Consensus Method Research Articles

Research on the Calculation and Analysis of Ski-Track Areas Based on Laser Point Clouds

To address the long-term statistical problem of ski-track area in the construction and operation of ski resorts, we propose a new ski-track point cloud boundary extraction method that improves the accuracy of boundary extraction and minimizes the offset of the area error. In this method, all point clouds are first projected onto the fitting plane using the random sample consensus (RANSAC) method. An improved point cloud boundary extraction algorithm is used to triangulate and extract the high-precision ski-track boundary. A discrete Green formula is then used to calculate and count the ski track’s exact area. It is demonstrated through five sets of test experiments that the error offset of the method proposed in this paper is smaller than that of other classical methods, which confirms its benefits and feasibility.

A novel hybrid consensus-based variational Bayesian filter with position uncertainty in multi-agent system

Most existing distributed state estimation approaches assume that the position of agent in multi-agent system (MAS) is precisely known. However, in target tracking applications, the position of agent is noisy, which makes traditional distributed state estimation methods suffer from accuracy reduction. This paper focuses on developing a novel hybrid consensus-based variational Bayesian filter (HCVB) for target tracking with position uncertainty in nonlinear MAS. To improve the nonlinear approximation accuracy, high-degree cubature information filter framework is used. To cope with agent position uncertainty, the measurement update is derived by using variational Bayesian (VB) method to approximate the joint posterior distribution of the target state and agent position, which can estimate the state and agent position jointly. To decrease the required consensus iterations, a hybrid consensus method is proposed, which can achieve global consistency of all agents with a few consensus iterations. Simulations on UAV tracking in MAS verify the superiority of the proposed method.

Diagnosing post-COVID-19 condition (long COVID) in adults.

[See related articles at www.cmaj.ca/lookup/doi/10.1503/cmaj.220823][1] and [www.cmaj.ca/lookup/doi/10.1503/cmaj.220824][2] The World Health Organization used a Delphi consensus method that involved people with lived experience and clinicians to provide a case definition for long COVID: symptoms

Observing expert opinion of medical affairs pharmaceutical physicians on the value of their clinical experience to the pharmaceutical industry using the Jandhyala method

Background The pharmaceutical industry requires a highly qualified workforce with diverse skillsets. Medical affairs pharmaceutical physicians (MAPPs) have unique qualifications among pharmaceutical company employees, but the exact contribution of their education and training is unknown. This study aimed to identify the medical education and training competencies MAPPs use in the pharmaceutical industry in relation to the four external stakeholders, regulators, payors, prescribers, and patients. Method Ten MAPPs were recruited using convenience sampling via professional networks. A systematic literature review and the Jandhyala method, a two-stage qualitative online consensus method, identified which of MAPPs’ medical education and training competencies they used in their work with each external stakeholder. Statistical analyses determined heterogeneity in the relevance of competencies and competency categories to each stakeholder. Results Nine MAPPs completed the study. Of the 59 competencies identified, 54 were relevant to all external stakeholders. Relevance of competencies varied significantly between external stakeholders (p = .0434). Binary competency scores varied significantly for three pairs of stakeholders, “patient vs. payor” (p = .025), “prescriber vs. regulator” (p = .013) and “prescriber vs. payor” (p = .008). Between-stakeholder overall frequency count varied significantly for two of the nine competency categories. Conclusion MAPPs develop a highly specialized set of competencies during medical education and training from which they use distinct subsets to meet the needs of external stakeholders in the pharmaceutical industry. Undergraduate and postgraduate competency-based medical education appears to prepare MAPPs for cognitive and technical work. Further exploration may aid understanding of how they develop soft skills.

Blockchain-Based E-Certificate Verification and Validation Automation Architecture to Avoid Counterfeiting of Digital Assets in Order to Accelerate Digital Transformation

The security and confidentiality of data are very important for institutions. Meanwhile, data fabrication or falsification of official documents is still common. Validation of the authenticity of documents such as certificates becomes a challenge for various parties, especially those who have to make decisions based on the validity of the document. Scanning-based signatures on printed and digital documents are still relatively easy to counterfeit and yet still difficult to distinguish from the original. The traditional approach is no longer reliable. Solutions to these problems require the existence of data security techniques, seamless online verification of the authenticity of printed documents, and e-certificates quickly. The objective of the study is to model the e-certificate verification process via blockchain and proof-of-stake consensus methods and use MD5 encryption. The data or identity listed on the e-certificate is secured with an embedded digital signature in the form of a QR code and can be checked for the truth online. A combination of technologies capable of suppressing or removing counterfeiting of digital assets will accelerate digital transformation across spectrums of modern life. The resulting architectural model can be used as a starting point for implementing a blockchain-based e-certificate verification and validation automation system.

