Time Delay Research Articles

Simplifying asynchronous optical sampling: an experimental approach toward industrial integration exploiting lock-in acquisition

Time-resolved optical spectroscopies are emerging as a go-to technique for non-destructive testing of nanomaterials. Inspecting the thermal and mechanical properties of a mesoscale device requires achieving delay times beyond the ns timescale in a nanoscopy setup, potentially in a vibration polluted environment. These requirements constitute a major challenge for traditional pump-probe techniques based on moving mechanical delay lines and lock-in detection. Asynchronous optical sampling (ASOPS) and electronically controlled optical sampling (ECOPS), avoiding any moving mechanical parts, are good alternatives. However, their detection scheme is based on fast-balanced photodiodes, which, as a technology, are not as widespread, not as developed, and lack the performance of lock-in based detection. In this study, we introduce what we believe is a novel approach that integrates ASOPS/ECOPS and lock-in detection methodologies, eliminating the necessity for a reference signal and streamlining the optical configuration. By leveraging the strengths of each technique, our approach enhances simplicity and efficiency. The scheme is first validated against standard approaches in the frame of a beam-depletion measurement in a sum frequency experiment. It is then tested in a paradigmatic case study to inspect the mechanics of a single gold nanodisk, with dimensions in the 100 nm range, nanopatterned on a sapphire substrate. These results widen the range of applicability of time-resolved optical techniques as a nano-metrology tool to industrial settings.

Reduction in chemotherapy relative dose intensity decreases overall survival of neoadjuvant chemoradiotherapy in patients with locally advanced esophageal carcinoma

BackgroundMany patients undergo dose reduction or early termination of chemotherapy to reduce chemoradiotherapy-related toxicity, which may increase their risk of survival. However, this strategy may result in underdosing patients with locally advanced esophageal squamous cell carcinoma (LA-ESCC). This study aimed to analyze the relationship between the relative dose intensity (RDI) and survival outcomes in patients with LA-ESCC.MethodsThis retrospective study assessed patients with LA-ESCC (cT2N + M0, cT3-4NanyM0) receiving neoadjuvant chemoradiotherapy (NCRT) with curative-intent esophagectomy. The patients received 2 courses of paclitaxel plus carboplatin (TC) combination radiotherapy prior to undergoing surgery. During NCRT, RDI was computed, defined as the received dose as a percentage of the standard dose, and the incidence of dose delays was estimated (≥ 7 days in any course cycle). The best RDI cutoff value (0.7) was obtained using ROC curve. The Kaplan–Meier survival curves were compared using the log-rank test, the treatment effect was measured using hazard ratios (HR) and 95% confidence intervals (CI).ResultsWe included 132 patients in this study, divided into RDI < 0.7 and RDI ≥ 0.7 groups using cut-off value of 0.7. RDI grade was an independent prognostic factor for OS. Baseline demographic and clinical characteristics were well balanced between the groups. There was no evidence that patients with RDI < 0.7 experienced less toxicity or those with RDI ≥ 0.7 resulted in more toxicity. However, patients with RDI < 0.7 who were given reduced doses had a worse overall survival [HR 0.49, 95% CI 0.27–0.88, P = 0.015]. The risk of a lower RDI increased with a longer dose delay time (P < 0.001).ConclusionThe RDI below 0.7 for avoiding chemoradiotherapy toxicity administration led to a reduction in the dose intensity of treatment and decreased overall survival.

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.

Complete synchronization of discrete-time variable-order fractional neural networks with time delays

This paper addresses complete synchronization issue of discrete-time variable-order fractional neural networks (DVFNNs) with time delays. By taking advantage of inequality technologies and nabla Laplace transform, two stability theories are derived in the form of Lyapunov lemmas, which, as generalizations of constant-order fractional versions, can be used to resolve complete synchronization issues of DVFNNs. In addition, under the designed controller, several complete synchronization criteria for DVFNNs are derived by utilizing inequality techniques and the Lyapunov method. In the end, a numerical example is provided to reveal the validity of our theoretical results.

