ABSTRACT
The most dangerous Coronavirus, COVID-19, is the source of this pandemic illness. This illness was initially identified in Wuhan, China, in December 2019, and currently sweeping the globe. The virus spreads quickly because it is so simple to transmit from one person to another. Fever is one of the obvious signs of COVID-19 and is one of its prevalent symptoms. The mucosal areas, such as the nose, eyes, and mouth, are among the most significant ways to catch this virus. In order to prevent and track the corona virus infection, this research suggests a face-touching detection and self-health report monitoring system. Their hygiene will immediately improve thanks to this system. In this pandemic circumstance, people use their hands in dirty environments like buses, trains, and other surfaces, where the virus can remain active for a very long time. With an accelerometer and a pulse oximeter sensor, this system alerts the user when they are carrying their hands close to their faces. © 2023 IEEE.
ABSTRACT
COVID-19 virus spread very rapidly if we come in contact to the other person who is infected, this was treated as acute pandemic. As per the data available at WHO more than 663 million infected cases reported and 6.7 million deaths are confirmed worldwide till Dec, 2022. On the basis of this big reported number, we can say that ignorance can cause harm to the people worldwide. Most of the people are vaccinated now but as per standard guideline of WHO social distancing is best practiced to avoid spreading of COVID-19 variants. This is difficult to monitor manually by analyzing the persons live cameras feed. Therefore, there is a need to develop an automated Artificial Intelligence based System that detects and track humans for monitoring. To accomplish this task, many deep learning models have been proposed to calculate distance among each pair of human objects detected in each frame. This paper presents an efficient deep learning monitoring system by considering distance as well as velocity of the object detected to avoid each frame processing to improve the computation complexity in term of frames/second. The detected human object closer to some allowed limit (1m) marked by red color and all other object marked with green color. The comparison of with and without direction consideration is presented and average efficiency found 20.08 FPS (frame/Second) and 22.98 FPS respectively, which is 14.44% faster as well as preserve the accuracy of detection. © 2023 IEEE.
ABSTRACT
This paper investigates the dynamics and drivers of informational inefficiency in the Bitcoin futures market. To quantify the adaptive pattern of informational inefficiency, we leverage two groups of statistics which measure long memory and fractal dimension to construct a global-local market inefficiency index. Our findings validate the adaptive market hypothesis, and the global and local inefficiency exhibits different patterns and contributions. Regarding the driving factors of the time-varying inefficiency, our results suggest that trading activity of retailers (hedgers) increases (decreases) informational inefficiency. Compared to hedgers and retailers, the role played by speculators is more likely to be affected by the COVID-19 crisis. Extremely bullish and bearish investor sentiment has more significant impact on the local inefficiency. Arbitrage potential, funding liquidity, and the pandemic exert impacts on the global and local inefficiency differently. No significant evidence is found for market liquidity and policy uncertainty related to cryptocurrency.
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The results of a study on the possible connection between the spread of the SARS-CoV-2 virus and the Earth's magnetic field based on the analysis of a large array digital data for 95 countries of the world are presented. The dependence of the spatial SARS-CoV-2 virus spread on the magnitude of the BIGRF Earth's main magnetic field modular induction values was established. The maximum diseases number occurs in countries that are located in regions with reduced (25. 0-30. 0 μT) and increased (48. 0-55. 0 μT) values, with a higher correlation for the first case. The spatial dependence of the SARS-CoV-2 virus spreading on geomagnetic field dynamics over the past 70 years was revealed. The maximum diseases number refers to the areas with maximum changes in it, both in decrease direction (up to - 6500 nT) and increase (up to 2500 nT), with a more significant correlation for countries located in regions with increased geomagnetic field. © 2022 EAGE. All Rights Reserved.
ABSTRACT
The COVID-19 pandemic, caused by the novel coronavirus, has had a significant impact on daily life, education, business, and trade. The virus spreads quickly through direct contact with droplets, fecal-oral transmission, and water contamination. The consequences of the pandemic can be classified into three categories: health, economic, and social. The physical, mental, and psychological behaviors of individuals have also changed due to the pandemic. This study aimed to assess the impact of COVID-19 on the general population. A survey questionnaire with ten questions was distributed through an online portal, and the responses were analyzed using SPSS software. The results showed that healthcare workers were among the most affected, with the primary impact on their social and psychological well-being. Although previous research suggested that all fields were equally affected, this study found that healthcare workers were the most impacted group. The study concluded that the COVID-19 pandemic had a significant impact on the social and psychological well-being of the general population, with healthcare workers being the most affected. © 2023 IEEE.