An adaptive consensus method based on feedback mechanism and social interaction in social network group decision making

Many consensus models in social network group decision making (SNGDM) have been reported to obtain a collective solution despite the initial opinions of decision makers (DMs) may be different. However, these models ignore the obstinacy of DMs to their initial opinions, which violates the sociological research results. Aiming at the consensus reaching of SNGDM where DMs are stubborn to their initial opinions, this paper proposes a novel consensus model based on the passive adjustment based on feedback mechanism (PA-FM) and active adjustment based on social interaction (AA-SI), which adaptively adopts AA-SI or PA-FM in each round according to the opinion distribution of DMs. Specially, the proposed consensus model assimilates the advantage of PA-FM and AA-SI, where adjustment intensity of DMs affects the consensus level of PA-FM, and stubbornness degree of DMs affects that of AA-SI. To elucidate the performance and advantages of the proposed consensus model, a hypothetical application and three simulation analyses are constructed. The results show that (1) In the decision-making group that is stubborn to the initial opinions, social interaction has a beneficial or harmful effect on the consensus reaching, depending on the stubbornness degree and opinion distribution of DMs; and (2) Given any stubbornness degree and adjustment intensity, the proposed consensus model outperforms existing consensus methods in consensus efficiency and success rate.

A sequential three-way decision-based group consensus method under probabilistic linguistic term sets

For complex decision-making problems, the joint evaluation of multiple experts is indispensable for efficiently addressing them. Therefore, multi-attribute group decision-making (MAGDM) problems have become popular in recent decades, however the development of MAGDM problems in the environment of probabilistic linguistic term sets (PLTSs) is not mature. To this end, the paper aims to study group consensus under PLTSs. Specifically, since some existing distance measures do not meet the basic principle of distance measures, and some ones can not measure the distance between PLTSs of different lengths, a new distance measure is defined to eliminate these two drawbacks. In the consensus reaching process, most of common orientation rules choose to change all evaluation information of experts, which is actually hard in reality. For the sake of guaranteeing the original information as much as possible with consensus reaching, the attribute discriminant value is proposed via using the idea of sequential three-way decision (STWD). Then, an STWD-based group consensus method is constructed under PLTSs, namely PLTS-STWD. Meanwhile, dynamic modification parameters are developed associated with attribute discriminant values. Afterwards, the regret-rejoice function is employed to rank all alternatives. Finally, the constructed method is applied to a realistic case and compared with other consensus methods to verify its effectiveness and feasibility.

A Novel Security Method for Exact Average Consensus

Consensus is a fundamental problem for multi-agent systems, and achieving average consensus is essential for many applications, such as load balancing and cooperative control, where all agents must reach an agreement through a communication network. However, conventional approaches require agents to disclose information to their neighbors, which is often undesirable. This paper presents a novel security method for achieving exact average consensus in a distributed manner using the Paillier cryptosystem without the involvement of a third party. To protect the privacy of an agent, we replace the agent's initial state and one of its neighbor's initial state with virtual values, which are constructed based on the Pailler cryptosystem, ensuring that attackers can only infer the virtual initial values. We rigorously prove that the privacy of an agent can be guaranteed even when all of its neighbors are untrusted.

Energy efficient RANSAC algorithm for flat surface detection in point clouds

Mobile robots control systems achieve greater efficiency through the use of robust environmental analysis algorithms based on data collected from optical sensors such as depth cameras, Light Detection and Ranging sensors (LIDARs). These data sources provide information about control object environment in point cloud. The work of such algorithms, as a rule, is aimed at detecting the objects of interest and searching for the specified objects, as well as relocating its own position on the scene. There are many different approaches for solving object detection problem in point clouds, but most of them require high computational resources. In this work, many variations of the random sample consensus (RANSAC) method are analyzed for objects defined by a mathematical model of an analytical form. Statistical characteristics of data analysis were used to compare the methods. The results demonstrate the most energy efficient flat surface detection method that processes 60 RGB-D camera frames per second.

Best Practices for Evaluation and Treatment of Agitated Children and Adolescents (BETA) in the Emergency Department: Consensus Statement of the American Association for Emergency Psychiatry.

