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The global financial crisis of 2008-2009 and the COVID-19 pandemic that started in 2019 along with the slow and unstable recovery of the global economy have raised concerns about the impact of global uncertainty on the macroeconomics of the countries. The paper used the Structural Vector Autoregression (SVAR) model to examine the impact of global uncertainty shocks on Vietnam's economy from the period 2008-2022. We found that Vietnam's output dropped following the shock of global uncertainty, the peak was in the third month, and lasted for one year. Inflation in Vietnam had a rapid downturn in the first month, peaked in the seventh month, and took a long time to cease. When the economy experienced the shock of increased global uncertainty, Vietnam's policy interest rate was adjusted downward. Additionally, we included a long-term interest rate to consider the overall impact of monetary policy into account. A decreasing trend was also found with this rate. The global uncertainty shock effects acted as the aggregate demand shocks, reducing output and inflation as the uncertainty increases and vice versa, thus monetary policy can be used to regulate Vietnam's economy to deal with negative shocks without the trade-offs between output and inflation as aggregate supply shocks.
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In this work we consider the problem of how to best allocate a limited supply of vaccines in the aftermath of an infectious disease outbreak by viewing the problem as a sequential game between a learner and an environment (specifically, a bandit problem). The difficulty of this problem lies in the fact that the payoff of vaccination cannot be directly observed, making it difficult to compare the relative effectiveness of vaccination on different population groups. Currently used vaccination policies make recommendations based on mathematical modelling and ethical considerations. These policies are static, and do not adapt as conditions change. Our aim is to design and evaluate an algorithm which can make use of routine surveillance data to dynamically adjust its recommendation. We evaluate the performance of our approach by applying it to a simulated epidemic of a disease based on real-world COVID-19 data, and show that our vaccination policy was able to perform better than existing vaccine allocation policies. In particular, we show that with our allocation method, we can reduce the number of required vaccination by at least 50% in order to keep the peak number of hospitalised patients below a certain threshold. Also, when the same batch sizes are used, our method can reduce the peak number of hospitalisation by up to 20%. We also demonstrate that our vaccine allocation does not vary the number of batches per group much, making it socially more acceptable (as it reduces uncertainty, hence results in better and more interpretable communication). © 2022 International Joint Conferences on Artificial Intelligence. All rights reserved.
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The purpose of this study is to present a comprehensive review of the use of structural equation modeling (SEM) in augmented reality (AR) studies in the context of the COVID-19 pandemic. IEEE Xplore Scopus, Wiley Online Library, Emerald Insight, and ScienceDirect are the main five data sources for data collection from Jan 2020 to May 2021. The preferred reporting items for systematic reviews and meta-analyses (PRISMA) approach was used to conduct the analysis. At the final stage, 53 relevant publications were included for analysis. Variables such as the number of participants in the study, original or derived hypothesized model, latent variables, direct/indirect contact with users, country, limitation/suggestion, and keywords were extracted. The results showed that a variety of external factors were used to construct the SEM models rather than using the parsimonious ones. The reports showed a fair balance between the direct and indirect methods to contact participants. Despite the COVID-19 pandemic, few publications addressed the issue of data collection and evaluation methods, whereas video demonstrations of the augmented reality (AR) apps were utilized. The current work influences new AR researchers who are searching for a theory-based research model in their studies. © 2022 Institute of Advanced Engineering and Science. All rights reserved.
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Due to the emergence of the COVID-19 pandemic, governments have implemented several urgent steps to minimize the disease’s effect and transmission. Supportive measures to trace contacts and warn people infected with COVID-19 were also implemented such as the COVID-19 contact tracing application. This study investigated the effects of variables influencing the intention to use the COVID-19 tracker. The extended Unified Theory of Acceptance and Use of Technology model was used to investigate user behavior using the COVID-19 tracker application. Google Form was used to construct and distribute the online survey to participants. Experiment results from 224 individuals revealed that performance expectations, trust, and privacy all have an impact on app usage intention. However, social impact, effort expectation, and facilitating conditions were not shown to be statistically significant. The conceptual model explained 60.07% of the amount of variation, suggesting that software developers, service providers, and policymakers should consider performance expectations, trust, and privacy as viable factors to encourage citizens to use the app. This study work’s recommendations and limitations are thoroughly discussed. © 2022. Vinh T. Nguyen and Chuyen T. H. Nguyen. This open-access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license.
