Intelligent Tourism Platform Model Construction Based on Artificial Intelligence: Taking Wudang Mountain as an example

The COVID-19 pandemic, which began in December 2019, has brought huge changes to people's lives. In terms of tourism, the prevention and control measures taken to stop the spread of the epidemic have led to a decline in the number of global trips, and the development of the tourism economy has entered a trough. The economic losses caused by the tourism industry and its corresponding service industry need to be resolved urgently. At the same time, with the development of artificial intelligence, virtual reality and other technologies, the concept of smart tourism was proposed. Based on this, we put forward a website platform model for tourists to inquire about tourism, which takes Wudang Mountain as an example and uses artificial intelligence as technical support. This platform model can meet the needs of users to carry out cloud tour of scenic spots online and enjoy scenic spots without leaving home. It can also conduct intelligent query of offline scenic spots, including route customization, ticket ordering, scenic spot recommendation and many other contents, to meet the various needs of tourists. In addition, the highlight of the platform model is the guide assistant that can conduct dialogues. Based on artificial intelligence technology, it can solve users' specific problems and give feasible solutions in the process of dialogues with users. © 2023 IEEE.

Effective Real Time Disaster Management Using Optimized Scheduling

In recent years we face many types of natural and man-created disasters such as tsunamis, earthquakes, hurricanes, Covid-19 pandemic, terrorist attacks, floods, etc. which cause diverse and worse effects on our daily lives and economy. In order to mitigate the impact of such disasters and reduce the causality, economic loss during disaster response cycle, the different disaster management resources such as rescue teams, transportation, healthcare and related services must be schedule and allocated efficiently. In this research, we proposed the Cluster-Based Real–Time Disaster Resource Management Framework which used edge and computing-based real-time scheduling of various resources and emergency services in disaster management. The edge computing resources are grouped into the cluster and a set of tasks is assigned to the cluster and scheduled on the edge computing cluster to increase resource utilization and acceptance rate which is the problem of existing partitioned scheduling and reduces response time, and overhead due to communication and migration which is the issue in exiting scheduling. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Trust based COVID-19 vaccine distribution using blockchain technology

The COVID-19 outbreak has impacted huge number of individuals all around the world and has caused a great economic loss all over the world. Vaccination is most effective solution to prevent this disease. It helps in protecting the whole community. It improves the human immune system and fights against corona virus reducing the death rate. This paper deals with the different types of COVID-19 vaccine and their related distribution, it includes measures to ensure safe and secured distribution of the vaccine through block chain technology with the help of supply chain. Any malfunction in the chain is identified by the trust value of the function point method and the value of the Markov Chain. © 2023 - IOS Press. All rights reserved.

How to design virus containment policies? A joint analysis of economic and epidemic dynamics under the COVID-19 pandemic.

We analyze the impact of different designs of COVID-19-related lockdown policies on economic loss and mortality using a micro-level simulation model, which combines a multi-sectoral closed economy with an epidemic transmission model. In particular, the model captures explicitly the (stochastic) effect of interactions between heterogeneous agents during different economic activities on virus transmissions. The empirical validity of the model is established using data on economic and pandemic dynamics in Germany in the first 6 months after the COVID-19 outbreak. We show that a policy-inducing switch between a strict lockdown and a full opening-up of economic activity based on a high incidence threshold is strictly dominated by alternative policies, which are based on a low incidence threshold combined with a light lockdown with weak restrictions of economic activity or even a continuous weak lockdown. Furthermore, also the ex ante variance of the economic loss suffered during the pandemic is substantially lower under these policies. Keeping the other policy parameters fixed, a variation of the consumption restrictions during the lockdown induces a trade-off between GDP loss and mortality. Furthermore, we study the robustness of these findings with respect to alternative pandemic scenarios and examine the optimal timing of lifting containment measures in light of a vaccination rollout in the population.

Implementation of Mask Wearing Recognition

Because of the pandemic of COVID-19 since 2020, it seriously affects people's daily life and causes huge economic loss. Recently, the international community has mostly adopted an attitude of coexisting with Covid-19. We cannot ignore the harm the virus can bring to us. In order to effectively protect everyone from the virus, the most basic and effective way is to wear a mask to keep you away from exposure to the virus when going to public areas. Vision intelligence can play an important role in public health issues. In this paper, we utilize the object detection method to implement an actual mask wearing recognition system which can detect if people have a face mask on their face, and send a warning message if not wearing a mask. YOLOv3 is the basic framework for our implementation. After training and fine-tuning processes, the implemented model can perform effectively and correctly. © 2022 IEEE.

