Aggressive scaling of semiconductor technology nodes has led to copper-based interconnects beginning to approach the maximum scaling limit of the material, beyond which unacceptably high increases in interconnect resistance due to electron scattering at grain boundaries and interfaces begins to cause degradation of device performance. New materials are required for interconnect applications beyond the 7 nm node to produce devices with acceptable signal delay and power consumption parameters. Topological semimetals are one family of materials that are of interest for the replacement of copper in interconnect applications due to the predicted favorable resistance scaling, which results from topologically protected surface states that suppress electron scattering and act as conduction pathways in nanoscale films. This decrease in interconnect resistance has the potential to improve the efficiency of integrated circuits through reduced RC delay and reduced energy consumption, which is under increased scrutiny due to increasing computing demands, such as generative artificial intelligence and cloud computing. In order to aid in the integration of these promising materials into production environments, scalable synthesis methods, such as atomic layer deposition (ALD), are needed. In addition to the development of deposition chemistries for these materials, insight into how processing conditions impact the performance of the resulting film are also of importance. Here, we report on a new thermal ALD deposition chemistry for molybdenum phosphide (MoP) using molybdenum(V) chloride (MoCl5) and tris(dimethylamino)phosphine (TDMAP) at temperatures between 350 °C and 425 °C. In-situ and ex-situ characterization of the resulting films was performed using quartz crystal microbalance (QCM), x-ray photoelectron spectroscopy (XPS), x-ray diffraction (XRD), atomic force microscopy (AFM), scanning electron microscopy (SEM), and four-point probe measurements. QCM measurements demonstrated a linear mass increase of 164 ng/cycle at 375 °C. Film deposition was confirmed through XRD and XPS chemical state analysis. The resulting films were near stoichiometric as determined via XPS. AFM and SEM characterization revealed a polycrystalline morphology with nanoscale grain sizes. Four-point probe measurements of the as-deposited films indicated non-ideal electrical performance which was subsequently improved through post deposition annealing. Although more work is needed to improve electrical performance, this new ALD chemistry may provide a method for the deposition of MoP films at the dimensions required for next generation technology nodes.

Traffic signalized intersections form crucial control nodes in urban networks, where fluctuations in vehicle arrival rates during peak periods often produce extended delays and unreliable performance. Traditional deterministic design approaches, based on mean hourly volumes, fail to represent short-term variability inherent in real-world traffic conditions. This paper presents a stochastic reliability analysis framework to quantify the effect of Peak Hour Factor (PHF) variability on intersection delay performance, integrating field traffic data from Palm Beach and Broward Counties, Florida. Using Monte Carlo simulation, delay probability distributions were generated, and key reliability metrics-including the probability of failure (Pf) and reliability index (β)-were evaluated for both morning (AM) and evening (PM) peaks. Results revealed that intersections with low PHF (< 0.80) exhibited higher probabilities of exceeding the critical 55 s/veh delay threshold, with PM peaks showing Pf ≈ 0.39 and β = 0.28, compared to AM Pf ≈ 0.27 and β = 0.61. Incorporating additional uncertainties-arrival-type randomness and saturation flow variability-increased unreliability by approximately 15%. The proposed framework demonstrates that reliability-based modeling provides a more realistic, risk-informed foundation for traffic signal timing, design evaluation, and urban mobility planning.

Accurate vehicle state estimation in a lane coordinate system is essential for safe and reliable operation of Advanced Driver Assistance Systems (ADASs) and autonomous driving. However, achieving robust lane-based state estimation using only low-cost sensors, such as a camera, an IMU, and a steering angle sensor, remains challenging due to the complexity of vehicle dynamics and the inherent signal delays in vision systems. This paper presents a lane-coordinate-based vehicle state estimator that addresses these challenges by combining a vehicle dynamics-based bicycle model with an Extended Kalman Filter (EKF) and a signal delay compensation algorithm. The estimator performs real-time estimation of lateral position, lateral velocity, and heading angle, including the unmeasurable lateral velocity about the lane, by predicting the vehicle's state evolution during camera processing delays. A computationally efficient camera processing pipeline, incorporating lane segmentation via a pre-trained network and lane-based state extraction, is implemented to support practical applications. Validation using real vehicle driving data on straight and curved roads demonstrates that the proposed estimator provides continuous, high-accuracy, and delay-compensated lane-coordinate-based vehicle states. Compared to conventional camera-only methods and estimators without delay compensation, the proposed approach significantly reduces estimation errors and phase lag, enabling the reliable and real-time acquisition of vehicle-state information critical for ADAS and autonomous driving applications.

