State Transition Time Research Articles

Bayesian regularization-based intelligent computing for peristaltic propulsion of curvature-dependent channel walls

This study investigates the numerical analysis of curvature-dependent symmetric channel walls filled with porous media, focusing on various flow characteristics using Artificial Neural Networks optimized with the Levenberg–Marquardt Backpropagation Scheme (ANNs-BLMS). The analysis explores the Electrically Conducting Peristaltic Propulsion of Carreau–Yasuda Ternary Hybrid Nanofluids (ECPPCY-THNFs) propagating through sinusoidal wave trains within a curved conduit. To streamline the analysis, the governing equations have been simplified under specific assumptions of lubrication theory. The simplified governing equations are solved using Adam and three-stage Lobatto IIIa formula numerical techniques to generate a dataset spanning the curvature-dependent channel walls, covering four cases and nine scenarios of ECPPCY-THNFs. This dataset encompasses four cases and nine scenarios of ECPPCY-THNFs, with a step size of 0.02. As a result, the domain is divided into 131 grid points for velocity and temperature profiles and 71 grid points for rates of heat transfer analysis. The dataset is divided into three parts: 10% for training, 10% for testing, and 80% for validation. To apply the proposed methodology, the dataset is constructed by varying the Hartmann number, flow rate, Darcy number, curvature parameter, and radiation parameter. Subsequently, an artificial intelligence-based algorithm is employed to derive solution expressions for various flow fields and to analyze the dataset. The results are presented through detailed tabular and graphical illustrations. Heat transfer analysis is performed using the proposed model, and the findings are validated through multiple techniques, including error histograms, regression plots, mean square error (MSE), time series analysis, error autocorrelation, and state transition. A comparative study between two numerical methods and Artificial Intelligence (AI)-generated predictions is also undertaken. The results obtained using the AI-based ANN-BLMS framework confirm the reliability and accuracy of the proposed methodology in effectively solving the ECPPCY-THNFs. The results demonstrate that the curvature parameter has a considerable effect on the mechanical and thermal aspects of the flow, and therefore, it must be incorporated into the modeling of flows through curved channels. Additionally, the flow rate of 7.5 is the critical value, representing the minimum required to sustain fluid flow in a curved channel. When the curvature parameter is below this critical value, an increase in the curvature results in a decrease in the temperature profile. However, when the curvature parameter exceeds the critical value, the temperature profile shows the opposite trend. Furthermore, the velocity of ternary hybrid nanofluids show concave-up shapes for flow rates (Θ) values greater than 7.5 and concave-down shapes for flow rates values less than 7.5. The highest and lowest velocities occur near the center of the curved channel for Θ>7.5 and Θ<7.5, respectively. Moreover, the coefficient of determination values, used as performance indicators, are found to be unity (1.000) for the ANN model. The MSE values and error histogram values for the heat transfer rates are 2.8467 × 10−11 and −3.05 × 10−7, respectively.

An efficient tracking differentiator based active disturbance rejection control for flight environment simulation system

The flight environment simulation system plays a crucial role in aeroengine testing, replicating real mission pressure and temperature conditions. However, existing systems face challenges in handling strong measurement noises and total disturbances during transient tests, leading to inaccurate simulations and potential actuator damage. To address these gaps, an enhanced tracking differentiator based active disturbance rejection control scheme is proposed and implemented. Our method introduces two key innovations: a discrete-time optimal control law that dynamically adjusts control inputs based on state transition and sampling times, and a phase-advancing method that mitigates filtering phase delays by incorporating derivative signals to predict input trends. These components are integrated into a novel tracking differentiator, enhancing the estimation capability of the extended state observer in active disturbance rejection control. Frequency-domain analysis demonstrates the proposed tracking differentiator's superior filtering performance and phase quality. Simulations of intake environment pressure during aeroengine transient tests reveal that, compared to traditional tracking differentiator-based active disturbance rejection control, the proposed approach significantly reduces both control signal oscillations and setpoint deviations by 75.44% and 53.9%, respectively. These improvements effectively address environment simulation deviations and unnecessary actuator wear, thereby enhancing the accuracy and reliability of aeroengine testing.

Costs of Severe to Profound Hearing Loss & Cost Savings of Cochlear Implants.

