Trigger Time Research Articles

Start Time Planning for Cyclic Queuing and Forwarding in Time-Sensitive Networks

Time-sensitive networking (TSN) is a kind of network communication technology applied in fields such as industrial internet and intelligent transportation, capable of meeting the application requirements for precise time synchronization and low-latency deterministic forwarding. In TSN, cyclic queuing and forwarding (CQF) is a traffic shaping mechanism that has been extensively discussed in the recent literature, which allows the delay of time-triggered (TT) flow to be definite and easily calculable. In this paper, two algorithms are designed to tackle the start time planning issue with the CQF mechanism, namely the flow–path–offset joint scheduling (FPOJS) algorithm and congestion-aware scheduling algorithm, to improve the scheduling success ratio of TT flows. The FPOJS algorithm, which adopts a novel scheduling object—a combination of flow, path, and offset—implements scheduling in descending order of a well-designed priority that considers the resource capacity and resource requirements of ports. The congestion-aware scheduling algorithm identifies and optimizes congested ports during scheduling and substantially improves the scheduling success ratio by dynamically configuring port resources. The experimental results demonstrate that the FPOJS algorithm achieves a 39% improvement in the scheduling success ratio over the naive algorithm, 13% over the Tabu-ITP algorithm, and 10% over the MSS algorithm. Moreover, the algorithm exhibits a higher scheduling success ratio under large-scale TSN.

Distributed Optimization on Matrix‐Weighted Networks

ABSTRACTThis article is intended to tackle the optimization problems for continuous‐time first‐order and second‐order multi‐agent systems (MASs) operating over matrix‐weighted networks. A matrix‐weighted network serves as a powerful framework to model the interdependence among agents' multidimensional states, providing an effective approach to analyze smart grids, intelligent transportation systems, and so forth. Our optimization objective is to facilitate the convergence of all agents toward the optimal value of a global cost function, which is formed by a sum of local cost functions. To achieve this goal, distributed optimization algorithms based on Hessian matrix and gradient information are constructed. Additionally, an edge‐based event‐triggered mechanism is utilized to avoid communicating with all neighbors at the time of event triggering. It is proved that this mechanism theoretically excludes Zeno behavior. The results show that the proposed algorithms ensure that the agents can achieve the optimization goal while reducing energy consumption. Finally, an application is presented to substantiate the theoretical results.

The Influence of Thermophysical Parameters on Thermal Runaway Characteristics for Pouch-Type Lithium-Ion Batteries

Abstract The thermal variation during the temperature rise process of batteries is closely related to multiple physical parameters. Establishing a direct relationship between these parameters and thermal runaway (TR) features under abusive conditions is challenging using theoretical equations due to complex electrochemical and thermal coupling. In this paper, a high-temperature thermal runaway model of pouch-type lithium-ion battery is established through electrical-thermal coupled approach, demonstrating a good agreement between the simulation and experimental results. The results reveal distinct trends in thermal parameters of the early temperature rise, trigger time for TR and peak temperature during TR process, for varying convective heat transfer coefficient, cell specific heat capacity, cell density and cell thermal conductivity. Across various convective heat transfer coefficients, the rates of temperature increase, moments of TR, and peak temperatures within a battery emerge as the cumulative outcomes of competing processes of the intricate exothermic secondary reactions within the battery, and the heat transfer with the surroundings. Batteries with lower heat capacity exhibit reduced thermal inertia and heightened sensitivity to temperature changes. Alterations in the thermal capacity of a battery wield a profoundly significant impact upon the moment of thermal runaway within the battery. Enhancing the thermal conductivity yields limited improvements in heat dissipation during thermal runaway primarily due to the relatively small geometrical scale of the battery. Results of this paper can provide valuable insights for size optimization design, thermal management system optimization design, thermal runaway safety warning and prevention of Lithium-ion batteries.

