Asynchronous Case Research Articles

Distributed beamforming design for UAV‐based integrated communication and jamming systems

AbstractThe integrated communication and jamming system is proposed by exploiting the diversity gain and flexible deployment of unmanned aerial vehicle (UAV)‐based distributed multiple input multiple output technology, which is expected to gain higher spectral efficiency and system scalability. This paper designs transmit beamforming of multiple UAVs so that they can simultaneously impose jamming signal to jamming targets and communication signal to communication users at the same frequency. The aim is to maximize the total jamming effect of all jamming targets while achieving a certain communication rate of communication users by optimizing the transmit beamforming matrices. Two cases are considered in which the UAV swarm works in clock synchronization and clock asynchronization. Specifically, the problem is formulated into a non‐convex optimization in the synchronous case. A near optimal solution is derived by decoupling the beamforming matrix into multi‐user interference suppression matrix and single‐user beamforming matrix. In the asynchronous case, the transmitter–receiver matching relation needs to consider and multiple optimization variables are highly coupled, penalty factors and intermediate variables to convert the optimization problem to convex programming are introduced. The simulation results show that the proposed beamforming design algorithm coordinates the communication and jamming signal rationally and outperforms the conventional beamforming design algorithms.

Input-to-state stability of discrete-time impulsive switched delayed systems with delay-dependent impulse under two asynchronous cases

This paper studies a class of discrete-time impulsive switched delayed systems with delay-dependent impulses, aiming to solving the input-to-state stability (ISS) problem in the case of asynchronous switching signal between subsystem and controller and asynchrony of switching signals and impulsive signals. Among them, the delay effect is handled by a new Lyapunov-Krasovskii function divided into a delay-dependent part and a delay-independent part. In contrast to admissible edge-dependent average dwell time (AED-ADT) methods that address switching signals, we propose admissible edge-dependent average impulsive interval (AED-AII) to deal with delayed impulses, thereby establishing the relationship between these methods and the decay rate. Meanwhile, a stabilizing controller for discrete impulsive switched linear delayed systems is proposed and the minimum average dwell time and controller gain are obtained. Compared with the previous work, the parameters of Lyapunov functional relation depend on the directed edges; the operation of subsystem is divided into asynchronous time and synchronous time, which are less conservative; the combination of AED-ADT and AED-AII can be applied to a wider range of impulsive switched systems. Finally, two examples are given to show the effectiveness of the results.

Exponential Synchronization of Markovian Jump Neural Networks Based on Asynchronous Delayed-Feedback Controller With Uncertain Hidden Information.

Due to the complex network environment, the feedback information cannot be timely received by the controller. This article proposes a method on the exponential synchronization for the Markovian jump neural networks, which is achieved by designing a new asynchronous delayed-feedback controller, with its feedback delay taken into account. The quantized relationship between the exponential synchronization and the feedback delay is derived from a new designed Lyapunov functional, to acquire delay boundaries. With the help of a hidden-Markov process, the designed controller shows asynchrony, which allows controller modes to run free. In particular, the detection probability is assumed to be bounded known, marking a breakthrough over existing results. Moreover, the proposed method proves to be applicable in both synchronous and asynchronous cases. By using the proposed method, the computation freedom of the controller gain matrix can be substantially augmented. Further, comparative numerical studies are implemented to validate the effectiveness and superiority of the proposed method.

Overcoming the paradigm of traditional teaching methodology in pharmacology monitoring: innovations in medical education driven by the pandemic