Objectives. The study sets out to parametrically investigate the impact of time delays within cyber-physical emulation circuits for signal audio modules. Specifically, it examines how delays introduced by analog-to-digital and digital-to-analog converters of the hardware/software interface, the central processor, and the visual graphical emulation (VGE) software environment are influenced by factors like the selected data input-output protocol and the VGE block preset configurations such as sampling rate, buffer size and time, and the number of channels.Methods. Used methods of architectural SPICE modeling of electrical circuits on the VGE Simulink software platforms leverage the resources of the Simscape library and LiveSPICE. Additional methods include those for incorporating differential equations in the numerical analysis of SPICE models designed for analog circuits and techniques for processing experimental data generated from cyber-physical emulation using the built-in Simulink environment and associated laboratory radio measurement tools.Results. The study introduces a novel approach to emulate analog audio devices using cyber-physical SPICE modeling. Through the use of digital twins, the study investigates the impact of modifiable parameters on signal delays within audio module circuits during cyber-physical emulation. Based on these findings, technical guidelines are provided for selecting appropriate delay correction settings between 20 and 120 ms to ensure efficient highspeed audio signal post-processing.Conclusions. By configuring the VGE software block’s settings identically to the ASIO data input/output protocol prevalent in audio interface technology (44.1 kHz sampling frequency, 8 buffer size) substantially decreased latency in typical audio module circuit nodes is achieved with cyber-physical emulation built into the VGE LiveSPICE environment. The achieved time delays of 5 ms direct transmission circuit contrast with the 7 ms latency observed in the cyber-physical emulation of the SPICE circuit when both are benchmarked within the VGE Simulink environment. The successful implementation of cyber-physical emulation for SPICE models is achieved through the use of particular settings, such as a 44.1 kHz sampling frequency, buffer sizes ranging from 512 to 1024 samples, and the use of the ASIO data input/output protocol.

The postoperative rehabilitation of ankle fractures, particularly in the home setting, has a crucial influence on the recovery of lower limb function. To enhance the portability, real‐time performance, and safety of postoperative remote rehabilitation training, this study proposes a novel robot‐assisted remote rehabilitation system tailored for postoperative ankle fracture patients. Based on a distributed system architecture, the hardware system enables modular decomposition and facilitates wireless control of the lower controller. The total weight of the robotic system is 2.634 kg. By combining a deep learning algorithm with an interpolation fitting method, the time delay in interaction force signals during remote communication is predicted and compensated. The control frequency is elevated to 100 Hz with a maximum normalized root mean square error of 10.89%, ensuring the precision and continuity of the robot control system. Additionally, a full‐cycle rehabilitation training strategy based on adaptive admittance control with system stiffness identification is proposed, encompassing passive, active–passive, isotonic, and active activities of daily living trainings. Experimental results indicate that the robotic system can execute the training strategies at each phase with high accuracy and safety, and the proposed adaptive control strategy has better compliance than fixed parameter admittance control and fuzzy admittance control methods.

Time delay of pulsar signals in astrophysical black hole spacetimes

Sensing-actuation system with zero signal delay for ultrasensitive recognition

Conflict resolution and response inhibition: A simultaneous EEG-EMG-pupillometry study.

With the rapid development of next-generation new energy systems, user-side resources are aggregated to participate in demand response for peak shaving and valley filling. However, volatile renewable outputs cause unpredictable power deviations, while unreliable communication devices introduce signal delays and data loss. To address this challenge, we investigate precise and robust demand response control considering dual-network coupling uncertainties. An cccurate model construction of dual-network coupling uncertainty is constructed based on robust optimization theory and Monte Carlo sampling. In particular, the impacts of communication device reliability, communication delay, and bit error characteristics are characterized. Then, an optimization problem with the objective of minimizing distribution network loss and maximizing microgrid operator income is formulated, which is transformed into a two-stage robust optimization model. Next, a data-driven, precise, and robust control method based on a dual-adversarial deep Q-network and sudden power outage perception is proposed. Two intelligent but adversarial agents are designed to iteratively confront each other in alternating iterations, improving convergence speed and obtaining robust optimal solutions. Considering the impact of sudden power outages, an emergency control preference model is established based on historical data of load retention priorities and economic incentive levels, reducing the risk of large-scale power outages and ensuring stable operation of the distribution network. Simulation results confirm the effectiveness of the proposed method in enhancing the accuracy and robustness of demand response.

