Communication Delays Research Articles

A-192 Effectiveness in Communicating Critical Results: impact of technology and consultive actions on improving performance indicators in a laboratory in Northeast Brazil

Abstract Background Critical results consist of laboratory test results that require immediate intervention or decision-making by the responsible medical team, as they pose a risk to the patient's life. To manage the process of communicating critical results, it is also necessary to define the indicators, goals and systematic comparability through benchmarking. Methods The performance of the indicators measuring the critical results process for the year 2023 was analyzed. Performance indicators such as resolution time, success and failure rate in communication, delay rate in communication and the rate of open incidents related to the communication of critical results were monitored using a Business Intelligence tool. Results In the first two months of 2023 the results reflected the absence of parameters in the LIS for effective communication of critical results. September and October there was a high turnover rate in the laboratory in addition to the change in the hospital care profile in which the laboratory medicine service was inserted. These causes culminated in a number of 122 (1.48%) communication failures, representing an increase of 1,120% compared to the first two months of the year. On the other hand, there was a significant improvement in the average communication time for critical results, decreasing from approximately 736 hours to 184 hours(reduction of around 75%). There was also a reduction in communication delay of approximately 67%. These numbers corroborate the parameterization actions in the system aimed at speeding up the process. As a continuation of the improvement actions, the laboratory implemented goal setting for the resolution time indicator and also the periodic meeting for critical analysis of the results.In addition to these actions, the laboratory approached the hospital's clinical staff, making it possible to provide clarity on the difference between compulsory notification and critical results. This clarification allowed for the establishment of a list of exams considered critical, in order to meet the hospital profile, as well as to define the communication flow. For the success indicator in communicating critical results, there was a 22% improvement when comparing the last two months of 2023 with the first two months of the same year. In the last two months of the year, this indicator performed at 99.4%, corroborating the study published by Noronha et al., (2016), where the effectiveness rate in critical results communication was 96.1%. Comparing with the study by Noronha et al., (2016), where the biochemistry sector had the highest rate of critical results communication failures (73.8%), followed by the Emergency Room (78.6% ) and ICU (16.7%), the findings of this study highlighted the top 3 detracting sectors: Biochemistry (68%), “UPA” - Emergency Care Unit (42.1%) and ICU (21.4%), making data superimposable. Conclusions The communication of critical results has challenges related to its effectiveness, especially in hospital contexts. As a critical and high-impact activity, constant actions are necessary to continuously improve the process. Actions should take into account: interactions with attending physicians, the development and training of the internal team, and constant checks of the LIS, as technology can support the process management.

Convolutional Long Short-Term Memory Predictor for Collaborative Remotely Operated Vehicle Trajectory Tracking in a Leader–Follower Formation Subject to Communication and Sensor Latency in the Presence of External Disturbances

Nowadays, collaborative operations between Remotely Operated Vehicles (ROVs) face considerable challenges, particularly in leader–follower schemes. The underwater environment imposes limitations on acoustic modems, leading to reduced transmission speeds and increased latency in ROV position and speed transmission. This complicates effective communication between the ROVs. Traditional methods, such as Recursive Least Squares (RLS) predictors and the Kalman filter, have been employed to address these issues. However, these approaches have limitations in handling non-linear patterns and disturbances in underwater environments. This paper introduces a Convolutional Long Short-Term Memory (ConvLSTM) predictor designed to enhance communication and trajectory tracking between ROVs in a leader–follower scheme. The proposed ConvLSTM aims to address the shortcomings of previous methods by adapting effectively to varying conditions, including ocean currents, communication delays, and signal interruptions. Simulations were conducted to evaluate ConvLSTM’s performance and compare it with other advanced predictors, such as Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM), under different conditions. The results demonstrated that ConvLSTM achieved a 13.9% improvement in trajectory tracking, surpassing other predictors in scenarios that replicate real underwater conditions and multi-vehicle communication. These results highlight ConvLSTM’s potential to significantly enhance the performance and stability of collaborative ROV operations in dynamic underwater environments.