Analysis of Prospective Scenarios: A Study on Educational REITS in Brazil

Among the problems caused by the pandemic of the new coronavirus (SARS-COV-2), besides the irreparable loss of loved ones and the damage to global health caused by the disease, the restrictions imposed and the economic losses incurred by them stood out. Given the sudden changes imposed on the routine of society and companies, many businesses went bankrupt, while the other survivors needed to adapt quickly, resulting in a routine based on home office, e-commerce and distance learning. The educational sector was strongly affected by these restrictions, as well as the assets linked to it, as highlighted by the cumulative annual drop of 22% of the IFIX (index of Real Estate Investment Funds), witnessed by investors during the arrival and spread of the pandemic in Brazilian territory. Thus, the work was based on the prospective analysis of scenarios through the Momentum method, producing three possible future scenarios for the recovery of the educational REITs (Pessimistic, Optimistic, and Trend), with the help of three financial planning specialists. At the end of this study, it was possible to configure the scenarios: "The recovery of education REITs” as optimistic scenario, "Challenges of education REITs” as trend scenario, and "The crisis of education REITs” as Pessimistic scenario. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Aggregated Anticrisis Model of Sustainable Development of Small Business

Under the COVID-19 pandemic condition, small businesses are developing in difficult economic and social conditions. Some small businesses were unable to overcome the crisis threshold and were forced to withdraw from the market, most tried to change the situation positively and keep their business, despite the difficult working conditions. The issue of timely determination of the crisis conditions of small businesses, their objective assessment, sustainable development, and the development of an effective anticrisis strategy is still acute. In our view, such a strategy should always be present in the development plans of small businesses, accompanying their long-term development. The anticrisis plan will allow small business minimize economic losses as much as possible. Drafting a document containing an anticrisis strategy requires the use of a special methodology for forecasting crisis phenomena and the formation of proactive anticrisis measures. The main goal of the research is to establish an effective system and tools for anticrisis regulation of small enterprises, the practical implementation of which provides the possibility of sustainable development of small enterprises. Based on this target orientation, the main stages of the anticrisis strategy are determined in the paper, and the SWOT analysis matrix is used to diagnose small companies in crisis conditions. A systematic approach to analysis is the basis for choosing small business stabilization strategies. Based on the systematic analysis, small enterprises are given the opportunity to establish an aggregated model of sustainable development, where the main stages and sequence of overcoming the crisis are presented. Diagnosing the external and internal environment of a small enterprise precedes the development of an anticrisis strategy and involves conducting a static and dynamic analysis of its development. The methodology of anticrisis strategic analysis developed by us allows to determine the cause-and-effect relationship of factors and indicators affecting the development of small enterprises, includes the identification of the main areas of sustainability of the development of small enterprises, local and integral assessments. The innovativeness of the above-mentioned approach lies in the fact that it can be used in the conditions of various characteristics of small business crisis research, and in this sense it has a universal-pragmatic character. © 2022 Bull. Georg. Natl. Acad. Sci.

Modeling the resumption of work and production of enterprises during COVID-19: An SIR-based quantitative framework.

The ongoing COVID-19 pandemic has evolved beyond being a public health crisis as it has exerted worldwide severe economic impacts, triggering cascading failures in the global industrial network. Although certain powerful enterprises can remain its normal operation during this global shock, what's more likely to happen for the majority, especially those small- and medium-sized firms, is that they are experiencing temporary suspension out of epidemic control requirement, or even permanent closure due to chronic business losses. For those enterprises that sustain the pandemic and only suspend for a relatively short period, they could resume work and production when epidemic control and prevention conditions are satisfied and production and operation are adjusted correspondingly. In this paper, we develop a novel quantitative framework which is based on the classic susceptible-infectious-recovered (SIR) epidemiological model (i.e., the SIR model), containing a set of differential equations to capture such enterprises' reactions in response to COVID-19 over time. We fit our model from the resumption of work and production (RWP) data on industrial enterprises above the designated size (IEDS). By modeling the dynamics of enterprises' reactions, it is feasible to investigate the ratio of enterprises' state of operation at given time. Since enterprises are major economic entities and take responsibility for most output, this study could potentially help policy makers better understand the economic impact caused by the pandemic and could be heuristic for future prevention and resilience-building strategies against suchlike outbreaks of public health crises.

