Response Delay Research Articles

Transmission dynamics informed neural network with application to COVID-19 infections

Since the end of 2019 the COVID-19 repeatedly surges with most countries/territories experiencing multiple waves, and mechanism-based epidemic models played important roles in understanding the transmission mechanism of multiple epidemic waves. However, capturing temporal changes of the transmissibility of COVID-19 during the multiple waves keeps ill-posed problem for traditional mechanism-based epidemic compartment models, because that the transmission rate is usually assumed to be specific piecewise functions and more parameters are added to the model once multiple epidemic waves involved, which poses a huge challenge to parameter estimation. Meanwhile, data-driven deep neural networks fail to discover the driving factors of repeated outbreaks and lack interpretability. In this study, aiming at developing a data-driven method to project time-dependent parameters but also merging the advantage of mechanism-based models, we propose a transmission dynamics informed neural network (TDINN) by encoding the SEIRD compartment model into deep neural networks. We show that the proposed TDINN algorithm performs very well when fitting the COVID-19 epidemic data with multiple waves, where the epidemics in the United States, Italy, South Africa, and Kenya, and several outbreaks the Omicron variant in China are taken as examples. In addition, the numerical simulation shows that the trained TDINN can also perform as a predictive model to capture the future development of COVID-19 epidemic. We find that the transmission rate inferred by the TDINN frequently fluctuates, and a feedback loop between the epidemic shifting and the changes of transmissibility drives the occurrence of multiple waves. We observe a long response delay to the implementation of control interventions in the four countries, while the decline of the transmission rate in the outbreaks in China usually happens once the implementation of control interventions. The further simulation show that 17 days’ delay of the response to the implementation of control interventions lead to a roughly four-fold increase in daily reported cases in one epidemic wave in Italy, which suggest that a rapid response to policies that strengthen control interventions can be effective in flattening the epidemic curve or avoiding subsequent epidemic waves. We observe that the transmission rate in the outbreaks in China is already decreasing before enhancing control interventions, providing the evidence that the increasing of the epidemics can drive self-conscious behavioural changes to protect against infections.

Hierarchical CNNPID Based Active Steering Control Method for Intelligent Vehicle Facing Emergency Lane-Changing

To resolve the response delay and overshoot problems of intelligent vehicles facing emergency lane-changing due to proportional-integral-differential (PID) parameter variation, an active steering control method based on Convolutional Neural Network and PID (CNNPID) algorithm is constructed. First, a steering control model based on normal distribution probability function, steady constant radius steering, and instantaneous lane-change-based active for straight and curved roads is established. Second, based on the active steering control model, a three-dimensional constraint-based fifth-order polynomial equation lane-change path is designed to address the stability problem with supersaturation and sideslip due to emergency lane changing. In addition, a hierarchical CNNPID Controller is constructed which includes two layers to avoid collisions facing emergency lane changing, namely, the lane change path tracking PID control layer and the CNN control performance optimization layer. The scaled conjugate gradient backpropagation-based forward propagation control law is designed to optimize the PID control performance based on input parameters, and the elastic backpropagation-based module is adopted for weight correction. Finally, comparison studies and simulation/real vehicle test results are presented to demonstrate the effectiveness, significance, and advantages of the proposed controller.

Подход к анализу гидрометеорологических условий, определяющих задержку в реагировании на морские разливы нефти в Арктической зоне Российской Федерации

The present study reviews the approach to assessing the oil spills response delay caused by hydrometeorological and geographical conditions at the operation site. To illustrate the method and discuss the results, the application of two mechanical response system in ice and ice-free conditions are considered. Two points of the northern and southern area of the Northern Sea Route are analyzed. It is found that the value of the OSR completion time can exceed a year with a very significant probability. The proposed analysis makes it possible to assess the possibilities of improving the response efficiency for existing and newly created oil spill response strategies in the region of interest, as well as to apply other measures to reduce the risks of the negative impact of accidental spills on Arctic ecosystems.

Temporal complexity in photoplethysmography and its influence on blood pressure.