Nerve conduction velocity studies are essential to understanding neurological disorders like ALS, Guillain-Barré syndrome, Charcot-Marie-Tooth disease, carpal tunnel syndrome, sciatic nerve disorders, and multiple sclerosis, which are marked by slowed signal conduction. Various ions in the extracellular space (ECS) and the nerve fiber regulate signal propagation, making it crucial to analyze ECS's impact on signal transmission. This study examines how a non-homogeneous extracellular space affects nerve conduction velocity using a modified cable model that incorporates ECS parameters such as its diameter and resistance. The results suggest that a non-homogeneous extracellular space significantly impacts the conduction velocity of propagating signals, leading to variations in the conduction velocity, signal delays, phase shifts, and resonance. The model has been thoroughly examined using various combinations of electrophysiological parameters of the ECS and nerve fibers to simulate a wide range of biological conditions, and the simulated results have been consistent and align with the existing findings.

Abstract. Electromagnetic signals commonly used in geodetic applications, such as the Global Navigation Satellite System (GNSS), undergo bending and delay in the neutral gas atmosphere of the Earth. The least travel time (LTT) concept is one of the approaches to model signal slant delays via a ray tracing (RT) procedure. In this study, we developed an LTT-based RT algorithm (LTT v2), where the three-dimensional refractivity field of the atmosphere is based on the atmospheric model data. This representation is complete in a sense that the domain of the RT conforms to the native grid geometry of the atmospheric model. In principle, the LTT-based RT algorithm is seen as an extension of an atmospheric model for signal delay evaluation. The atmospheric states are generated using a global numerical weather prediction model, the Open Integrated Forecast System of the European Centre for Medium-Range Weather Forecasts. In the LTT v2 model, some physical and numerical approximations are improved compared to the original implementation, called “LTT v1”. We compare the slant delays products of the two models. Additionally, a comparable modelling setup is created with the state-of-the-art VieVS Ray Tracer (RADIATE). The skill of slant delay estimation is assessed using metrics that are indicative of the quality of GNSS products derived using the GROOPS (Gravity Recovery Object Oriented Programming System) orbit solver software toolkit of the Graz University of Technology. The metrics used are GNSS orbit midnight discontinuities (MDs) and residuals of ground station precise positioning with respect to the IGS14 reference. Employment of slant delay products of the LTT RT algorithm in GNSS processing shows similar performance with v1 and v2. The GNSS orbit MDs are reduced by around 3 % when using the LTT v2 model, while root-mean-square residuals of ground station precise positioning are 5 % lower with LTT v1. The consistency of both metrics is improved slightly using LTT v2, as seen by the metrics' standard deviation values. Intercomparison with RADIATE indicates significantly better performance of LTT v2, which we attribute entirely to the much larger amount and lossless utilization of weather model data as input to LTT v2 versus RADIATE.

This article investigates the controllability of networked sampled-data systems with various time delays on both control and transmission channels. Necessary and sufficient controllability conditions are first derived for systems with a single delay and then extended to systems with multiple delays. It is found that delays in control signals have no effects on the overall controllability. For a networked system whose topology matrix has only zero eigenvalues, delays of neither control nor transmission signals will affect the overall controllability. It is proved that an uncontrollable mode 1 of such a networked sampled-data system cannot be altered by arbitrary delays. Finally, the networked sampled-data system with first-order holders is discussed, which is modeled as a variant of time-delayed system, and some easy-to-verify algebraic conditions on the controllability are given based on matrix rank checking.

The Impact of Signal Delay and Power Surge on the Performance of Parity-Time Symmetric Wireless Power Transfer System

Developing porous low-dielectric (low-k) materials is one of the methods to achieve high-performance integrated circuits (ICs), which is critical for reducing signal delay and power consumption. This review focuses on the classification, synthesis, and application of these materials, with a spotlight on recent advances in polyimide (PI) and silica-based variants. The PI materials are characterized based on the specific preparation methods employed, while the silica-based materials are classified according to their organic and inorganic properties. Additionally, an investigation is conducted on various porous low-k materials. These materials are engineered to achieve a balance between low dielectric constants and retained mechanical integrity, which is essential for advanced IC performance. The review highlights the challenges in material synthesis and integration, emphasizing the need for scalable and reliable solutions. Through a qualitative analysis of the literature, this review compares properties and fabrication techniques, exploring both traditional and emerging materials. The summary of recent advancements and the identification of gaps in understanding aim to guide future research and experimental development, ensuring the continued progress of microelectronic devices.