Android App based on CNN for Covid-19 Detection using Chest X-ray Images

Covid_19 pandemic is considered a global emergency that has caused great human as well as economic loss. To attack this pandemic, we developed a convolutional neural network model (CNN) the detection of COVID-19 using Chest X-Ray images (CXR). The model is then deployed on the Android app RT2022 which is tested on real CXR images and the first results obtained, after experiments, testify to the performance of the RT2022 model. In COVID-19 detection, we achieved an accuracy of 90.60% © 2022 IEEE.

Influence of COVID-19 on the sustainability of livestock performance and welfare on a global scale.

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is currently spreading worldwide. The pandemic has already had significant adverse effects on human civilization, the environment, and the ecosystem at national and global levels. Moreover, the various sectors of the food production chain, particularly agriculture and livestock, have also been significantly affected in terms of production sustainability and economic losses. The global pandemic has already resulted in a sharp drop in meat, milk, and egg production. Restrictions of movement at national and international levels, implemented as a part of control strategies by public health sectors, have negatively impacted business related to the supply of raw materials for livestock farmers and farm outputs, veterinary services, farmworkers, and animal welfare. This review highlights the significant impacts of COVID-19 on the sustainability of livestock performance, welfare on a global scale, and strategies for mitigating these adverse effects.

Interpretable AI for policy-making in pandemics

Since the first wave of the COVID-19 pandemic, governments have applied restrictions in order to slow down its spreading. However, creating such policies is hard, especially because the government needs to trade-off the spreading of the pandemic with the economic losses. For this reason, several works have applied machine learning techniques, often with the help of special-purpose simulators, to generate policies that were more effective than the ones obtained by governments. While the performance of such approaches are promising, they suffer from a fundamental issue: since such approaches are based on black-box machine learning, their real-world applicability is limited, because these policies cannot be analyzed, nor tested, and thus they are not trustable. In this work, we employ a recently developed hybrid approach, which combines reinforcement learning with evolutionary computation, for the generation of interpretable policies for containing the pandemic. These policies, trained on an existing simulator, aim to reduce the spreading of the pandemic while minimizing the economic losses. Our results show that our approach is able to find solutions that are extremely simple, yet very powerful. In fact, our approach has significantly better performance (in simulated scenarios) than both previous work and government policies. © 2022 ACM.

Evaluating the economic impacts of COVID-19 pandemic on shipping and port industry: A case study of the port of Shanghai.

The stability of shipping and port operations are crucial for international trade and global supply chain. However, the COVID-19 pandemic hit the shipping and port industry enormously in late 2019, and continues till now. It is important to identify the impacts of the pandemic on shipping and port operations and evaluate the potential economic impacts for better setting future development strategies and policies. A System Dynamics (SD) model is proposed to depict the impact transmission within the supply chain considering 5 sub-systems (shipping, port, transportation, manufacturing and social). Potential economic impacts which are represented by the shipping loss and port loss will be assessed. 6 scenarios with different epidemic durations and capacity recovery degrees have been set to investigate the economic impacts. The port of Shanghai, together with the container shipping business is selected as input for case study. Results indicate that in the first few months the port and carriers may suffer economic loss due to shrunken demand caused by COVID-19. But later carriers may enjoy an increase of income compared to non-pandemic scenario owing to strong recovery in most scenarios. Moreover, we found that manufacturing, transportation and port operation capacities would jointly affect the recovery process and economic impacts. The findings can facilitate policy makers in making port management and future industry development decisions.

Investigating the effects of various control measures on economy and spread of COVID-19 in Turkey: a system dynamics approach.

