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During the COVID-19 pandemic, the internet has significantly spread information, providing people with knowledge and advice about health protection regarding COVID-19. While a previous study demonstrated that health and eHealth literacy are related to COVID-19 prevention behaviors, few studies have focused on the relationship between health literacy, eHealth literacy, and COVID-19-related health behaviors. The latter includes not only preventative behaviors but also conventional health behaviors.

The objective of this study was to develop and verify a COVID-19-related health behavior questionnaire, explore its status and structure, and examine the associations between these behaviors and participants’ health literacy and eHealth literacy.

A snowball sampling method was adopted to recruit participants to complete anonymous cross-sectional questionnaire surveys online that assessed sociodemographic information, self-reported coronavirus knowledge, health literacy, eHealth literacy, and COVID-19-related he-19-related health behaviors during the pandemic, which may be helpful to reduce the risk of COVID-19 infection among college students.This work investigates the stabilization problem of uncertain stochastic Markovian jump systems (MJSs) under communication constraints. To reduce the bandwidth usage, a discrete-time Markovian chain is employed to implement the stochastic communication protocol (SCP) scheduling of the sensor nodes, by which only one sensor node is chosen to access the network at each transmission instant. Moreover, due to the effect of amplitude attenuation, time delay, and random interference/noise, the transmission may be inevitably subject to the Rice fading phenomenon. All of these constraints make the controller only receive the fading signal from one activated sensor node at each instant. A merge approach is first used to deal with two Markovian chains; meanwhile, a compensator is designed to provide available information for the controller. By a compensator and mode-based sliding-mode controller, the resulting closed-loop system is ensured to be input-to-state stable in probability (ISSiP), and the quasisliding mode is attained. Moreover, an iteration optimizing algorithm is provided to reduce the convergence domain around the sliding surface via searching a desirable sliding gain, which constitutes an effective GA-based sliding-mode control strategy. Finally, the proposed control scheme is verified via the simulation results.This article investigates the model-free containment control of multiple underactuated unmanned surface vessels (USVs) subject to unknown kinetic models. A novel cooperative control architecture is presented for achieving a containment formation under switching topologies. Specifically, a path-guided distributed containment motion generator (CMG) is first proposed for generating reference points according to the underlying switching topologies. Next, guiding-vector-field-based guidance laws are designed such that each USV can track its reference point, enabling smooth transitions during topology switching. Finally, data-driven neural predictors by utilizing real-time and historical data are developed for estimating total uncertainties and unknown input gains, simultaneously. Based on the learned knowledge from neural predictors, adaptive kinetic control laws are designed and no prior information on kinetic model parameters is required. By using the proposed method, the fleet is able to converge to the convex hull spanned by multiple virtual leaders under switching topologies regardless of fully unknown kinetic models. Through stability analyses, it is proven that the closed-loop control system is input-to-state stable and the tracking errors are uniformly ultimately bounded. Simulation results verify the effectiveness of the proposed cooperative control architecture for multiple underactuated USVs with fully unknown kinetic models.Constrained multiobjective optimization problems (CMOPs) involve both conflicting objective functions and various constraints. Due to the presence of constraints, CMOPs’ Pareto-optimal solutions are very likely lying on constraint boundaries. see more The experience from the constrained single-objective optimization has shown that to quickly obtain such an optimal solution, the search should surround the boundary of the feasible region from both the feasible and infeasible sides. In this article, we extend this idea to cope with CMOPs and, accordingly, we propose a novel constrained multiobjective evolutionary algorithm with bidirectional coevolution, called BiCo. BiCo maintains two populations, that is 1) the main population and 2) the archive population. To update the main population, the constraint-domination principle is equipped with an NSGA-II variant to move the population into the feasible region and then to guide the population toward the Pareto front (PF) from the feasible side of the search space. While for updating the archive population, a nondominated sorting procedure and an angle-based selection scheme are conducted in sequence to drive the population toward the PF within the infeasible region while maintaining good diversity. As a result, BiCo can get close to the PF from two complementary directions. In addition, to coordinate the interaction between the main and archive populations, in BiCo, a restricted mating selection mechanism is developed to choose appropriate mating parents. Comprehensive experiments have been conducted on three sets of CMOP benchmark functions and six real-world CMOPs. The experimental results suggest that BiCo can obtain quite competitive performance in comparison to eight state-of-the-art-constrained multiobjective evolutionary optimizers.Spectral-domain optical coherence tomography (SD-OCT) images inevitably suffer from multiplicative speckle noise caused by random interference. This study proposes an unsupervised domain adaptation approach for noise reduction by translating the SD-OCT to the corresponding high-quality enhanced depth imaging (EDI)-OCT. We propose a structure-persevered cycle-consistent generative adversarial network for unpaired image-to-image translation, which can be applied to imbalanced unpaired data, and can effectively preserve retinal details based on a structure-specific cross-domain description. It also imposes smoothness by penalizing the intensity variation of the low reflective region between consecutive slices. Our approach was tested on a local data set that consisted of 268 SD-OCT volumes and two public independent validation datasets including 20 SD-OCT volumes and 17 B-scans, respectively. Experimental results show that our method can effectively suppress noise and maintain the retinal structure, compared with other traditional approaches and deep learning methods in terms of qualitative and quantitative assessments.