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The development and approval of COVID-19 vaccines have generated optimism for the end of the COVID-19 pandemic and a return to normalcy. However, vaccine hesitancy, often fueled by misinformation, poses a major barrier to achieving herd immunity.

We aim to investigate Twitter users' attitudes toward COVID-19 vaccination in Canada after vaccine rollout.

We applied a weakly supervised aspect-based sentiment analysis (ABSA) technique, which involves the human-in-the-loop system, on COVID-19 vaccination-related tweets in Canada. Automatically generated aspect and opinion terms were manually corrected by public health experts to ensure the accuracy of the terms and make them more domain-specific. Then, based on these manually corrected terms, the system inferred sentiments toward the aspects. We observed sentiments toward key aspects related to COVID-19 vaccination, and investigated how sentiments toward "vaccination" changed over time. In addition, we analyzed the most retweeted or liked tweets by observingccines (P<.001) and vaccination (P=.009). Further investigation of the most retweeted or liked tweets revealed two opposing trends in Twitter users who showed negative sentiments toward vaccines the "anti-vaxxer" population that used negative sentiments as a means to discourage vaccination and the "Covid Zero" population that used negative sentiments to encourage vaccinations while critiquing the public health response.

Our study examined public sentiments toward COVID-19 vaccination on tweets over an extended period in Canada. selleck chemicals llc Our findings could inform public health agencies to design and implement interventions to promote vaccination.

Our study examined public sentiments toward COVID-19 vaccination on tweets over an extended period in Canada. Our findings could inform public health agencies to design and implement interventions to promote vaccination.

The prevalence of depression in the United States is >3 times higher mid-COVID-19 versus prepandemic. Racial/ethnic differences in mindsets around depression and the potential impact of the COVID-19 pandemic are not well characterized.

This study aims to describe attitudes, mindsets, key drivers, and barriers related to depression pre- and mid-COVID-19 by race/ethnicity using digital conversations about depression mapped to health belief model (HBM) concepts.

Advanced search, data extraction, and artificial intelligence-powered tools were used to harvest, mine, and structure open-source digital conversations of US adults who engaged in conversations about depression pre- (February 1, 2019-February 29, 2020) and mid-COVID-19 pandemic (March 1, 2020-November 1, 2020) across the internet. Natural language processing, text analytics, and social data mining were used to categorize conversations that included a self-identifier into racial/ethnic groups. Conversations were mapped to HBM concepts (ie, perceireceive mental health help, such as the constructs that comprise the HBM.

There were considerable racial/ethnic differences in drivers and barriers to seeking help and treatment for depression pre- and mid-COVID-19. As expected, COVID-19 has made conversations about depression more negative and with frequent discussions of barriers to seeking care. Applying concepts of the HBM to data on digital conversation about depression allowed organization of the most frequent themes by race/ethnicity. Individuals of all groups came online to discuss their depression. These data highlight opportunities for culturally competent and targeted approaches to addressing areas amenable to change that might impact the ability of people to ask for or receive mental health help, such as the constructs that comprise the HBM.

Although telehealth appears to have been accepted among some obstetric populations before the COVID-19 pandemic, patients' receptivity and experience with the rapid conversion of this mode of health care delivery are unknown.

In this study, we examine patients' prenatal care needs, preferences, and experiences during the COVID-19 pandemic, with the aim of supporting the development of successful models to serve the needs of pregnant patients, obstetric providers, and health care systems during this time.

This study involved qualitative methods to explore pregnant patients' experiences with prenatal health care delivery at the onset of the COVID-19 pandemic. We conducted in-depth interviews with pregnant patients in the first and second trimester of pregnancy who received prenatal care in Cleveland, Ohio, from May to July 2020. An interview guide was used to probe experiences with health care delivery as it rapidly evolved at the onset of the pandemic.