In the course of the last decades there has been a transformation of medical education, aiming to change from a traditional model centered on the teacher to active methodologies, which involve students in a participative, practical and critical way. The pandemic highlighted the importance of differentiated approaches, such as dialoged classes and the inverted classroom. While they bring benefits, they also present challenges, such as preparedness and the necessary infrastructure. Training assessment and Think-Pair-Share (TPS) methodology are also important in this context. Accordingly, the adaptation of the ative methodologies for the teaching of pharmacology is considered relevant for forming doctors prepared for the future demands. The objective of the present study was to report the experience of the monitors in the discipline of Pharmacology in the context of adaptation between the pandemic scenario with remote activities and the return to face-to-face teaching, maintaining the strategy of active methodologies and the formative evaluation, which was initially structured during the pandemic in a digital context. The discipline of Pharmacology offered to the undergraduate course in Medicine of the State University of Rio de Janeiro has therefore started to integrate medical theory and practice, involving classroom, asynchronous and clinical cases. In this way, the teachers teach the subjects and the student monitors, with the supervision of the teachers, select clinical cases based on practical experiences, and use the strategy of the TPS for discussions with small groups of students, which, afterwards, are shared with the whole group. Clinical cases are based on supporting texts, but the discussions encourage the addition of new information to widen the understanding of the discipline. In this context, the monitoring project in Pharmacology involves an active teaching approach, encouraging students to participate in clinical case discussions and broaden theoretical knowledge. This practice enriches academic skills, arouses interest in teaching and is well evaluated by students. The approach aligns with Glasser's learning pyramid, favoring knowledge retention. The use of the TPS methodology promotes collaboration and clinical reasoning and the impact has been positive, which is evidenced by student evaluations and feedback to the discipline and to the monitors. Therefore, the restructuring of the Pharmacology discipline adopted more dynamic and active teaching strategies, seeking to stimulate the active participation of students and critical reasoning. Monitoring played an important role in this approach, promoting debate, proactivity, and clinical skills. The monitoring experience has been positive, deepening the technical knowledge of the monitors and challenging them to constantly improve teaching strategies. Accordingly, the work reflects the importance of more ative, critical medical teaching that is aligned with social demands.

Bumpless transfer proportional-integral-derivative control of switched positive systems

This paper addresses the bumpless transfer proportional-integral-derivative (PID) control for discrete-time switched systems with average dwell time (ADT). First, a bumpless transfer performance is proposed to reduce the control bumps caused by switching of the systems. Then, a PID controller is constructed to achieve the positivity and stability under synchronous switching. Under the PID control, the system is transformed into a time-delayed system. Sufficient conditions are derived to preserve the positivity, stability, and bumpless transfer performance. Moreover, a PID controller is proposed under the asynchronous switching case. All gain matrices of the PID controller are described via a matrix decomposition approach. Using the copositive Lyapunov function and linear programming, the positivity, stability, and bumpless transfer performance of the system are achieved under synchronous and asynchronous switching cases, respectively. Finally, two examples are provided to illustrate the effectiveness of the proposed approaches.

Robust fuzzy predictive switching control for nonlinear multi-phase batch processes with synchronous vs asynchronous cases

Multi-phase batch processes (MPBP) have the characteristics of nonlinearity and various cases of switching, where the switching between two adjacent phases can be divided into synchronous vs asynchronous cases. The present study introduces a robust fuzzy predictive method for switching control to ensure that an MPBP can still operate stably in the above two cases. First, considering the multi-phase characteristics of MPBP, multiple stable sub-models are established under the synchronous case. On this basis, unstable sub-models are established between adjacent phases under the asynchronous case. Second, in order to deal with the inherent nonlinearity of MPBP, each sub-model is locally linearized and a mixed Takagi-Sugeno (T-S) model is established. Third, using a mapping relationship between controller and local subsystem including direct mapping and fully associative mapping, a global output control law is designed. By combining the Lyapunov stability theory, linear matrix inequality (LMI) theory, mode-dependent average dwell time method, etc., corresponding LMI conditions are derived by the mapping relationship of different cases, making each phase asymptotically stable and each batch exponentially stable. Then, the distribution compensation gains of the corresponding controllers, the shortest operating time of the stable subsystem and the longest operating time of the unstable subsystem can be obtained by solving corresponding LMI conditions in a synchronous or an asynchronous case. Finally, if the MPBP is in an asynchronous case, according to the longest operating time of the unstable subsystem, a time compensation method based on the obtained operating time is proposed to avoid the occurrence of an unstable subsystem. The simulation results demonstrate that the proposed method has greater advantages than the reference methods.

Developing a Web-Based Asynchronous Case Discussion Format on Social Media to Teach Clinical Reasoning: Mixed Methods Study.