To estimate costs of severe to profound hearing loss, including costs and cost-savings associated with cochlear implantation. Data was obtained from the National Health Interview Survey, the National Health and Nutrition Examination Survey and national Medicare rates. We used continuous time state transition models with individual patient simulations to estimate the costs of severe to profound hearing loss (SPHL) across the lifespan. The model included four states, normal hearing, severe to profound hearing loss, cochlear implantation, and death. The estimated lifetime cost of an individual born with SPHL is $489,274 [377,518; 616,519]. Costs are lower for those who received a cochlear implant before 18 months of age $390,931 [311,976; 471,475], compared to those who are not implanted $608,167 [442,544; 791,719]. For individuals with a later onset of hearing loss (60 years old) lifetime costs were $154,536 [7,093; 302,936]. The annual societal costs for the US population were estimated to be $37 [8; 187] billion. SPHL is a costly condition, with the primary driver being lost productivity. Medical costs were higher for cochlear implantation, however, the higher income earnings offset the higher medical costs. Overall, early implantation substantially reduced lifetime costs. Access to hearing health care and technology is critical given the documented benefits for language, education, and quality of life. Government and insurance policies should be modified to allow for equal access and coverage for hearing technology, which will ultimately reduce lifetime and societal costs. N/A The current study used existing nationally representative datasets. Thus, these levels of evidence do not apply. Laryngoscope, 134:4358-4365, 2024.

An optimized Closed-loop Precharging Strategy for Modular Multilevel Converters Based on Power Balancing Control under start or fault restart conditions

Modular multilevel converter (MMC)-based high-voltage DC (HVDC) systems have found rapid growth in power grids. To reduce the inrush current, it is necessary to precharge the submodule (SM) capacitors to the rated voltage before normal operation. In this paper, an optimized pre-charging strategy for MMC is proposed based on power balancing . First, the charging power is obtained by a designed closed-loop control of the SM capacitors. Then the total power released by the AC or DC side could be calculated by power conservation and feedback by controlling the charging current. At the same time, the power difference between the upper and lower bridge arm of each phase and the internal voltage balancing of bridge arms are also considered by the proposed bridge arm and SM voltage algorithms. By comparing with conventional closed-loop pre-charging strategies, the pre-charging speed is greatly enhanced as the achievement of synchronous charging of upper and lower bridge arm SMs. Moreover, as the same voltage and current double closed-loop control applied in both MMC pre-charging and normal state, the state transition time could be reduced. Finally, a series of simulations and prototype experiments are conducted to verify the feasibility of the proposed pre-charging strategy.

Experimental assessment of a JANUS-based consensus protocol

This paper proposes a distributed, JANUS-based protocol that enables an underwater acoustic network to reach consensus on arbitrary local opinions as numeric state variables. An envisioned scenario where nodes shall agree on parameters describing the acoustic environment is used to evaluate the protocol. The scenario exemplifies the protocol’s potential in future applications where nodes use the environment description to decide on appropriate modulation and coding schemes.The evaluation is based on numerical simulations and sea experiments in a challenging acoustic environment. The numerical simulations allowed examining the performance for different parameter values regarding the timing of transmission events and state transitions in the finite state machine implementation of the protocol. The best parameter configuration was used in the sea experiments conducted in a bay in the Baltic Sea. The experiments comprised several deployments of five to six commercial modems.Results from the experiments show that the protocol can achieve a consensus up to 89% of the time in the tested environment, and up to 96% of the time if the state variables are permitted to differ by one discretisation step maximum across the network. In addition, if the network separates due to environmental conditions, connected components appear to achieve consensus more often when the links are more reliable. Finally, it is shown that when different consensus processes are active in parallel, packets from one process do not interfere with the opinions in different processes, besides the existing probability of packet loss due to packet collision.

