Everyday Morbid Geography: Street Life and COVID-19 State Regulation in Manila and Hanoi

After a full year of intermittent observation of pandemic conditions, this research analyzes the way street life of neighborhoods in two Southeast Asian cities has adapted to government-intensified sanitation measures, scarcity of essential goods and services, and movement restrictions that characterize the persistence of the COVID-19 influenza in selected sites in and around Manila, in the Philippines and in Hanoi, Vietnam. This study describes how citizens negotiate the morbid geography - the reshaping of public space as well as its encompassed social, institutional and economic processes in response to the pervasive state of pandemic - in relation to well-meaning but sometimes draconian government health regulations in the Global South. We draw on the dynamics of institutional strategy and citizen tactics as a theoretical lens, as informed by literature on coping and transgressive practices. Learning from comparable patterns of citizens' everyday life on the streets, even in countries with distinct administrative traditions, the study highlights the significance of agency under crisis and emphasizes the different meanings of public space for varied social groups. It suggests how urban planning and administration can be improved to prepare cities for future health emergencies and make them more resilient. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

Inadvertent Treatment of a Vertical Banded Gastroplasty Stricture With a LAMS for Two Years

Introduction: Lumen-apposing metal stents (LAMS) are innovative endoscopic devices representing the next significant advancement in stent technology. LAMS have demonstrated success, most notably with improving drainage of pancreatic fluid collections. Other clinical indications for using LAMS include biliary drainage, gastroenterostomy, or the managment of luminal tract strictures. The stent has a larger lumen diameter than previously created stents as well as a unique "dumbbell" shape to limit migration. Studies have demonstrated advantages such as shorter procedure times and overall reduced repeat endoscopic procedures. As LAMS has gained notoriety, there have been increasing studies demonstrating potential complications of the device. Most common consequences of LAMS include bleeding, biliary stricture, and buried LAMS syndrome. As the anatomical design has decreased migration risk, prompt removal is recommended to prevent buried LAMS syndrome, where the stent is embedded in the wall of the gastric mucosa and can eventually not be visualized endoscopically. In this case, we will present a patient with an endoscopically placed LAMS, which was successfully removed with minimal complications after two years in place. Case Description/Methods: Our patient is a 68 year old female with a Vertical Banded Gastroplasty Stricture. She had required multiple repeat endoscopies for dilation therapy but the stricture was refractory to dilation, as a result, she underwent LAMS placement Due to the onset of the COVID pandemic, patient was lost to follow up. On a repeat EGD two years after placement, the stent remained in its original location. There were signs of mild gastric tissue overgrowth at the right lateral side of the LAMS. The stent was then removed easily with no signs of bleeding. After removal, the stricture remained dilated as the scope could be passed without difficulty. Over course of COVID she ate better than she had in years. (Figure) Discussion: LAMS have demonstrated significant success in a variety of endoscopic interventions. Their potential complications are well documented in various studies. This case is unique in regards to the length of time in which the LAMS remained in position. From a literature review, no study has demonstrated a LAMS in place as long as two years for stricture management. More remarkable is the lack of complications from the stent such as no bleeding with removal and no true buried LAMS syndrome as there was minimal tissue overgrowth. (Figure Presented).

Government policy, IT capabilities, digital transformation, and innovativeness in Post-Covid context: case of Vietnamese SMEs

Purpose: COVID-19 has made businesses increasingly dependent on technology to be competitive and efficient. Small and medium enterprises (SME) digitalisation and innovation research are widespread. SME digital transformation and innovation require government policies, initiatives and assistance. How the government can help SMEs achieve these goals is unclear. So, this paper aims to investigate how government policy may assist Vietnamese SMEs to boost innovation performance and digital transformation. Design/methodology/approach: The study will take a quantitative approach, with questionnaires distributed to 659 respondents from SMEs in Vietnam through snowball and convenience sampling procedures. The structural equational modelling method is used for data analysis. Findings: The study indicated that government policies supported Vietnamese SMEs' innovation and information technology (IT) capabilities. Government policy assistance also boosted IT capabilities and innovation. Furthermore, mediation effects show that digital transformation fully mediates the relationship between innovativeness and firm performance, whereas IT capabilities partially mediate this relationship. Research limitations/implications: Further research that replicates the findings and analyses contextual heterogeneities between nations is advised because Vietnam's pandemic setting was both similar and dissimilar. Practical implications: The study demonstrated government-company interactions through supportive policy. It investigated whether SMEs seeking digital transformation and innovativeness might gain competitive benefits by implementing effective knowledge management and enhancing their IT capabilities. Originality/value: A resource-based theoretical framework is extended to study how innovation, public policy and digital transformation for SMEs interact. The study confirms government policy strongly influences enterprises' digital development. Specifically, the new mediating effects of IT capabilities and digital transformation are explored and provide new insights into the existing literature. © 2023, Emerald Publishing Limited.

