Hybrid teaching after COVID-19: advantages, challenges and optimization strategies
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ABSTRACT:
SUBMITTER: Wang X
PROVIDER: S-EPMC11241882 | biostudies-literature | 2024 Jan
REPOSITORIES: biostudies-literature
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Project description:Multiple vaccines have recently been developed, and almost all the countries are presently vaccinating their population to tackle the COVID-19 pandemic. Most of the COVID-19 vaccines in use are administered via intramuscular (IM) injection, eliciting protective humor and cellular immunity. COVID-19 intranasal (IN) vaccines are also being developed that have shown promising ability to induce a significant amount of antibody-mediated immune response and a robust cell-mediated immunity as well as hold the added ability to stimulate protective mucosal immunity along with the additional advantage of the ease of administration as compared to IM injected vaccines. By inducing secretory IgA antibody responses specifically in the nasal compartment, the intranasal SARS-CoV-2 vaccine can prevent virus infection, replication, shedding, and disease development, as well as possibly limits virus transmission. This article highlights the current progress, advantages, prospects, and challenges in developing intranasal COVID-19 vaccines for countering the ongoing pandemic.
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Project description:This research paper develops a novel hybrid approach, called hybrid particle swarm optimization-teaching-learning-based optimization (hPSO-TLBO), by combining two metaheuristic algorithms to solve optimization problems. The main idea in hPSO-TLBO design is to integrate the exploitation ability of PSO with the exploration ability of TLBO. The meaning of "exploitation capabilities of PSO" is the ability of PSO to manage local search with the aim of obtaining possible better solutions near the obtained solutions and promising areas of the problem-solving space. Also, "exploration abilities of TLBO" means the ability of TLBO to manage the global search with the aim of preventing the algorithm from getting stuck in inappropriate local optima. hPSO-TLBO design methodology is such that in the first step, the teacher phase in TLBO is combined with the speed equation in PSO. Then, in the second step, the learning phase of TLBO is improved based on each student learning from a selected better student that has a better value for the objective function against the corresponding student. The algorithm is presented in detail, accompanied by a comprehensive mathematical model. A group of benchmarks is used to evaluate the effectiveness of hPSO-TLBO, covering various types such as unimodal, high-dimensional multimodal, and fixed-dimensional multimodal. In addition, CEC 2017 benchmark problems are also utilized for evaluation purposes. The optimization results clearly demonstrate that hPSO-TLBO performs remarkably well in addressing the benchmark functions. It exhibits a remarkable ability to explore and exploit the search space while maintaining a balanced approach throughout the optimization process. Furthermore, a comparative analysis is conducted to evaluate the performance of hPSO-TLBO against twelve widely recognized metaheuristic algorithms. The evaluation of the experimental findings illustrates that hPSO-TLBO consistently outperforms the competing algorithms across various benchmark functions, showcasing its superior performance. The successful deployment of hPSO-TLBO in addressing four engineering challenges highlights its effectiveness in tackling real-world applications.
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Project description:BackgroundCOVID-19 screening is an effective nonpharmaceutical intervention for identifying infected individuals and interrupting viral transmission. However, questions have been raised regarding its effectiveness in controlling the spread of novel variants and its high socioeconomic costs. Therefore, the optimization of COVID-19 screening strategies has attracted great attention.ObjectiveThis review aims to summarize the evidence and provide a reference basis for the optimization of screening strategies for the prevention and control of COVID-19.MethodsWe applied a methodological framework for scoping reviews and the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) checklist. We conducted a scoping review of the present publications on the optimization of COVID-19 screening strategies. We searched the PubMed, Web of Science, and Elsevier ScienceDirect databases for publications up to December 31, 2022. English publications related to screening and testing strategies for COVID-19 were included. A data-charting form, jointly developed by 2 reviewers, was used for data extraction according to the optimization directions of the screening strategies.ResultsA total of 2770 unique publications were retrieved from the database search, and 95 abstracts were retained for full-text review. There were 62 studies included in the final review. We summarized the results in 4 major aspects: the screening population (people at various risk conditions such as different regions and occupations; 12/62, 19%), the timing of screening (when the target population is tested before travel or during an outbreak; 12/62, 19%), the frequency of screening (appropriate frequencies for outbreak prevention, outbreak response, or community transmission control; 6/62, 10%), and the screening and detection procedure (the choice of individual or pooled detection and optimization of the pooling approach; 35/62, 56%).ConclusionsThis review reveals gaps in the optimization of COVID-19 screening strategies and suggests that a number of factors such as prevalence, screening accuracy, effective allocation of resources, and feasibility of strategies should be carefully considered in the development of future screening strategies.
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Project description:The COVID-19 pandemic literally stopped most human movement and activities as it initially spread, which included dental practices and dental education. This defined the need for significative changes in teaching and learning with the use of "e-learning" methods, also for traineeships. This study was designed to determine the undergraduate student perception of these new methods as part of their education. This involved 353 students attending the Dental School of the G. D'Annunzio University of Chieti-Pescara, from the first to the sixth years. A questionnaire in Italian and was set-up using "Google Forms" and sent by email to the students. The questionnaire was divided into three parts: the first part included questions for general information, including age, sex and year of course; the second part had multiple choice questions related to their evaluation of the e-learning teaching, using a scale of opinion in the replies to each question (e.g., "scarce", "fair", "satisfying", "very good" and "excellent"); and the third part included two open questions to indicate the strengths and limitations of these new teaching and learning approaches. The categorical variables in the first and second parts of the questionnaire were evaluated using Chi squared tests, setting significance at p < 0.05, while the comments were evaluated qualitatively. The student feedback showed significant appreciation (p < 0.05) of the new methods and the efforts that the lecturers put in to provide lectures of as high a quality as possible. However, a lack of practical training was significantly perceived as an important problem in the structure of their new curriculum (p < 0.05). COVID-19 has been an epic tragedy that has hit the human population not only in terms of health and healthcare, but also quality of life. This includes the quality of dental education within universities. However, the pandemic can be seen to also represent motivation to invest in the necessary technological innovation to deliver the best possible education to our future dentists.
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