The coronavirus disease 2019 (COVID-19) which began in Wuhan in December 2019 has permeated all over the world in such a short time and was declared as a pandemic by World Health Organization (WHO). The pandemic that is erupting all of a sudden attracts the researchers to examine the spread and effects of the disease as well as the possible treatments and vaccine developments. In addition to the analytical models, such as compartmental modeling, Markov decision process, and so on, simulation and system dynamics (SD) are also widely applied in this field. In this study, we adopt the compartmental modeling stages to build an SD approach for the spread of the disease. A dynamic control measure decision support system (DSS) that varies depending on the number of daily cases is incorporated to the model. Furthermore, the economic loss in the gross domestic product (GDP) and workforce due to hospital stay and death caused by the COVID-19 are also investigated. The model is tested with various numerical parameters and the results are presented. The results on the spread of the disease and the associated economic loss provide meaningful insights into when control measures need to be imposed at which level. We also provide some policy insights, including some alternative policies, such as increasing awareness of people and vaccination in addition to control measures. The results reveal that the total number of cases and deaths is approximately 37% higher in the absence of dynamic DSS. However, everything comes at a price and applying such control measures brings about an increase in the economic loss about 47%.

Relationship Between Economic Loss and Anxiety During the Coronavirus Disease 2019 Pandemic: Moderating Effects of Knowledge, Gratitude, and Perceived Stress.

Objectives: The prolonged coronavirus disease 2019 (COVID-19) pandemic has caused individuals to suffer economic losses, in particular due to the implementation of intensive quarantine policies. Economic loss can cause anxiety and has a negative psychological impact on individuals, worsening their mental health and satisfaction with life. We examined the protective and risk factors that can influence the relationship between economic loss and anxiety during the COVID-19 pandemic. Methods: Panel data from 911 participants were collected in April and May 2020 and again 6 months later. We analyzed the relationship between economic loss and anxiety and investigated the moderating effects of knowledge about COVID-19, gratitude, and perceived stress. Moreover, we investigated whether there were any changes in moderating effects over time or in different demographic groups. Results: In the early stages of the spread of COVID-19, gratitude (B = -0.0211, F = 4.8130, p < 0.05) and perceived stress (B = 0.0278, F = 9.3139, p < 0.01) had moderating effects on the relationship between economic loss and anxiety. However, after 6 months, only perceived stress had a significant moderating effect (B = 0.0265, F = 7.8734, p < 0.01). Conclusion: In the early stages of COVID-19, lower levels of gratitude and higher perceived stress led to greater anxiety. In later stages of the prolonged pandemic, only perceived stress had a continued moderating effect on the relationship between economic loss and anxiety. This study suggests that psychological interventions to reduce perceived stress are needed to treat the possible adverse effects of the spread of infectious diseases on mental health.

Estimating global burden of COVID-19 with disability-adjusted life years and value of statistical life metrics.

BACKGROUND: Global burden of COVID-19 has not been well studied, disability-adjusted life years (DALYs) and value of statistical life (VSL) metrics were therefore proposed to quantify its impacts on health and economic loss globally. METHODS: The life expectancy, cases, and death numbers of COVID-19 until 30th April 2021 were retrieved from open data to derive the epidemiological profiles and DALYs (including years of life lost (YLL) and years loss due to disability (YLD)) by four periods. The VSL estimates were estimated by using hedonic wage method (HWM) and contingent valuation method (CVM). The estimate of willingness to pay using CVM was based on the meta-regression mixed model. Machine learning method was used for classification. RESULTS: Globally, DALYs (in thousands) due to COVID-19 was tallied as 31,930 from Period I to IV. YLL dominated over YLD. The estimates of VSL were US$591 billion and US$5135 billion based on HWM and CVM, respectively. The estimate of VSL increased from US$579 billion in Period I to US$2160 billion in Period IV using CVM. The higher the human development index (HDI), the higher the value of DALYs and VSL. However, there exits the disparity even at the same level of HDI. Machine learning analysis categorized eight patterns of global burden of COVID-19 with a large variation from US$0.001 billion to US$691.4 billion. CONCLUSION: Global burden of COVID-19 pandemic resulted in substantial health and value of life loss particularly in developed economies. Classifications of such health and economic loss is informative to early preparation of adequate resource to reduce impacts.

The trend of modern cold chain management: Characteristics, challenges and opportunities of vaccine logistics

The high demand for Covid-19 vaccines due to the pandemic increases the need for vaccine transportation and storage worldwide, and a cold chain is often required for many vaccines. The existing cold chains are confronting some obstacles today but are facing many great opportunities at the same time with the emerging new technologies. This paper aims to address the trend of modern cold chain management of vaccine logistics through a literature review on the topic of Covid-19 vaccine transportation and storage. Empirical studies are needed in future research. The transportation and storage of the Covid-19 vaccines are important for the overall vaccine administrations and can save large amounts of money and lives when improved. Failures of the cold chain can lead to great economic loss and damage of the vaccine potency;therefore, it is essential to study the characteristics of the current cold chain and how related innovations influence its development. © 2021 IEEE.