Distributed leader-following bipartite consensus for one-sided Lipschitz multi-agent systems via dual-terminal event-triggered mechanism

This article analyses leader-following bipartite consensus for one-sided Lipschitz multi-agent systems by dual-terminal event-triggered output feedback control approach. A distributed observer is designed to estimate unknown system states by employing relative output information at triggering time instants, and then an event-triggered output feedback controller is proposed. Dual-terminal dynamic event-triggered mechanisms are proposed in sensor–observer channel and controller–actuator channel, which can save communication resources to a great extent, and the Zeno behavior is ruled out. A new generalized one-sided Lipschitz condition is proposed to handle the nonlinear term and achieve bipartite consensus. Some stability conditions are presented to guarantee leader-following bipartite consensus. Finally, one-link robot manipulator systems are introduced to demonstrate the availability of the designed scheme. The results demonstrate that the agents of the robot manipulators can track the reference trajectories bi-directionally, and effectively reduce communication resources by 61.22% and 68.04% at the sensor–observer and controller–actuator channels, respectively.

Optimizing Virtual Network Embedding for Avionics with Time-Triggered Traffic Scheduling

Network virtualization technology abstracts the nodes and links resources in the physical network, and enables multiple virtual networks (VNs) to share the substrate network (SN) resources through the Virtual Netw ork Embedding (VNE) method. For time-triggered Ethernet (TTE) used in avionics, a time-triggered traffic scheduling oriented virtual network embedding (TT-VNE) method is proposed. While meeting the total resource constraints of traditional VNE problem, it ensures the strict periodicity of time-triggered (TT) traffic. During the solution process, the method sorts the virtual nodes according to the TT traffic bandwidth requirements and the total bandwidth requirements of the links connected to the virtual nodes, embeds the virtual nodes using the breadth first search algorithm, and plans the virtual links in candidate shortest path aggregation. If the TT traffic in the current virtual link is not schedulable, the iterative design of local virtual node re embedding and path planning is carried out. The simulation shows that the request acceptance rate of TT-VNE is not lower than that of existing methods, and when the number of virtual network requests in the star topology exceeds 30 , its request acceptance rate is about 14.3%higher than the VNE-NTANRC-D method which only considers the network topology and resource attributes.

Self-triggered MPC with adaptive prediction horizon for nano-satellite attitude control system

Nano-satellites are essential tools for various applications, including scientific experiments, deep space exploration and astronomical observation. Achieving precise model predictions is crucial for their successful operation. To address the intricate constraints of nano-satellites and enhance control performance, the Model Predictive Control (MPC) algorithm is an effective solution. However, implementing an MPC-based attitude control system in actual engineering scenarios presents significant challenges, primarily due to the substantial computational burden, especially given the limited onboard computing resources of nano-satellites. In this paper, we introduce a modified adaptive self-triggered model predictive control (ST-MPC) algorithm designed to stabilize the attitude of nano-satellites, while simultaneously reducing communication and computational overhead compared to traditional MPC methods. The proposed self-triggered mechanism dynamically determines the next trigger time according to the system state. Moreover, we incorporate considerations for the efficiency of actuators to address the constraints imposed by the magnetic torque characteristics within the modified self-triggered mechanism. Additionally, a strategy for adaptive prediction horizon is proposed to balance computation load and control accuracy. The results of our simulations demonstrate the effectiveness of the modified ST-MPC algorithm in comparison to both traditional MPC and standard ST-MPC approaches. This algorithm may have the potential to significantly impact attitude control applications for nano-satellites.

Expert Opinion on Refined and Extended Key Performance Indicators for Individualized Ovarian Stimulation for ART