Rms–Flux Slope in MAXI J1820+070: A Measure of Disk–Corona Coupling

The linear rms–flux relation has been well established in different spectral states of all accreting systems. In this work, we study the evolution of the frequency-dependent rms–flux relation of MAXI J1820+070 during the initial decaying phase of its 2018 outburst with Insight-HXMT over a broad energy range of 1–150 keV. As the flux decreases, we first observe a linear rms–flux relation at frequencies from 2 mHz to 10 Hz, while such a relation breaks at varying times for different energies, leading to a substantial reduction in the slope. Moreover, we find that the low-frequency variability exhibits the highest sensitivity to the break, which occurs prior to the hard-to-hard state transition time determined through time-averaged spectroscopy, and the time deviation increases with energy. The overall evolution of the rms–flux slope and intercept suggests the presence of a two-component Comptonization system. One component is radially extended, explaining the strong disk–corona coupling before the break, while the other component extends vertically, contributing to a reduction of disk–corona coupling after the break. A further vertical expansion of the latter component is required to accommodate the dynamic evolution observed in the rms–flux slope. In conclusion, we suggest that the rms–flux slope in the 1–150 keV band can be employed as an indicator of disk–corona coupling, and the hard-to-hard state transition in MAXI J1820+070 could be partially driven by changes in the coronal geometry.

Early Prediction of Human Intention for Human–Robot Collaboration Using Transformer Network

Abstract Human intention prediction plays a critical role in human–robot collaboration, as it helps robots improve efficiency and safety by accurately anticipating human intentions and proactively assisting with tasks. While current applications often focus on predicting intent once human action is completed, recognizing human intent in advance has received less attention. This study aims to equip robots with the capability to forecast human intent before completing an action, i.e., early intent prediction. To achieve this objective, we first extract features from human motion trajectories by analyzing changes in human joint distances. These features are then utilized in a Hidden Markov Model (HMM) to determine the state transition times from uncertain intent to certain intent. Second, we propose two models including a Transformer and a Bi-LSTM for classifying motion intentions. Then, we design a human–robot collaboration experiment in which the operator reaches multiple targets while the robot moves continuously following a predetermined path. The data collected through the experiment were divided into two groups: full-length data and partial data before state transitions detected by the HMM. Finally, the effectiveness of the suggested framework for predicting intentions is assessed using two different datasets, particularly in a scenario when motion trajectories are similar but underlying intentions vary. The results indicate that using partial data prior to the motion completion yields better accuracy compared to using full-length data. Specifically, the transformer model exhibits a 2% improvement in accuracy, while the Bi-LSTM model demonstrates a 6% increase in accuracy.

Real-Time Monitoring With Timing Side Information

Real-time monitoring plays a pivotal role in the Industrial Internet of Things (IIoT) with potential applications in factory automation, automated driving, and telesurgery, thereby attracting considerable recent attention in anticipation of the development of the sixth-generation (6G) of wireless networks. In this paper, we present a paradigm-shift data compression method that makes use of timing side information (TSI) obtained by observing two synchronized clocks at a remote sensor and a monitor. In particular, the TSI is found to allow the transmitter to send fewer bits consumed in characterizing the changing or holding time of a piecewise-constant stochastic process. We borrow the idea of source coding with side information to reveal the performance limits of both TSI-based lossless and lossy compression, and to develop practical low-complexity source coding schemes. To further reduce the implementation complexity and the hardware cost, we also present a real-time monitoring scheme where the sensor does not necessarily measure the state transition time. A statistical signal processing algorithm is adopted to estimate the changing time accurately. Our theoretical and numerical results show that the compression gain owing to the TSI is quite substantial, especially when the communication latency and the delay jitter are limited.

Weakly Correlated Local Cortical State Switches under Anesthesia Lead to Strongly Correlated Global States.

