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Abstract The aim of this study is to develop a hesitant fuzzy linguistic prioritized superiority and inferiority ranking method, which is used to solve multi-criteria decision making (MCDM) problems. In the MCDM problems, all criteria are expressed at different priority levels and the importance degrees of criteria and the evaluation values of alternatives are both expressed by hesitant fuzzy linguistic term sets (HFLTSs). The contribution of this study concerns the following: (1) A novel algorithm is proposed to determine the real-valued relative weights of criteria according to the given importance degrees of criteria, which are expressed by HFLTSs; (2) We model the prioritization relationships among criteria by revising the real-valued weights of criteria. The revision is based on the satisfaction degrees of evaluation values of alternatives under higher priority criteria to the decision maker (DM)'s requirements; (3) Based on the intrinsic characteristics of HFLTSs, we propose a hesitant fuzzy linguistic prioritized superiority and inferiority ranking method, in which the prioritization relationships among criteria and the DM's requirements for criteria are taken into account; (4) The proposed method is applied to the sustainable energy technology evaluation, and then, a comparative analysis is completed to show its practicability and availability. [ABSTRACT FROM AUTHOR]

The insufficiency of labeled training data is a major obstacle in automatic image annotation. To tackle this problem, we propose a semi-supervised manifold kernel density estimation (SSMKDE) approach based on a recently proposed manifold KDE method. Our contributions are twofold. First, SSMKDE leverages both labeled and unlabeled samples and formulates all data in a manifold structure, which enables a more accurate label prediction. Second, the relationship between KDE-based methods and graph-based semi-supervised learning (SSL) methods is analyzed, which helps to better understand graph-based SSL methods. Extensive experiments demonstrate the superiority of SSMKDE over existing KDE-based and graph-based SSL methods. [ABSTRACT FROM AUTHOR]

• A robust state-error IDA-PBC approach for tracking the desired trajectory of the USV system is constructed. • In order to handle the input saturation of actuator and the unknown external disturbances, an auxiliary dynamic system and a novel reduced-order ESO are coupled in the controller design. • The proposed control law can not only improve tracking performance but also reduce system energy consumption. A novel robust state error Interconnection and Damping assignment Passivity-based Control (IDA-PBC) controller for Unmanned Surface Vessels (USVs) with input saturation and disturbances is proposed. A reduced-order extended state observer, the state error IDA-PBC technique, and an auxiliary dynamic system are used in the controller design. Firstly, a reduced-order extended state observer is constructed to estimate the external disturbances. Then, the state error IDA-PBC approach reduces system energy consumption and is easy to implement. We construct an auxiliary dynamic system to handle input saturation. All signals of the whole system can guarantee uniformly ultimate boundedness. Simulations demonstrate the robustness and effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Social rewarding is a common but significant mechanism that promotes the evolution of cooperation. However, besides social rewarding, antisocial rewarding is also ordinary. Thus, we study the evolution of cooperation on prison dilemma game with strategy-neutral rewarding, namely a mechanism including social and antisocial rewarding. Two additional strategies, rewarding cooperators (RC) and rewarding defectors (RD), which establish union-like support to aid akin players are introduced. We show that the new mechanism greatly promotes the evolution of cooperation even in the presence of antisocial rewarding. The rewarding cooperators can enjoy both the benefits of their prosocial contributions and the corresponding rewards, thus they can form cooperative clusters to resist the aggression of defectors. On the other hand, due to their inherent greedy, rewarding defectors fail to secure a sustainable future. Our research might provide valuable insights into further exploring the nature of cooperation in the real world. [ABSTRACT FROM AUTHOR]

A self-optimizing multi-domain auxiliary fusion deep complex convolution recurrent network for speech enhancement (AMDCCRN) was developed to further improve the feature completeness, correlation expression, and global optimization efficiency of the deep speech enhancement model for speech signals. By constructing a multi-domain auxiliary fusion deep complex convolution recurrent network (MDCCRN), the correlation representation of speech features and features in different spatial domains was enriched. The sparrow search algorithm (PGSSA), which is based on a game theory parallel and global optimization strategy, was introduced to improve parallel search capability and to further augment the efficiency and performance of the model's hyperparameter adaptive search. At the same time, PGSSA was used for adaptive optimization of six key model parameters required for AMDCCRN construction. Finally, a speech enhancement model suitable for multi-domain auxiliary fusion with self-learning ability was constructed. Test results on two common speech corpus data sets, THCHS-30 and WSJ0, showed that the proposed method achieved a better speech enhancement effect than other existing methods and that the validity and generalization of the proposed method had been verified. [ABSTRACT FROM AUTHOR]