Systemic risk and financial system network using financial risk meter: the case of Vietnam

In the financial market, systemic risk is defined as the possibility that an event at the company level could trigger severe instability or collapse of an entire industry or the whole economy. Thus, understanding systemic risk is crucial for the financial institutions, large corporations, investors and regulators. This article investigates systemic risk and spillover effect using the new Financial Risk Meter ((Formula presented.)) index, which is obtained from running quantile linear regression and Least Absolute Shrinkage and Selection Operator ((Formula presented.)) method. The (Formula presented.) index is obtained to identify the highly risky periods, the contributors to systemic risk and the potential activators of spillover effect. Moreover, interconnection between firms can be visualized as a network. We use a data set consisting of daily stock returns from 35 financial institutions and real estate firms in Vietnam, combined with 4 macroeconomic variables over the period from November 2011 to December 2020. The findings indicate that over the considered period, some detected highly risky periods are 2012, 2018 and 2020, probably due to the non-performing loan crisis in Vietnam, US-China trade war and global COVID-19 outbreak. Some active activators of risk spillover effect are also identified. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

Rural Communication in the Covid-19 Pandemic: an Empirical Analysis from Thua Thien Hue Province, Central Vietnam

The world has witnessed the outbreak of the Covid-19 epidemic. Mainstream and social media are playing an important role in Covid-19 pandemic prevention. This research explores awareness, communication channels and effectiveness of communication in the Covid-19 pandemic in rural areas of Thua Thien Hue province, Central Vietnam. Primary information was collected from 181 respondents, who are farmers, non-farmers and students. Secondary information was collected from reports and statistical data. Television, word of mouth and local loudspeakers are the main channels of mainstream media while social media mentions the role of Facebook and Zalo to transfer Covid-19 pandemic information. Mainstream media is still the main channel of farmers and old people while non-farmers and young people tend to access information through social media. Communication has significantly contributed to improving awareness and action of rural people in the Covid-19 epidemic prevention. © 2022 World Association for Triple helix and Future strategy studies. All rights reserved.

Empirical Study of Green Practices Fostering Customers' Willingness to Consume via Customer Behaviors: The Case of Green Restaurants in Ho Chi Minh City of Vietnam

The COVID-19 pandemic has caused the population to change their consumption behavior and habits to a green living style to protect the environment. The aim of this study was to explore the theory of planned behavior (TPB) model to identify the effect of green practices on willingness to consume through customers' emotional attachment, attitudes, and satisfaction towards eco-friendly restaurants. We used a quantitative method with a self-administrated questionnaire and convenience sampling at eco-friendly restaurants in Ho Chi Minh City, Vietnam. Using a partial least square (PLS) structural equation model (SEM), we analyzed 1095 samples. The results of this study reveal that green practices significantly and positively affect customers' emotional attachment, satisfaction, and attitudes, but eco-friendliness did not have an effect on customers' emotional attachment. Moreover, the customers' satisfaction, attitudes, and emotional attachment were shown to significantly and positively affect their willingness to consume, as well as to pay 5 percent more for green products. Additionally, a mediating effect of emotional attachment, satisfaction, and attitudes was proven. The government needs to prioritize policies and programs to support these restaurants in order to apply sustainable business models and to build a green marketing strategy involving restaurants to protect environmental sustainability. © 2023 by the authors.