HOLISMOKES - XII. Time-delay measurements of strongly lensed Type Ia supernovae using a long short-term memory network

Strongly lensed Type Ia supernovae (LSNe Ia) are a promising probe with which to measure the Hubble constant ($H_0$) directly. To use LSNe Ia for cosmography, a time-delay measurement between multiple images, a lens-mass model, and a mass reconstruction along the line of sight are required. In this work, we present the machine-learning network which is a combination of a long short-term memory network (LSTM) and a fully connected neural network (FCNN). The is designed to measure time delays on a sample of LSNe Ia spanning a broad range of properties, which we expect to find with the upcoming Rubin Observatory Legacy Survey of Space and Time (LSST) and for which follow-up observations are planned. With follow-up observations in the $i$ band (cadence of one to three days with a single-epoch $5 depth of 24.5 mag), we reach a bias-free delay measurement with a precision of around 0.7 days over a large sample of LSNe Ia. The is far more general than previous machine-learning approaches such as the random forest (RF) one, whereby an RF has to be trained for each observational pattern separately, and yet the outperforms the RF by a factor of roughly three. Therefore, the is a very promising approach to achieve robust time delays in LSNe Ia, which is important for a precise and accurate constraint on $H_0$.

Variable-step [formula omitted] method combined with time two-grid algorithm for multi-singularity problems arising from two-dimensional nonlinear delay fractional equations

In this paper, we focus on the numerical simulation for two-dimensional nonlinear fractional sub-diffusion equations in the presence of time delay. Firstly, we investigate the existence, uniqueness and regularity of the solution for such problems. The theoretical result implies that the solution at τ+ is smoother than that at 0+, where τ is a constant time delay, and this is an improvement for the work (Tan et al., 2022). Secondly, a high-order difference scheme based on L1 method is constructed. For the sake of repairing the convergence order in temporal direction and improving the computational efficiency, an efficient time two-grid algorithm based on nonuniform meshes is first developed. The convergence order of the two-grid scheme reaches O(NF−min{rα,2−α}+NC−min{2rα,4−2α}+h12+h22), where NF and NC represent the number of the fine and coarse grids respectively, while h1 and h2 are the space-step sizes. Furthermore, stability and convergence analysis of the proposed scheme are carefully verified by energy method. Finally, numerical experiments are carried out to show the validity of theoretical statements.

Asymptotic stability analysis of time delayed fractional-order replicator dynamics with government’s intervention

This paper investigates the asymptotic stability of fractional-order replicator dynamics with government involvement. Firstly, we extend replicator dynamics incorporating government intervention into a fractional-order framework. Subsequently, we introduce a novel property for the vector Lyapunov function tailored to fractional-order replicator dynamics with government involvement. We then use this new property to elucidate the asymptotic stability of fractional-order replicator dynamics with government intervention. Additionally, we tackle the scenario of fractional-order replicator dynamics with time delay, analyzing the system's asymptotic stability in the presence of delay. Finally, we validate the correctness and practicality of these novel theoretical results through numerical simulations conducted under various conditions.

Neural Network-Based Adaptive Tracking Control for Denitrification and Aeration Processes With Time Delays.

Wastewater treatment process (WWTP), consisting of a class of physical, chemical, and biological phenomena, is an important means to reduce environmental pollution and improve recycling efficiency of water resources. Considering characteristics of the complexities, uncertainties, nonlinearities, and multitime delays in WWTPs, an adaptive neural controller is presented to achieve the satisfying control performance for WWTPs. With the advantages of radial basis function neural networks (RBF NNs), the unknown dynamics in WWTPs are identified. Based on the mechanistic analysis, the time-varying delayed models of the denitrification and aeration processes are established. Based on the established delayed models, the Lyapunov-Krasovskii functional (LKF) is used to compensate for the time-varying delays caused by the push-flow and recycle flow phenomenon. The barrier Lyapunov function (BLF) is used to ensure that the dissolved oxygen (DO) and nitrate concentrations are always kept within the specified ranges though the time-varying delays and disturbances exist. Using Lyapunov theorem, the stability of the closed-loop system is proven. Finally, the proposed control method is carried out on the benchmark simulation model 1 (BSM1) to verify the effectiveness and practicability.

Hopf-Hopf bifurcation, period [formula omitted] solutions, slow-fast phenomena, and chimera of an optoelectronic reservoir computing system with single delayed feedback loop

In this paper, we investigate the co-dimension two bifurcations and complicated dynamics of an optoelectronic reservoir computing (RC) system with single delayed feedback loop. We focuses primarily on its underlying system’s Hopf-Hopf bifurcation. Firstly, we apply DDE-BIFTOOL built in Matlab to sketch the bifurcation diagrams with respect to two bifurcation parameters, namely feedback strength β and time delay τ, and find the existence of the Hopf-Hopf bifurcation points. Then, using the multiple scales method, we obtain their normal forms, and using the normal form method, we unfold and classify their local dynamics. Then numerical simulations are conducted to verify these results. We discover rich dynamical behaviors of the system in specific regions. Besides, other complicated dynamics, such as fast-slow phenomena, Period n solutions, and chimera, are found in the system. All these rich dynamical phenomena can provide excellent performance potentially for this optoelectronic reservoir computing system with single delayed feedback loop.