Abstract. Atmospheric rivers (ARs) are narrow filaments of high moisture flux responsible for most of the horizontal transport of water vapor from the tropics to mid-latitudes. Improving forecasts of ARs through numerical weather prediction (NWP) is important for increasing the resilience of the western United States (US) to flooding and droughts. These NWP forecasts rely on the improved understanding of AR physics and dynamics from satellite, radar, aircraft, and in situ observations, and now airborne radio occultation (ARO) can contribute to those goals. The ARO technique is based on precise measurements of Global Navigation Satellite Systems (GNSS) signal delays collected from a receiver on board an aircraft from setting or rising GNSS satellites. ARO inherits the advantages of high vertical resolution and all-weather capability of spaceborne RO observations and has the additional advantage of continuous and dense sampling of the targeted storm area. This work presents a comprehensive ARO dataset recovered from 4 years of AR Reconnaissance (AR Recon) missions over the eastern Pacific. The final dataset is comprised of ∼ 1700 ARO profiles from 39 flights over approximately 260 flight hours from multiple GNSS constellations. Profiles extend from aircraft cruising altitude (13–14 km) down into the lower troposphere, with more than 50 % of the profiles extending below 4 km, below which the receiver loses or cannot initiate signal tracking. The horizontal drift of the tangent points that comprise a given ARO profile greatly extends the area sampled from just underneath the aircraft to both sides of the flight track (up to ∼ 400 km). The estimated refractivity accuracy with respect to dropsondes is ∼ 1.2 % in the upper troposphere, where the sample points are closely colocated. For the lower troposphere, the agreement is within ∼ 7 %, which is the level of consistency expected given the nature of atmospheric variations over the 300–700 km separation between the lowest point and the dropsonde.

This article investigates the Cauchy problem for a stochastic partial differential-functional equation (SPDFE) of a special form, which models dynamic processes with memory under the influence of random perturbations. In particular, we examine the mathematical conditions ensuring the existence and uniqueness of the solution. The theoretical results are based on modern tools of stochastic analysis and functional differential calculus. Since analytical solutions to such equations are generally unavailable, the paper explores approaches for approximate numerical solutions. We describe the discretization of space and time, and techniques for approximating the functional (memory-dependent) terms using a historical buffer. Stochastic perturbations are modeled as space-time noise based on the Wiener process. We developed a Python-based software implementation using the Euler–Maruyama method to validate the theoretical results and provide a practical illustration of memory-driven dynamics. The considered equation models the evolution of a system under spatial diffusion, damping, memory effects (via past state dependence), and stochastic noise. For the numerical solution, we use discretized approximations of the memory integral as an average over a buffer of previous values. A graphical visualization of the spatiotemporal evolution is constructed, including a heatmap that shows how the system state u(t, x) «spreads» and fluctuates under the combined influence of memory and noise. The results are promising for further applications in the theory and practice of computational modeling of complex dynamical systems with memory and stochasticity. In particular, the developed approaches may be applied in modeling processes in physics (heat conduction with delay, diffusion in memory-structured media), biology and ecology (population dynamics or epidemic spread with incubation delays), financial mathematics (volatility depending on past states), and technical or information control systems with stochastic effects and signal delays.

In this paper, a robust neural adaptive controller is proposed for the trajectory tracking control problem of unmanned surface vessels (USVs), considering model uncertainty, time-varying environmental disturbance, and actuator saturation. First, measurement errors in acceleration signals are eliminated through filtering techniques and a series of auxiliary variables, and after linearly parameterizing the USV dynamic model, a parameter adaptive update law is developed based on Lyapunov’s second method to estimate unknown dynamic parameters in the USV dynamics model. This parameter adaptive update law enables online identification of all USV dynamic parameters during trajectory tracking while ensuring convergence of the estimation errors. Second, a radial basis function neural network (RBF-NN) is employed to approximate unmodeled dynamics in the USV system, and on this basis, a robust damping term is designed based on neural damping technology to compensate for environmental disturbances and unmodeled dynamics. Subsequently, a trajectory tracking controller with parameter adaptation law and robust damping term is proposed using Lyapunov theory and adaptive control techniques. In addition, finite-time auxiliary variables are also added to the controller to handle the actuator saturation problem. Signal delay compensators are designed to compensate for input signal delays in the control system, thereby enhancing controller reliability. The proposed controller ensures robustness in trajectory tracking under model uncertainties and time-varying environmental disturbances. Finally, the convergence of each signal of the closed-loop system is proved based on Lyapunov theory. And the effectiveness of the control system is verified by numerical simulation experiments.