Tailored intelligence to detect unusual epidemic activity following the explosion at Vector, Russia

We used open source data from the EpiWATCH observatory to monitor for early disease signals in Russia and surrounding countries following an explosion at a BSL 4 laboratory, Vector, in Siberia in September 2019. Upon news of the explosion at Vector on September 16th 2019, the EpiWATCH team added the Russian language and key words Russia, Siberia, Novosibirsk, and Koltsovo to the Standard Operating Procedures, in addition to the usual epidemic-specific keywords used in EpiWATCH. We also searched for outbreak reports in countries bordering Siberia, including Mongolia, Kazakhstan and China. Given local spread of an epidemic could manifest in these countries, we included searching in Chinese, Mongolian and Kazakh. We added "Ukraine” as a key word, given current conflict between Russia and Ukraine. Data collection began in September 2019, one week after the explosion, with this considered the baseline. We demonstrate a method for rapid epidemic intelligence following an incident of concern, the explosion at Vector. There were some unexplained outbreaks in Russia in the three months following the explosion. No unexplained outbreaks were detected in countries bordering Russia, nor in Ukraine in the three months following the explosion. We detected an accidental release of brucella from a laboratory in China in early December 2019 and two reports of severe pneumonia prior to official reports, which could have been early COVID-19 cases. Best practice in preparedness should include surveillance for disease events in the months following an event of concern at local, national and global levels. In the absence of official surveillance data, open source intelligence may be the only available means of detecting outbreaks and enabling early response and mitigation for the rest of the world. EpiWATCH was able to identify reports of Russian outbreaks in the weeks and months following the Vector explosion, which allowed monitoring of outbreaks of concern without a known cause. © 2020 The Author(s).

A Study of Performance Evaluation for Textile and Garment Enterprises

Vietnam's textile and garment enterprises make an important contribution to the country with the second largest export turnover. The existence and development of textile and garment enterprises have a significant influence on the socioeconomic development of Vietnam. Currently, Vietnam's textile and garment industry is facing difficulties caused by the COVID-19 pandemic, along with competition from foreign direct investment (FDI) enterprises. Therefore, it is imperative for managers to assess competitiveness by measuring their past and current performance indicators. This study assesses the performance of Vietnam's 10 textile and garment enterprises from 2017 to 2020 by combining the DEA–Malmquist productivity index (MPI) and epsilon-based measure (EBM) model. The proposed model considered three inputs (total assets, cost of goods sold, and liabilities) and two outputs (total revenue and gross profit). In addition to showing the best-performing companies from certain aspects during the period (2017–2020), the results show that the EBM method combined with the Malmquist model in the field can be successfully applied. This study is a reference for companies in the textile and garment industry to identify their position to improve their operational efficiency and overcome their weaknesses. © 2022 by the authors.

Disease Diagnosis Based on Symptoms Description

When one feels unwell, it is crucial to arrange a time as soon as possible to meet a doctor for early detection of potential health-related problems. However, a relatively large number of Vietnamese people usually avoid going to the hospital as they are afraid of long waits at such crowded places, while the current COVID-19 pandemic means being at those places poses a higher risk of contracting the disease. For simpler health problems, people would prefer a solution that, given their symptoms, provides a reliable diagnosis in a shorter time. This study presents an approach in building a deep-learning-based disease predictor of health conditions conducted from given symptoms in Vietnamese. The proposed method combines a tokenizer and bi-directional recurrent neural networks and achieved an accuracy of 98.96% (compared to a certified doctor's diagnosis) in selected test cases, demonstrating its promising capabilities in the task. The application is expected to easily be integrated into a mobile application and open the way for other deep-learning-based solutions which analyze people's symptoms to help them have their health conditions diagnosed at home. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Towards De Novo Drug Design for the Coronavirus: A Drug-Target Interaction Prediction Approach using Atom-enhanced Graph Neural Network with Multi-hop Gating Mechanism

