Ayalapoulsen7366

al media given the infodemic and emotional contagion through online social networks. Online platforms may be used to monitor the toll of the pandemic on mental health. ©Michael Y Ni, Lin Yang, Candi M C Leung, Na Li, Xiaoxin I Yao, Yishan Wang, Gabriel M Leung, Benjamin J Cowling, Qiuyan Liao. Originally published in JMIR Mental Health (http//mental.jmir.org), 12.05.2020.BACKGROUND The outbreak of coronavirus disease 2019 (COVID-19) has caused significant stress and mental health problems among the general public. However, persons at greatest risk for poor mental health outcomes, such as persons with serious mental illness, have been largely overlooked. OBJECTIVE To examine the mental health impact of COVID-19 and social distancing behaviors among persons with serious mental illness and the behaviors taken to prevent COVID-19 infection. METHODS Participants will include individuals with serious mental illness (e.g., schizophrenia, bipolar disorder) and non-psychiatric control participants who are currently or previously participated in several ongoing parent observational studies. Data will be collected from April, 2020 through August, 2020. Participants will complete phone interviews at two time points to assess their current emotional functioning and measures they have taken to prevent COVID-19 infection. Baseline (pre-COVID-19) mental health, sampled by ecological momentary assessment over an extended period, will be compared with current mental health, sampled by ecological momentary assessment over an extended period, and demographic, cognitive and psychosocial factors at baseline will be used to examine risk and resilience to current mental health and coping. RESULTS Study results will be published in peer-reviewed scientific journals. CONCLUSIONS Findings have broad implications for understanding the psychological consequences of COVID-19 among vulnerable persons with serious mental illness, and will provide the opportunity to identify targets to reduce negative outcomes in the future. We also hope our efforts will provide a roadmap and resources for other researchers who would like to implement a similar approach. CLINICALTRIALMultilabel classification (MLC) has received much attention recently. The existing MLC algorithms usually learn multiple classifiers simultaneously by exploiting the correlations among different labels. However, it is difficult and/or expensive to collect a large amount of multilabeled data in practice. The lack of labeled data significantly deteriorates the performance of classification. Moreover, the existing algorithms belong to centralized learning, that is, all the data with their labels must be transmitted to a fusion center for processing. But in many real applications, data may be dispersedly collected/stored in distributed nodes of networks. Due to the concerns of communication cost, processing ability, and data privacy, it is impossible to transmit and/or process the data centrally at one node. Considering this, the problem of distributed MLC over networks is studied, and two distributed information-theoretic semisupervised multilabel learning (dITS²ML²) algorithms are proposed, which are, respectively, used for solving linear and nonlinear MLC problems. In the proposed algorithms, a cost-sensitive objective function is designed, in which a new label correlation term defined on some anchor data is suggested. Besides, to decentralize the global objective function, a distributed matrix completion algorithm is developed to distributively complete the label matrix of the anchor data. Then, by exchanging and combining a few intermediate quantities instead of the original data for both linear and nonlinear cases, the model parameters can be adaptively estimated. The convergence of the proposed dITS²ML² algorithms is analyzed, and their effectiveness in MLC is verified by simulations on various real datasets.Recently, increasing works have been proposed to drive evolutionary algorithms using machine-learning models. Usually, the performance of such model-based evolutionary algorithms is highly dependent on the training qualities of the adopted models. Since it usually requires a certain amount of data (i.e., the candidate solutions generated by the algorithms) for model training, the performance deteriorates rapidly with the increase of the problem scales due to the curse of dimensionality. To address this issue, we propose a multiobjective evolutionary algorithm driven by the generative adversarial networks (GANs). At each generation of the proposed algorithm, the parent solutions are first classified into real and fake samples to train the GANs; then the offspring solutions are sampled by the trained GANs. PEG300 chemical structure Thanks to the powerful generative ability of the GANs, our proposed algorithm is capable of generating promising offspring solutions in high-dimensional decision space with limited training data. The proposed algorithm is tested on ten benchmark problems with up to 200 decision variables. The experimental results on these test problems demonstrate the effectiveness of the proposed algorithm.This article studies the coordinated control problem of networked multiagent systems via distributed cloud computing. A distributed cloud predictive control scheme is proposed to achieve desired coordination control performance and compensate actively for communication delays between the cloud computing nodes and between the agents. This scheme includes the design of a multistep state predictor and optimization of control coordination. The multistep state predictor provides a novel way of predicting future immeasurable states of agents in a large horizontal length. The optimization of control coordination minimizes the distributed cost functions which are presented to measure the coordination between the agents so that the optimal design of the coordination controllers is simple with little computational increase for large-scale-networked multiagent systems. Further analysis derives the conditions of simultaneous stability and consensus of the closed-loop-networked multiagent systems using the distributed cloud predictive control scheme.