Objective: The temporal complexity of photoplethysmography (PPG) provides valuable information about blood pressure (BP). In this study, we aim to interpret the stochastic PPG patterns with a model-based simulation, which may help optimize the BP estimation algorithms. Methods: The classic four-element Windkessel model is adapted in this study to incorporate BP-dependent compliance profiles. Simulations are performed to generate PPG responses to pulse and continuous stimuli at various timescales, aiming to mimic sudden or gradual hemodynamic changes observed in real-life scenarios. To quantify the temporal complexity of PPG, we utilize the Higuchi fractal dimension (HFD) and autocorrelation function (ACF). These measures provide insights into the intricate temporal patterns exhibited by PPG. To validate the simulation results, continuous recordings of BP, PPG, and stroke volume from 40 healthy subjects were used. Results: Pulse simulations showed that central vascular compliance variation during a cardiac cycle, peripheral resistance, and cardiac output (CO) collectively contributed to the time delay, amplitude overshoot, and phase shift of PPG responses. Continuous simulations showed that the PPG complexity could be generated by random stimuli, which were subsequently influenced by the autocorrelation patterns of the stimuli. Importantly, the relationship between complexity and hemodynamics as predicted by our model aligned well with the experimental analysis. HFD and ACF had significant contributions to BP, displaying stability even in the presence of high CO fluctuations. In contrast, morphological features exhibited reduced contribution in unstable hemodynamic conditions. Conclusion: Temporal complexity patterns are essential to single-site PPG-based BP estimation. Understanding the physiological implications of these patterns can aid in the development of algorithms with clear interpretability and optimal structures.

TastePeptides-EEG: An Ensemble Model for Umami Taste Evaluation Based on Electroencephalogram and Machine Learning.

In the field of food, the sensory evaluation of food still relies on the results of manual sensory evaluation, but the results of human sensory evaluation are not universal, and there is a problem of speech fraud. This work proposed an electroencephalography (EEG)-based analysis method that effectively enables the identification of umami/non-umami substances. First, the key features were extracted using percentage conversion, standardization, and significance screening, and based on these features, the top four models were selected from 19 common binary classification algorithms as submodels. Then, the support vector machine (SVM) algorithm was used to fit the outputs of these four submodels to establish TastePeptides-EEG. The validation set of the model achieved a judgment accuracy of 90.2%, and the test set achieved a judgment accuracy of 77.8%. This study discovered the frequency change of α wave in umami taste perception and found the frequency response delay phenomenon of the F/RT/C area under umami taste stimulation for the first time. The model is published at www.tastepeptides-meta.com/TastePeptides-EEG, which is convenient for relevant researchers to speed up the analysis of umami perception and provide help for the development of the next generation of brain-computer interfaces for flavor perception.

Two-stage approach to causality analysis-based quality problem solving for discrete manufacturing systems

In discrete manufacturing systems, the efficiency of quality problem solving always matters, but it can decrease continually due to increasing systematic complexity, uncertainty, and fuzzy internal mechanism. These factors often hinder situation awareness, confuse decision-making, and delay response times in quality problem solving. In such context, Bayesian network performs poorly in precisely locating objects to-be-adjusted in complex causal relationship networks and predicting the impact of interventions to-be-implemented. To address this challenge, a two-stage approach to causality analysis-based quality problem solving is proposed. In the first stage, an improved Bayesian network is proposed to identify the likely root causes that have a direct causality on the quality indicator. In the second stage, causal inference is used to estimate the effect of likely root causes on the quality indicator. The proposed approach compensates for Bayesian network in precisely identifying root causes that should have causation rather than correlation with quality problems, and facilitating a quantitative tuning process to create suitable solutions. To assess the effectiveness of the proposed approach, a quality problem solving case in an aerospace shell parts spinning process was conducted. The results demonstrate that the approach can accurately identify likely root causes and determine the appropriate intervention degree.