For humans, the COVID-19 pandemic and Coronavirus have undeniably been a nightmare. Although there are effective vaccines, specific drugs are still urgent. Normally, to identify potential drugs, one needs to design and then test interactions between the drug and the virus in an in silico manner for determining candidates. This Drug-Target Interaction (DTI) process, can be done by molecular docking, which is too complicated and time-consuming for manual works. Therefore, it opens room for applying Artificial Intelligence (AI) techniques. In particular, Graph Neural Network (GNN) attracts recent attention since its high suitability for the nature of drug compounds and virus proteins. However, to introduce such a representation well-reflecting biological structures of biological compounds is not a trivial task. Moreover, since available datasets of Coronavirus are still not highly popular, the recently developed GNNs have been suffering from overfitting on them. We then address those issues by proposing a novel model known as Atom-enhanced Graph Neural Network with Multi-hop Gating Mechanism. On one hand, our model can learn more precise features of compounds and proteins. On the other hand, we introduce a new gating mechanism to create better atom representation from non-neighbor information. Once applying transfer learning from very large databanks, our model enjoys promising performance, especially when experimenting with Coronavirus. © 2022 IEEE.

Application of the lean method in designing the layout of face mask manufacturing plants for the prevention of covid-19

The prolonged global coronavirus pandemic (Covid-19) has affected all aspects of life, economy, and society, especially small and medium businesses. To meet this challenge, many companies are transforming models and reorganizing production and operations to adapt to this situation. These companies have adopted a variety of philosophies to remove non-value-Added activities from their production processes to maintain efficiency, flexibility, and profitability. In the context of Industry 4.0, solutions are ready to combine automation technology together with the Lean manufacturing approach. Furthermore, when it comes to efficient use of resources (financial, labor, materials, machine, and equipment), Industry 4.0 should be applied to Lean Processes. Thus, this article shows how to apply the Lean method to optimize plant design to cut waste, improve the efficiency of input resources, increase labor productivity by reducing labor costs, waiting (man-To-man;man-machine), reduced movement, and redundancy of operations in the workflow. Especially, the paper uses the SLP (Systematic Layout Planning) method to arrange the areas, material flow, and supply chain in the factory, Lean application to visualize the factory and combines IoT (Internet of Things). Moreover, using automation and Lean Production theory will support much for factory construction in the future, minimizing irrationalities when applied in practice. The result of the paper will mention a case study on the design and simulation of a face mask plant © 2022 IEEE.

Bank provisioning practice during the pandemic: evidence from the COVID-19 outbreak

We investigate whether, and to what extent, banks exploit their discretion over loan loss provisions to achieve their management purposes during the pandemic. Using a sample of US banks during the current COVID-19 outbreak, we find that banks are more eager to use discretionary loan loss provisions in response to the worsening pandemic situation. We find in particular that banks use discretionary loan loss provisions to manage regulatory capital, smooth income and signal private information to outsiders. Overall, this paper enriches the literature on bank discretionary behaviour during the difficult time, especially during the current COVID-19 pandemic, and therefore, it has important implications for banking supervisor and bank stakeholders.

Disease Diagnosis Based on Symptoms Description

When one feels unwell, it is crucial to arrange a time as soon as possible to meet a doctor for early detection of potential health-related problems. However, a relatively large number of Vietnamese people usually avoid going to the hospital as they are afraid of long waits at such crowded places, while the current COVID-19 pandemic means being at those places poses a higher risk of contracting the disease. For simpler health problems, people would prefer a solution that, given their symptoms, provides a reliable diagnosis in a shorter time. This study presents an approach in building a deep-learning-based disease predictor of health conditions conducted from given symptoms in Vietnamese. The proposed method combines a tokenizer and bi-directional recurrent neural networks and achieved an accuracy of 98.96% (compared to a certified doctor’s diagnosis) in selected test cases, demonstrating its promising capabilities in the task. The application is expected to easily be integrated into a mobile application and open the way for other deep-learning-based solutions which analyze people’s symptoms to help them have their health conditions diagnosed at home. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Barriers to social health insurance enrollment for accessing antiretroviral therapy among people living with HIV/AIDS at early stages of shifting to insurance-based systems: a narrative literature review