ObjectiveTo assess the adequate ovarian follicular development and oocyte recovery between ovarian potential (antral follicle count [AFC]) before the start of ovarian stimulation (OS) and oocyte quantity and quality at oocyte retrieval. A holistic overview of the current key performance indicators (KPIs) was applied to identify the complementary strengths and identify where the current repertoire can be expanded. DesignExpert opinion. SubjectsNot applicable. InterventionNone. Main Outcome MeasuresTo formulate a proposal for a refined and expanded repertoire of KPIs for individualized OS for Assisted Reproductive Technology. ResultsThe performance and outcomes of OS on ovarian follicular development can be evaluated through the application of defined KPIs. Current KPIs for OS are the ovarian sensitivity index (OSI), the follicular output rate (FORT), the oocyte retrieval rate (ORR), and the follicle to oocyte index (FOI). Notably, there are no specific KPIs dedicated to the assessment of follicular development (i.e., recruitment, selection, growth and dominance). In light of this, we recommend expanding the current KPIs for OS to include “Early FORT” (accounting for the number of follicles measuring ≥10–11 mm on Day 5/6 of OS relative to AFC) and “Modified FORT” (the ratio between the number of follicles measuring ≥12 mm at the time of oocyte maturation triggering and AFC); the extension of ORR to include two discrete categories at oocyte retrieval: follicles ≥12 mm and follicles ≥16 mm, to ensure all responsive follicles are accounted for; and FOI to be measured at oocyte maturation triggering and oocyte retrieval ("Advanced FOI”). ConclusionsOnce validated and adopted in clinical practice, we envisage that the proposed expanded KPIs measuring the effect of OS on follicular development (recruitment, selection, growth and dominance) will increase the understanding of the relationship between ovarian reserve measured by AFC and oocyte quantity and quality at oocyte retrieval. This understanding will enable physicians to better evaluate the direct effect of different gonadotropins and doses on ovarian response, leading to a more personalized approach to OS in the context of ART treatment.

Direct ink writing 3D printed graphene oxide nanocomposite aerogel for intelligent fire-warning and exceptional fire-shielding

Fabricating intelligent fire-warning sensors with controllable 3D structures and shapes by advanced manufacturing technology is a formidable challenge but remains the critical factor in broadening fire prevention and protection. Herein, the novel fire-warning aerogel with a high-fidelity 3D porous structure and diversiform geometrical features based on graphene oxide (GO), cellulose nanofiber (CNF) and ammonium polyphosphate (APP) was fabricated via direct ink writing (DIW) 3D printing combined with freeze-drying technology. Benefiting from the hydrogen bonding multi-interactions between the molecules as well as the ideal rheological properties, the ink containing 20 wt% of CNF (GA-CNF-20) had a dynamic yield stress of 620 Pa. As a result, the corresponding aerogel had a significantly improved elasticity modulus of 153 kPa and a compressive stress of 400 kPa at a deformation of 39.7 %. Meanwhile, GA-CNF-20 displayed an ultrafast fire-warning trigger time (1.32 s) and a long-term response time (170 s) because of the thermal reduction property of GO together with the in-situ phosphorus doping reaction of APP under fire. Besides, the GA-CNF-20 also exhibited a strong heat-insulating capability, high char-forming property, self-extinguishing performance as well as structure integrity in the burning test. The relevant improvement mechanisms were attributed to the non-flammable gas diluting effect, intumescent char layer barrier effect and fire blowing-out effect in both gas and condensed phases. Such self-supporting printed aerogel can potentially be used as a fire-warning sensor for designated systems with specific precision structure in multi-domains of electrical and traffic engineering.

An Improved MOEA/D with an Auction-Based Matching Mechanism

Multi-objective optimization problems (MOPs) constitute a vital component in the field of mathematical optimization and operations research. The multi-objective evolutionary algorithm based on decomposition (MOEA/D) decomposes a MOP into a set of single-objective subproblems and approximates the true Pareto front (PF) by optimizing these subproblems in a collaborative manner. However, most existing MOEA/Ds maintain population diversity by limiting the replacement region or scale, which come at the cost of decreasing convergence. To better balance convergence and diversity, we introduce auction theory into algorithm design and propose an auction-based matching (ABM) mechanism to coordinate the replacement procedure in MOEA/D. In the ABM mechanism, each subproblem can be associated with its preferred individual in a competitive manner by simulating the auction process in economic activities. The integration of ABM into MOEA/D forms the proposed MOEA/D-ABM. Furthermore, to make the appropriate distribution of weight vectors, a modified adjustment strategy is utilized to adaptively adjust the weight vectors during the evolution process, where the trigger timing is determined by the convergence activity of the population. Finally, MOEA/D-ABM is compared with six state-of-the-art multi-objective evolutionary algorithms (MOEAs) on some benchmark problems with two to ten objectives. The experimental results show the competitiveness of MOEA/D-ABM in the performance of diversity and convergence. They also demonstrate that the use of the ABM mechanism can greatly improve the convergence rate of the algorithm.