ENVISIONing a critical period to preserve development: communication delays in SCN1A+ dravet syndrome

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Constant-Power and Maximum Efficiency Tracking Control of Fuzzy IPT Systems With Communication Delays

Constant-Power and Maximum Efficiency Tracking Control of Fuzzy IPT Systems With Communication Delays

A cooperative distributed droop gains adjustment in DC microgrids

Direct Current (DC) microgrids are a very promising solution to integrate renewable energy sources, energy storage systems and loads. The main goals of a microgrid controller are to achieve voltage regulation and load sharing among multiple distributed generators (DGs). Distributed control techniques are widely adopted to address these two objectives. Most of the existing distributed methods in the literature adjust the deviation caused by the conventional droop control by adding two additional terms. In this work, a new cooperative distributed controller to adjust the droop gains of DGs is proposed. The proposed method achieves average voltage regulation and proportional current sharing in a unified manner. It does not require additional corrective terms, resulting then in a simple control structure. Moreover, it relies only on exchanging one communication variable among neighboring DGs, which reduces the communication overhead. The stability of the proposed controller is analyzed using a small signal model of the system. The effectiveness of the proposed controller is evaluated under common load changes, local load variations, communication link failures and communication delays. The designed control scheme is verified through Hardware-In-the-Loop (HIL) tests using NI PXIe real-time simulator.

Design of voice command recognition chip based on heterogeneous acceleration

Abstract Speech, as one of the earliest forms of communication used by humans, can effectively convey information. However, the current deep neural network models for speech recognition are generally large in scale and can only be deployed in the cloud, which imposes high deployment environment requirements and power consumption, thereby limiting their implementation on embedded devices. In the context of end-to-end speech recognition, a series of challenges are encountered, including power consumption constraints, computing power limitations, network dependencies, privacy protection, bandwidth restrictions, and communication delays. To address these issues, this paper proposes the design of an end-to-end voice command recognition chip based on deep neural networks specifically for recognizing voice commands in specific scenarios. This chip achieves low power consumption and minimal delay in recognition. Additionally, we introduce a weighted, overloadable chip architecture to enable seamless scene migration, ultimately aiming to resolve the aforementioned challenges.

Research on Differential Protection of Distribution Network Based on 5G Communication Technology

Abstract The application of traditional optical fiber in differential protection technology in distribution networks faces many challenges. Due to the physical characteristics and transmission limitations of optical fiber, there is a blind area for line fault isolation, which cannot meet the growing demand for access, and is not conducive to the popularization and application of differential protection in distribution networks. This paper proposes a differential protection method for distribution networks based on 5G communication technology. 5G communication technology has the characteristics of high bandwidth, low delay, high precision network timing and safety and reliability, and provides a new wireless communication method for differential protection of distribution network. The introduction of 5G communication technology can not only provide a more stable and fast data transmission channel to ensure that the differential protection system can respond quickly after receiving the fault signal, but also realize the precise monitoring of each node of the distribution network. At the same time, this paper combines network slicing technology, uplink scheduling optimization, bit error rate optimization, air retransmission optimization, transmission priority optimization and other methods to reduce the communication delay and delay jitter, improve the reliability of the method.

Event-triggered cascade predictor for a class of nonlinear MIMO systems with large communication delays

The event-triggered predictor design problem for a class of nonlinear MIMO systems with large time delays is investigated in this paper. A periodic event-triggered mechanism is designed to avoid unnecessary data transmission and save communication energy. The triggering condition is only determined by sampled outputs of the system, such that it is applicable in case of the communication delay. Then a novel observer-based cascade predictor is proposed to reconstruct the original system state based on the delayed and event-triggered measurements, which is composed of a continuous-discrete observer and several sub-predictors in a chain structure. The stability of proposed predictor is analyzed through the Lyapunov approach. By using a sufficient number of sub-predictors and applying appropriate parameters, the prediction errors converge to bounded regions exponentially under large time delays. Finally, simulations are performed to verify the effectiveness of the proposed predictor and the event-triggered communication mechanism.