Case-based learning conferences are valuable to trainees, but growing clinical demands hinder consistent attendance. Social media increasingly acts as a venue for trainees to supplement their education asynchronously. We designed and implemented a web-based asynchronous clinical case discussion series on the Twitter social media platform to fill this educational gap. The aim of this mixed methods study is to examine the nature of interactions among web-based case discussion participants and assess local attitudes regarding the educational intervention. Starting in February 2018, we posted clinical vignettes to a dedicated Twitter account with the prompt "What else do you want to know?" to stimulate discussion. The authors replied in real time when case discussion participants requested additional details. Additional data about the case were posted at regular intervals to the discussion thread to advance the overall case discussion. Participants were asked to explain their reasoning and support their conclusions when appropriate. Web-based engagement was assessed using Twitter Analytics. Participants' posts were qualitatively analyzed for themes, with special attention to examples of using clinical reasoning skills. A codebook of types of participant posts and interactions was refined iteratively. Local engagement and attitudes at our institution were assessed by surveying internal medicine trainees (n=182) and faculty (n=165) after 6 months. Over a 6-month period, 11 live case discussions were engaged with by users 1773 times. A total of 86 Twitter profiles spanning 22 US states and 6 countries contributed to discussions among participants and the authors. Participants from all training levels were present, ranging from students to faculty. Interactions among participants and the case moderators were most commonly driven by clinical reasoning, including hypothesis-driven information gathering, discussing the differential diagnosis, and data interpretation or organization. Of 71 respondents to the local survey, 29 (41%) reported having a Twitter account. Of the 29 respondents with Twitter accounts, 17 (59%) reported participating in the case discussions. Respondents agreed that case participation increased both their clinical reasoning skills (15/17, 88%) and clinical knowledge (13/17, 76%). A social media-based serialized case discussion was a feasible asynchronous teaching method for engaging web-based learners of all levels in a clinical reasoning discussion. Further study should examine what factors drive trainee participation in web-based case discussions and under what circumstances asynchronous discussion might be preferred over in-person teaching activities.

A Stackelberg Game Approach to the Stability of Networked Switched Systems Under DoS Attacks

This paper investigates the stability of networked switched systems under DoS attacks by a Stackelberg game approach. Firstly, the non-cooperative competition between the attacker and the controller is characterized as a switched Stackelberg game. The delivery rate of data packets and controller gains compose the strategy sets of the attacker and the controller, respectively. Then, the switched optimal Stackelberg strategy is obtained from the corresponding Stackelberg equilibrium. Considering the open-loop and asynchronous switching cases caused by DoS attacks, sufficient conditions for the exponential mean-square stability of the system are obtained. Finally, switched RLC circuit systems are provided to verify the effectiveness of the proposed methods.

Impact of the plasticity modeling on fatigue life prediction for steels showing modest non-proportional hardening

This study evaluated the influence of Tanaka parameter-based cyclic plasticity modeling on fatigue life estimation under multiaxial loading. The life predictions based on the experimental stress–strain data, simulated by the Chaboche cyclic plasticity model and Chaboche model with the implemented Tanaka parameter, were compared for this purpose. The fatigue life was then calculated using three popular prediction models for the two steel grades exhibiting moderate non-proportional hardening. Eleven multiaxial loadings were investigated, including proportional, 90° out-of-phase, and asynchronous cases. Modeling the cyclic material response using the coupled Chaboche and Tanaka models revealed to have improved all fatigue life predictions.

