Robust Consensus Research Articles

Robust Data-Driven Compensation Iterative Learning Consensus Tracking Control for MIMO Multiagent Systems With Random Packet Dropouts

Robust Data-Driven Compensation Iterative Learning Consensus Tracking Control for MIMO Multiagent Systems With Random Packet Dropouts

An efficient Proof-of-Authority consensus scheme against cloning attacks

Proof-of-Authorization (PoA) consensus algorithms are widely used in permissioned blockchain networks due to their high throughput, security, and efficiency. However, PoA is susceptible to cloning attacks, where attackers copy the authenticator identity and key, thereby compromising the consensus integrity. This study proposes a novel randomized authenticator within the PoA framework to mitigate cloning attacks and solve the leader selection bottleneck. The main contributions include 1) Introducing unpredictability in leader selection through Verifiable Random Functions (VRFs) to prevent identity duplication.2) Dynamic group management using a hierarchical decentralized architecture of distributed ledgers that balances security and performance.3) Using threshold signatures to avoid a single point of failure among validators.4) Comprehensively analyzing attacks, security, randomness, and availability.5) Evaluating the effectiveness of a randomized authenticator by means of OMNET++ simulations to assess efficiency. By integrating randomness into leader selection and robust consensus design, the approach enables reliable and secure dynamic group management in decentralized networks.

A clustering ensemble algorithm for handling deep embeddings using cluster confidence

Abstract Clustering ensemble, which aims to learn a robust consensus clustering from multiple weak base clusterings, has achieved promising performance on various applications. With the development of big data, the scale and complexity of data is constantly increasing. However, most existing clustering ensemble methods typically employ shallow clustering algorithms to generate base clusterings. When confronted with high-dimensional complex data, these shallow algorithms fail to fully utilize the intricate features present in the latent data space. As a result, the quality and diversity of the generated base clusterings are insufficient, thus affecting the subsequent ensemble performance. To address this issue, we propose a novel clustering ensemble algorithm for handling deep embeddings using cluster confidence (CEDECC) to improve the robustness and performance. Instead of simply combining deep clustering with clustering ensembles, we take into consideration that the performance of existing deep clustering methods heavily relies on the quality of low-dimensional embeddings generated during the pre-training stage. The quality of embeddings is unstable due to the influence of different initialization parameters. In CEDECC, specifically, we first construct a cluster confidence measure to evaluate the quality of low-dimensional embeddings. Typically, high-quality low-dimensional embeddings yield accurate clustering results with the same model parameters. Then, we utilize multiple high-quality embeddings to generate the base partitions. In the ensemble strategy phase, we consider the cluster-wise diversity and propose a novel ensemble cluster estimation to improve the overall consensus performance of the model. Extensive experiments on three benchmark datasets and four real-world biological datasets have demonstrated that the proposed CEDECC consistently outperforms the state-of-the-art clustering ensemble methods.

Robust Consensus Design of Uncertain Multiagent Systems With Bounded Gains and Incremental Nonlinear Interactions

Robust Consensus Design of Uncertain Multiagent Systems With Bounded Gains and Incremental Nonlinear Interactions

A two-stage distributionally robust maximum expert consensus model with asymmetric costs and risk aversion

The maximum expert consensus model (MECM) emerges as a widely used consensus optimization model in group decision making (GDM). However, contemporary complexities in decision-making environment lead to the asymmetry and uncertainty of adjustment costs of decision-makers (DMs), which are critical during the consensus reaching process (CRP). Additionally, the risks emerging with the uncertainty during CRP should also be analyzed. Therefore, this paper focuses on developing the two-stage distributionally robust MECM (DRO-MECM) with asymmetric adjustment costs under an uncertain environment to improve the CRP. Specifically, we propose a MECM with asymmetric costs. Moreover, we build the two-stage DRO-MECM based on the mean-CVaR under two uncertain scenarios, allowing it to manage uncertain costs effectively while considering the risk preferences of DMs. The first stage aims to maximize the number of DM within consensus and the second stage seeks to minimize the consensus cost. Finally, the applicability of the proposed models is demonstrated by applying them to the allocation of healthcare and security capacity enhancement subsidy funds in China. The efficiency of the models is further corroborated by sensitivity analysis and comparison analysis.