The article presents a study of hybrid adder architectures, focused on increasing their efficiency by minimizing signal propagation delay in digital systems. The main attention is paid to the comparative analysis of integrating different principles of adder construction, particularly serial and parallel circuits, with the aim of achieving an optimal balance between speed and hardware costs. For a detailed study of the time characteristics of hybrid solutions, modeling in the specialized software environment OrCAD/PSpice was applied. Critical parameters that significantly affect calculation speed and signal delay under different input pulse configurations were experimentally determined. Approaches to optimizing these parameters to ensure stable and fast operation are proposed. The research results demonstrate how the use of hybrid architectures can significantly improve the overall performance of arithmetic logic units. This creates prerequisites for the development of more high-speed and energy-efficient microchips, which is critically important for modern computing systems. A comparative characteristic of the studied adders is provided, confirming the effectiveness of the chosen approach. The obtained data emphasize the significant potential of hybrid adders as a key element in the design of new generation high-performance digital systems. Figs.: 9. Tabl.: 1. Refs.: 8 titles.

The current literature still lacks robust real-life evidence to support the practical implications of telesurgery. We conduct the current study to evaluate the safety and effectiveness of telesurgery over a high-speed remote communication network (the 5G network) in the real world. A total of 37 patients were enrolled across the participating hospitals from December 2023 to June 2024. Patients underwent various telesurgeries including radical prostatectomy, partial nephrectomy and other urological surgeries. The primary end point is the success rate of remote surgery. The secondary end points include network transmission status, robot-assisted surgery time, intraoperative blood loss, subjective evaluation of intraoperative experience, postoperative PSA level, positive surgical margin. Other evaluation index includes the surgeon's physiological and psychological stress during surgery, equipment failure and adverse events as postoperative complications. The overall success rate for remote surgery was 100% [95% confidence interval (CI) 90.5-100, p = 0.019]. The mean network signal delay was 55.30 ± 39.64ms. The total delay at the local and remote ends were 250.30 ± 39.64ms and 249.78 ± 40.00ms, respectively. The average packet loss rate was less than 0.01% at both ends. The evaluation of the remote surgeons' intraoperative experience excellent. A total of 6 adverse events occurred and none were related to the instruments of robot. No postoperative complications of Clavien-Dindo grade III-IV occurred. No serious or repairable equipment failure occurred at either the remote or local end. It is feasible to perform urological telesurgery over the 5G high-speed remote communication network to overcome geographical barriers and improve patient care.

In the field of modern optical communication, radar signal processing and optical sensors, true time delay technology, as a key means of signal processing, can achieve the accurate control of the time delay of optical signals. This study presents a novel design that integrates a 2 × 2 Multi-Mode Interference (MMI) structure with a Mach–Zehnder modulator on a silicon nitride–lithium niobate (SiN-LiNbO3) heterogeneous integrated optical platform. This configuration enables the selective interruption of optical wave paths. The upper path passes through an ultralow-loss Archimedes’ spiral waveguide delay line made of silicon nitride, where the five spiral structures provide delays of 10 ps, 20 ps, 40 ps, 80 ps, and 160 ps, respectively. In contrast, the lower path is straight through, without introducing an additional delay. By applying an electrical voltage, the state of the SiN-LiNbO3 switch can be altered, facilitating the switching and reconfiguration of optical paths and ultimately enabling the combination of various delay values. Simulation results demonstrate that the proposed optical true delay line achieves a discrete, adjustable delay ranging from 10 ps to 310 ps with a step size of 10 ps. The delay loss is less than 0.013 dB/ps, the response speed reaches the order of ns, and the 3 dB-EO bandwidth is broader than 67 GHz. In comparison to other optical switches optical true delay lines in terms of the parameters of delay range, minimum adjustable delay, and delay loss, the proposed optical waveguide digital adjustable true delay line, which is based on an optical switch and an Archimedes’ spiral structure, has outstanding advantages in response speed and delay loss.