Network-enabled rapid growth of a US-based remote asynchronous oncology subspecialist case review service.

e13507 Background: AccessHope (AH) is a company that provides remote subspecialist insight on cancer care for patients via an asynchronous model of review of medical records followed by a detailed assessment of current and potential future management options sent to the patient’s local oncologist. This summary characterizes the rapid and ongoing scaling and growth as demand for this service has expanded and as AccessHope has transitioned from a single site to a geographically diverse US-based network of National Cancer Institute-designated comprehensive cancer centers (NCI-CCCs) as foundational partners. Methods: Cases were identified based on a trigger list of high complexity cancers or by request for case review by participating patients with cancer. Using descriptive statistics, we assessed measures of eligible patient, completed case volumes, and number of unique expert reviewers over time; the distribution of cancer types; the extent of concordance between care outlined by the local care team and that recommended by the reviewing expert; and potential cost savings from recommended changes. Results: From 1/2019-9/2022, the number of sponsor organizations and eligible “covered lives” increased by 1462% and 393% respectively; case volume increased from 42 to 525 cases/quarter over this interval. The most common cancer diagnoses were gastrointestinal (30%), hematologic/lymphoid (18%), lung (14%), and breast (12%). While reviewers recommended an alternative treatment in 29% of cases and agreed overall with current management in 71%, only 4% agreed and had no proposed refinements associated with anticipated evidence-based improvements. Recommendations leading to cost savings if adopted were identified in 16.1% of cases, translating to projected cost savings of $13,008/patient. Conclusions: After initially demonstrating the feasibility of completing a rapid turnaround of cases of lung cancer either patient-initiated for review or prospectively triggered by diagnosis and stage, this program of asynchronous second opinions has grown from a single institution to a network of five NCI-CCCs around the US. Demand for this service has rapidly escalated, so that AH has been able to provide input and support to local oncologists for many-fold more physicians and patients over the past few years.

Research on Asynchronous Information Fusion Technology of the Distributed Active/Passive Imaging Detection System

In the process of target cooperative tracking of the distributed multi-imaging detection system, aiming at the problem of asynchronous measurement information from different detectors, a distributed Asynchronous and Heterogeneous Information Matrix Fusion algorithm based on the Cubature Kalman Filter (CKF-AHIMF) is proposed. According to the Local Detector Driven Communication (LDDC) method and CKF nonlinear mapping, IMF distributed fusion algorithm is generalized into nonlinear, asynchronous, and heterogeneous cases to integrate the information from a multi-imaging detection system and to obtain global high-precision target trajectory. Through the digital simulation, it is verified that the CKF-AHIMF algorithm under the LDDC method proposed in this paper is more effective and has higher tracking accuracy than other distributed fusion algorithms in dealing with the problem of target cooperative tracking under asynchronous information from local imaging detectors, which is also valid to implement in precision detection and guidance system.

Two-Tier 360-Degree Video Delivery Control in Multiuser Immersive Communications Systems

In the immersive communications systems, video information is stringently delivered for extended reality applications. Since users may demand various immersive experience and have different levels of view prediction accuracy, the two-tier 360-degree video delivery frame structure with dynamic transmission time interval duration needs to be designed appropriately. To maximize the system's quality of experience, two new frame structures and power control are proposed. Meanwhile, the synchronous and asynchronous cases with different computational complexities are addressed. The results demonstrate the effectiveness of the proposed design and indicate that flexible frame structure is beneficial for multiuser immersive communications.

Everyone Knows That Everyone Knows: Gossip Protocols for Super Experts

A gossip protocol is a procedure for sharing secrets in a network. The basic action in a gossip protocol is a pairwise message exchange (telephone call) wherein the calling agents exchange all the secrets they know. An agent who knows all secrets is an expert. The usual termination condition is that all agents are experts. Instead, we explore protocols wherein the termination condition is that all agents know that all agents are experts. We call such agents super experts. We also investigate gossip protocols that are common knowledge among the agents. Additionally, we model that agents who are super experts do not make and do not answer calls, and that this is common knowledge. We investigate conditions under which protocols terminate, both in the synchronous case, where there is a global clock, and in the asynchronous case, where there is not. We show that a commonly known protocol with engaged agents may terminate faster than the same commonly known protocol without engaged agents.

