional differences, are demonstrated. Finally, a BPNN prediction model considering regional changes of multiple meteorological factors is proposed, and the validity of the model is verified by long-term collected data. Experimental results show that the proposed model can effectively predict the propagation delay fluctuation in the next few days, and its overall performance is significantly improved compared with that of the existing linear model and simple neural network model.

Assessment of GNSS zenith tropospheric delay responses to atmospheric variables derived from ERA5 data over Nigeria

Tropospheric delay is a major error caused by atmospheric refraction in Global Navigation Satellite System (GNSS) positioning. The study evaluates the potential of the European Centre for Medium-range Weather Forecast (ECMWF) Reanalysis 5 (ERA5) atmospheric variables in estimating the Zenith Tropospheric Delay (ZTD). Linear regression models (LRM) are applied to estimate ZTD with the ERA5 atmospheric variables. The ZTD are also estimated using standard ZTD models based on ERA5 and Global Pressure and Temperature 3 (GPT3) atmospheric variables. These ZTD estimates are evaluated using the data collected from the permanent GNSS continuously operating reference stations in the Nigerian region. The results reveal that the Zenith Hydrostatic Delay (ZHD) from the LRM and the Saastamoinien model using ERA5 surface pressure are of identical accuracy, having a Root Mean Square (RMS) error of 2.3 mm while the GPT3-ZHD has an RMS of 3.4 mm. For the Zenith Wet Delay (ZWD) component, the best estimates are derived using ERA5 Precipitable Water Vapour (PWV). These include the ZWD derived by the LRM having an average RMS of 20.9 mm and Bevis equation having RMS of 21.1 mm and 21.0 mm for global and local weighted mean temperatures, respectively. The evaluation of GPT3-ZWD estimates gives RMS of 45.8 mm. This study has provided a valuable insight into the application of ERA5 data for ZTD estimation. In line with the findings of the study, the ERA5 atmospheric variables are recommended for improving the accuracy in ZTD estimation, required for GNSS positioning.

Barriers to timely identification of bipolar disorder in youth: a multidimensional perspective

Bipolar disorder (BD) in youth often goes unrecognized and therefore untreated. However, little is known about pathways to treatment of youth with BD and factors that influence the time taken for each stage of these pathways. In this article, we use the conceptual framework by Scott and colleagues called the Model of Pathways to Treatment as a foundation to explore the components of delay in the diagnosis and treatment of youth with BD. The total time from the onset of symptoms until treatment initiation was divided into four sequential intervals; i.e., the Appraisal, the Help-seeking, the Diagnostic and the Pre-treatment intervals and potential disease, patient, and healthcare system/provider factors that influence each interval were identified. This multidimensional conceptual framework can offer a systematic approach to understanding and exploring barriers to early identification and interventions in BD, which is a crucial step in the development of strategies to facilitate prompt diagnosis and treatment. We hope this work contributes to the discussion on delayed diagnosis and treatment of youth with BD and provides a roadmap to inform future research studies and policy decisions.

Understanding components and predictors of delay of first treatment for mental health problems: A hospital-based study in China.

Understanding components and predictors of delay of first treatment for mental health problems are crucial to inform interventions for earlier treatment. However, Chinese-context knowledge of this theme is still limited. We conducted an inpatient survey among 206 patients with various mental disorders in China. Delay of first utilization of mental healthcare (Delay-Total) and its two components of help-seeking delay (Delay-H) and referral delay (Delay-R) were assessed in terms of occurrence and duration. Binary logistic regression was performed to test predictors of Delay-Total, Delay-H and Delay-R, and multiple linear regression was used to test predictors of delay durations. Overall, 66.0% patients experienced Delay-Total, with a duration range of 0 to 353 months; 49.5% patients had Delay-H (duration range = 0-207 months) and 29.6% with Delay-R (duration range = 0-323 months). Multivariate logistic regression analysis found that the diagnosis of severe mental disorders was a consistent predictor for a reduced chance of Delay-Total, Delay-H and Delay-R. Multiple linear regression analysis demonstrated that younger age of disorder onset and disorder onset before 2016 were significantly associated with longer delay. Delay of first treatment for mental health problems is still common in China. However, the development of mental health policy and services promotes shorter treatment delay. The diagnosis of common mental disorders and younger age of onset are risk factors of the occurrence and duration of delay, respectively. Thus, education of the public and non-mental-healthcare professionals are needed for better disorder recognition and more efforts should be inputted to support youngsters' utilization of mental healthcare.