During recovery from anesthesia, brain activity switches abruptly between a small set of discrete states. Surprisingly, this switching also occurs under constant doses of anesthesia, even in the absence of stimuli. These metastable states and the transitions between them are thought to form a "scaffold" that ultimately guides the brain back to wakefulness. The processes that constrain cortical activity patterns to these states and govern how states are coordinated between different cortical regions are unknown. If state transitions were driven by subcortical modulation, different cortical sites should exhibit near-synchronous state transitions. Conversely, spatiotemporal heterogeneity would suggest that state transitions are coordinated through corticocortical interactions. To differentiate between these hypotheses, we quantified synchrony of brain states in male rats exposed to a fixed isoflurane concentration. States were defined from spectra of local field potentials recorded across layers of visual and motor cortices. A transition synchrony measure shows that most state transitions are highly localized. Furthermore, while most pairs of cortical sites exhibit statistically significant coupling of both states and state transition times, coupling strength is typically weak. States and state transitions in the thalamic input layer (L4) are particularly decoupled from those in supragranular and infragranular layers. This suggests that state transitions are not imposed on the cortex by broadly projecting modulatory systems. Although each pairwise interaction is typically weak, we show that the multitude of such weak interactions is sufficient to confine global activity to a small number of discrete states.SIGNIFICANCE STATEMENT The brain consistently recovers to wakefulness after anesthesia, but this process is poorly understood. Previous work revealed that, during recovery from anesthesia, corticothalamic activity falls into one of several discrete patterns. The neuronal mechanisms constraining the cortex to just a few discrete states remain unknown. Global states could be coordinated by fluctuations in subcortical nuclei that project broadly to the cortex. Alternatively, these states may emerge from interactions within the cortex itself. Here, we provide evidence for the latter possibility by demonstrating that most pairs of cortical sites exhibit weak coupling. We thereby lay groundwork for future investigations of the specific cellular and network mechanisms of corticocortical activity state coupling.

How Good Is the Vibronic Hamiltonian Repetition Approach for Long-Time Nonadiabatic Molecular Dynamics?

Multiple applied studies of slow nonadiabatic processes in nanoscale and condensed matter systems have adopted the "repetition" approximation in which long trajectories for such simulations are obtained by concatenating shorter trajectories, directly available from ab initio calculations, many times. Here, we comprehensively assess this approximation using model Hamiltonians with parameters covering a wide range of regimes. We find that state transition time scales may strongly depend on the length of the repeated data, although the convergence is not monotonic and may be slow. The repetition approach may under- or overestimate the time scales by a factor of ≤7-8, does not directly depend on the dispersion of energy gap and nonadiabatic coupling (NAC) frequencies, but may depend on the magnitude of the NACs. We suggest that the repetition-based nonadiabatic dynamics may be inaccurate in simulations with very small NACs, where intrinsic transition times are on the order of ≥100 ps.

Modeling and reliability verification of industrial control network protocol based on time state transition matrix

SummaryWith the improvement of industrial informatization, various industrial control system network protocols have also been widely used. The reliability of these protocols will directly affect the safety of industrial control systems. As an effective method that can automatically analyze system reliability, model checking has been widely used in the verification of various safety‐critical systems. In this paper, we propose a modeling design method for industrial control network protocol based on time semantic reconstruction of time state transition matrix (TSTM). In addition, we provide a TSTM model checking method based on linear temporal logic (LTL). In order to effectively alleviate the state space explosion, the method adopts bounded model checking (BMC) technology. Furthermore, we implement a TSTM model verification tool called ICPV. Finally, we apply the above method to the modeling and verification of the industrial control network protocol Powerlink and through a comparison experiment with UPPAAL to illustrate the effectiveness of the method proposed in this paper.

A Review of HMM-Based Approaches of Driving Behaviors Recognition and Prediction

Current research and development in recognizing and predicting driving behaviors plays an important role in the development of Advanced Driver Assistance Systems (ADAS). For this reason, many machine learning approaches have been developed and applied. Hidden Markov Model (HMM) is a suitable algorithm due to its ability to handle time series data and state transition descriptions. Therefore, this contribution will focus on a review of HMM and its applications. The aim of this contribution is to analyze the current state of various driving behavior models and related HMM-based algorithms. By examining the current available approaches, a review is provided with respect to: i) influencing factors of driving behaviors corresponding to the research objectives of different driving models, ii) summarizing HMM related methods applied to driving behavior studies, and iii) discussing limitations, issues, and future potential works of the HMM-based algorithms. Conclusions with respect to the development of intelligent driving assistant system and vehicle dynamics control systems are given.