Although advantages of telehealth were noted, therments, our study suggests that there may be specific needs and concerns among the diverse patient groups using this modality during the pandemic. More research is needed to understand patients' experiences with telehealth during the pandemic and develop approaches that are responsive to the needs and preferences of patients.Highly constrained multiobjective optimization problems (HCMOPs) refer to constrained multiobjective optimization problems (CMOPs) with complex constraints and small feasible regions, which are commonly encountered in many real-world applications. Current constraint-handling techniques will face two difficulties when dealing with HCMOPs 1) feasible solution is hard to be found and too much search effort is spent in locating the feasible region and 2) since the total feasible region of an HCMOP can consist of several disconnected subregions, the search process might be stuck in the comparatively larger feasible subregion, which does not contain the whole Pareto front (PF). To address these two issues, an evolutionary algorithm with constraint relaxation strategy based on differential evolution algorithm, that is, CRS-DE, is proposed in this article. In each generation, the CRS-DE relaxes the constraints by dividing the infeasible solutions into two subpopulations based on total constraint violation, that is, tet of well-distributed optimal solutions for HCMOPs.Due to the effectiveness and advantages of interval-valued intuitionistic fuzzy sets (IVIFSs) in evaluating uncertainty and risk, we introduce IVIFSs into loss functions of decision-theoretic rough sets (DTRSs) and propose an optimization-based approach to interval-valued intuitionistic fuzzy three-way decisions. First, based on the classical DTRSs and two previous optimization models, we construct a new concise linear programming model for simultaneously determining the threshold pair. Our model is mathematically equivalent to the DTRSs and the previous models under the Karush-Kuhn-Tucker (KKT) condition. Second, we extend the constructed model via the IVIFSs of loss functions and we discuss the relations between these loss functions based on a similarity measure function-based ranking method and a multiple score function-based ranking method for IVIFSs. Third, we develop our extended models via two ranking methods and we prove the existence and uniqueness of the optimal solution of the model. The optimization-based method, along with its algorithm for three-way decisions, is designed in an interval-valued intuitionistic fuzzy environment. Compared to the latest existing methods, our method has three advantages (see Advantages 1-3). Finally, an illustrative example is considered, and the advantages of our approach are demonstrated by this example.The fault-tolerant consensus of nonlinear multiagent systems (MASs) is studied by using the dynamic event-triggered control and the adaptive control techniques. First, a general dynamic adaptive event-triggered mechanism (DAETM) is proposed, which promotes and improves many existing dynamic event-triggered mechanism and static event-triggered mechanism. On this basis, a new distributed dynamic adaptive event-triggered fault-tolerant controller (DDAETFTC) is designed. Then, two simple and clear criteria are derived, respectively, to ensure consensus can be reached asymptotically for nonlinear MASs with directed networks and with undirected networks under the new DDAETFTC. The obtained results also can apply to linear MASs. Furthermore, it is proven that there is no agent to show the Zeno behavior in MASs under the new DAETM. Finally, an example is given to simulate the obtained results.Prototype-based learning (PbL) using a winner-take-all (WTA) network based on minimum Euclidean distance (ED-WTA) is an intuitive approach to multiclass classification. By constructing meaningful class centers, PbL provides higher interpretability and generalization than hyperplane-based learning (HbL) methods based on maximum inner product (IP-WTA) and can efficiently detect and reject samples that do not belong to any classes. In this article, we first prove the equivalence of IP-WTA and ED-WTA from a representational power perspective. Then, we show that naively using this equivalence leads to unintuitive ED-WTA networks in which the centers have high distances to data that they represent. We propose ±ED-WTA that models each neuron with two prototypes one positive prototype, representing samples modeled by that neuron, and a negative prototype, representing the samples erroneously won by that neuron during training. We propose a novel training algorithm for the ±ED-WTA network, which cleverly switches between updating the positive and negative prototypes and is essential to the emergence of interpretable prototypes. Unexpectedly, we observed that the negative prototype of each neuron is indistinguishably similar to the positive one. The rationale behind this observation is that the training data that are mistaken for a prototype are indeed similar to it. The main finding of this article is this interpretation of the functionality of neurons as computing the difference between the distances to a positive and a negative prototype, which is in agreement with the BCM theory. Our experiments show that the proposed ±ED-WTA method constructs highly interpretable prototypes that can be successfully used for explaining the functionality of deep neural networks (DNNs), and detecting outlier and adversarial examples.The salient progress of deep learning is accompanied by nonnegligible deficiencies, such as 1) interpretability problem; 2) requirement for large data amounts; 3) hard to design and tune parameters; and 4) heavy computation complexity. Despite the remarkable achievements of neural networks-based deep models in many fields, the practical applications of deep learning are still limited by these shortcomings. This article proposes a new concept called the lightweight deep model (LDM). LDM absorbs the useful ideas of deep learning and overcomes their shortcomings to a certain extent. We explore the idea of LDM from the perspective of partial least squares (PLS) by constructing a deep PLS (DPLS) model. The feasibility and merits of DPLS are proved theoretically, after that, DPLS is further generalized to a more common form (GDPLS) by adding a nonlinear mapping layer between two cascaded PLS layers in the model structure. The superiority of DPLS and GDPLS is demonstrated through four practical cases involving two regression problems and two classification tasks, in which our model not only achieves competitive performance compared with existing neural networks-based deep models but also is proven to be a more interpretable and efficient method, and we know exactly how it improves performance, how it gives correct results.