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The emergence of variants of Covid-19, the persistence of lockdowns in many countries, and the necessity to maintain sustainable education have resulted in a shift from the traditional classroom to virtual space. As such, there is a strong need to leverage technological advances while mitigating the challenges faced by primary teachers. Through the incorporation of eight elements, the authors sought to better understand factors that influence teacher readiness to deliver sex education in primary schools. Structural Equation Modeling was employed to assess the proposed conceptual model. The online survey was designed and distributed by Google Forms. Based on the results from 383 individuals, the findings revealed that facilitating conditions, educational policy, and parental involvement all had a relationship with teacher readiness. Digital content positively influenced performance expectancy and effort expectancy. Sexual knowledge had a statistically significant and positive influence on effort expectancy. Finally, openness had a statistically significant and positive influence on performance expectancy. The significant exceptions were that effort expectancy was not found to predict teacher readiness, and performance expectancy was not found to influence teacher readiness. The reasons for these non-significant correlations were briefly discussed and more studies on this topic are called to investigate these unexpected outcomes in more detail. The level of readiness, as well as theoretical and practical implications for scholars and practitioners, were discussed. ©Authors
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Background: Our high-volume, academic liver transplant center accepts many interhospital transplant evaluation referrals to inpatient teams with resident frontline providers (RFLPs). Though standardized communication occurs between sending hospitals and accepting fel-lows/attendings, no such process exists for residents, who are first to evaluate these patients and triage them to teams for care to begin. Most patients arrive at night, when fellows/ attendings are offsite. Our quality improvement project sought to improve clinical information sharing for interhospital transfers such that housestaff were aware of 100% of incoming transfers and had access to their clinical summaries. Interventions: Two QI committee-approved, HIPAA-compliant communication initiatives were launched. In 2/2020, an email notification system to triage residents shared planned arrival time for patients pending transfer. In 7/2020, a clinical data repository (“Transfer Log”) where fellows documented updated clinical notes and management recommendations was made available to RFLPs for use overnight. Measures: Qualitative and quantitative data were assessed at different timepoints. Likert scale surveys assessing resident comfort with the transfer process were administered before 2/2020 email intervention (pre) and after 7/2020 transfer log intervention (post). Time from patient arrival to team assignment (TTA) in the electronic health record was used as a proxy for time to patient assessment by a resident;this was measured before and after each intervention separately (email/transfer log). Patients arriving during the first COVID-19 surge were excluded because redeployment altered team/triage structures. Results: Intervention emails were delivered for 159/176 patients. Housestaff respondents reported frequency of access to clinical information as follows: pre-interventions 4/31 some-times/very often;27/31 never/rarely. Post-interventions 11/26 sometimes/very often;15/26 never/rarely (Fisher's exact p=0.02). Pre-interventions 12/39 felt “not at all prepared” vs. 27/39 somewhat/adequately prepared;post-interventions 2/24 felt “not at all prepared” vs. 22/24 somewhat/adequately (Fisher's exact p=0.06). For TTA, there were 178 “pre-email” and 176 “post-email” patients. There were 259 “pre-transfer log” (including 178 “pre-email”) and 95 “post-transfer log” patients. There was no significant difference in mean TTA pre-vs. post-email (p=0.86) (Fig 1). There was a significant difference in mean TTA pre- vs. post-transfer log (55 minutes pre vs. 40 post, p=0.02);variance was smaller in the posttransfer log group (33 vs. 58, F statistic 3.06, p<0.01) (Fig 2). Early notification and increased access to clinical information for RFLPs were associated with better sense of preparedness for admitting housestaff;access to clinical information may account for this positive change. $Φgure
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This paper presents a deep learning approach to predict new COVID-19 infected cases in a specific country with insufficient data at the onset of the outbreak. We collected data on daily new confirmed cases in several countries of the region where COVID-19 occurred earlier and caused more severe effects than in Vietnam. Then we computed some deep machine learning models to adapt the spreading speed of Delta strain in each nation to generate various scenarios for the epidemic situation in Vietnam. We used models based on recurrent neural networks (RNN) architectures such as long-short term memory (LSTM), gated recurrent unit (GRU), and several hybrid structures between LSTM and GRU. Learning from the experiments in this research, we built a set of circumstances for COVID-19 in Vietnam. We also found that GRU always gives the best performance in terms of MSE, while LSTM is the worst. © 2021, Springer Nature Singapore Pte Ltd.