Modeling and Analysis of Latency Distribution in the 40-100Gbps Dual-Mode Energy Efficient Ethernet

Recently, the 40-100Gbps Energy Efficient Ethernet (EEE) is standardized by IEEE 802.3bj. Unlike the 1-10Gbps EEE, the 40-100Gbps EEE includes two energy-saving states with different power consumption and state transition times. The default Dual-Mode strategy is the fundamental policy, which utilizes these states to make a trade-off between the energy saving and the incurred latency of frames. Although numerous analytical works have been proposed for EEE strategies, most of them can only be applied to the single energy-saving state condition of 1-10Gbps EEE, or can only capture the mean latency of frames. However, the interactive applications always place a soft or hard deadline on frame transmission. Accordingly, the tail latency of frames becomes crucial. Therefore, we model and analyze the latency distribution of frames in the 40-100Gbps Dual-Mode EEE in this work. The guidelines provided by analytical results help the Dual-Mode strategy to fulfill the given tail latency requirement and make a better trade-off between the energy saving and the incurred latency under different traffic loads. In addition, this model is powerful as it can generate the major results of several existing works under the corresponding simplified scenarios. Finally, the analytical results are verified by trace-driven simulations based on NS3.

Early-Warning Signals of Drought-Flood State Transition over the Dongting Lake Basin Based on the Critical Slowing Down Theory

In this study, the standardized precipitation index (SPI) data in Hunan Province from 1961 to 2020 is adopted. Based on the critical slowing down theory, the moving t-test is firstly used to determine the time of drought-flood state transition in the Dongting Lake basin. Afterwards, by means of the variance and autocorrelation coefficient that characterize the phenomenon of critical slowing down, the early-warning signals indicating the drought-flood state in the Dongting Lake basin are explored. The results show that an obvious drought-to-flood (flood-to-drought) event occurred around 1993 (2003) in the Dongting Lake basin in recent 60 years. The critical slowing down phenomena of the increases in the variance and autocorrelation coefficient, which are detected 5–10 years in advance, can be considered as early-warning signals indicating the drought-flood state transition. Through the studies on the drought-flood state and related early-warning signals for the Dongting Lake basin, the reliabilities of the variance and autocorrelation coefficient-based early-warning signals for abrupt changes are demonstrated. It is expected that the wide application of this method could provide important scientific and technological support for disaster prevention and mitigation in the Dongting Lake basin, and even in the middle and lower reaches of the Yangtze River.

HMM‐TCN‐based health assessment and state prediction for robot mechanical axis

Aiming at the problems of high manual cost, low efficiency, and low precision of the mechanical axis health management in industrial robot applications, this paper proposes a health assessment and state prediction algorithm based on hidden Markov model (HMM) and temporal convolutional networks (TCN). First, the MPdist similarity comparison algorithm is used to construct the mechanical axis health index. Then the hidden Markov model is trained with observable sensor data. After that, the temporal convolution neural network is used to predict state transition time iteratively, and the predicted results are decoded by HMM. The experimental results show that the HMM-TCN model can accurately assess the health state of the mechanical axis and predict the state transition in real-time. The prediction accuracy of this method reaches 87.5%, and the error interval locates in [−3,9] time steps. The accuracy, early/late prediction indicators are better than HMM-RNN, HMM-LSTM, and HMM-GRU.

Toward securing the control plane of 5G mobile networks against DoS threats: Attack scenarios and promising solutions

With the advent of the fifth generation (5G) technology, a plethora of revolutionary applications can now be supported. This tremendous growth will be certainly accompanied by a wider and fast-evolving security threat landscape, especially when the potential of massive devices connectivity will be fully unleashed. In this paper, we outline the security challenges faced by the 5G radio access network (5G-RAN) control plane as a result of the functional and architectural enhancements made at the radio resource control (RRC) protocol layer. We correspondingly analyze the dynamics of the new 5G RRC three-states model for a machine-type traffic pattern under an attack-free situation. Afterwards, we introduce and describe two RRC-based denial of service (DoS) attacks threatening the 5G-RAN resources availability. In the first threat scenario, the attacker maliciously manipulates the timing of state transitions in an attempt to overload the control plane. The second attack can be carried out through faking huge on-demand system information requests to prevent legitimate consumers from access to the cell and cause failed and interrupted 5G mobile services. Through numerical simulations, we measure the potential impact of these attacks on both 5G devices using the collision probability along with the access delay metrics, and on the critical gNB-centralized unit using the signaling overhead and the resource occupancy time metrics. Our observations reveal that both attacks can have disastrous effects on network stability and resiliency. Finally, some promising solutions are listed, with a special emphasis on injecting randomness into system parameters to complicate the task of designing such DoS attacks.