Stabilization of delayed Markovian jump systems with sampled controllers

This paper mainly studies the stabilization problem of continuous-time delayed Markovian jump systems by a sampled controller. Not only the state is sampled, but also the switching signals, where the latter sampled signal makes the synthesis and analysis of delayed systems more complex and difficult. To address these issues, this paper develops an augmented system approach, resulting in a novel system model that incorporates two delayed exponential matrices. It has been demonstrated that the stability properties of original delayed system can be ensured by the constructed auxiliary system. The correlation among Markov process, sampling interval and time delay is first established, and several stabilization results are given. More special situations about the proposed sampled are further considered. The validity and superiority of the method proposed in this paper are verified through two numerical examples.

Towards an optimal 3-D design and deployment of 6G UAVs for interference mitigation under terrestrial networks

Unmanned aerial vehicles (UAVs) have opened new communication possibilities by being able to access remote areas. Their ability to serve a large number of users based on demand and adaptability is a key strength. In Sixth Generation (6G) networks, UAVs are highly valued for their cost-efficiency and versatile deployment. However, the mobility of UAVs introduces different types of interference issues, resulting in a decrease in network performance and quality of service (QoS) for edge users. To address these challenges, this paper introduces a clustering-based solution involving three main steps. Firstly, UAVs are deployed in three-dimension (3D) space based on user requests using mini-batch K-mean clustering Subsequently, re-clustering is explored to tackle load balancing within clusters. Finally, outliers and boundary users are classified to enhance QoS for edge users. This model effectively reduces interference and boosts UAV reliability in terrestrial networks. Also, a case study is presented to show how UAVs can mitigate interference in maritime communication within terrestrial networks. Numerical results demonstrated that the proposed scheme increases throughput by 33.06% and reduces energy consumption and time delay by 73.15% and 9.15%, respectively, as compared to the existing baseline schemes.

Entropy-Extreme Model for Predicting the Development of Cyber Epidemics at Early Stages

The approaches used in biomedicine to analyze epidemics take into account features such as exponential growth in the early stages, slowdown in dynamics upon saturation, time delays in spread, segmented spread, evolutionary adaptations of the pathogen, and preventive measures based on universal communication protocols. All these characteristics are also present in modern cyber epidemics. Therefore, adapting effective biomedical approaches to epidemic analysis for the investigation of the development of cyber epidemics is a promising scientific research task. The article is dedicated to researching the problem of predicting the development of cyber epidemics at early stages. In such conditions, the available data is scarce, incomplete, and distorted. This situation makes it impossible to use artificial intelligence models for prediction. Therefore, the authors propose an entropy-extreme model, defined within the machine learning paradigm, to address this problem. The model is based on estimating the probability distributions of its controllable parameters from input data, taking into account the variability characteristic of the last ones. The entropy-extreme instance, identified from a set of such distributions, indicates the most uncertain (most negative) trajectory of the investigated process. Numerical methods are used to analyze the generated set of investigated process development trajectories based on the assessments of probability distributions of the controllable parameters and the variability characteristic. The result of the analysis includes characteristic predictive trajectories such as the average and median trajectories from the set, as well as the trajectory corresponding to the standard deviation area of the parameters’ values. Experiments with real data on the infection of Windows-operated devices by various categories of malware showed that the proposed model outperforms the classical competitor (least squares method) in predicting the development of cyber epidemics near the extremum of the time series representing the deployment of such a process over time. Moreover, the proposed model can be applied without any prior hypotheses regarding the probabilistic properties of the available data.

State Estimation of Switched Time-Delay Complex Networks With Strict Decreasing LKF.

State estimation issue is investigated for a switched complex network (CN) with time delay and external disturbances. The considered model is general with a one-sided Lipschitz (OSL) nonlinear term, which is less conservative than Lipschitz one and has wide applications. Adaptive mode-dependent nonidentical event-triggered control (ETC) mechanisms for only partial nodes are proposed for state estimators, which are not only more practical and flexible but also reduce the conservatism of the results. By using dwell-time (DT) segmentation and convex combination methods, a novel discretized Lyapunov-Krasovskii functional (LKF) is developed such that the value of LKF at switching instants is strict monotone decreasing, which makes it easy for nonweighted L2 -gain analysis without additional conservative transformation. The main results are given in the form of linear matrix inequalities (LMIs), by which the control gains of the state estimator are designed. A numerical example is given to illustrate the advantages of the novel analytical method.

