Résumé
To stop the COVID-19 spread, artificial intelligence and new technologies are also actively participating in the battle. For fighting against the virus, disinfection is one of the effective ways to block the spread of the virus. According to the preliminary market research, only in the large distribution centers is the operator-controlled machine disinfection, at present most of the small and medium-sized express logistics stations are through human resources to carry out disinfection, and sorting of express packages, so the dependence on human costs, medical resources can be imagined. To this end, we designed ultrasonic atomization disinfection, sorting, and whole load notification integrated machine based on deep learning [1] and Internet of Things [2] technology to cope with the trend of normalization and recurrence of the epidemic. After testing logistics with different labels, the experimental results show that the system can effectively distinguish different labels and carry out a series of operations such as disinfection, sorting, and notification of full load, which can be put into production and contribute to epidemic prevention work. © 2023 IEEE.
Résumé
The global outbreak of coronavirus disease 2019 (COVID-19) has spread to more than 200 countries worldwide, leading to severe health and socioeconomic consequences. As such, the topic of monitoring and predicting epidemics has been attracting a lot of interest. Previous work reported search volumes from Google Trends are beneficial in decoding influenza dynamics, implying its potential for COVID-19 prediction. Therefore, a predictive model using the Wiener methods was built based on epidemic-related search queries from Google Trends, along with climate variables, aiming to forecast the dynamics of the weekly COVID-19 incidence in Washington, DC, USA. The Wiener model, which shares the merits of interpretability, low computation costs, and adaptation to nonlinear fluctuations, was used in this study. Models with multiple sets of features were constructed and further optimized by the highest weight selecting strategy. Furthermore, comparisons to the other two commonly used prediction models based on the autoregressive integrated moving average (ARIMA) and long short-term memory (LSTM) were also performed. Our results showed the predicted COVID-19 trends significantly correlated with the actual (rho <inline-formula> <tex-math notation="LaTeX">$=$</tex-math> </inline-formula> 0.88, <inline-formula> <tex-math notation="LaTeX">$p $</tex-math> </inline-formula> <inline-formula> <tex-math notation="LaTeX">$<$</tex-math> </inline-formula> 0.0001), outperforming those with ARIMA and LSTM approaches, indicating Google Trends data as a useful tool in terms of COVID-19 prediction. Also, the model using 20 search queries with the highest weighting outperformed all other models, supporting the highest weight feature selection as a feasible criterion. Google Trends search query data can be used to forecast the outbreak of COVID-19, which might assist health policymakers to allocate health care resources and taking preventive strategies. IEEE
Résumé
The world is currently dealing with the aftermath of the coronavirus disease 2019 (COVID-19) pandemic, which has resulted in momentous changes, the likes of which had not been witnessed within the previous century. These adverse events and the resulting uncertainty have posed enormous challenges to organizations, and many are on the verge of collapse. Organizations urgently need to enhance their risk management abilities and capacity to cope with crises, and organizational resilience, as such a tool, has attracted widespread attention in China and abroad. The purpose of this study is to understand the research status and development trend of organizational resilience. In this study, we applied CiteSpace to perform a visual analysis. Searching for topics related to organizational resilience, we retrieved papers published from 1990-2022 in the Web of Science Core database. Second, we constructed an author, institution, and country/region collaboration network to identify the most prolific authors, institutions and countries, respectively. The distribution of core journals determined by journal co-citations, the document co-citation network, and a clustering analysis revealed the research topics and knowledge structure, the author co-citation network revealed which authors were influential, the keyword co-citation network indicated popular research topics, and the keyword bursts highlighted the research fields. This paper analyzes the main contributions of organizational resilience research at the author, institution, and country levels;knowledge sources;interdisciplinary characteristics and research areas in organizational resilience;and direction of future research. © 2013 IEEE.
Résumé
Covid-19 has turned our world upside down and transformed nearly every aspect of daily life, business and travel. The COVID-19 pandemic has had severe negative impacts on human beings in all spheres of economic, social and even geopolitical life worldwide. The impact of this crisis will be the subject of numerous studies in all fields of scientific research in the coming years. According to all official data, the tourism sector is one of the most affected sectors of the economy. Despite the reported partial recovery in the sphere of tourism revenues in the period May-September 2020, the tourism industry is far from the reported in recent years' growth regarding the number of visits and tourism revenues. On the contrary, serious declines were reported, reaching 80% decline in revenue, compared to 2019 This study aims to examine the impact of the COVID-19 crisis on Bulgarian tourism and the behaviour of Bulgarian tourists. To achieve this, a situational analysis of the impact of travel restrictions on tourists and on the tourism sector in Bulgaria has been made. Changes in the number of tourist visits to Bulgaria in the period 2015-2020 have been studied. The study examines the attitudes of Bulgarian tourists and changes in their tourist consumption during the period of travel restrictions. The obtained results give us grounds to make recommendations for faster dealing with the losses suffered by the business and the slump in demand reported by the tourists as a result of the imposed COVID-19 restrictions. The research importance stems from taking into consideration and reporting the impact of this large-scale phenomenon in all its aspects, which will allow businesses and countries to tackle COVID-19 challenges more quickly. © 2021 International Multidisciplinary Scientific Geoconference. All rights reserved.