Agitation in children and adolescents in the emergency department (ED) can be dangerous and distressing for patients, family and staff. We present consensus guidelines for management of agitation among pediatric patients in the ED, including non-pharmacologic methods and the use of immediate and as-needed medications. Using the Delphi method of consensus, a workgroup comprised of 17 experts in emergency child and adolescent psychiatry and psychopharmacology from the the American Association for Emergency Psychiatry and the American Academy of Child and Adolescent Psychiatry Emergency Child Psychiatry Committee sought to create consensus guidelines for the management of acute agitation in children and adolescents in the ED. Consensus found that there should be a multimodal approach to managing agitation in the ED, and that etiology of agitation should drive choice of treatment. We describe general and specific recommendations for medication use. These guidelines describing child and adolescent psychiatry expert consensus for the management of agitation in the ED may be of use to pediatricians and emergency physicians who are without immediate access to psychiatry consultation.Reprinted from West J Emerg Med 2019; 20:409-418, with permission from the authors. Copyright © 2019.

Semi-automated 18F-FDG PET segmentation methods for tumor volume determination in Non-Hodgkin lymphoma patients: a literature review, implementation and multi-threshold evaluation

In the treatment of Non-Hodgkin lymphoma (NHL), multiple therapeutic options are available. Improving outcome predictions are essential to optimize treatment. The metabolic active tumor volume (MATV) has shown to be a prognostic factor in NHL. It is usually retrieved using semi-automated thresholding methods based on standardized uptake values (SUV), calculated from 18F-Fluorodeoxyglucose Positron Emission Tomography (18F-FDG PET) images. However, there is currently no consensus method for NHL. The aim of this study was to review literature on different segmentation methods used, and to evaluate selected methods by using an in house created software tool.A software tool, MUltiple SUV Threshold (MUST)-segmenter was developed where tumor locations are identified by placing seed-points on the PET images, followed by subsequent region growing. Based on a literature review, 9 SUV thresholding methods were selected and MATVs were extracted. The MUST-segmenter was utilized in a cohort of 68 patients with NHL. Differences in MATVs were assessed with paired t-tests, and correlations and distributions figures.High variability and significant differences between the MATVs based on different segmentation methods (p < 0.05) were observed in the NHL patients. Median MATVs ranged from 35 to 211 cc.No consensus for determining MATV is available based on the literature. Using the MUST-segmenter with 9 selected SUV thresholding methods, we demonstrated a large and significant variation in MATVs. Identifying the most optimal segmentation method for patients with NHL is essential to further improve predictions of toxicity, response, and treatment outcomes, which can be facilitated by the MUST-segmenter.

Distributed Fixed-Time Optimal Resource Management for Microgrids

This article studies the problem of distributed cooperative resource management for Microgrids. In order to maximize the social welfare, a distributed fixed-time consensus algorithm is first proposed to integrate economic dispatch and demand response, which optimally assigns the energy between generation participants and load participants. Based on the designed algorithm, each participant is able to quickly obtain its optimal operation and only requires partial calculation and communication without a central control coordination, thus it offers high flexibility, strong robustness, and better privacy. Moreover, compared with the existing distributed optimization algorithms, the proposed algorithm simultaneously possesses the advantages of fixed-time consensus, no initialization conditions, and supporting switching topology, etc. Furthermore, it is proved that each participant can achieve consensus in a fixed-time manner and converge to the global optimal point if the parameters of the algorithm satisfy some conditions. Finally, numerical simulations in the 6-Bus and the IEEE 30-Bus systems are provided to validate the effectiveness of the presented algorithm. Note to Practitioners—This article is motivated by the problem of distributed cooperative resource management for Microgrids to make the system economical and safe operation. The existing distributed methods only converge in an infinite time horizon. However, asymptotic convergence is undesirable in practice, especially in multiperiod optimisation problems. To address this issue and integrate more distributed resources in the future, in this article, we first propose a distributed fixed-time consensus method, where each participant can achieve consensus in a fixed time and obtain its optimal operation locally. Moreover, the proposed method is robust to communication failures and adaptive to topology changes. Our future work will aim at considering more practical constraints, such as power flow constraints, ramping rate constraints, etc.

Distributed Aggregative Optimization via Finite-time Dynamic Average Consensus

This paper studies the distributed aggregative optimization problem with local constraint sets over an undirected graph. The local objective function of each agent depends on its own decision variables and an aggregation function composed of all agents' decision variables. Taking advantage of the dynamic average consensus method and the projection operator, a continuous-time algorithm with nonuniform gradient gains is proposed to seek the optimal decision variable, which only requires the sign of relative state information between agents' neighbours and has an advantage in reducing communication cost. It is proved that auxiliary variables for estimating the aggregation function achieve consensus in finite time and the proposed algorithm converges asymptotically to the optimal decision variable based on the Lyapunov stability theory. Finally, numerical examples are provided to show the effectiveness of theoretical results.