Future smoking prevalence by socioeconomic status in England: a computational modelling study

BackgroundThe difference in smoking across socioeconomic groups is a major cause of health inequality. This study projected future smoking prevalence by socioeconomic status, and revealed what is needed to achieve...

Joint planning of maintenance, buffer stock and quality control for unreliable, imperfect manufacturing systems

A stochastic model is developed in this study for joint planning of maintenance, production and quality control in manufacturing systems. The process may deteriorate during the production and the process state may transit from an in-control to an out-of-control. The state transition time follows a general distribution. Sampling inspections are conducted on the process during the production run and the collected information is plotted on a proper control chart to monitor the process. With respect to the process state, PM action or CM action is conducted on the process. The time duration to conduct maintenance actions was considered as a continuous random variable. During conducting maintenance action the production process is interrupted and the demand is satisfied from a safety stock. The aim of the model is to integrate the decisions on the sampling inspections, control chart design, maintenance schedule and stock level determination to minimize the total cost. A numerical study was conducted and the sensitivity of the model was analyzed for important parameters. A genetic algorithm was applied for optimization and finally, the integrated model of three aspects and the mathematical model of maintenance and production were compared to investigate the impact of joint planning of these aspects on the management of manufacturing systems.

Effects of Vessel Distance and Sex on the Behavior of Endangered Killer Whales

Accurate knowledge of behavior is necessary to effectively manage the effects of human activities on wildlife, including vessel-based whale-watching. Yet, the wholly aquatic nature of cetaceans makes understanding their basic behavioral ecology quite challenging. An endangered population of killer whales faces several identified threats including prey availability and disturbance from vessels and sound. We used bio-logging tags that were temporally attached to individuals of the endangered Southern Resident killer whale population to more fully understand their subsurface behavior and to investigate vessel effects on behavior, including foraging behavior involving prey capture. We collected tag data over three field seasons in the waters surrounding the San Juan Islands, WA, United States, corresponding to the core summer area of the critical habitat of the population. Here, we used hidden Markov models to identify latent behavioral states that include characterization of different foraging states from sound and movement variables recorded by the multi-sensor tags. We tested a number of vessel variables (e.g., vessel counts, distance, and speed) on state transition probabilities, state occurrence and time spent within each behavioral state. Whales made fewer dives involving prey capture and spent less time in these dives when vessels had an average distance less than 400 yd (366 m). Additionally, we found both a sex and vessel distance effect on the state transition probabilities, suggesting that females and males respond differently to nearby vessels. Specifically, females were more likely to transition to a non-foraging state when vessels had an average distance less than 400 yd (366 m). A female’s decision to forego foraging states due to the close proximity of vessels could have cascading effects on the ability to meet energetic requirements to support reproductive efforts. This is particularly concerning in an endangered population that is in decline. Our findings, suggesting that female killer whales are at greater risk to close approaches by vessels, highlight the importance of understanding sex-specific responses to disturbance. These findings can inform future management decisions seeking to preserve foraging opportunities and enhance recovery efforts relevant to many cetacean species, including vulnerable and endangered populations.

Formation of viable, but putatively non-culturable (VPNC) cells of beer-spoilage lactobacilli growing in biofilms

Bacteria belonging to the genus Lactobacillus are the most commonly-found beer-spoilage microorganisms in breweries. This work compares the ability of biofilm and planktonic cells of beer-spoilage Lactobacillus sp. to enter a viable, but putatively non-culturable (VPNC) state. Planktonic and biofilm cells of five beer-spoilage species of Lactobacillus were incubated at 26 °C in the lager beer with sub-culturing. Both biofilm and planktonic cells could be induced to enter the VPNC state, however, the state-transition time is variable: it took a minimum of 63 days for the biofilm cells to enter the VPNC state, while the time for planktonic cells was 119–217 days. The presence of VPNC cells in non-culturable biofilms was further confirmed by PCR coupled with propidium monoazide pretreatment that amplified genes 16S rDNA and horA. Taken together, this study provides evidence that Lactobacillus biofilm cells can enter the VPNC state at a rate faster than the planktonic cells under stressful conditions of the beer (low pH, high concentrations of hop bitter compounds and ethanol, and limited carbon/nitrogen sources) in the brewing process.