Optimal planning and management strategies for minimally invasive lung segmentectomies: an international Delphi consensus report.

CALGB140503/JCOG0802 RCTs comparing lobectomy with sublobar resection in stage IA NSCLC have confirmed the non-inferiority of segmentectomy. Additional insight is needed to improve preoperative work-up and intraoperative strategies to increase safety and promote the dissemination of minimally invasive segmentectomy (MIS). A Delphi panel study assessed the level of consensus among surgeons for the planning and management of MIS. Twenty-one expert lung surgeons represented academic institutions, major teaching hospitals, and community hospitals from Europe, North America, and Asia. A 3-round Delphi methodology was used to analyse the answers of each panellist. Recognizing that questions with fewer response options have a higher consensus probability due to limited variability, weighted consensus thresholds were modified based on the number of response options. The 21 panellists responded to all 3 rounds of questions. Based on the most robust consensus (94.4%), 3D chest CT reconstructions are recommended only when planning complex segmentectomies. Surgeons should perform 3D reconstructions chest CT scans (consensus = 83.3%). The most effective and safest technique is image-guided VATS in a hybrid operating room (consensus = 83.3%). Dyes with intravenous administration are the safest technique for identifying the intersegmental plane during MIS (consensus = 72.2%). Augmented/mixed reality will probably not immediately help reduce perioperative complications (consensus = 72.2%). This Delphi consensus supports 3D reconstructions and preoperative pulmonary nodule localization before complex MIS. These recommendations should be considered when allocating resources to improve MIS's safety and oncologic efficacy for patients with small, early-stage lung cancers.

Developing and Validating Entrustable Professional Activities (EPAs) for Rheumatology Fellowship Training Programs in Saudi Arabia: A Delphi Study.

Entrustable professional activities (EPAs) define the core tasks that a graduating rheumatologist needs to perform independently in practice. The objective of this study was to develop and validate EPAs for rheumatology fellowship training programs in Saudi Arabia. Experts met to develop an initial set of potential end-of-training EPAs by conducting a comprehensive literature review of EPAs and studying the Saudi rheumatology fellowship curriculum. Then, to validate the EPAs, we conducted two rounds of the modified Delphi technique among rheumatology experts in Saudi Arabia. A response rate of 80% was considered and the minimum number of experts needed to be 25 to 30. Descriptive statistics were utilized to describe participants' demographic characteristics and group responses to each statement in all rounds. The experts were asked to rate the relevancy of each EPA using a 5-point Likert scale in both Delphi rounds. In the preliminary phase, four rheumatologists developed an initial set of 36 core EPAs for rheumatology training program in Saudi Arabia. For the two-rounds Delphi techniques, 32 experts were invited to complete the study. The response rate of the first and second round were, 78.12% (25) and 93.75% (30), respectively. The first-round Delphi resulted in a robust consensus on 31 EPAs for rheumatology training.Five EPAs were excluded,and one new EPA was proposed.In the subsequent round,all 32 EPAs achieved strong consensus.The eliminated EPAs likely fell short in one or more of the following areas: relevance to rheumatology practice in Saudi Arabia, overlapping with other EPAs, or practical challenges in the implementation. We have developed and validated a core set of EPAs for rheumatology fellowship training programs in Saudi Arabia. Mapping and identifying milestones for these EPAs are essential steps to follow to enhance workplace curriculum development.

Distributed Neurodynamics-Based Backstepping Optimal Control for Robust Constrained Consensus of Underactuated Underwater Vehicles Fleet.