Improved PBFT Consensus Algorithm Based on Node Role Division

The PBFT (Practical Byzantine Fault Tolerance, PBFT) consensus algorithm, which addressed the issue of malicious nodes sending error messages to disrupt the system operation in distributed systems, was challenging to support massive network nodes, the common participation over all nodes in the consensus mechanism would lead to increased communication complexity, and the arbitrary selection of master nodes would also lead to inefficient consensus. This paper offered a PBFT consensus method (Role Division-based Practical Byzantine Fault Tolerance, RD-PBFT) to address the above problems based on node role division. First, the nodes in the system voted with each other to divide the high reputation group and low reputation group, and determined the starting reputation value of the nodes. Then, the mobile node in the group was divided into roles according to the high reputation value, and a total of three roles were divided into consensus node, backup node, and supervisory node to reduce the number of nodes involved in the consensus process and reduced the complexity of communication. In addition, an adaptive method was used to select the master nodes in the consensus process, and an integer value was introduced to ensure the unpredictability and equality of the master node selection. Experimentally, it was verified that the algorithm has lower communication complexity and better decentralization characteristics compared with the PBFT consensus algorithm, which improved the efficiency of consensus.

Prioridades de políticas de salud. Encuentro SESPAS en la XXXIII Escuela de Salud Pública de Menorca

ObjectiveIn September 2022, the Sociedad Española de Salud Pública y Administración Sanitaria (SESPAS) brought together a panel of experts with the aim of defining and prioritizing health policy proposals, from the perspective of the Spanish State as a whole, to adapt the National Health System (NHS) to current risks and to contemporary/present-day society. MethodExpert meeting structured using a mix of procedures adapted from brainstorming, nominal group and Rand consensus method techniques. Relevance and feasibility of proposals identified were assessed individually by each panelist. Proposals were then ordered thematically and ranked according to the median and quartile deviation of relevance scores. ResultsPanelists identified and prioritized several proposals in different areas: governance and funding of the NHS, reform of the portfolio of services and benefits and of the NHS human resources, public health and health policy, actions against inequality and poverty, and healthcare delivery reform, including socio-sanitary, primary and end-of-life care. ConclusionsThe results of the meeting show the urgent need to address in-depth changes in many state-wide health policies, including a major reconfiguration of governance, public health, and health care structures. They also point out potential areas of improvement, constituting a tentative guide of prioritized issues to be addressed.

Cost efficient management of complex financial energy trading systems: Knowledge-based blockchain technique

Industry 4.0 has led to the growth of smart cities utilizing the Internet of Things (IoT). Wireless sensor network (WSN) has drawn considerable interest as an important element of the IoT. The Energy Internet (EI) is growing in importance as a significant component of constructing the intelligent city, especially in terms of its reliability and security. EI is moving in a new direction of energy trading advancement where distributed energy transaction models are replacing the conventional centralized layout. Blockchain technology, which is the underlying support, has gained interest as a result of its benefits, namely integrity, and nonrepudiation. Privacy disclosure, despite the benefits, is a concern for many blockchain-enabled trading layouts. The paper presents encryption according to the cryptographic text properties as the main scheme for reconstructing the dealing layout in order to resolve the issue in the electricity market. In particular, the blockchain energy trade plan with privacy (BETPWP) has been developed to manage the distributed transaction. Transaction arbitration in ciphertext form is employed for achieving precise access control. As a result of this method, the transaction layout could be made considerably more secure and reliable, as well as maximizing the security of private data. BETPWP also proposes a credibility-enabled equity proof consensus method that would significantly improve operational performance. A study of security and the test assessment of the suggested method are performed in order to demonstrate its effectiveness.

Consensus-based statements for assessing clinical competency in podiatry-related work integrated learning