Trajectory Tracking of Unmanned Logistics Vehicle Based on Event-Triggered and Adaptive Optimization Parameters MPC

Unmanned logistics vehicle (ULV) realize the automation and intelligence of cargo transportation, which improves the efficiency, cost-effectiveness and safety of logistics and distribution, while the trajectory tracking control of ULV is the key technology to ensure their safe and efficient delivery of goods. In order to solve the trajectory tracking problem of ULV in the process of delivering goods, this paper proposes a model predictive control (MPC) method based on event-triggered and fuzzy adaptive optimization parameters. Firstly, the dynamics model of the ULV is established. Secondly, an event-triggered mechanism is introduced to establish ET-MPC, while a disturbance observer is designed considering the external disturbance and the controller calculation discarding the nonlinear term. Thirdly, the advantages of fuzzy control and MPC algorithms are integrated, and the four important parameters in the MPC controller are adaptively optimized by fuzzy control, and the improved MPC control strategy is designed. Finally, the CarSim-Matlab/Simulink co-simulation platform and the experimental vehicle platform are constructed to verify the effectiveness of the improved MPC trajectory tracking controller proposed in this paper. The results show that the improved MPC control strategy can reduce the computation time of the controller, and the total number of triggering times of the controller is reduced by 46.44% compared with the classical MPC, which reduces the computational complexity of the controller and improves the accuracy and smoothness of the trajectory tracking of the ULV.

EVENT-TRIGGERED CONTROL OF MILLING PHOTOELECTRIC TRACKING SERVO SYSTEMS BASED ON MULTI-INNOVATION INTEREST PARAMETER IDENTIFICATION

Given the coupling of speed and current and the nonlinearity of electronic devices, the photoelectric tracking servo system for milling machines make it difficult to determine the model parameters, and traditional proportional–integral (PI) control hardly meets the high-performance requirements of milling machine optoelectronic tracking servo systems, especially in the development of system networking. In this study, an event-triggered intelligent PI position control strategy based on a multi-innovation identification model was proposed to improve the low control accuracy and dynamic performance caused by the strong coupling and nonlinearity in the optoelectronic tracking servo system of computerized numerical control (CNC) milling machines. A discredite model of the system was established through a multi-innovation identification model, and the PI control parameter was quickly determined using an improved multiverse optimization (IMVO) algorithm. At the same time, an event-triggering mechanism was introduced, thus reducing the number of controller triggers and saving system resources while ensuring the dynamic performance of the system. Finally, experiment results were compared with typical second-order system engineering design PI (SSED-PI) control, pole placement PI (PP-PI) control, and multiverse optimization (MVO)-PI control. Results demonstrate that the proposed multi-innovation stochastic gradient identification model fully utilizes the historical turning angle information of the optoelectronic tracking servo system and has higher accuracy than traditional stochastic gradient identification (parameter accuracy improved by 6.9 times, quantization error reduced by 6.7 times). The proposed event-triggered IMVO-PI (ET-IMVO-PI) has a triggering frequency of 3.5% compared with time-triggered IMVO-PI, with an overshoot of less than 0.5%, which can meet the needs of most engineering practices (less than 5%). Compared with event-triggered SSED-PI, PP-PI, and IMVO-PI, ET-IMVO-PI has higher dynamic performance and fewer triggering times, which can effectively meet the requirements of high-performance network control. The proposed method serves a crucial theoretical guide and important reference for the upgrading and transformation of the photoelectric tracking servo system of CNC milling machines. Keywords: Event-triggered control, Multiple innovative parameter identification, Multiverse optimization PI, Optoelectronic tracking servo system

Adjustment of scan delay for bolus tracking with cardiothoracic ratio of CT scout image for hepatic artery phase of hepatic dynamic CT.