Nonlinear-Model-Inversion Control for Stall-Flutter Suppression of an Airfoil via Camber Morphing

This paper presents a nonlinear-model-inversion control law to suppress stall flutter of an airfoil with active trailing-edge morphing. First, a nonlinear aeroelastic model is proposed utilizing two nonlinear autoregressive neural networks with exogenous inputs , which are used to predict aerodynamic moments on an airfoil due to large-amplitude oscillation and camber morphing, respectively. Afterward, a nonlinear-model-inversion control system is designed upon the mentioned aeroelastic system to suppress stall flutter via the camber morphing. A fluid–structure–control (FSC) coupling strategy is developed with structure and control systems embedded in the high-fidelity computational-fluid-dynamics environment to validate the control effect. The FSC high-fidelity simulations show that the nonlinear-model-inversion controller can suppress pitching oscillation completely, whereas a linear proportional–derivative controller without time delay only performs a limited suppression rate by 25.6%. The flowfield evolution result infers that the active camber morphing can generate a converse training-edge vortex, which counteracts the leading-edge vortex during stall flutter. From the perspective of an energy hysteresis, active camber morphing works well by converting injected aerodynamic energy from positive to negative.

Time-Delay Following Model for Connected and Automated Vehicles Considering Multiple Vehicle Safety Potential Fields

Connected and automated vehicles (CAVs) represent a significant development in the transport industry owing to their intelligent and interconnected features. Potential field theory has been extensively used to model CAV driving behaviour owing to its objectivity, universality, and measurability. However, existing car-following models do not consider the impact of time delays and the influence of information from multiple vehicles ahead and behind. This paper focuses on the driving-safety risks associated with CAVs, aiming to enhance vehicle safety and reliability during travelling. We developed a multi-vehicle car-following model based on safety potential fields (MIDM-SPF), taking into account the characteristics of multi-vehicle connected information and time delays. To enhance the model’s precision, real-world data from urban roads were employed, alongside an improved optimisation algorithm to fine-tune the car-following model. The simulation experiment revealed that MIDM-SPF significantly reduces stop-and-go traffic, thereby improving traffic flow stability in urban areas. Additionally, we validated the stability of our model under varying market penetration rates in large-scale mixed traffic. Our findings indicate that increasing the CAV proportion improves the stability of mixed traffic flows, which has important implications for alleviating traffic congestion and guiding the large-scale implementation of autonomous driving in the future.

Finite-time quasi-projective synchronization of fractional-order reaction-diffusion delayed neural networks

This paper investigates the finite-time quasi-projective synchronization (FTQPS) issue of fractional-order reaction-diffusion neural networks (FORDNNs). To the best of our knowledge, this paper introduces the concept of FTQPS for the first time. First, an integral-type Lyapunov function is constructed relying on the characterization of the reaction-diffusion term and some inequality methods. Subsequently, the nonlinear feedback control strategy is designed to achieve the FTQPS goal and some sufficient conditions are obtained to guarantee FTQPS of FORDNNs. Further, the system's synchronization speed is measured by estimating the settling time. It should be noted that the above control strategy is also applicable to conventional integer-order reaction-diffusion neural networks with time delays. Finally, a numerical example is used to illustrate the validity of the theoretical analysis presented.

An 8-bit successive-approximation register analog-to-digital converter operating at 125 kS/s with enhanced comparator in 180 nm CMOS technology

Data converters are necessary for the conversion process of analog and digital signals. Successive approximation register (SAR) analog-to-digital converters (ADC) can achieve high levels of accuracy while consuming relatively low amounts of power and operating at relatively high speeds. This paper describes a design of 8-bit 125 kS/s SAR ADC with a proposed high-speed comparator design based on dynamic latch architecture. The proposed design of the comparator enhances the performance compared to a conventional dynamic comparator by adding two parallel clocked input complementary metal-oxide semiconductor (CMOS) transistors which reduce the parasitic resistance in the latch ground path and serve to minimize the latch delay time. The design of each sub-system for the ADC is explained thoroughly, which contains a sample and hold circuit, successive approximation register, charge redistribution types digital-to-analog converter, and the new proposed comparator. The proposed design is implemented using 180 nm CMOS technology with a power supply of 1.2 V. The average inaccuracy in differential non-linearity (DNL) is +0.6/−0.8 LSB (least significant bit), and integral non-linearity (INL) is +0.4/−0.7 LSB. The proposed design exhibits a delay time of 157 ps at 1 MHz clock frequency.