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According to a new study by the International Labor Organization (ILO), the COVID-19 pandemic has had a strong impact on the garment industry in the Asia-Pacific region. A sharp drop in retail sales in key export markets has affected workers and businesses across supply chains. To ensure the effectiveness and efficiency of garment supply chain, choosing a sustainable supplier should be a main concern of all businesses. The supplier selection problem in garment industry involves multiple quantitative and qualitative criteria. There have been many research and literatures about the development and application of Multicri-teria Decision Making (MCDM) models in solving decision-making problems in different industry sectors such as supplier selection or investment assessment. Many different MCDM models have been introduced over the years, and each model is uniquely dedicated into solving a particular problem. There is very little MCDM models incorporated with fuzzy set theory to support decision makers with decision-making problem in uncertain environments. This paper introduces a Fuzzy MCDM-based approach to the problem by utilizing Fuzzy-Analytic Hier-archical Process (FAHP) and Weighted Aggregated Sum Product Assessment (WASPAS) methods to support the decision makers. The aim of the paper is developing a decision-making tool that supports the decision maker in deciding the suitable supplier in garment industry under fuzzy environment. The proposed MCDM model is applied to a real-world case study to demonstrate the application steps of the model as well as its feasibility. The model assisted in successfully its proposed goals that resulted in an optimal supplier in garment industry. © 2022, Tech Science Press. All rights reserved.
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The two main approaches that countries are using to ease the strain on healthcare infrastructure is building temporary hospitals that are specialized in treating COVID-19 patients and promoting preventive measures. As such, the selection of the optimal location for a temporary hospital and the calculation of the prioritization of preventive measures are two of the most critical decisions during the pandemic, especially in densely populated areas where the risk of transmission of the virus is highest. If the location selection process or the prioritization of measures is poor, healthcare workers and patients can be harmed, and unnecessary costs may come into play. In this study, a decision support framework using a fuzzy analytic hierarchy process (FAHP) and a weighted aggregated sum product assessment model are proposed for selecting the location of a temporary hospital, and a FAHP model is proposed for calculating the prioritization of preventive measures against COVID-19. A case study is performed for Ho Chi Minh City using the proposed decision-making framework. The contribution of this work is to propose a multiple criteria decision-making model in a fuzzy environment for ranking potential locations for building temporary hospitals during the COVID-19 pandemic. The results of the study can be used to assist decision-makers, such as government authorities and infectious disease experts, in dealing with the current pandemic as well as other diseases in the future. With the entire world facing the global pandemic of COVID-19, many scientists have applied research achievements in practice to help decision-makers make accurate decisions to prevent the pandemic. As the number of cases increases exponentially, it is crucial that government authorities and infectious disease experts make optimal decisions while considering multiple quantitative and qualitative criteria. As such, the proposed approach can also be applied to support complex decision-making processes in a fuzzy environment in different countries. © 2021 Tech Science Press. All rights reserved.
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Virtual 3D conferences are emerging communication channels as a substitution for face-to-face fashion due to the advancement of technologies and the covid-19 pandemic. Current efforts focus on bringing contents into 3D virtual space while delivering them to the color vision deficiency have not been taken into account. To alleviate the stated issue, this paper presents a prototype for color-blind people to simulate the same experience as normal ones. Our method helps users: 1) understand the presented content through adjusted color filtering in such a way that similar colors can be differentiated by the brightness, 2) apparently-identical colors can be varied by the color transformation. Our proposed prototype is demonstrated through three use cases setup in three conditions such as traffic lights, fruit color differentiation, and graph reading in a virtual meeting room. A pilot study conduct with 29 participants shows that our proposed method can improve color differentiation and accuracy for color-blind. © 2021 ACM.
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The COVID 19 pandemic has led to a new global recession and is still causing a lot of issues because of the delays in the employment of people. This scenario has severe consequences for many countries’ labor markets in the world. This problem’s complexity and importance requires an integrated method of subjective and objective evaluation rather than intuitive decisions. This research aims to investigate sustainable indexes for assessing the unemployment problem by using a Multi-Criteria Decision-Making Model (MCDM). Grey theory and Decision Making Trial and Evaluation Laboratory (GDEMATEL) are deployed to transform the experts’ opinions into quantitative data. The analysis based on 20 crucial criteria is employed to determine the weights of sustainability of unemployment problems. The results revealed that the top ten of determinants are Economic growth, Industrialization, Foreign direct investment, Real GDP per capita, Education level, Trade Openness, Capacity Utilization Rate, Urbanization, Employability skills, Education system expansion, which have the most significant effects on the unemployment rate under COVID 19 impacts. Furthermore, GDEMATEL could effectively assess the sustainable indicators for unemployment problems in “deep and wide”aspects. The study proposes the Grey MCDM model, contributes to the literature, provides future research directions, and helps policymakers and researchers achieve the best solutions to the unemployment problems under “economic shocks.” © Copyright: The Author(s) This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.