THE EFFECTIVENESS OF EXPERIENTIAL LEARNING MODELS IN TEACHER TRAINING: A CASE STUDY IN THAI NGUYEN PROVINCE, VIETNAM

The quality of education may be raised in large part by investing in teacher training. It contributes to the expansion and updating of professional knowledge, teaching abilities, and new and improved methods of approaching students. Teachers can also continue to grow individually via the refresher process, reflect on their teaching methods, and adjust to shifts in the educational landscape and the needs of their students. The purpose of the project is to identify an experiential learning-based teacher training paradigm that is efficient and workable. A pedagogical experiment using this model was conducted with two classes and 35 high school teachers in Thai Nguyen Province, Vietnam. The findings of the research, which used the document research approach together with expert opinion, demonstrate that learning via experience in teacher training has accomplished the desired objective in 5 key stages: (1) (1) Choosing a few standard lessons to organize to teach illustrations; (2) preparing to organize the illustration teaching after the standard lesson has been chosen; (3) practicing demonstration teaching; (4) organizing the discussion after attending the demonstration lesson; and (5) making personal plans to organize the lesson in accordance with the training's content.  Article visualizations

Two Spot Coupled Ring Resonators

Abstract. We consider a model of two coupled ring waveguides with constant linear gain and nonlinear absorption with space-dependent coupling. This system can be implemented in various physical situations as optical waveguides, atomic Bose-Einstein condensates, polarization condensates, etc. It is described by two coupled nonlinear Schrödinger equation. For numerical simulations, we take local two-gaussian coupling.It is found in our previous papers that, depending on the values of involved parameters, we can obtain several interesting nonlinear phenomena, which include spontaneous symmetry breaking, modulational instability leading to generation of stable circular flows with various vorticities, stable inhomogeneous states with interesting structure of currents flowing between rings, as well as dynamical regimes having signatures of chaotic behavior. This research will be associated with experimental investigation planned in Freie Universität Berlin, in the group of prof. Michael Giersig

Dielectrophoresis can control the density of CNT membranes as confirmed by experiment and dissipative particle simulation

In forests and membranes, Carbon nano tubes (CNT) are not individual, instead they tend to be agglomerated into bundles because of the strong van der Waals interaction. CNTs usually form into bundles containing up to hundreds or thousands of parallel CNTs named as fibres which create networks within a CNT membrane. Recently, CNT based macrostructures (yarn and membrane) have increasingly been used in various applications in electronics, medical and bioengineering. Meanwhile the volume density of CNTs impacts on mechanical and physical properties of macrostructures, the controlling of the density of membranes is very complex. Thus, in this paper, an electric processing to dilate CNT membrane is sufficiently studied and investigated by both the experiment and particle based numerical simulation. Several initially potential applications of the method are also represented not only to control the density of CNTs but also to improve the CNTs’ alignment in macro-structures

Kinetics of neutralizing antibodies against Omicron variant in Vietnamese healthcare workers after primary immunization with ChAdOx1-S and booster immunization with BNT162b2

We studied the development and persistence of neutralizing antibodies against SARS-CoV-2 ancestral strain, and Delta and Omicron (BA.1 and BA.2) variants in Vietnamese healthcare workers (HCWs) up to 15 weeks after booster vaccination. We included 47 HCWs, including group 1 (G1, N = 21) and group 2 (G2; N = 26) without and with breakthrough Delta variant infection before booster immunization, respectively). The study participants had completed primary immunization with ChAdOx1-S and booster vaccination with BNT162b2. Neutralizing antibodies were measured using a surrogate virus neutralization assay. Of the 21 study participants in G1, neutralizing antibodies against ancestral strain, Delta variant, BA.1, and BA.2 were (almost) abolished at month 8 after the second dose, but all had detectable neutralizing antibodies to the study viruses at week 2 post booster dose. Of the 26 study participants in G2, neutralizing antibody levels to BA.1 and BA.2 were significantly higher than those to the corresponding viruses measured at week 2 post breakthrough infection and before the booster dose. At week 15 post booster vaccination, neutralizing antibodies to BA.1 and BA.2 dropped significantly, with more profound changes observed in those without breakthrough Delta variant infection. Booster vaccination enhanced neutralizing activities against ancestral strain and Delta variant compared with those induced by primary vaccination. These responses were maintained at high levels for at least 15 weeks. Our findings emphasize the importance of the first booster dose in producing cross-neutralizing antibodies against Omicron variant. A second booster to maintain long-term vaccine effectiveness against the currently circulating variants merits further research