Building a hospitable and reliable dialogue system for android robots: a scenario-based approach with large language models

Dialogue systems implemented with android robots are expected to provide not only advanced conversational ability but also reliability and hospitality due to their human-like appearance. In this study, we aim to develop a hospitable dialogue system by encouraging open-ended utterances and responding adaptively to give users the feeling of being heard. To achieve this, utilizing large language models (LLMs) is a promising option, but task-oriented dialogue systems implemented with only LLMs often generate irrelevant, inconsistent, or contradictory utterances. Therefore, we propose a scenario-based dialogue system that subdivides the task into smaller sub-tasks, such as summarization, information extraction, and response generation, and uses LLMs in a fine-grained manner to overcome such shortcomings of the LLM-based dialogue system. Our system was evaluated in the tourist-spot recommendation task of the Dialogue Robot Competition 2022 and achieved second place in the preliminary round and first place in the final round, outperforming other rule-based dialogue systems. However, we also identified several challenges when using LLMs for android dialogue systems, including response delays due to computational complexity, hallucinations, and coordination issues between generated utterances and robot control.

Social-Aware Decentralized Cooperative Caching for Internet of Vehicles

As the industry related to intelligent vehicles becomes increasingly mature, emerging in-vehicle applications and services are mushrooming. They have strict requirements on service response delay and network bandwidth. In the face of dynamic environment in the Internet of Vehicles (IoV), how to design an adaptive edge caching strategy becomes a challenge. To cope with this challenge, some researchers have introduced optimization methods based on learning algorithms in cooperative caching. However, general learning algorithms tend to waste bandwidth and computing resources on repetitive task. To this end, we make full use of the idle resources in the road to build a cooperative caching system and propose a Social-Aware Decentralized Cooperative caching (SADC) for IoV. This strategy uses the federated learning framework to train the collaborative caching algorithm based on Deep Reinforcement Learning (DRL). Among them, the Road Side Unit (RSU) is responsible for the training and updating of the global model, and vehicles use local data to provide local updates to the RSU, which then averages the updates provided by all vehicles to improve the shared model. In addition, we use the social network of vehicle users to obtain vehicle contact rates in different areas. The SADC strategy can reduce the content transmission latency and response time, thereby improving the experience quality of vehicle users. Compared with traditional caching strategies, this strategy reduces the average content access delay by about 20%. We also demonstrate the effectiveness of this strategy using an extensive set of experiments.

Aggregation Dispatch and Control Strategies for Multi-Type Loads in Industrial Parks

With the continuous expansion of renewable energy construction, the power system requires a larger-scale flexible dispatchable and controllable resource for power balance. Fully tapping into the power regulation capability of multi-type loads in industrial parks, making them a low-cost flexible dispatchable and controllable resource, is an effective approach to establish power regulation capability at scale in the new power system. However, the control characteristics of multi-type loads in industrial parks vary greatly, and their control delay characteristics, response speed, and sustainable response time are all different. Traditional dispatch and control methods cannot achieve precise control of the massive and multi-type loads in industrial parks. Therefore, this paper establishes unified models for the control characteristics of multi-type loads in industrial parks, quantitatively characterizes their control delay characteristics, start–stop characteristics, and control response speed. Based on this, the aggregated dispatch and control model and dispatch and control strategy for multi-type loads in industrial parks are developed, which provide a predictive control rate for individual loads considering the delay and segmented response characteristics to achieve precise aggregation control of multi-type loads in industrial parks. Simulation results show that the aggregated dispatch and control model and its aggregated dispatch and control strategy achieve precise control of multi-type loads in industrial parks. Flexible dispatchable and controllable loads can provide low-cost power regulation capability for the new power system.

Localization of Epileptic Brain Responses to Single-Pulse Electrical Stimulation by Developing an Adaptive Iterative Linearly Constrained Minimum Variance Beamformer.

Delayed responses (DRs) to single pulse electrical stimulation (SPES) in patients with severe refractory epilepsy, from their intracranial recordings, can help to identify regions associated with epileptogenicity. Automatic DR localization is a large step in speeding up the identification of epileptogenic focus. Here, for the first time, an adaptive iterative linearly constrained minimum variance beamformer (AI-LCMV) is developed and employed to localize the DR sources from intracranial electroencephalogram (EEG) recorded using subdural electrodes. The prime objective here is to accurately localize the regions for the corresponding DRs using an adaptive localization method that exploits the morphology of DRs as the desired sources. The traditional closed-form linearly constrained minimum variance (CF-LCMV) solution is meant for tracking the sources with dominating power. Here, by incorporating the morphology of DRs, as a constraint, to an iterative linearly constrained minimum variance (LCMV) solution, the array of subdural electrodes is used to localize the low-power DRs, some not even visible in any of the electrode signals. The results from the cases included in this study also indicate more distinctive locations compared to those achievable by conventional beamformers. Most importantly, the proposed AI-LCMV is able to localize the DRs invisible over other electrodes.