Measurement-based channel characterization in a large hall scenario at 300 GHz

Sub-terahertz (Sub-THz), defined as the frequency bands in 100-300 GHz, is promising for future generation communications and sensing applications. Accurate channel measurement and modeling are essential for development and performance evaluation of the future communication systems. Accurate channel modeling relies on realistic channel data, which should be collected by high-fidelity channel sounder. This paper presents the measurement-based channel characterization in a large indoor scenario at 299-301 GHz. We firstly review the state-of-the-art channel measurements at sub-THz frequency bands. We then presented a VNA-based channel sounder for long-range measurements, which uses the radio-over-fiber techniques. Channel measurements using this channel sounder are conducted in a large hall scenario. Based on the measurement data, we calculated and analyzed key propagation channel parameters, e.g., path loss, delay spread, and angular spread. The results are also analyzed both in the line-of-sight (LoS) and none-LoS (NLoS) cases. The large delay components in the measurements demonstrate the possibility of the longrange channel measurement campaign at 300 GHz.

H∞ Control design for discrete‐time nonlinear delayed systems

SummaryThis manuscript concerns the stabilization of discrete‐time Takagi‐Sugeno (T‐S) fuzzy systems with additive time‐varying delay components. By constructing a suitable fuzzy Lyapunov‐Krasovskii functional (LKF) with membership functions, the sufficient condition of the stability criteria has been established for the discrete‐time T‐S fuzzy systems with additive time‐varying delays. To do this, the extended version of Wirtinger's inequality is used to bound the forward differences of the LKF. By utilizing the T‐S fuzzy model approach, the dynamics of each fuzzy rule is expressioned by a linear system. Then, a fuzzy controller is designed to guarantee the performance of the closed‐loop system formulated in terms of linear matrix inequalities (LMIs). Finally, the numerical simulations are carried out to demonstrate the effectiveness and feasibility of our theoretical findings.

Joint Estimation of Ground Displacement and Atmospheric Model Parameters in Ground-Based Radar

Atmospheric delay is the primary error in ground-based synthetic aperture radar (GBSAR). The existing compensation methods include the external meteorological data correction method, the polynomial fitting method, and the persistent scatterers SAR interferometry (PSInSAR) calibration method. Combining the polynomial fitting and the persistent scatterers targets is the most popular method of GBSAR atmospheric delay compensation. However, the displacement component of the coherent target is always ignored in the atmospheric delay compensation, which is unpractical. A joint estimation method of ground displacement and atmospheric model parameters is developed in this paper. The displacement component is determined by the spatial and temporal features of the objects. The atmospheric delay component is regarded as a systematic error represented by a quadratic polynomial related to distance. The result is resolved by the least-square method. Compared to the existing method, the root-mean-square error (RMSE) of the proposed method had a significant improvement in the validation experiment. In the real in situ experiment, the time series obtained by the GBSAR had a similar trend to that acquired by the Global Positioning System (GPS) receiver. It is indicated that the proposed method can lead to a better deformation estimation by taking the deformation component into account in the atmospheric compensation.