BackgroundThe training of undergraduate and graduate-entry podiatry students in Australia and New Zealand includes practical sessions in a simulated and real-life clinical setting and Work Integrated Learning (WIL) comprising professional clinical placements. Student performance during WIL is evaluated by their Clinical Educators using clinical competency tools. Having a standardised and validated clinical assessment tool for WIL in podiatry would facilitate consistency in assessment, promote standardisation between programs, and ensure that all podiatry students are assessed against a set of criteria over the course of their clinical programs to the point of threshold clinical competency. Therefore, the aim of this study was to develop a series of consensus-based statements via Delphi technique as the first step towards developing guidelines to direct the assessment of podiatry students during WIL.MethodsThis study used a three-round modified Delphi consensus method. A panel of 25 stakeholders was sought. Specifically, representation from each of the universities in Australia and New Zealand who provide entry level programs, Clinical Educators, podiatry student representatives, new podiatry graduates and consumers (podiatrists hiring new graduates). The survey for Round 1 aimed for consensus and consisted of five open-ended questions. Questions one to three asked respondents to nominate what they considered were the important elements that needed to be assessed for podiatry students undertaking WIL for: Clinical performance/skills, Communication and Professional behaviour, Question 4 asked respondents to identify further/other elements of importance, whilst Question 5 asked a) how these elements should be evaluated and b) how should overall competency and ability to progress within the program be determined. Round 2 and 3 aimed to gather agreement and the questions were based on the responses from previous rounds.ResultsTwenty-five participants agreed to participate, 17 females (68%) and eight males (32%). The panel consisted of 10 podiatry educators (40%), nine Clinical Educators (36%), two student representatives (8%), two new podiatry graduates (8%) and two consumers (8%). From the 25 recruited participants, 21 responded to Round one, 18 to Round two and 17 in Round three. At the conclusion of the Delphi survey, 55 statements had reached consensus or agreement.ConclusionsThis Delphi study is the first of its kind for the podiatry profession to develop consensus-based statements regarding the assessment of WIL. Fifty-five statements pertinent to the assessment of WIL were identified. This is an important first step toward the development of a consistent WIL assessment tool which may be applied across entry-level podiatry programs across Australia and New Zealand.

A SURVEY A HYBRID MODEL FOR CENTRAL BANK DIGITAL CURRENCY BASED ON BLOCKCHAIN

With the development of blockchain technology, research on digital currency is gaining popularity, especially Central Bank Digital Currency (CBDC), which is essential to the growth of a nation's economy. However, CBDC requires a more complicated system than other crypto currencies do. a stronger focus on supervision and controllable decentralization Therefore, the technical foundation that is in accordance with economic ecology, efficient consensus methods, and network architecture that conserves computer resources are essential elements of CBDC. In this study, we propose a modularity network-enabled hybrid blockchain system for CBDC. Common digital currencies are recorded using the account system, especially for numerous small payment transactions, whereas digital assets and smart contracts with major value fluctuations and low liquidity are recorded using the Unspent Transaction Output (UTXO) scheme. To boost the concurrency of this structured network, a sliced data storage solution is developed, and a modular blockchain design is recommended. The DPOS-BFT technique is enhanced based on the block chain’s CBDC supervision mode, which condenses the original algorithm's two rounds of consensus into a single round. The effectiveness of this approach in speeding up consensus and transaction processing is shown in three simulated studies on scheme, network, and consensus.

Recent advances in estimating protein structure model accuracy

The quality assessment of protein models is a key technology in protein structure prediction and has become a prominent research focus in the field of structural bioinformatics since advent of CASP7. Model quality assessment method not only guides the refinement of protein structure model but also plays a crucial role in selecting the best model from multiple candidate conformations, offering significant value in biological research and practical applications. This study begins with reviewing the critical assessment of protein structure prediction (CASP) and continuous automated model evaluation (CAMEO), and model evaluation metrics for monomeric and complex proteins. It primarily summarizes the development of model quality assessment methods in the last five years, including consensus methods (multi-model methods), single-model methods, and quasi-single-model methods, and also introduces the evaluation methods for protein complex models in CASP15. Given the remarkable progress of deep learning in protein prediction, the article focuses on the in-depth application of deep learning in single-model methods, including data set generation, protein feature extraction, and network architecture construction. Additionally, it presents the recent efforts of our research group in the field of model quality assessment. Finally, the article analyzes the limitations and challenges of current protein model quality assessment technology, and also looks forward to future development trends.

Optimal consensus control for partially unknown multi-agent systems with switching communication topology*

Since the local neighborhood tracking error system for switching multi-agent systems (MASs) is time-varying, the existing optimal consensus method for fixed MASs cannot be used in switching MASs. This paper proposes a new approach to solve the optimal consensus problems of linear switching MASs. Firstly, a new augment local neighborhood tracking error (ALNTE) system, which is based on the joint communication topology, is developed to construct the control object, and the open-loop estimation method is used to estimate the ALNTE for individuals. Secondly, based on the ALNTE system, the performance index is designed, and the value iteration method is used to solve the coupled Hamilton-Jacobi-Bellman equations and obtain the optimal control. Finally, theoretical analysis is given to prove the learning stability and control stability of the value iteration method, and a numerical simulation is used to verify the effectiveness of the proposed method.