COVID-19 Pandemic, Air Quality, and PM2.5 Reduction-Induced Health Benefits: A Comparative Study for Three Significant Periods in Beijing

Previous studies have estimated the influence of control measures on air quality in the ecological environment during the COVID-19 pandemic. However, few have attached importance to the comparative study of several different periods and evaluated the health benefits of PM2.5 decrease caused by COVID-19. Therefore, we aimed to estimate the control measures' impact on air pollutants in 16 urban areas in Beijing and conducted a comparative study across three different periods by establishing the least squares dummy variablemodel and difference-in-differencesmodel. We discovered that restriction measures did have an apparent impact on most air pollutants, but there were discrepancies in the three periods. The Air Quality Index (AQI) decreased by 7.8%, and SO2, NO2, PM10, PM2.5, and CO concentrations were lowered by 37.32, 46.76, 53.22, 34.07, and 19.97%, respectively, in the first period, while O-3 increased by 36.27%. In addition, the air pollutant concentrations in the ecological environment, including O-3, reduced significantly, of which O-3 decreased by 7.26% in the second period. Furthermore, AQI and O-3 concentrations slightly increased compared to the same period in 2019, while other pollutants dropped, with NO2 being the most apparent decrease in the third period. Lastly, we employed health effects and environmental value assessment methods to evaluate the additional public health benefits of PM2.5 reduction owing to the restriction measures in three periods. This research not only provides a natural experimental basis for governance actions of air pollution in the ecological environment, but also points out a significant direction for future control strategies.

A Study on People's Mental Health on Twitter during the COVID-19 Pandemic

The COVID-19 pandemic has been one of the most highly discussed topics throughout its entire lifespan. This pandemic has caused huge losses throughout the world, ranging from economic losses to a massive death toll of over five million that is still growing. One aspect has not been touched on nearly as much as the impact on mental health worldwide. We decided to study the pandemic's impact on peoples' mental health, specifically depression and the many symptoms that come with it. Twitter was used for this study as it has many raw and unfiltered personal sentiments from its many users. Twitter also has a helpful developer API that allows tweets to be queried at a massive scale. Throughout our research, we found that the number of people discussing COVID-19 depression was much higher during the first six months of the pandemic. However, during the following six months, those still suffering from such depression were experiencing worse symptoms with greater frequency. © 2022 ACM.

Impact of COVID-19 on Dry fish Production and Marketing from India

Drying is one of the oldest known techniques for preservation of fishes. Dry fish is a commodity which has demand both within and outside India. Indian dry fish exports contribute 7.45% to the total marine product exports valued at USD 156.94 million during 2020-21. The COVID-19 imposed lockdown led to disruptions in several segments of the economy and dry fish sector was no exception. The dry fish production is carried out as a small-scale activity along the coastal belt of the country providing source of livelihood for thousands of fisherfolk especially women. The dry fish value chain involves many players starting from fresh fish supplier, dry fish processor, dry fish agent, wholesaler, retailers and consumers. Owing to the outbreak of COVID-19 and the subsequent lockdown enforced from 24th March 2020, small scale dry fish processors suffered losses which have been estimated to the tune of Rs.0.5 to 1 lakh per month depending on the capacity of the unit. Women involved in household drying of fish experienced losses amounting to Rs.10,000 to Rs.12,000 per month. The dry fish exports from India, although seasonal has also registered a decline during the first three months of the pandemic and ensuing lockdown period. This paper presents an assessment of the impacts on the dry fish sector in the country.

The Impact of Innovation and Digitalization on Service Sector at Post-pandemic era

This study aims to investigate the economic impact of the COVID-19 on service sector companies. During this international pandemic, the economic losses are so destructive that world economies are eyeing future recessions. Poverty and unemployment and are always low, and the assist of IT is needed to eliminate the harmful results. This paper also identified the industries which have been strongly impacted at the pandemic time and are in need of comprehensive reform. Just some few industries are showing less losses or operating well during that pandemic. The research aims to show the effect of COVID-19 on different industries while identifying the most affected industry. This study also explains the role of Innovation, Digitalization, and IT during COVID-19 and how they can assist humans and businesses deal with exacerbating situations all-over the world. © 2021 ACM.