Coseismic Deformation Field and Fault Slip Distribution Inversion of the 2020 Jiashi Ms 6.4 Earthquake: Considering the Atmospheric Effect with Sentinel-1 Data Interferometry

Due to some limitations associated with the atmospheric residual phase in Sentinel-1 data interferometry during the Jiashi earthquake, the detailed spatial distribution of the line-of-sight (LOS) surface deformation field is still not fully understood. This study, therefore, proposes an inversion method of coseismic deformation field and fault slip distribution, taking atmospheric effect into account to address this issue. First, an improved inverse distance weighted (IDW) interpolation tropospheric decomposition model is utilised to accurately estimate the turbulence component in tropospheric delay. Using the joint constraints of the corrected deformation fields, the geometric parameters of the seismogenic fault and the distribution of coseismic slip are then inverted. The findings show that the coseismic deformation field (long axis strike was nearly east-west) was distributed along the Kalpingtag fault and the Ozgertaou fault, and the earthquake was found to occur in the low dip thrust nappe structural belt at the subduction interface of the block. Correspondingly, the slip model further revealed that the slips were concentrated at depths between 10 and 20 km, with a maximum slip of 0.34 m. Accordingly, the seismic magnitude of the earthquake was estimated to be Ms 6.06. Considering the geological structure in the earthquake region and the fault source parameters, we infer that the Kepingtag reverse fault is responsible for the earthquake, and the improved IDW interpolation tropospheric decomposition model can perform atmospheric correction more effectively, which is also beneficial for the source parameter inversion of the Jiashi earthquake.

Previous experience with delays affects delay discounting in animal model of ADHD

BackgroundADHD is a disorder where a common symptom is impulsive behaviour, a broad term associated with making sub-optimal choices. One frequently used method to investigate impulsive behaviour is delay discounting, which involves choosing between a small, immediate reinforcer and a delayed, larger one. Choosing the small immediate reinforcer is by itself, however, not sufficient for terming the choice impulsive, as all organisms eventually switch to choosing the small, immediate reinforcer when the delay to the larger reinforcer becomes long. This switch can be termed impulsive only when it occurs more frequently, or at shorter LL delays, than typically observed in normal controls. A poorly understood aspect is how choice is influenced by previous experience with delays. Using an animal model of Attention-Deficit/Hyperactivity Disorder, the Spontaneously Hypertensive Rat, we manipulated the order of exposure to delays in a delay discounting task. Following a preference test, the Ascending group experienced gradually increasing delays between choice and reinforcer while the Descending group were exposed to these delays in reverse order.ResultsThe results showed that the Descending group chose the small, immediate reinforcer over the larger delayed to a much larger extent than the Ascending group, and continued to do so even when the delay component was ultimately removed. Strain effects were found in the Ascending group, with SHRs switching to the small, immediate reinforcer earlier than controls as the delay to the larger reinforcer increased.ConclusionThe data suggests that delay discounting is affected by history of exposure to delayed consequences. When reinforcement contingencies are incrementally changed from having no response-reinforcer delay to a long delay, discounting of delayed consequences is gradual. However, a sudden change from no delay to a long delay, without intermediate training, results in a rapid switch to the small, immediate reinforcer option, and this behaviour is somewhat resilient to the shortening and eventual removal of the large reinforcer delay. The implication is that attempting to reduce already existing impulsive behaviour in children with ADHD will require gradual habituation and not sudden changes in reinforcement contingencies.

Bi-Criteria Approximation for a Multi-Origin Multi-Channel Auto-Scaling Live Streaming Cloud

Live video traffic has been widely observed to vary significantly within short timescale. In order to manage such traffic dynamic of overlay live streaming, the Content Provider (CP) may deploy a set of geo-dispersed auto-scaling servers where the pay-as-you-go deployment cost is charged by the amount of resources used due to server uploading and data transmission between servers. To support geo-distributed user demands, we study a novel multi-origin multi-channel auto-scaling live streaming cloud that pushes each channel stream in the core network overlay as a tree covering the end servers who have local demand for the channel. The Origin-to-End (O2E) delay from an origin to an end server is due to the Server-to-Server (S2S) delays of the overlay links along the path. By optimizing the overlay of the core network, we seek to minimize the deployment cost and O2E delays of the channels (i.e., a bi-criteria problem), which can be equivalently phrased as minimizing the deployment cost while meeting certain given maximum O2E delay constraints. We formulate a realistic problem capturing the major cost and delay components, and show its NP-hardness. We propose Cost-optimized Multi-Origin Multi-Channel Overlay Streaming (COCOS), a novel, efficient and near-optimal bi-criteria approximation algorithm with proven approximation ratio. Trace-driven extensive experimental results based on real-world live streaming service data validate that COCOS outperforms other state-of-the-art schemes by a wide margin (cutting the cost in general by more than 50%).