Résumé
Finding items with potential to increase sales is of great importance in online market. In this paper, we propose to study this novel and practical problem: rising star prediction. We call these potential items Rising Star, which implies their ability to rise from low-turnover items to bestsellers in the future. Rising stars can be used to help with unfair recommendation in e-commerce platform, balance supply and demand to benefit the retailers and allocate marketing resources rationally. Although the study of rising star can bring great benefits, it also poses challenges to us. The sales trend of rising star fluctuates sharply in the short-term and exhibits more contingency caused by some external events (e.g., COVID-19 caused increasing purchase of the face mask) than other items, which cannot be solved by existing sales prediction methods. To address above challenges, in this paper, we observe that the presence of rising stars is closely correlated with the early diffusion of user interest in social networks, which is validated in the case of Taocode (an intermediary that diffuses user interest in Taobao). Thus, we propose a novel framework, RiseNet, to incorporate the user interest diffusion process with the item dynamic features to effectively predict rising stars. Specifically, we adopt a coupled mechanism to capture the dynamic interplay between items and user interest, and a special designed GNN based framework to quantify user interest. Our experimental results on large-scale real-world datasets provided by Taobao demonstrate the effectiveness of our proposed framework. IEEE
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We synthesize scenarios of hourly electricity price, which is known as the system marginal price (SMP), for thirty-years based on the oil price. Hourly SMP scenarios are very important when planning new generators because the revenue and cost of new capacity margins are determined based on the SMP. Because the SMP contains both short-term and long-term periodic patterns, designing a single model based on these patterns to predict the SMP is difficult. Although oil price affects SMP, they can not be directly used in the forecasting model because the resolution of SMP is at hourly intervals, but that of oil price is at yearly intervals. To overcome these problems, we decompose the SMP into annual, monthly, and daily components, and the components are predicted based on different models. The model for the annual component (AC) is designed to predict the long-term trend based on fuel price scenarios. The model for the monthly component (MC) is designed to predict the seasonal trends based on the long short term memory (LSTM) model. The model for the daily component (DC) is designed to predict the daily SMP fluctuation. Finally, we synthesize SMP scenarios by aggregating three components. We make three types of SMP scenarios (high, reference, and low), and the performance of the scenarios is tested using previous data for two years on the basis of mean absolute error (MAE). Due to the global COVID-19 pandemic, the low type of SMP scenario is most accurate. We also verify that the reliability of long-term scenarios can be secured by using oil price while maintaining monthly and daily patterns. Author
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This article examines the Google Trends data related to the second COVID-19 wave in India. We investigate the phenomenon of cyberchondria, which potentially causes individuals to avoid getting tested and quarantined directly upon experiencing symptoms for fear of losing their salaries or jobs. We utilize Google Trends data to predict future disease statistics, like the pandemic's impact on human activities and health-related issues in India. By means of a bootstrapped Pearson correlation, a time-lead correlation, and a quantile regression, we found a strong relationship between Google Trend searches and COVID-19 cases. Contextualizing the second COVID-19 wave in India through the lenses of cyberchondria and protection motivation theory, our article notes that, when people develop COVID-19 symptoms, they turn to Google for confirmation and treatment, rather than getting themselves checked early, only getting medically tested, and treated when their health deteriorates. At that stage, given the patients’critical conditions, hospitalization is the only option. This places an unsustainable burden on hospitals, resulting in capacity constraints and increased mortality rates. We suggest using Google Trends data to forecast COVID-19 waves and mobilize the health infrastructure to save lives and facilitate friction-free growth. IEEE
Résumé
Since its inception, COVID-19 has changed several dynamics in society, both on a personal and professional level. Mobility was one of the most affected aspects, as a result of the necessary social distancing and preventive measures that had to be enacted by the various countries and which restricted, at various times, freedom of movement. The impact that COVID-19 had, and still has, on mobility is important to be understood so that the necessary measures can be taken in order to return to normality and, for example, not regress in the steps that were being taken in encouraging the use of public transport as a measure to combat the carbon footprint as well as traffic congestion in cities. This paper intends to analyze the reality of Spain and Portugal, in the period between May 10th and July 2nd, 2021, in which both countries had already finished restricting mobility measures. The study used data from Google Community Mobility Reports and was done by regions, taking into account the average age of inhabitants and the number of inhabitants in each region. The analysis focused on different categories of places such as retail and recreation, groceries and pharmacies, parks, transit stations, workplaces, and residential. Author