Evaluation of awake prone positioning effectiveness in moderate to severe COVID-19

Evidence mainly from high income countries suggests that lying in the prone position may be beneficial in patients with COVID-19 even if they are not receiving invasive ventilation. Studies indicate that increased duration of prone position may be associated with improved outcomes, but achieving this requires additional staff time and resources. Our study aims to support prolonged (≥ 8hours/day) awake prone positioning in patients with moderate to severe COVID-19 disease in Vietnam. We use a specialist team to support prone positioning of patients and wearable devices to assist monitoring vital signs and prone position and an electronic data registry to capture routine clinical data

Acceptance and user experiences of a wearable device for the management of hospitalized patients in COVID-19–designated wards in Ho Chi Minh City, Vietnam: action learning project

Background: Wearable devices have been used extensively both inside and outside of the hospital setting. During the COVID-19 pandemic, in some contexts, there was an increased need to remotely monitor pulse and saturated oxygen for patients due to the lack of staff and bedside monitors. Objective: A prototype of a remote monitoring system using wearable pulse oximeter devices was implemented at the Hospital for Tropical Diseases in Ho Chi Minh City, Vietnam, from August to December 2021. The aim of this work was to support the ongoing implementation of the remote monitoring system. Methods: We used an action learning approach with rapid pragmatic methods, including informal discussions and observations as well as a feedback survey form designed based on the technology acceptance model to assess the use and acceptability of the system. Based on these results, we facilitated a meeting using user-centered design principles to explore user needs and ideas about its development in more detail. Results: In total, 21 users filled in the feedback form. The mean technology acceptance model scores ranged from 3.5 (for perceived ease of use) to 4.4 (for attitude) with behavioral intention (3.8) and perceived usefulness (4.2) scoring in between. Those working as nurses scored higher on perceived usefulness, attitude, and behavioral intention than did physicians. Based on informal discussions, we realized there was a mismatch between how we (ie, the research team) and the ward teams perceived the use and wider purpose of the technology. Conclusions: Designing and implementing the devices to be more nurse-centric from their introduction could have helped to increase their efficiency and use during the complex pandemic period

Regulatory T cells and their role in rheumatic diseases: a potential target for novel therapeutic development

Regulatory T cells have an important role in limiting immune reactions and are essential regulators of self-tolerance. Among them, CD4+CD25high regulatory T cells are the best-described subset. In this article, we summarize current knowledge on the phenotype, function, and development of CD4+CD25high regulatory T cells. We also review the literature on the role of these T cells in rheumatic diseases and discuss the potential for their use in immunotherapy

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks

The global response: How cities and provinces around the globe tackled Covid-19 outbreaks in 2021

Background: Tackling the spread of COVID-19 remains a crucial part of ending the pandemic. Its highly contagious nature and constant evolution coupled with a relative lack of immunity make the virus difficult to control. For this, various strategies have been proposed and adopted including limiting contact, social isolation, vaccination, contact tracing, etc. However, given the heterogeneity in the enforcement of these strategies and constant fluctuations in the strictness levels of these strategies, it becomes challenging to assess the true impact of these strategies in controlling the spread of COVID-19.Methods: In the present study, we evaluated various transmission control measures that were imposed in 10 global urban cities and provinces in 2021 Bangkok, Gauteng, Ho Chi Minh City, Jakarta, London, Manila City, New Delhi, New York City, Singapore, and Tokyo.Findings: Based on our analysis, we herein propose the population-level Swiss cheese model for the failures and pit-falls in various strategies that each of these cities and provinces had. Furthermore, whilst all the evaluated cities and provinces took a different personalized approach to managing the pandemic, what remained common was dynamic enforcement and monitoring of breaches of each barrier of protection. The measures taken to reinforce the barriers were adjusted continuously based on the evolving epidemiological situation.Interpretation: How an individual city or province handled the pandemic profoundly affected and determined how the entire country handled the pandemic since the chain of transmission needs to be broken at the very grassroot level to achieve nationwide control