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This paper addresses the event-triggered global consensus issue of discrete-time double-integrator multi-agent systems (MASs) with asymmetric input saturation, where the saturation bound could be zero. First, for the leaderless scenario, we develop an event-triggered scheme and present the necessary and sufficient criterion for global consensus. Second, the above result is extended to the leader-following scenario, and global consensus is still ensured. It is worth emphasizing that a basic idea of the proposed event-triggered scheme is to preset a minimum triggering interval, so that the triggering function is inactive within this interval after the last triggering time. Such a treatment can ensure that the minimum time between two update instances can be strictly greater than one. Finally, several simulations are given to illustrate the effectiveness of suggested theoretical findings.

This article aimed to study the global consensus problem of high-order multi-agent systems with a saturation constraint and communication delay. Among them, all agents are described by discrete-time systems. Firstly, in order to compensate for the communication delay, a networked predictive control method is adopted and a predictive-based control protocol is designed. Secondly, for the neutrally stable agent model, leaderless and leader-following situations are considered and it is proven that, under a fixed communication topology, adopting the prediction-based control protocol makes the multi-agent systems with saturation constraint and communication delay achieve a global consensus. Finally, the results are illustrated via numerical simulation.

Purpose To evaluate the severity distribution of chemical burn-induced Limbal stem cell deficiency (LSCD) according to the novel global consensus classification and to compare the treatment approach, before and after the global consensus. Methods Medical records of 127 eyes of 109 patients with LSCD were included. LSCD stages were categorized according to the global consensus classification published by “International LSCD Working Group”. Results The mean age was 37.5 ± 16.5(6–75). The distribution of LSCD stage according to the global consensus scale was as follows: Stage 1A in 5 eyes(3.9%);Stage 1B in 16 eyes(12.6%);Stage 1C in 4 eyes(3.2%);Stage 2A in 15 eyes(11.8%);Stage 2B in 36 eyes(28.3%);Stage 3 in 51 eyes(40.2%). A total of 88(69.3%) eyes underwent surgery for LSCD. Of these, 80 had surgery prior to the publication of the global consensus (before October 2020), 58(72.5%) had preoperative severe (≥ Stage 2B) LSCD and 22(27.5%) had preoperative early stage (≤ Stage 2A) LSCD. As of October 2020, all 8 eyes that underwent surgery had preoperative severe (≥ Stage 2B) LSCD, as recommended by global consensus. Conclusion Recently, a global consensus has been established on both the classification and the management of LSCD. This study is one of the first to present small-scale epidemiological data on the severity distribution of LSCD in the light of the global consensus. It was observed that surgery was performed on 27.5% of the eyes that were not recommended for surgery according to this new consensus. With the increasing awareness of this consensus, it might be possible to avoid unnecessary surgical intervention.

This paper examines the roles of the matrix weight elements in matrix-weighted consensus. The consensus algorithms dictate that all agents reach consensus when the weighted graph is connected. However, it is not always the case for matrix weighted graphs. The conditions leading to different types of consensus have been extensively analysed based on the properties of matrix-weighted Laplacians and graph theoretic methods. However, in practice, there is concern on how to pick matrix-weights to achieve some desired consensus, or how the change of elements in matrix weights affects the consensus algorithm. By selecting the elements in the matrix weights, different clusters may be possible. In this paper, we map the roles of the elements of the matrix weights in the systems consensus algorithm. We explore the choice of matrix weights to achieve different types of consensus and clustering. Our results are demonstrated on a network of three agents where each agent has three states.

This study is concerned with the problem of dynamical distributed consensus for multi-agent systems with nonlinear dynamics. Following the nearest neighbour rule, an adaptive consensus protocol is designed for such systems without using any global information, where the coupling weight of an agent from its neighbours adaptively updates according to the differences from the mean activity of the agent and its neighbours. The analysis shows that, under some mild assumptions, the adaptive law can achieve local and global consensus for any network with connected communication graph. Numerical simulations, illustrated by a common second-order consensus example, are performed to demonstrate the effectiveness of the presented results.