Effects of Tropospheric Refraction on Precise Point Positioning in Brazil

Troposphere is one of the most limiting sources of error in the accuracy of 15 Precise Point Positioning method. This work aims to analyze the effects of tropospheric 16 delay on this positioning technique by applying the Bernese scientific program to GNSS 17 data belonging to the Brazilian Network for Continuous Monitoring of GNSS Systems. 18 GNSS data are related to several climatic zones, including Amazon region, referring to four 19 seasons. The data were processed considering six strategies, each one using different 20 troposphere models and mapping functions for analysis. Results were evaluated according 21 to the Root Mean Square, estimated for 15 processing days for each season, involving 89 22 GNSS stations. Results show that the greatest effects of the tropospheric delay occurred in 23 the equatorial region, related to the altimetric component, in all seasons of the year. This 24 climatic zone is under a strong influence of the Amazon region, which presents high annual 25 humidity values. In addition, it can occur a great humidity variation in this region, which 26 can compromise the process of estimating wet component of tropospheric delay. Finally, 27 results showed that the best processing strategy was the use of the Vienna mapping function 28 in conjunction with corrections based on the Numerical Weather Forecast model.

The M/M/k with Deterministic Setup Times

Capacity management, whether it involves servers in a data center, or human staff in a call center, or doctors in a hospital, is largely about balancing a resource-delay tradeoff. On the one hand, one would like to turn off servers when not in use (or send home staff that are idle) to save on resources. On the other hand, one wants to avoid the considerable setup time required to turn an ''off'' server back ''on.'' This paper aims to understand the delay component of this tradeoff, namely, what is the effect of setup time on average delay in a multi-server system? Surprisingly little is known about the effect of setup times on delay. While there has been some work on studying the M/M/k with Exponentially-distributed setup times, these works provide only iterative methods for computing mean delay, giving little insight as to how delay is affected by k , by load, and by the setup time. Furthermore, setup time in practice is much better modeled by a Deterministic random variable, and, as this paper shows, the scaling effect of a Deterministic setup time is nothing like that of an Exponentially-distributed setup time. This paper provides the first analysis of the M/M/k with Deterministic setup times. We prove a lower bound on the effect of setup on delay, where our bound is highly accurate for the common case where the setup time is much higher than the job service time. Our result is a relatively simple algebraic formula which provides insights on how delay scales with the input parameters. Our proof uses a combination of renewal theory, martingale arguments and novel probabilistic arguments, providing strong intuition on the transient behavior of a system that turns servers on and off.

Delay-Dependent Switching Approaches for Stability Analysis of Two Additive Time-Varying Delay Neural Networks.

This article analyzes the exponentially stable problem of neural networks (NNs) with two additive time-varying delay components. Disparate from the previous solutions on this similar model, switching ideas, that divide the time-varying delay intervals and treat the small intervals as switching signals, are introduced to transfer the studied problem into a switching problem. Besides, delay-dependent switching adjustment indicators are proposed to construct a novel set of augmented multiple Lyapunov-Krasovskii functionals (LKFs) that not only satisfy the switching condition but also make the suitable delay-dependent integral items be in the each corresponding LKF based on each switching mode. Combined with some switching techniques, some less conservativeness stability criteria with different numbers of switching modes are obtained. In the end, two simulation examples are performed to demonstrate the effectiveness and efficiency of the presented methods comparing other available ones.