A Nonlinear Optimization Design Algorithm for Nearly Linear-Phase 2D IIR Digital Filters

In this paper, a new optimization method for the design of nearly linear-phase two-dimensional infinite impulse (2D IIR) digital filters with a separable denominator is proposed. A design framework for 2D IIR digital filters is formulated as a nonlinear constrained optimization problem where the group delay deviation in the passband is minimized under prescribed soft magnitude constraints and hard stability requirements. To achieve this goal, sub-level sets of the group delay deviations are utilized to generate a sequence of filters, from which the one with the best performance is selected. The quality of the obtained filter is evaluated using three quality factors, namely, the passband magnitude quality factor Qh and the group delay deviation quality factor Qτ, while the third one is a new quality factor Qs that assesses the performance in the stopband relative to the minimum filter gain in the passband. The proposed framework is implemented using the interior-point (IP) method in a MATLAB environment, and the experimental results show that filters designed using the proposed method have good magnitude response and low group delay deviation. The performance of the resulting filters is compared with the results of other methods.

Novel Edge Caching Approach Based on Multi-Agent Deep Reinforcement Learning for Internet of Vehicles

Along with the development of Internet of Vehicles (IoV) and wireless technology, the usage of applications that require low latency, such as autonomous driving and intelligent navigation, is increasing rapidly, and the demand for content is increasing greatly. This paper proposes an edge caching approach for the IoV based on multi-agent deep reinforcement learning (ECSMADRL) so as to resolve the problem of excessive response delay due to the large increase of data traffic in the IoV. The approach jointly considers content distribution and caching in dynamic environments. In other words, each moving vehicle in the IoV can be seen as an agent, and it can make decisions about content caching and content access adaptively according to the changing environment to minimize the delay in the process of content distribution. It is proved by experiments that compared with other methods, the proposed edge caching (EC) approach has better performance in reducing content distribution delay, improving content hit rate and success rate.

A TP-DDPG Algorithm Based on Cache Assistance for Task Offloading in Urban Rail Transit

With the rapidly increase demand on reliable wireless communication for rail scenarios, especially for real-time services, the resource limitations of on-board users are becoming increasingly prominent. The mobile edge computing (MEC) technology can expand the computing capability of terminal equipment with the edge server and reduce the end-to-end delay in the communication structure. To meet the requirements on the response delay of task execution of on-board users, deploying MEC servers in the rail communication system could be an efficient scheme. In this paper, a task offloading scheme based on task preprocessing deep deterministic policy gradient (TP-DDPG) algorithm is proposed, and a cache-assisted framework is introduced. Considering the high-speed characteristics of trains, a relevant environment interaction model is designed by using agents and interactive training to obtain an optimized task offloading and resource allocation scheme. Simulation results show that compared with other algorithms, the proposed algorithm can effectively reduce the system delay and improve the quality of service.

Doppler Robustness of Joint Communication and Radar Systems Using the Wiener Filter

The orthogonal frequency-division multiplexing (OFDM) is a promising waveform for joint radar and communication systems. In this article, a unified approach is proposed to analyse both radar and communication robustness with respect to Doppler mismatch. The analytical study leads to an expression that links the radar performance to the communication one. The performance of a delay-Doppler radar is quantified by modelling the delay response of the OFDM radar with an appropriate random variable. Different delay filters are analysed, among which the Wiener filter. The analytical results are validated by simulations, they show that the radar system benefits from the Wiener filter, and the configurations where this filter outperforms the other ones are outlined.

Ultralow-Dielectric-Constant Atomic Layers of Amorphous Carbon Nitride Topologically Derived from MXene.