This study aimed to determine the scan delay for bolus tracking in the hepatic artery phase (HAP) of hepatic dynamic computed tomography (CT) using the cardiothoracic ratio (CTR) from CT scout images. We retrospectively studied 188 patients who underwent hepatic dynamic CT, 24 of whom had scan delays adjusted for CTR. The contrast enhancement of the abdominal aorta, portal vein, hepatic vein, and hepatic parenchyma was calculated for HAP. The adequacy of the scan timing for HAP was assessed using three classifications: early, appropriate, or late. The effect of HAP on scan timing adequacy was determined using multivariate logistic regression analysis, and the optimal cutoff value of CTR was evaluated using receiver operating characteristic analysis. The trigger times for bolus tracking (odds ratio: 1.58) and CTR (odds ratio: 1.23) were significantly affected by the appropriate scan timing of the HAP. The optimal cutoff value of CTR was 59.3%. The scan timing of HAP with a scan delay of 15s was 14% of early and 86% of appropriate, and the proportion of early in CTR ≥ 60% (early, 52%; appropriate, 48%) was higher than that in CTR < 60% (early, 6%; appropriate, 94%). Adjusting the scan delay to 20s in CTR ≥ 60% increased the proportion of appropriate (early, 4%; appropriate, 96%). The CTR of a CT scout image is an effective index for determining the scan delay for bolus tracking. Adjusting the scan delay by CTR can provide appropriate HAP images in more patients. Trial registration number: R-080; date of registration: 9 March 2023, retrospectively registered.

A New Deep Learning Model to Detect Gamma-Ray Bursts in the AGILE Anticoincidence System

The AGILE space mission was launched in 2007 to study X-ray and gamma-ray astrophysics. AGILE operated in spinning mode from 2009 until 2024 February 14, when it re-entered the Earth’s atmosphere. This work uses data acquired from the AGILE anticoincidence system (ACS) from 2019 January 1 to 2022 December 31. The ACS is designed to reject charged background particles. It also detects X-ray photons in the 50–200 KeV energy range and saves each panel count rate in the telemetry as ratemeter data, a time series with a resolution of 1.024 s. We developed a method that uses a deep learning model to predict the background count rates of the AGILE ACS top panel (perpendicular to the pointing direction of the payload detectors) using the satellite’s orbital parameters as input. Then, we use the difference between predicted and acquired count rates to detect gamma-ray bursts (GRB). We trained the model with a background-only data set. After the training, the model can predict the ACS count rates with a mean reconstruction error of 3.8%. We used the GRBs listed in the GRBweb catalog to search for significant anomalies in the ACS data. We extracted light curves of 140 bins of 1.024 s for each GRB from the AGILE ACS to cover the trigger time of the GRBs. The model detected 39 GRBs with a significance of σ ≥ 3. The results contain four GRBs detected for the first time in the AGILE data.

Bipartite consensus for multi-agent systems over signed networks: A novel dynamic event-triggered mechanism

This paper is concerned with the problem of dynamic event-triggered bipartite consensus control for nonlinear multi-agent systems based on signed networks. A new dynamic event-triggered control mechanism is proposed, whereby an adjustment variable is introduced to dynamically schedule the triggering frequency, enabling the minimization of the triggering times while ensuring system performance. Based on the designed event-triggered control algorithm, sufficient conditions are derived to guarantee that bipartite consensus can be reached by the considered nonlinear multi-agent system. Furthermore, it is proved that Zeno behavior will not occur. Numerical examples are provided in this paper to verify the effectiveness of the proposed control algorithm. The simulation results reveal that, compared with the static event-triggered mechanism and the traditional dynamic event-triggered mechanism, the proposed event-triggered algorithm can further reduce the triggering times and enhance the flexibility of the event-triggered mechanism.