Robust constrained formation tracking control of underactuated underwater vehicles (UUVs) fleet in 3-D space is a challenging but practical problem. To address this problem, this article develops a novel consensus-based optimal coordination protocol and a robust controller, which adopts a hierarchical architecture. On the top layer, the spherical coordinate transform is introduced to tackle the nonholonomic constraint, and then a distributed optimal motion coordination strategy is developed. As a result, the optimal formation tracking of UUVs fleet can be achieved, and the constraints are fulfilled. To realize the generated optimal commands better and, meanwhile, deal with the underactuation, at the lower-level control loop a neurodynamics-based robust backstepping controller is designed, and in particular, the issue of "explosion of terms" appearing in conventional backstepping-based controllers is avoided and control activities are improved. The stability of the overall UUVs formation system is established to ensure that all the states of the UUVs are uniformly ultimately bounded in the presence of unknown disturbances. Finally, extensive simulation comparisons are made to illustrate the superiority and effectiveness of the derived optimal formation tracking protocol.

Robust Adaptive Average Consensus Over a Time-Varying and Nonbalanced Environment

Robust Adaptive Average Consensus Over a Time-Varying and Nonbalanced Environment

Robust Consensus Analysis in Fractional-Order Nonlinear Leader-Following Systems with Delays: Incorporating Practical Controller Design and Nonlinear Dynamics

This article investigates the resilient-based consensus analysis of fractional-order nonlinear leader-following systems with distributed and input lags. To enhance the practicality of the controller design, an incorporation of a disturbance term is proposed. Our modeling framework provides a more precise and flexible approach that considers the memory and heredity aspects of agent dynamics through the utilization of fractional calculus. Furthermore, the leader and follower equations of the system incorporate nonlinear functions to explore the resulting changes. The leader-following system is expressed by a weighted graph, which can be either undirected or directed. Analyzed using algebraic graph theory and the fractional-order Razumikhin technique, the case of leader-following consensus is presented algebraically. To increase robustness in multi-agent systems, input and distributive delays are used to accommodate communication delays and replicate real-time varying environments. This study lays the groundwork for developing control methods that are more robust and flexible in complex networked systems. It does so by advancing our understanding and practical application of fractional-order multi-agent systems. Additionally, experiments were conducted to show the effectiveness of the design in achieving consensus within the system.

Output Reachable Set-Based Leader-Following Consensus of Positive Agents Over Switching Networks.

This work addresses the output reachable set-based leader-following consensus problem, focusing on a group of positive agents over directed dwell-time switching networks. Two types of non-negative disturbances, namely, 1) L1 -norm bounded disturbances and 2) L∞,1 -norm bounded disturbances are studied. Meanwhile, a class of directed dwell-time switching networks for modeling the communication protocol of positive agents is investigated. To deal with the presence of disturbances, an output-feedback control protocol is developed to achieve a robust consensus with positivity preserved based on the output reachable set. By exploiting the positive characteristics, switched linear copositive Lyapunov functions are adopted to establish output reachable set-based consensus conditions. These conditions can facilitate the control protocol design by solving a bilinear programming problem, and also generate hyperpyramidal regions to confine the output consensus error. A particle swarm optimization-based (PSO-based) algorithm is applied to compute the controller gains and optimize the volume of the hyperpyramids. The proposed methods are verified by the presented numerical case studies.

Poster 371: ChatGPT: An Effective Tool for Patients to Understand Platelet-Rich Plasma in Orthopedic Surgery

Objectives: Web-based technology has augmented healthcare delivery for both patients and providers. ChatGPT, a free and easily accessible artificial intelligence (AI) chatbot, has the potential to further optimize current practice. The chatbot has cemented itself as an informative resource for orthopaedic patients, but its use in ortho-biologics, particularly platelet-rich plasma (PRP), has not been described. The purpose of this study was to assess the quality of information provided by ChatGPT in response to frequently asked questions about the use of PRP in orthopedic surgery. Methods: ChatGPT was posed 10 frequently asked questions about the use of PRP in orthopedic surgery in a conversational thread. Responses were recorded and no follow-up queries or clarification was obtained. The responses were subsequently graded “A,” “B,” “C,” or “F,” corresponding with “excellent response,” “adequate response needing mild clarification,” “inadequate response needing moderate clarification,” and “poor response needing severe clarification.” Results: Of the 10 responses, 1 was grade “A,” 7 were grade “B,” 2 were grade “C,” and 0 were grade “F.” Overall, ChatGPT provided good quality responses to the prompts. Conclusions: ChatGPT provided sufficient responses to questions regarding PRP in orthopedic surgery, utilizing appropriate language to provide responses representative of the current literature surrounding PRP. The biggest limitation to superior chatbot performance is the lack of robust consensus on the use and preparation of PRP. This study accentuated the possibility for ChatGPT to serve as an additional informative resource for orthopedic surgery patients who want to learn about PRP.