Introduction: Global funding for human immunodeficiency virus (HIV)/acquired immunodeficiency syndrome (AIDS) responses in low- and middle-income countries is decreasing, and becomes unpredictable in the future due to co-existing pandemic of COVID-19. Therefore, lessons learned from a developing country that successfully shifted HIV/AIDS programs to insurance-based systems are in need. Aim of the study was to identify the barriers to enrollment and the use of health insurance (HI) for antiretroviral therapy (ART) in Vietnam. Material(s) and Method(s): This study is a narrative literature assessment of peer-reviewed publications on HI for accessing ART in Vietnam. Conceptual framework was developed based on the study's objectives with related factors analyzed from user's perspective, provider's perspective, and socioeconomic and cultural factors considered. Result(s): From user's perspective, the barriers to HI enrollment and the use of HI included awareness of the benefits from HI, affordability for enrollment into HI, fear of stigma and discrimination, fear of responsibility to pay for co-payment, and pre-conception of services provided by HI. From provider's perspective, the barriers were health workers' attitudes, quality of care and treatment services as well as inconsistent and insufficient guidance on social health insurance coverage of care and treatment for people living with HIV (PLHIV). Conclusion(s): A comprehensive information package on HI and the benefits of HIV/AIDS services integrated into HIV programs should be considered to improve the enrollment into and the use of HI among PLHIV. Additionally, it is very important to encourage the government and local authorities to secure adequate funds for co-payment of ART. Copyright © 2022 Termedia Publishing House Ltd.. All rights reserved.

CovRelex: A COVID-19 Retrieval System with Relation Extraction

This paper presents CovRelex, a scientific paper retrieval system targeting entities and relations via relation extraction on COVID-19 scientific papers. This work aims at building a system supporting users efficiently in acquiring knowledge across a huge number of COVID-19 scientific papers published rapidly. Our system can be accessed via https://www.jaist.ac.jp/is/labs/ nguyen-lab/systems/covrelex/.

Agricultural Supply Chain Risks Evaluation with Spherical Fuzzy Analytic Hierarchy Process

The outbreak of the COVID-19 pandemic has impacted the development of the global economy. As most developing and third world countries are heavily dependent on agriculture and agricultural imports, the agricultural supply chains (ASC) in all these countries are exposed to unprecedented risks following COVID-19. Therefore, it is vital to investigate the impact of risks and create resilient ASC organizations. In this study, critical risks associated with ASC were assessed using a novel Analytical Hierarchy Process based on spherical fuzzy sets (SF-AHP). The findings indicated that depending on the scope and scale of the organization, supply risks, demand risks, financial risks, logistics and infrastructure risks, management and operational risks, policy and regulatory risks, and biological and environmental risks all have a significant impact on ASC. This research highlighted that themost significant criterion is specified as Transportation (TP), followed by Market (MA) and Policy (PO), respectively. Meanwhile, Technology (TL) is the least significant criterion. The study's findings can help managers with a holistic view of the agriculture supply chain risk mitigation. Furthermore, this study may assist managers in sharing information about the processing of agricultural products from top to bottom to manage risk in the supply chain.

Misinfo Reaction Frames: Reasoning about Readers' Reactions to News Headlines

Even to a simple and short news headline, readers react in a multitude of ways: cognitively (e.g. inferring the writer's intent), emotionally (e.g. feeling distrust), and behaviorally (e.g. sharing the news with their friends). Such reactions are instantaneous and yet complex, as they rely on factors that go beyond interpreting factual content of news. We propose Misinfo Reaction Frames (MRF), a pragmatic formalism for modeling how readers might react to a news headline. In contrast to categorical schema, our free-text dimensions provide a more nuanced way of understanding intent beyond being benign or malicious. We also introduce a Misinfo Reaction Frames corpus, a crowdsourced dataset of reactions to over 25k news headlines focusing on global crises: the Covid-19 pandemic, climate change, and cancer. Empirical results confirm that it is indeed possible for neural models to predict the prominent patterns of readers' reactions to previously unseen news headlines. Additionally, our user study shows that displaying machine-generated MRF implications alongside news headlines to readers can increase their trust in real news while decreasing their trust in misinformation. Our work demonstrates the feasibility and importance of pragmatic inferences on news headlines to help enhance AI-guided misinformation detection and mitigation.