The current societal demands and technological developments have resulted in the participation of a large number of experts in making decisions as a group. Conflicts are imminent in groups and conflict management is complex and necessary especially in a large group. However, there are few studies that quantitatively research the conflict detection and resolution in the large-group context, especially in the multicriteria large-group decision making (GDM) context. This article proposes a dynamic adaptive subgroup-to-subgroup conflict model to solve multicriteria large-scale GDM problems. A compatibility index is proposed based on two kinds of conflicts among experts 1) cognitive conflict and 2) interest conflict. Then, the fuzzy c-means clustering algorithm is used to classify experts into several subgroups. A subgroup-to-subgroup conflict detection method and a weight-determination approach are developed based on the clustering results. Afterward, a conflict resolution model, which can dynamically generate feedback suggestion, is introduced. Finally, an illustrative example is provided to demonstrate the effectiveness and applicability of the proposed model.This article investigates the task planning problem where one vehicle needs to visit a set of target locations while respecting the precedence constraints that specify the sequence orders to visit the targets. The objective is to minimize the vehicle's total travel distance to visit all the targets while satisfying all the precedence constraints. We show that the optimization problem is NP-hard, and consequently, to measure the proximity of a suboptimal solution from the optimal, a lower bound on the optimal solution is constructed based on the graph theory. Then, inspired by the existing topological sorting techniques, a new topological sorting strategy is proposed; in addition, facilitated by the sorting, we propose several heuristic algorithms to solve the task planning problem. The numerical experiments show that the designed algorithms can quickly lead to satisfying solutions and have better performance in comparison with popular genetic algorithms.Brain electroencephalography (EEG), the complex, weak, multivariate, nonlinear, and nonstationary time series, has been recently widely applied in neurocognitive disorder diagnoses and brain-machine interface developments. With its specific features, unlabeled EEG is not well addressed by conventional unsupervised time-series learning methods. In this article, we handle the problem of unlabeled EEG time-series clustering and propose a novel EEG clustering algorithm, that we call mwcEEGc.  https://www.selleckchem.com/products/jw74.html  The idea is to map the EEG clustering to the maximum-weight clique (MWC) searching in an improved Fréchet similarity-weighted EEG graph. The mwcEEGc considers the weights of both vertices and edges in the constructed EEG graph and clusters EEG based on their similarity weights instead of calculating the cluster centroids. To the best of our knowledge, it is the first attempt to cluster unlabeled EEG trials using MWC searching. The mwcEEGc achieves high-quality clusters with respect to intracluster compactness as well as intercluster scatter. We demonstrate the superiority of mwcEEGc over ten state-of-the-art unsupervised learning/clustering approaches by conducting detailed experimentations with the standard clustering validity criteria on 14 real-world brain EEG datasets. We also present that mwcEEGc satisfies the theoretical properties of clustering, such as richness, consistency, and order independence.For social robots to effectively engage in human-robot interaction (HRI), they need to be able to interpret human affective cues and to respond appropriately via display of their own emotional behavior. In this article, we present a novel multimodal emotional HRI architecture to promote natural and engaging bidirectional emotional communications between a social robot and a human user. User affect is detected using a unique combination of body language and vocal intonation, and multimodal classification is performed using a Bayesian Network. The Emotionally Expressive Robot utilizes the user's affect to determine its own emotional behavior via an innovative two-layer emotional model consisting of deliberative (hidden Markov model) and reactive (rule-based) layers. The proposed architecture has been implemented via a small humanoid robot to perform diet and fitness counseling during HRI. In order to evaluate the Emotionally Expressive Robot's effectiveness, a Neutral Robot that can detect user affects but lacks an emotional display, was also developed. A between-subjects HRI experiment was conducted with both types of robots. Extensive results have shown thsdgfdsfatat both robots can effectively detect user affect during the real-time HRI. However, the Emotionally Expressive Robot can appropriately determine its own emotional response based on the situation at hand and, therefore, induce more user positive valence and less negative arousal than the Neutral Robot.This article deals with the exponential synchronization problem for complex dynamical networks (CDNs) with coupling delay by means of the event-triggered delayed impulsive control (ETDIC) strategy. This novel ETDIC strategy combining delayed impulsive control with the event-triggering mechanism is formulated based on the quadratic Lyapunov function. Among them, the event-triggering instants are generated whenever the ETDIC strategy is violated and delayed impulsive control is implemented only at event-triggering instants, which allows the existence of some network problems, such as packet loss, misordering, and retransmission. By employing the Lyapunov-Razumikhin (L-R) technique and impulsive control theory, some sufficient conditions with less conservatism are proposed in terms of linear matrix inequalities (LMIs), which indicates that the ETDIC strategy can guarantee the achievement of the exponential synchronization and eliminate the Zeno phenomenon. Finally, a numerical example and its simulations are presented to verify the effectiveness of the proposed ETDIC strategy.Domain adaptation is suitable for transferring knowledge learned from one domain to a different but related domain. Considering the substantially large domain discrepancies, learning a more generalized feature representation is crucial for domain adaptation. On account of this, we propose an adaptive component embedding (ACE) method, for domain adaptation. Specifically, ACE learns adaptive components across domains to embed data into a shared domain-invariant subspace, in which the first-order statistics is aligned and the geometric properties are preserved simultaneously. Furthermore, the second-order statistics of domain distributions is also aligned to further mitigate domain shifts. Then, the aligned feature representation is classified by optimizing the structural risk functional in the reproducing kernel Hilbert space (RKHS). Extensive experiments show that our method can work well on six domain adaptation benchmarks, which verifies the effectiveness of ACE.