Identifying research priorities for improving information and support for patients undergoing breast cancer surgery: a UK patient-centred priority setting project

PurposeTo use robust consensus methods with individuals with lived breast cancer experience to agree the top 10 research priorities to improve information and support for patients undergoing breast cancer surgery in the UK.MethodsResearch uncertainties related to information and support for breast cancer surgery submitted by patients and carers were analysed thematically to generate summary questions for inclusion in an online Delphi survey. Individuals with lived breast cancer experience completed two Delphi rounds including feedback in which they selected their top 10 research priorities from the list provided. The most highly ranked priorities from the survey were discussed at an in-person prioritisation workshop at which the final top 10 was agreed.ResultsThe 543 uncertainties submitted by 156 patients/carers were categorised into 63 summary questions for inclusion in the Delphi survey. Of the 237 individuals completing Round 1, 190 (80.2%) participated in Round 2. The top 25 survey questions were carried forward for discussion at the in-person prioritisation workshop at which 17 participants from across the UK agreed the final top 10 research priorities. Key themes included ensuring patients were fully informed about all treatment options and given balanced, tailored information to support informed decision-making and empower their recovery. Equity of access to treatments including contralateral mastectomy for symmetry was also considered a research priority.ConclusionThis process has identified the top 10 research priorities to improve information and support for patients undergoing breast cancer surgery. Work is now needed to develop studies to address these important questions.

Finite-time consensus of second-order multi-agent connectivity preserving based on adaptive sliding mode control

This study addresses the problem of robust finite-time connectivity preserving consensus for second-order multi-agent systems (MASs) with a limited communication range. Considering that the communication ability of agents in practical applications is limited, this study introduces an innovative approach to the consensus protocol, which involves the incorporation of a potential function aimed at ensuring sustained communication connection within a MAS. In addition, a finite-time fault-tolerant consensus protocol for a second-order MAS with actuator additive faults and external disturbances is designed by combining sliding mode control (SMC) and adaptive control. Following the homogeneous theorem and the finite-time consensus theory, this study concludes that, under specific conditions, a MAS can achieve finite-time consensus. Moreover, an advanced control protocol named a super-twisting sliding mode control protocol is introduced to mitigate the undesirable chattering phenomenon in the SMC. Finally, the effectiveness and superiority of the proposed method are verified by numerical examples.

Robust median consensus cubature Kalman filter for distributed sensor networks

Distributed estimation of multiagent systems has gained increasing attention in recent years, with consensus algorithms being one of the popular research topics. However, most existing algorithms aim to reach a consensus on the average value of all nodes, which can be significantly affected by outliers and disturbances. To achieve a more resilient distributed estimation, this paper focuses on the median value consensus strategy. Specifically, a continuous median consensus method with an adaptive parameter is presented and discretized using time-varying steps. This approach eliminates chattering errors caused by discretization and enhances convergence speed considerably. Furthermore, utilizing the proposed algorithm, a median consensus cubature Kalman filter (MC-CKF) is designed for nonlinear scenarios. Simulation results demonstrate that our proposed algorithm outperforms traditional algorithms when facing a denial-of-service attack.