Ground Deformation in Yuxi Basin Based on Atmosphere-Corrected Time-Series InSAR Integrated with the Latest Meteorological Reanalysis Data

Time-series interferometric synthetic aperture radar (TS-InSAR) is often affected by tropospheric artifacts caused by temporal and spatial variability in the atmospheric refractive index. Conventional temporal and spatial filtering cannot effectively distinguish topography-related stratified delays, leading to biased estimates of the deformation phases. Here, we propose a TS-InSAR atmospheric delay correction method based on ERA-5; the robustness and accuracy of ERA-5 data under the influence of different atmospheric delays were explored. Notably, (1) wet delay was the main factor affecting tropospheric delay within the interferogram; the higher spatial and temporal resolution of ERA-5 can capture the wet delay signal better than MERRA-2. (2) The proposed method can mitigate the atmospheric delay component in the interferogram; the average standard deviation (STD) reduction for the Radarsat-2 and Sentinel-1A interferograms were 19.68 and 14.75%, respectively. (3) Compared to the empirical linear model, the correlation between the stratified delays estimated by the two methods reached 0.73. We applied this method for the first time to a ground subsidence study in the Yuxi Basin and successfully detected three subsidence centers. We analyzed and discussed ground deformation causes based on rainfall and fault zones. Finally, we verified the accuracy of the proposed method by using leveling monitoring data.

Measurement of clinical delay intervals among younger adults with colorectal cancer using health administrative data: a population-based analysis

BackgroundClinical delays may be important contributors to outcomes among younger adults (<50 years) with colorectal cancer (CRC). We aimed to describe delay intervals for younger adults with CRC using health...

Synchronization of Markovian jump neural networks for sampled data control systems with additive delay components: Analysis of image encryption technique

In an modern world, image encryption played an vital role to prevent our data from illegal abuser entrée. Based on this, in this paper, the Markovian jump neural networks for synchronization of sampled‐data control systems with two additive delay components are used on the looped functional method, and its direct application is applied in image encryption. Meanwhile, and denotes the information states with tuning parameter and a few slack variable which is introduced in the derived result. Furthermore, the sampled‐data controller is intended both the present and delayed state information, to enroll the control performance and flexibility. Finally by using the new technique, the several examples are highlighted in the numerical section, and also, the effectiveness of an image encryption is studied.

Simultaneous multiple bands time delay and frequency component estimation based on expectation–maximization

Acoustic sensor systems often operate in complex environments containing multiple sound and noise sources. This paper proposes a new multi-band multi-source time delay estimation and frequency analysis algorithm based on the Expectation–Maximization (EM) method. The algorithm improves the multi-source signal superposition model and introduces the EM method to iteratively find parameters for each frequency band. The algorithm also provides multiple time delay estimates while simultaneously estimating the intensity distribution of the frequency bands for each of the corresponding time delays. The distributions are valuable for subsequent tasks such as target classification and identification. The performance of the algorithm is assessed by simulations and field experiments.

Influence of environmental variables on leaf area index in loblolly pine plantations

The productivity of even-aged forest stands varies from one year to the next as a function of canopy size and its interaction with the effective radiation used for photosynthesis. To characterize this relation, ecologists use leaf area index (LAI), a metric that serves as an indicator of the photosynthetic capacity on a given site. In this research, we proposed a model describing leaf area index dynamics in loblolly pine plantations growing in the southeastern United States. The model implements a delayed differential equation using periodic coefficients that enforce the seasonality in resource availability. The equation was further expanded to accommodate climatic variables to evaluate their contribution in reducing the observed variability. The proposed model uses environmental modifiers to account for the changes in resource availability and to adjust the carrying capacity in forest stands. From a range of tested variables, we found monthly maximum temperature and monthly excess water to be the most influential on leaf area index dynamics. With the addition of environmental modifiers and a local carrying capacity parameter, root mean square error was reduced to 0.3802 units LAI (m2/m2) from a base model RMSE of 0.4427 units LAI (m2/m2). The results indicate that the delay component has a small 41-day effect in the model, which is contrary to our initial hypothesis that stored within-tree carbohydrates can be used for further seasons to build foliage.