Estimation of the invariant density for discretely observed diffusion processes: impact of the sampling and of the asynchronicity

We aim at estimating in a non-parametric way the density π of the stationary distribution of a d-dimensional stochastic differential equation , for , from the discrete observations of a finite sample ,…, with . We propose a kernel density estimator and we study its convergence rates for the pointwise estimation of the invariant density under anisotropic Hölder smoothness constraints. First of all, we find some conditions on the discretization step that ensures it is possible to recover the same rates as if the continuous trajectory of the process was available. As proven in the recent work [Amorino C, Gloter A. Minimax rate of estimation for invariant densities associated to continuous stochastic differential equations over anisotropic Holder classes; 2021. arXiv preprint arXiv:2110.02774], such rates are optimal and new in the context of density estimator. Then we deal with the case where such a condition on the discretization step is not satisfied, which we refer to as the intermediate regime. In this new regime we identify the convergence rate for the estimation of the invariant density over anisotropic Hölder classes, which is the same convergence rate as for the estimation of a probability density belonging to an anisotropic Hölder class, associated to n iid random variables . After that we focus on the asynchronous case, in which each component can be observed at different time points. Even if the asynchronicity of the observations complexifies the computation of the variance of the estimator, we are able to find conditions ensuring that this variance is comparable to the one of the continuous case. We also exhibit that the non-synchronicity of the data introduces additional bias terms in the study of the estimator.

Stability of Lurie-type systems with asynchronous and synchronous switching and constant delays

The problems of analysis for systems with synchronous and asynchronous switching have been actively studied for the linear case. In this paper, a switched system of difference-differential equations, in which the right-hand side consists of a linear term and an essentially nonlinear part containing sector-type components is considered. This kind of systems belongs to the class of Lurie indirect control systems. Sufficient conditions on the system parameters and the switching law are investigated for asymptotic stability to be guaranteed both in the case of synchronous switching between subsystems and in asynchronous one. In the latter case it is supposed that the nonlinear delayed part switches with a lag equal to the corresponding delay. It is required that stability should be preserved for any constant positive delays. The problem is solved using the Lyapunov — Krasovsky approach. The functional is chosen that includes a quadratic form and integrals of nonlinearities. Restrictions that ensure asymptotic stability for an arbitrary switching law are found. With such an approach for the asynchronous case these conditions turn out to be less restrictive. By using multiple functionals the restrictions on the lengths of intervals between switchings are also obtained. This type of conditions are similar for both cases of synchronous and asynchronous switching. Theoretical results are demonstrated by a specially selected example.

Developing and Integrating Asynchronous Web-Based Cases for Discussing and Learning Clinical Reasoning: Repeated Cross-sectional Study.

Trainees rely on clinical experience to learn clinical reasoning in pediatric emergency medicine (PEM). Outside of clinical experience, graduate medical education provides a handful of explicit activities focused on developing skills in clinical reasoning. In this paper, we describe the development, use, and changing perceptions of a web-based asynchronous tool to facilitate clinical reasoning discussion for PEM providers. We created a case-based web-based discussion tool for PEM clinicians and fellows to post and discuss cases. We examined website analytics for site use and collected user survey data over a 3-year period to assess the use and acceptability of the tool. The learning tool had more than 30,000 site visits and 172 case comments for the 55 published cases over 3 years. Self-reported engagement with the learning tool varied inversely with clinical experience in PEM. The tool was relevant to clinical practice and useful for learning PEM for most respondents. The most experienced clinicians were more likely than fellows to report posting commentary, although absolute rate of commentary was low. An asynchronous method of case presentation and web-based commentary may present an acceptable way to supplement clinical experience and traditional education methods for sharing clinical reasoning.

Analysis and evaluation of synchronous and asynchronous FLchain

Motivated by the heterogeneous nature of devices participating in large-scale federated learning (FL) optimization, we focus on an asynchronous server-less FL solution empowered by blockchain technology. In contrast to mostly adopted FL approaches, which assume synchronous operation, we advocate an asynchronous method whereby model aggregation is done as clients submit their local updates. The asynchronous setting fits well with the federated optimization idea in practical large-scale settings with heterogeneous clients. Thus, it potentially leads to higher efficiency in terms of communication overhead and idle periods. To evaluate the learning completion delay of BC-enabled FL, namely FLchain, we provide an analytical model based on batch service queue theory. Furthermore, we provide simulation results to assess the performance of both synchronous and asynchronous mechanisms. Important aspects involved in the BC-enabled FL optimization, such as the network size, link capacity, or user requirements, are put together and analyzed. As our results show, the synchronous setting leads to higher prediction accuracy than the asynchronous case. Nevertheless, asynchronous federated optimization provides much lower latency in many cases, thus becoming an appealing solution for FL when dealing with large datasets, tough timing constraints (e.g., near-real-time applications), or highly varying training data.