Reflecting on the Science of Climate Tipping Points to Inform and Assist Policy Making and Address the Risks they Pose to Society

AbstractThere is a diverging perception of climate tipping points, abrupt changes and surprises in the scientific community and the public. While such dynamics have been observed in the past, e.g., frequent reductions of the Atlantic meridional overturning circulation during the last ice age, or ice sheet collapses, tipping points might also be a possibility in an anthropogenically perturbed climate. In this context, high impact—low likelihood events, both in the physical realm as well as in ecosystems, will be potentially dangerous. Here we argue that a formalized assessment of the state of science is needed in order to establish a consensus on this issue and to reconcile diverging views. This has been the approach taken by the Intergovernmental Panel on Climate Change (IPCC). Since 1990, the IPCC has consistently generated robust consensus on several complex issues, ranging from the detection and attribution of climate change, the global carbon budget and climate sensitivity, to the projection of extreme events and their impact. Here, we suggest that a scientific assessment on tipping points, conducted collaboratively by the IPCC and the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services, would represent an ambitious yet necessary goal to be accomplished within the next decade.

Distributed multivariate‐observer‐based robust consensus control of nonlinear multiagent systems against time‐varying attacks on actuators and sensors

AbstractThis article investigates the robust consensus problem for nonlinear multiagent systems against time‐varying false data injection attacks on actuators and sensors. First, the root‐mean‐square (RMS) theory is used to extend the assumption of the slow‐varying or constant attack signals to the case of time‐varying attack signals. Second, a novel distributed multivariate observer (DMO) is designed to estimate the followers' system states and the time‐varying attack signals on actuators and sensors. With the help of the outputs of DMO, a distributed robust consensus control arithmetic is proposed, which can compensate for actuator attacks and isolate sensor attacks so that exponential consensus and robust consensus are achieved. In particular, the robust performance of estimation errors and consensus errors is ensured by establishing the RMS gain index via linear matrix inequality, in which the zero initial conditions of estimation errors and consensus errors are not required. Finally, two simulation examples, including a network of four aircraft longitudinal dynamic systems, are given to verify the effectiveness of the proposed arithmetic.

Distributionally Robust Two-Stage Minimum Asymmetric Adjustment Cost Consensus Model with Risk Aversion

Distributionally Robust Two-Stage Minimum Asymmetric Adjustment Cost Consensus Model with Risk Aversion

Robust maximum fairness consensus models with aggregation operator based on data-driven method

In group decision-making (GDM), when decision-makers (DMs) feel it is unfair, they may take uncooperative measures to disrupt the consensus-reaching process (CRP). On the other hand, it is difficult for the moderator to objectively determine each DM’s unit consensus cost and weight in CRP. Hence, this paper proposes data-driven robust maximum fairness consensus models (RMFCMs) to address these. First, this paper uses the robust optimization method to construct multiple uncertainty sets to describe the uncertainty of the DMs’ unit adjustment cost and proposes the RMFCMs. Subsequently, based on the DMs’ historical data, the DMs’ weights in the CRP are determined by a data-driven method based on the kernel density estimation (KDE) method. Finally, this paper also applies the proposed models to the carbon emission reduction negotiation process between governments and enterprises, and the experimental results verify the rationality and robustness of the proposed consensus model.

Pediatric Orbital Juvenile Xanthogranuloma: A Case Series and Review of the Literature.

Juvenile xanthogranuloma (JXG) is a subtype of histiocytosis characterised histologically by foamy non-Langerhan cells with Touton giant cells. It typically manifests as a single self-limiting cutaneous nodule in the pediatric population. Orbital JXG is extremely rare, and its clinical course and management are not well understood or defined. Herein we present 3 cases of orbital JXG and provide a detailed literature review. Review of 3 cases with orbital JXG and literature review of all published cases. Three presented cases demonstrate the heterogeneous clinical course of orbital JXG. Although centred around the use of steroids, there is neither robust evidence nor consensus on its management. The wider JXG literature is currently concentrated around the classification of JXG with respect to histiocytosis, especially the exclusion of extracutaneous JXG as separate diseases. This separation is based on clinical, histopathological, and molecular findings. It is unclear where orbital JXG best fits in this emerging classification of JXG. Our review of the cases and literature on orbital JXG show that it may manifest with variable clinical course and its molecular pathogenic mechanism may be different to that of the cutaneous JXG.