Numerical simulation study on the impact of convective heat transfer on lithium battery air cooling thermal model

To enhance the accuracy of lithium battery thermal models, this study investigates the impact of temperature-dependent convective heat transfer coefficients on the battery’s air cooling and heat dissipation model, based on the sweeping in-line robs bundle method proposed by Zukauskas. By calculating and fitting the relationship between the convective heat transfer coefficient and temperature at flow rates of 0.05 m/s, 0.15 m/s, 0.25 m/s, and 0.35 m/s, it was found that the relationship is complex. An electrochemical-thermal coupling model was established using the operational characteristics of lithium batteries, and a thermal runaway reaction kinetics model was created using isothermal thermal runaway experiments and least squares optimization. The temperature-dependent convective heat transfer coefficient was then integrated into both models. Numerical simulations revealed that during normal discharge, the maximum temperature difference in the battery when the convective heat transfer coefficient is a function of temperature is less than 1 % compared to when it is constant. However, in the high-temperature thermal runaway model, the impact of temperature-dependent convective heat transfer coefficients on the thermal runaway critical parameters is minimal at flow rates of 0.05 m/s and 0.15 m/s. When the flow rate increases to 0.25 m/s and 0.35 m/s, the impact on the trigger time of thermal runaway is 17.34 % and 18.07 %, respectively. Experimental validation and research results indicate that the temperature effect on the convective heat transfer coefficient should be considered in high-temperature thermal runaway and thermal management models to calculate the convective heat transfer more accurately within the battery pack, improving model accuracy and reducing the risks of thermal runaway.

Investigation on Thermal Runaway Hazards of Cylindrical and Pouch Lithium-Ion Batteries under Low Pressure of Cruise Altitude for Civil Aircraft

Thermal runaway characteristics and hazards of lithium-ion batteries under low ambient pressure in-flight conditions are studied in a dynamic pressure chamber. The influence of ambient pressures (95 kPa and 20 kPa) and packaging forms (cylindrical and pouch commercial batteries) were especially investigated. The results show that the values of heat release, temperature, and CO2 concentration decrease with the reduction in pressure from 95 kPa to 20 kPa, while the total hydrocarbon and CO increase. Without violent fire, explosion, and huge jet flames, the thermal hazards of TR fire under 20 kPa are lower, but the amount of toxic/flammable gas emissions increases greatly. The amount of CO and hydrocarbons varies inversely with the thermal hazards of fire. Under low-pressure environments of cruise altitude, the thermal hazards of TR fire for pouch cells and the toxic/potentially explosive hazards of gas emissions of cylindrical cells need more attention. The performance of TR hazards for two packaging types of battery is also different. Pouch cells have higher thermal hazards of fire and lower combustible/toxic emitted gases than cylindrical cells. The thermal runaway intensity of individual cells decreases under lower ambient pressure, but the burning intensity increases dramatically when thermal runaway occurs in a battery pack. The open time of a safety valve (rupture of the bag) is shortened, but the trigger time for a thermal runaway varies for different formats of batteries under 20 kPa. Those results may be helpful for the safety warning and hazard protection design of Li batteries under low-pressure conditions.

Development of Wood-Based functional composites for advanced sensing and energy conversion applications

With the low carbon-driven development of modern intelligent, research towards novel materials is concentrating on the sustainable biomass-based advanced functional composites. The advanced functionalized wood toward sensing, optoelectronic devices, and energy conversion has been contributed to fast electron transport upon temperature or photo stimulus. Fast electron transport has been highly depended on the interfacial adhesion between the functional layer and wood matrix as well as graphitization degree during carbonization, which promote the electronic excitation and jumps. Here, we develop wood-based functional composites for advanced fire warning and energy conversion by loading metal–insulator transition (MIT) function factor (Ti3O5) onto different woods via liquid crosslinkers, and systematically establish the interfacial adhesion-carbonization-electron transport processes and their relationships on the sensing and photothermal conversion properties. Firstly, balsa with high porosity (pore diameter ≈50 μm), low density (0.16 g/cm3) and extractives content (total extractives content 16.27 %) facilitates crosslinker penetration due to the more holding space and lower nonpolar components. A favorable permeation can promote better interfacial adhesion, and the formation of a continuous conductive network of char-layers during carbonization. Secondly, the crosslinker (chitosan) with excellent adhesion (1 level) and char-forming ability (char residues is 21.7 %), facilitates the formation of a compact network of highly graphitized char-layers (ID/IG=0.81), which increases the electrical conductivity (highest conductivity 5.52 × 10-4 S/m). We found that the most sensitive flame trigger time (0.86 s) for fire warning and the highest evaporation flux (1.63 kg m-2h−1) for desalination can be obtained by using balsa which coupled the chitosan as well as the Ti3O5.