Machine Learning for Optimizing Macro-ergonomics in Pharmaceutical Supply Chain

This study endeavors to enhance the macro-ergonomics of pharmaceutical supply chains by introducing an innovative hybrid AI methodology, incorporating fuzzy data envelopment analysis (FDEA). A comprehensive case study is conducted to evaluate the efficiency of the pharmaceutical supply chain, focusing on macro-ergonomic work system assessment. This evaluation aims to identify design flaws contributing to communication delays between physicians and patients. The proposed integrated approach utilizes a hybrid AI framework, specifically FDEA, to accurately measure macro-ergonomic influences on the healthcare supply chain under uncertain conditions. The case study involves a prominent urban outpatient medical facility with 20 clinics, selecting 20 Decision-Making Units for a holistic perspective. Results uncover factors causing delays, emphasizing weak or absent feedback structures as critical elements affecting the healthcare supply chain’s effectiveness. In conclusion, the study recommends modifications to optimize the pharmaceutical supply chain, enhancing overall healthcare efficiency. The findings provide valuable insights for healthcare management, underscoring the crucial role of advanced AI methodologies in addressing complex challenges within healthcare supply chains.

Learning Underwater Intervention Skills Based on Dynamic Movement Primitives

Improving the autonomy of underwater interventions by remotely operated vehicles (ROVs) can help mitigate the impact of communication delays on operational efficiency. Currently, underwater interventions for ROVs usually rely on real-time teleoperation or preprogramming by operators, which is not only time-consuming and increases the cognitive burden on operators but also requires extensive specialized programming. Instead, this paper uses the intuitive learning from demonstrations (LfD) approach that uses operator demonstrations as inputs and models the trajectory characteristics of the task through the dynamic movement primitive (DMP) approach for task reproduction as well as the generalization of knowledge to new environments. Unlike existing applications of DMP-based robot trajectory learning methods, we propose the underwater DMP (UDMP) method to address the problem that the complexity and stochasticity of underwater operational environments (e.g., current perturbations and floating operations) diminish the representativeness of the demonstrated trajectories. First, the Gaussian mixture model (GMM) and Gaussian mixture regression (GMR) are used for feature extraction of multiple demonstration trajectories to obtain typical trajectories as inputs to the DMP method. The UDMP method is more suitable for the LfD of underwater interventions than the method that directly learns the nonlinear terms of the DMP. In addition, we improve the commonly used homomorphic-based teleoperation mode to heteromorphic mode, which allows the operator to focus more on the end-operation task. Finally, the effectiveness of the developed method is verified by simulation experiments.

Artificial intelligence for cybersecurity monitoring of cyber-physical power electronic converters: a DC/DC power converter case study.

Power electronic converters are widely implemented in many types of power applications such as microgrids. Power converters can make a physical connection between the power resources and the power application. To control a power converter, required data such as the voltage and the current of that should be measured to be used in a control application. Therefore, a communication-based structure including sensors and communication links can be used to measure the desired data and transmit that to the controllers. So, a power converter-based system can be considered as a type of cyber-physical system, and it can be vulnerable to cyber-attacks. Then, it can strongly be recommended to use a strategy for a power converter-based system to monitor the system and identify the existence of cyber-attack in the system. In this study, artificial intelligence (AI) is deployed to calculate the value of the false data (i.e., constant false data, and time-varying false data) and detect false data injection cyber-attacks on power converters. Besides, to have a precise technical evaluation of the proposed methodology, that is evaluated under other issues, i.e., noise, and communication link delay. In the case of noise, the proposed strategy is examined under noises with different signal-to-noise ratios . Further, for the case of the communication delay, the system is examined under both symmetrical (i.e., same communication delay on all inputs) and unsymmetrical communication delays (i.e., different communication delay/delays on the inputs). In this work, artificial neural networks are implemented as the AI-based application, and two types of the networks, i.e., feedforward (as a basic type) and long short-term memory (LSTM)-based network as a more complex network are tested. Finally, three important AI-based techniques (regression, classification, and clustering) are examined. Based on the obtained results, this work can properly identify and calculate the false data in the system.

The time lag in local field potential signals for the development of its Bayesian belief network

PurposeThe objective is to suggest time as an important variable to consider in the network model, specifically when discussing causality.MethodsThere is a consideration of the context of functional connectivity because of the time importance of observing the feature inside the neuroscience context. A network model was constructed using the Bayesian network method, utilizing a dataset consisting of three rats’ local field potentials. The model took into consideration the time delay of communication among brain areas, as recorded in this study. In pursuit of this objective, the delayed mutual information method was employed to ascertain the temporal delay between local field potentials and K2 score for the purpose of model comparison.ResultsBayesian network depicted the probabilistic relationship among rat’s brain areas. Delayed mutual information captured the lag among brain areas, and after its appliance on the Bayesian network model, posed better results.ConclusionThe primary novelty of this research lies in its integration of minor delays within the Bayesian network approach, accomplished through the utilization of the delayed mutual information technique prior to its implementation. The suggested methodology incorporates an essential feature that supports the analysis of functional connectivity among brain areas, thereby providing support for the dynamics of neurophysiology.