A hybrid observer for estimating the state of a distributed linear system

A hybrid observer is described for estimating the state of an m>0 channel, n-dimensional, continuous-time, linear system of the form ẋ=Ax,yi=Cix,i∈{1,2,…,m}. The system’s state x is simultaneously estimated by m agents assuming each agent i senses yi and receives appropriately defined data from each of its current neighbors. Neighbor relations are characterized by a time-varying directed graph N(t) whose vertices correspond to agents and whose arcs depict neighbor relations. Agent i updates its estimate xi of x at “event times” ti1,ti2,ti3,… using a local continuous-time linear observer and a local parameter estimator which iterates q times during each event time interval [ti(s−1),tis),s≥1 to obtain an estimate of x(tis). Subject to the assumptions that none of the Ci’s are zero, the neighbor graph N(t) is strongly connected for all time, and the system whose state is to be estimated is jointly observable, it is shown that for any number λ>0, it is possible to choose q and the local observer gains so that each estimate xi converges to x at least as fast as e−λt does. This result holds whether or not agents communicate synchronously, although in the asynchronous case it is necessary to assume that N(t) changes in a suitably defined sense. Exponential convergence is also assured if the event time sequences of the m agents are slightly different than each other, although in this case only if the system being observed is exponentially stable; this limitation however, is primarily a robustness issue shared by all state estimators, centralized or not, which are operating in “open loop” in the face of small modeling errors. The result also holds facing abrupt changes in the number of vertices and arcs in the inter-agent communication graph upon which the algorithm depends.

Dynamic event-triggered synchronization of complex networks with switching topologies: Asynchronous observer-based case

In this paper, the issue of observer-based synchronization for discrete complex networks with Markov switching topologies is investigated. First, the asynchronous phenomenon between the observer and the node is considered. By resorting to a hidden Markov model (HMM), an non-homogenous observer with an asynchronous topological structure and gain is developed to estimate the state of nodes. Second, for saving precious communication resources, the remote controller updates the control signal based on a dynamic event-triggered scheme (DETS). In this scheme, the threshold of DETS is improved into a diagonal threshold matrix, and the desired system performance can be obtained by flexibly arranging each entry of threshold matrix. To unify the works of the synchronization and state observation, an augmented error system (AES) is established. Subsequently, sufficient conditions for H∞ synchronization of complex networks with asynchronous constraints are proposed by choosing a suitable Lyapunov function. Finally, two simulation examples are implemented to demonstrate the effectiveness of the above results.

Data-Based Optimal Consensus Control for Multiagent Systems With Policy Gradient Reinforcement Learning.

This article investigates the optimally distributed consensus control problem for discrete-time multiagent systems with completely unknown dynamics and computational ability differences. The problem can be viewed as solving nonzero-sum games with distributed reinforcement learning (RL), and each agent is a player in these games. First, to guarantee the real-time performance of learning algorithms, a data-based distributed control algorithm is proposed for multiagent systems using offline system interaction data sets. By utilizing the interactive data produced during the run of a real-time system, the proposed algorithm improves system performance based on distributed policy gradient RL. The convergence and stability are guaranteed based on functional analysis and the Lyapunov method. Second, to address asynchronous learning caused by computational ability differences in multiagent systems, the proposed algorithm is extended to an asynchronous version in which executing policy improvement or not of each agent is independent of its neighbors. Furthermore, an actor-critic structure, which contains two neural networks, is developed to implement the proposed algorithm in synchronous and asynchronous cases. Based on the method of weighted residuals, the convergence and optimality of the neural networks are guaranteed by proving the approximation errors converge to zero. Finally, simulations are conducted to show the effectiveness of the proposed algorithm.