Amiodarone-Induced Anaphylaxis.

Background: Amiodarone-induced anaphylaxis is seldom reported. The mechanism of this anaphylaxis is unknown. Methods: A literature search was carried out with keywords "Amiodarone" and "Anaphylaxis" and "polysorbate 80" or "hypotension." A search using "amiodarone" in the FDA Adverse Event Reporting System (FAERS) from 1969 to 2024 was also conducted. Results: There are a total of 10 cases of amiodarone-induced anaphylaxis in the literature. Six patients were male. Ages ranged from 15 to 86years old. Nine cases were triggered by intravenous injection (IV) and one by oral administration. Eight patients did not have previous exposure to amiodarone. The trigger times for IV amiodarone were immediate to 90 minutes. All nine cases of IV amiodarone resulted in hypotension (90%), with an immeasurable blood pressure (70%). Presentations included bronchospasm or a skin rash (60%), angioedema (40%), and unconsciousness (20%). Only one patient had a history of allergy to penicillin and sulfonamide. An amiodarone skin test was positive on one patient. Increased blood tryptase (4 cases), positive basophil activation test to amiodarone (2 cases), increased eosinophil count (1 case), and increased serum IgE (1 case) were reported. Amiodarone was terminated in 80% of the patients. Epinephrine, norepinephrine, antihistamine-1, or steroids were used to rescue patients. Four patients were intubated. All patients fully recovered. In the FAERS database, 89 cases of amiodarone-associated anaphylaxis were reported, resulting in 14 deaths. Conclusions: Solvent polysorbate 80, amiodarone, and iodide may contribute to amiodarone-induced anaphylaxis. Prompt treatment is the key to saving patients.

Load-balanced Routing Heuristics for Bandwidth Allocation of AVB Flow in TSN

Time-Sensitive Networking (TSN) is a new technology developed from Ethernet that guarantees deterministic transmission of various types of flows, such as Time-triggered (TT) flows and Audio-video-bridging (AVB) flows, in the same network. Currently, Time-aware Shaping (TAS) and Credit-based Shaping (CBS) are widely used for scheduling hybrid flows, where the credit value in CBS is directly linked to the logical bandwidth value idleSlope , which affects the real-time performance of AVB flows. However, as the network scale increases, existing bandwidth allocation methods result in higher computation times and lower allocation success rates. In this paper, the aforementioned problem is addressed by two-step: first, we design a load-balanced routing heuristic (LBRH) to improve the allocation success rate; second, we accelerate the CBS bandwidth allocation by using unified bandwidth allocation scheme, which integrates LBRH and further improves the allocation success rate. Performance evaluation in multiple test cases shows that LBRH can improve the success rate of bandwidth allocation, and the unified bandwidth allocation scheme integrating LBRH significantly reduces the overall execution time while improving the success rate of bandwidth allocation, which is more suitable for large-scale TSN networks with complex routing.

Dynamic event‐triggered leader‐following bipartite consensus of second‐order multi‐agent systems under DoS attacks

AbstractThis article investigates the bipartite secure consensus of the second‐order leader‐follower multi‐agent systems (MASs) against aperiodic denial of service (DoS) attacks. First, a new dynamic event‐triggered strategy is proposed for the consensus control. Compared with the static event‐triggered strategy, the dynamic event‐triggered protocol can reduce the trigger times and save resources by introducing an internal dynamic variable. Different from the secure consensus results which only discussed the cooperative links, based on the theory of the structurally balanced graph, this article considers cooperative and antagonistic interactions among agents simultaneously for complex behaviors of them. Besides, the characteristics of the DoS attacks are analyzed in detail and the secure consensus is achieved by utilizing the Lyapunov theory and inductive methods. Finally, the Zeno behavior is excluded and the availability of the designed algorithms is demonstrated by numerical simulations.