A high-precision and near-zero residue laser-controlled gel propellant

Micro-nano satellites, with their advantages of small size, low power consumption, short development cycles, and capability for formation flying and networking, play a significant role in scientific research, national defense, and commercial applications. However, currently developed micro-nano satellites globally possess very limited maneuverability and lack effective attitude and orbit propulsion control. Solid propellants can offer some advantages such as high energy density and low cost, and they are extensively used in micro-and nano satellite propulsion systems. However, most of these propellants exhibit poor controllability and produce massive amounts of residual by-products. Laser-controlled gel propellants have been confirmed to be the most effective solution to address these issues in this study. The present study exhibits the preparation of ADN-based laser-controlled gel propellants using the freeze–thaw method and their controllable combustion characteristics under laser irradiation. The results show that the ignition delay time of gel propellants decreases from 93.0 ms to 34.6 ms, the extinction delay time is stable at around 2 ms, and the average burning rate increases from 0.6 mm/s to 1.5 mm/s under continuous laser irradiation with the power density ranging from 0.6 to 1.3 W/mm2. The average utilization rate of gel propellant is as high as 99.2 %. Upon testing, the residual solid matter was identified as unreacted propellant that had not been irradiated by the laser. Laser-controlled gel propellants represent a new type of energetic material that can be modulated to control its ignition, extinction, and burning rate. These capabilities overcome the challenges associated with the inability of traditional chemical propellants to extinguish and restart, thereby fulfilling the propulsion system requirements of micro-nano satellites.

Assessment of B-type natriuretic peptide as an early predictor of mortality in acutely poisoned patients with cardiotoxicities.

Cardiotoxicity is a major toxic effect induced by several types of drugs. An electrocardiogram is done routinely in cardiovascular drug exposures. Cardiac troponin I (cTnI) is the usual biomarker for diagnosing myocardial injury. B-type natriuretic peptide (BNP) is a well-established predictor of disease state in suspected heart failure. The study aimed to assess BNP's role as an early predictor of mortality compared with cTnI and ECG changes in acutely poisoned patients with cardiotoxicities. This study enrolled 70 patients with acute cardiotoxicity by drugs and toxins known to cause cardiac injury admitted to Tanta University Poison Control Center (TUPCC). Collected data included socio-demographic data, toxicological history, vital signs, ECG changes, Poison Severity Score (PSS), BNP, and cTnI serum levels. Patients were classified as survivors and non-survivors. Significantly more delay time was recorded in non-survivors. Moreover, vital signs were significantly abnormal in non-survivors. There was no statistical significance regarding the initial ECG abnormalities between survivors and non-survivors. BNP and cTnI levels were significantly higher among non-survivors. For mortality prediction, BNP had good predictive power (AUC=0.841) with 100% sensitivity and 79.7% specificity while cTnI had an acceptable predictive power (AUC=0.786), with 83.3% sensitivity and 78.1% specificity with insignificant difference between both biomarkers. BNP and cTnI levels can predict mortality in acute cardiotoxicity compared to ECG which has no statistically significant prediction. BNP has a higher discriminatory power than cTnI for the prediction of mortality.

Design and numerical analysis of high contrast ratio and ultra-compact all-optical 2-bit reversible comparator

In this paper, a novel interference-based nanostructure was designed and simulated to realize an all-optical 2-bit reversible comparator by employing a novel technique. The plane wave expansion (PWE) method was adopted to analyze the encoder design and frequency modes. Aside from downsizing, the finite-difference time-domain (FDTD) method was utilized for the simulation and numerical analysis of the design proposed herein. An ultra-compact nanostructure with a 129.8 μm2 footprint was utilized for the all-optical 2-bit reversible comparator. One of the noteworthy characteristics of the proposed nanostructure was its excellent contrast ratio (i.e., 13.8 dB) in comparison to other nanostructures. The bitrate and delay time in this nanostructure were 3.33 Tb/s and 300 fs, respectively. Based on the findings of the simulations conducted at a central wavelength of 1.55 μm, it is recommended to employ the nanostructure proposed herein during the third telecom window. A photonic crystal nano-resonator was utilized to design the high-performance all-optical 2-bit reversible comparator, which may also be employed in integrated optical circuits (IOCs).