A maiden application of an MGO-tuned cascade controller for the secondary frequency control in biogas-powered marine shipboard microgrid

The use of renewable energy sources in marine microgrids has become crucial to limiting the emissions from the shipping industry. Hence, sources like biogas, solar photo-voltaic, sea wave energy, etc. are being integrated rapidly into marine microgrids. In this study, a novel implementation of cascaded controllers like 1 + PI-TID and 1 + PD-PI is modelled for the secondary load frequency control of the shipboard system. The proposed controllers are then tuned with a newly developed metaheuristic algorithm i.e. mountain gazelle optimisation. Moreover, a comparative study is done with other newly developed algorithms. The performance of these controllers is checked in diverse scenarios. The proposed method gives a 23.79% and 54.07% increase stability for the conventional PI controllers. Furthermore, for studying cyber-attacks in the proposed system, various durations of communication delay are introduced and its stability is evaluated. Finally, sensitivity and uncertainty analysis is performed for the validation of the effectiveness of the proposed controller.

Jointly Optimization of Delay and Energy Consumption for Multi-Device FDMA in WPT-MEC System.

With the rapid development of mobile edge computing (MEC) and wireless power transfer (WPT) technologies, the MEC-WPT system makes it possible to provide high-quality data processing services for end users. However, in a real-world WPT-MEC system, the channel gain decreases with the transmission distance, leading to "double near and far effect" in the joint transmission of wireless energy and data, which affects the quality of the data processing service for end users. Consequently, it is essential to design a reasonable system model to overcome the "double near and far effect" and make reasonable scheduling of multi-dimensional resources such as energy, communication and computing to guarantee high-quality data processing services. First, this paper designs a relay collaboration WPT-MEC resource scheduling model to improve wireless energy utilization efficiency. The optimization goal is to minimize the normalization of the total communication delay and total energy consumption while meeting multiple resource constraints. Second, this paper imports a BK-means algorithm to complete the end terminals cluster to guarantee effective energy reception and adapts the whale optimization algorithm with adaptive mechanism (AWOA) for mobile vehicle path-planning to reduce energy waste. Third, this paper proposes an immune differential enhanced deep deterministic policy gradient (IDDPG) algorithm to realize efficient resource scheduling of multiple resources and minimize the optimization goal. Finally, simulation experiments are carried out on different data, and the simulation results prove the validity of the designed scheduling model and proposed IDDPG.

Triplex event-triggered recursive quantizer-based networked control system under communication delay and packet dropouts

In light of network congestion, communication delay, and packet dropouts, a triplex event-triggered recursive quantizer-based networked control system (TERQNCS) is proposed in this paper. To constrain quantization error while alleviating network congestion, the recursive quantizer is constructed in the TERQNCS. Considering it is insufficient to adequately alleviate network congestion only with either the quantizer or the event trigger, a triplex event-triggered mechanism is designed through the recursive quantizer. For revealing the impacts of communication delay and packet dropouts, four Bernoulli random variables are adopted to model the network channels in the TERQNCS. Then, the model-based predictive controller is designed by virtue of the predictive states, estimated states, and Bernoulli-based model. The proposed TERQNCS can achieve the desired system performance under communication delay and packet dropouts while adequately alleviating network congestion and constraining quantization error. The stability of the TERQNCS is proved by theoretical analysis. Besides, the advantages of the TERQNCS are substantiated by a tunnel diode circuit networked control system.

Recovery control of autonomous underwater vehicles based on modified delay-product-type functional

This paper focuses on the recovery process of autonomous underwater vehicles, emphasizing a hierarchical framework in which autonomous underwater vehicles are categorized into a mothership and sub-vessels. In the recovery phase, following the completion of an underwater mission, sub-vessels navigate towards a location designated by the mothership. The crux of the recovery hinges on the design of the controller for adapting communication delays induced by environmental in underwater communication transmissions. The mothership and sub-vessels constitute a collaborative multi-autonomous underwater vehicles network equipped with these controllers, making them operate through the synchronized adjustment of their states represented in error terms. A delay-dependent controller condition criterion is proposed based on the modified delay-product-type Lyapunov-Krasovskii functional. The controller with the gain obtained from the criterion manages the system effectively and ensures successful recovery. The effectiveness of the proposed approach is demonstrated through a case study involving a network comprising one leading and four following autonomous underwater vehicles.