Low-dielectric-constant materials such as silicon dioxide serving as interconnect insulators in current integrated circuit face a great challenge due to their relatively high dielectric constant of ≈4, twice that of the recommended value by the International Roadmap for Devices and Systems, causing severe parasitic capacitance and associated response delay. Here, novel atomic layers of amorphous carbon nitride (a-CN) are prepared via a topological conversion of MXene-Ti3 CNTx under bromine vapor. Remarkably, the assembled a-CN film exhibits an ultralow dielectric constant of 1.69 at 100 kHz, much lower than the previously reported dielectric materials such as amorphous carbon (2.2) and fluorinated-doped SiO2 (3.6), ascribed to the low density of 0.55g cm-3 and high sp3 C level of 35.7%. Moreover, the a-CN film has a breakdown strength of 5.6 MVcm-1 , showing great potential in integrated circuit application.

Demographic and condition-specific factors influencing how symptoms are experienced over time: results of the MOMENT study

Abstract Funding Acknowledgements Type of funding sources: Private grant(s) and/or Sponsorship. Main funding source(s): Australian Catholic University Background Adults with chronic illness (e.g., heart disease, diabetes) commonly experience intractable symptoms. The response to symptoms is often delayed until hospitalization is required. Acute symptoms prompt treatment-seeking, but variability in severity and bothersomeness of chronic symptoms may contribute to a delay in response. Purpose The purpose of this study was to describe symptom severity, bothersomeness, and variability over time and how explore these differ by demographic and clinical groups. Methods We conducted a longitudinal observational study, consenting and enrolling a convenience sample of adults ≥ 18 years with one or more symptomatic chronic conditions living in Italy, the Netherlands, Sweden, or the United States. Recruitment was done through social media. Inclusion criteria were ≥ 3 months experience with heart failure (HF), diabetes mellitus (DM), arthritis, asthma, and/or chronic obstructive pulmonary disease (COPD) and experiencing symptom(s) ≥ 3 times weekly. Symptoms characteristics were assessed twice daily on smartphones using ecological daily assessment methods to minimize recall bias. Data were analyzed using descriptive statistics, and Cohen’s d as an effect size when not significant. Results Eighty people completed the study. Most participants were middle-aged, female, and well-educated. Most lived in the EU. Variability in symptom severity and bothersomeness over time were strongly associated (r = 0.62, p<0.001) but this variability was not associated with either average symptom severity or bothersomeness (all p>0.05). There was a significant association between age and variability in symptom bothersomeness, with less variability and more stability in symptom bothersomeness over time as age increased (r = -0.29, p = 0.011). Women had lower average symptom severity and average bothersomeness than men (d = 0.33), but the differences were not significant. Comparing respondents with and without various conditions, in HF, there was a slight but non-significant difference in variability in symptom bothersomeness over time (d = 0.55). In DM, average symptom severity over time was less in persons with DM (t=-2.0, p = 0.024). Average symptom bothersomeness over time was less in DM (t=2.2, p = 0.007). In asthma, variability in symptom severity was greater over time (t=2.3, p = 0.012). In COPD, average symptom bothersomeness over time was greater (t=3.2, p = 0.001), variability in symptom bothersomeness over time was less (t=-1.9, p = 0.034), and the average symptom severity over time was greater (t=3.7, p<0.001). In arthritis, bothersomeness was higher and there was a slight but non-significant difference in average bothersomeness over time (d = -0.44). Conclusion There are both demographic (age, gender) and condition-specific factors that influence how symptoms are experienced over time in adults with chronic illness. Understanding of these patterns can be useful in designing interventions for chronic illness groups.

FAC-fed: Federated adaptation for fairness and concept drift aware stream classification

Federated learning is an emerging collaborative learning paradigm of Machine learning involving distributed and heterogeneous clients. Enormous collections of continuously arriving heterogeneous data residing on distributed clients require federated adaptation of efficient mining algorithms to enable fair and high-quality predictions with privacy guarantees and minimal response delay. In this context, we propose a federated adaptation that mitigates discrimination embedded in the streaming data while handling concept drifts (FAC-Fed). We present a novel adaptive data augmentation method that mitigates client-side discrimination embedded in the data during optimization, resulting in an optimized and fair centralized server. Extensive experiments on a set of publicly available streaming and static datasets confirm the effectiveness of the proposed method. To the best of our knowledge, this work is the first attempt towards fairness-aware federated adaptation for stream classification, therefore, to prove the superiority of our proposed method over state-of-the-art, we compare the centralized version of our proposed method with three centralized stream classification baseline models (FABBOO, FAHT, CSMOTE). The experimental results show that our method outperforms the current methods in terms of both discrimination mitigation and predictive performance.