In the present world, International Consensus Frameworks, commonly called global frameworks or global agendas, guide international development policies and practices. They guide the development of all countries and influence the development initiatives by their respective governments. Recent global frameworks, adopted mostly post-2015, include both a group of over-arching frameworks (eg, the Sendai Framework for Disaster Risk Reduction [SFDRR]) and a group of frameworks addressing specific issues (eg, the Dhaka Declaration on Disability and Disaster Risk Management). These global frameworks serve twin purposes: first, to set a global development standard, and second, to set policies and approaches to achieve these standards. A companion group of professional standards, guidelines, and tools (ie, Sphere’s Humanitarian Charter and Minimum Standards) guide the implementation and operationalization of these frameworks on the ground. This paper gathers these global frameworks and core professional guidelines in one place, presents an analytical review of their essential features, and highlights the commonalities and differences between and among these frameworks. The aim of this paper is to facilitate understanding of these frameworks and to help in designing development and resilience policy, planning, and implementation, at international and national levels, where these frameworks complement and contribute to each other. This Special Report describes an important and evolving aspect of the discipline and provides core information necessary to progress the science. Additionally, the report will help governments and policy makers to define their priorities and to design policies/strategies/programs to reflect the global commitments. Development practitioners can pre-empt the focus of the international community and the assistance coming from donors to the priority sectors, as identified in the global agenda. This would then help governments and stakeholders to develop and design a realistic plan and program and prepare the instruments and mechanisms to deliver the goals.

Equality of opportunity is about leveling the playing field so that circumstances such as gender, ethnicity, place of birth, or family background do not influence a person's life chances. Success in life should depend on people's choices, effort and talents, not to their circumstances at birth. 'Measuring Inequality of Opportunities in Latin America and the Caribbean' introduces new methods for measuring inequality of opportunities and makes an assessment of its evolution in Latin America over a decade. An innovative Human Opportunity Index and other parametric and non-parametric techniques are presented for quantifying inequality based on circumstances exogenous to individual efforts. These methods are applied to gauge inequality of opportunities in access to basic services for children, learning achievement for youth, and income and consumption for adults.

Primary immunodeficiencies are intrinsic defects in the immune system that result in a predisposition to infection and are frequently accompanied by a propensity to autoimmunity and/or immunedysregulation. Primary immunodeficiencies can be divided into innate immunodeficiencies, phagocytic deficiencies, complement deficiencies, disorders of T cells and B cells (combined immunodeficiencies), antibody deficiencies and immunodeficiencies associated with syndromes. Diseases of immune dysregulation and autoinflammatory disorder are many times also included although the immunodeficiency in these disorders are often secondary to the autoimmunity or immune dysregulation and/or secondary immunosuppression used to control these disorders. Congenital primary immunodeficiencies typically manifest early in life although delayed onset are increasingly recognized. The early diagnosis of congenital immunodeficiencies is essential for optimal management and improved outcomes. In this International Consensus (ICON) document, we provide the salient features of the most common congenital immunodeficiencies.

In the field of machine learning, an ensemble approach is often utilized as an effective means of improving on the accuracy of multiple weak base classifiers. A concern associated with these ensemble algorithms is that they can suffer from the Curse of Conflict, where a classifier’s true prediction is negated by another classifier’s false prediction during the consensus period. Another concern of the ensemble technique is that it cannot effectively mitigate the problem of Imbalanced Classification, where an ensemble classifier usually presents a similar magnitude of bias to the same class as its imbalanced base classifiers. We proposed an improved ensemble algorithm called “Sieve” that overcomes the aforementioned shortcomings through the establishment of the novel concept of Global Consensus. The proposed Sieve ensemble approach was benchmarked against various ensemble classifiers, and was trained using different ensemble algorithms with the same base classifiers. The results demonstrate that better accuracy and stability was achieved.

This paper investigates secure semi-global bipartite consensus (SSGBC) of linear multi-agent systems (MASs) with input saturation under denial-of-service (DoS) attacks via dynamic event-triggered control (DETC). A distributed DETC protocol is proposed for avoiding redundant information transmission. Subsequently, by utilizing Lyapunov stability theory and a low-gain feedback based bipartite consensus algorithm, it is proved that SSGBC of linear MASs can be achieved under the proposed DETC protocol under the assumption that the frequency and the duration are limited. Moreover, Zeno behavior of each follower can be excluded. Finally, a simulation example is given to verify effectiveness of the proposed DETC protocol.