Reputation-Driven Asynchronous Federated Learning for Optimizing Communication Efficiency in Big Data Labeling Systems

With the continuous improvement of the performance of artificial intelligence and neural networks, a new type of computing architecture-edge computing, came into being. However, when the scale of hybrid intelligent edge systems expands, there are redundant communications between the node and the parameter server; the cost of these redundant communications cannot be ignored. This paper proposes a reputation-based asynchronous model update scheme and formulates the federated learning scheme as an optimization problem. First, the explainable reputation consensus mechanism for hybrid intelligent labeling systems communication is proposed. Then, during the process of local intelligent data annotation, significant challenges in consistency, personalization, and privacy protection posed by the federated recommendation system prompted the development of a novel federated recommendation framework utilizing a graph neural network. Additionally, the method of information interaction model fusion was adopted to address data heterogeneity and enhance the uniformity of distributed intelligent annotation. Furthermore, to mitigate communication delays and overhead, an asynchronous federated learning mechanism was devised based on the proposed reputation consensus mechanism. This mechanism leverages deep reinforcement learning to optimize the selection of participating nodes, aiming to maximize system utility and streamline data sharing efficiency. Lastly, integrating the learned models into blockchain technology and conducting validation ensures the reliability and security of shared data. Numerical findings underscore that the proposed federated learning scheme achieves higher learning accuracy and enhances communication efficiency.

Family poverty, neighbourhood greenspace and perinatal outcomes.

The relationship between low income and adverse perinatal outcomes, such as low birth weight and developmental delays, is well established making the search for protective factors important. One such factor may be neighbourhood greenspace. This study elucidates the role of urban neighbourhood greenspace in the relationship between income and perinatal outcomes in a nationally representative birth cohort from the UK. Data on 14 050 infants participating in the initial wave at age 9 months of the Millennium Cohort Study were used (51% male, 20% non-white, 52% living in disadvantaged areas). We tested whether the association between income and perinatal outcomes is moderated by urban greenspace (measured in deciles) before and after adjustments for confounding. The perinatal outcomes included birth weight, gestational age (in days), communication and motor delays. The models were adjusted for the infant's sex and ethnicity, mother's age, education, substance use and mental health as well as area disadvantage and air pollution. Neighbourhood greenspace moderated the association between income and gestational age, even after adjustment for all confounders, b=-0.11, 95% CI (-0.215, -0.004). For births in low-income households, in particular, it was associated with an increase in gestational age by an average of approximately 3 days. However, after adjustment, greenspace was not found to influence birth weight, communication or motor delays at age 9 months. The biophilic design of urban environments is a modifiable factor for improving perinatal outcomes in the UK as urban greenspaces appear to be mitigating the risk of preterm birth associated with family poverty.

Design and implementation of an intelligent building security system using Arduino GIGA R1 Wi-Fi

In the face of evolving security challenges, the integration of Internet of Things (IoT) and Artificial Intelligence (AI) technologies has become essential for modern building security systems. This paper explores the design and implementation of an intelligent building security system utilizing the Arduino GIGA R1 Wi-Fi, OV7670 camera, and Giga Display Shield, enhanced by Convolutional Neural Networks (CNNs). The system is designed to detect and respond to security threats in real-time, offering a proactive approach to building security. Leveraging the processing power of the Arduino platform and the image recognition capabilities of CNNs, the proposed system distinguishes between authorized and unauthorized access with a demonstrated accuracy of 92%. The study also identifies challenges, including performance in low-light conditions and communication delays, which affect the system's efficiency. Future work will focus on overcoming these limitations and further enhancing the system's functionality. The results indicate that the proposed solution is a promising step toward more intelligent and responsive building security systems, providing a foundation for further innovation in the field.

Enhanced load frequency control using predictive reduced order generalized active disturbance rejection control under communication delay and cyber-attack

Enhanced load frequency control using predictive reduced order generalized active disturbance rejection control under communication delay and cyber-attack