Ontogenetic differences in the response of the cold-water coral Caryophyllia huinayensis to ocean acidification, warming and food availability

Cold-water corals (CWCs) are considered vulnerable to environmental changes. However, previous studies have focused on adult CWCs and mainly investigated the short-term effects of single stressors. So far, the effects of environmental changes on different CWC life stages are unknown, both for single and multiple stressors and over long time periods. Therefore, we conducted a six-month aquarium experiment with three life stages of Caryophyllia huinayensis to study their physiological response (survival, somatic growth, calcification and respiration) to the interactive effects of aragonite saturation (0.8 and 2.5), temperature (11 and 15 °C) and food availability (8 and 87 μg C L−1). The response clearly differed between life stages and measured traits. Elevated temperature and reduced feeding had the greatest effects, pushing the corals to their physiological limits. Highest mortality was observed in adult corals, while calcification rates decreased the most in juveniles. We observed a three-month delay in response, presumably because energy reserves declined, suggesting that short-term experiments overestimate coral resilience. Elevated summer temperatures and reduced food supply are likely to have the greatest impact on live CWCs in the future, leading to reduced coral growth and population shifts due to delayed juvenile maturation and high adult mortality.

'I Had It. I Don't Think I Have It…But I Do Feel It Will Come Back Somewhere': A Qualitative Investigation of the Experience of People With Non-Muscle Invasive Bladder Cancer.

Very little is known about the impact of living with non-muscle invasive bladder cancer (NMIBC). NMIBC patients' experiences of their illness-in terms of their perceptions, coping strategies and psychological wellbeing-were explored. This study describes an interpretative phenomenological analysis (IPA) of individuals' accounts of living with NMIBC while on routine surveillance for cancer recurrence. Ten individuals took part in face-to-face semi-structured interviews. Three superordinate themes were derived from the data. The first theme, Being Diagnosed and Treated for NMIBC, concerned the observation that participants considered the physical implications, timeline and practicalities of their illness of primary importance and focused less on its psychological aspects. The second theme, Grappling with the Illness, outlined the impact of the doctor-patient relationship. The final theme, 'I don't treat it as a problem. I treat it as an issue', delineated how participants managed difficult emotions in the context of the illness. Findings from this study demonstrated that participants generally found effective ways to cope with their illness and experience of ongoing surveillance, though delay of emotional responses was common. Clinical implications for healthcare professionals are outlined including the importance of high-quality communication with the urology team.

Fluid Osmolarity Modulates the Rate of Spontaneous Contraction of Lymphatic Vessels and Lymph Flow by Means of a Cooperation between TRPV and VRAC Channels.

Lymphatic vessels are capable of sustaining lymph formation and propulsion via an intrinsic mechanism based on the spontaneous contraction of the lymphatic muscle in the wall of lymphatic collectors. Exposure to a hyper- or hypo-osmolar environment can deeply affect the intrinsic contraction rate and therefore alter lymph flow. In this work, we aimed at defining the putative receptors underlying such a response. Functional experiments were conducted in ex vivo rat diaphragmatic specimens containing spontaneously contracting lymphatic vessels that were exposed to either hyper- or hypo-osmolar solutions. Lymphatics were challenged with blockers to TRPV4, TRPV1, and VRAC channels, known to respond to changes in osmolarity and/or cell swelling and expressed by lymphatic vessels. Results show that the normal response to a hyperosmolar environment is a steady decrease in the contraction rate and lymph flow and can be prevented by blocking TRPV1 channels with capsazepine. The response to a hyposmolar environment consists of an early phase of an increase in the contraction rate, followed by a decrease. The early phase is abolished by blocking VRACs with DCPIB, while blocking TRPV4 mainly resulted in a delay of the early response. Overall, our data suggest that the cooperation of the three channels can shape the response of lymphatic vessels in terms of contraction frequency and lymph flow, with a prominent role of TRPV1 and VRACs.