ABSTRACT
Beside the unexpected toll of mortality and morbidity caused by COVID-19 worldwide, low- and middle-income countries are more suffering from the devastating issues on economic and social life. This disease has fostered mathematical modelling. In this paper, a SEIAR mathematical model is presented to illustrate how policymakers may apply efficient strategies to end or at least to control the devastating wide spread of COVID-19. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
Building ventilation significantly impacts healthy and safe indoor conditions preventing airborne virus spread between people. Therefore, ventilation strategy is a globally essential and health-promoting research topic. Previous studies showed the importance of sufficient ventilation for diluting the virus concentration and reducing the infection risk. The present study investigates the probability of coronavirus infection in the typical room calculated with the Wells Riley proposes recommendations for further research of indoor airflow effect on the virus transmission. © 2022 17th International Conference on Indoor Air Quality and Climate, INDOOR AIR 2022. All rights reserved.
ABSTRACT
This study investigates whether and how the pandemic is priced in the bond market in China. Using the city-level COVID-19 cases on a daily basis, we find a significant positive relationship between the pandemic outbreak and corporate credit spreads, implying that investor risk perception on pandemic exposure attracts a premium. Consistent with the default risk channel, corporate financial resilience alleviates pandemic pricing. Information asymmetry and tail risk can amplify the pricing effect because of reduced investor risk-bearing capacity. These findings are robust in addressing endogeneity concerns. We contribute to the emerging literature on the pandemic effect on credit markets. © 2023 Taylor & Francis Group, LLC.
ABSTRACT
The effect of financial liberalization implementation and technological development, especially after 1980's in financial globalization process,trust has begun to get more important both governments and private firms. In this process, rapid developed and integration in financial markets and variety of financial instruments, countries credit risks are increased. Due to this credit risk measurument is getting more important. In this study we aim to examine10 countries which credit risks are diffrent each other, the effects of macroeconomic and fiscal determinants on credit ratings and credit default swaps by using ordered probit model between 2007-2021.
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The speed of spread of Coronavirus Disease 2019 led to global lockdowns and disruptions in the academic sector. The study examined the impact of mobile technology on physics education during lockdowns. Data were collected through an online survey and later evaluated using regression tools, frequency, and an analysis of variance (ANOVA). The findings revealed that the usage of mobile technology had statistically significant effects on physics instructors' and students' academics during the coronavirus lockdown. Most of the participants admitted that the use of mobile technologies such as smartphones, laptops, PDAs, Zoom, mobile apps, etc. were very useful and helpful for continued education amid the pandemic restrictions. Online teaching is very effective during lock-down with smartphones and laptops on different platforms. The paper brings the limelight to the growing power of mobile technology solutions in physics education. © 2018 Tsinghua University Press.
ABSTRACT
The World Health Organization (WHO) has suggested a successful social distancing strategy for reducing the COVID-19 virus spread in public places. All governments and national health bodies have mandated a 2-m physical distance between malls, schools, and congested areas. The existing algorithms proposed and developed for object detection are Simple Online and Real-time Tracking (SORT) and Convolutional Neural Networks (CNN). The YOLOv3 algorithm is used because YOLOv3 is an efficient and powerful real-time object detection algorithm in comparison with several other object detection algorithms. Video surveillance cameras are being used to implement this system. A model will be trained against the most comprehensive datasets, such as the COCO datasets, for this purpose. As a result, high-risk zones, or areas where virus spread is most likely, are identified. This may support authorities in enhancing the setup of a public space according to the precautionary measures to reduce hazardous zones. The developed framework is a comprehensive and precise solution for object detection that can be used in a variety of fields such as autonomous vehicles and human action recognition. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
We rely on the ESG ratings assigned by four distinct agencies (MSCI, Refinitiv, Robeco, and Sustainalytics) to study the link between ESG scores and firms' cost of debt financing during the Covid-19 pandemic. We document the existence of a statistically and economically significant ESG premium, i.e. better rated companies access debt at a lower cost. Despite some differences across rating agencies, this result is robust to additional controls for the issuer's credit standing as well as several bond and issuer's characteristics. We find that this effect is mainly driven by firms domiciled in advanced economies, whereas creditworthiness considerations prevail for firms in emerging markets. Lastly, we show that the lower cost of capital for highly rated ESG firms is explained both by investors' preference for more sustainable assets and by risk-based considerations unrelated to firms' creditworthiness, such as exposure to climate change risks.
ABSTRACT
The recent COVID-19 pandemic has necessitated the need to develop effective COVID-19 pandemic control strategies. One of the crucial steps for individual protection is to stop the virus spread by the wearing face masks. The proposed method is developed to monitor the infected people in the crowded public areas like shopping centers, wedding hall, workplace, school or college. The abnormal temperature is detected by using sensor and the obtained signal will then be sent to the Arduino device connected to the controller. In order to stop the spread of COVID 19 viruses, this study intends to design and develop a novel system to automatically limit the room capacity based on temperature. The proposed Atmega328 microcontroller-based body temperature detection and a room capacity measuring device is connected with the android smart phone of the user. © 2022 IEEE.
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This paper aims at applying optimal control principles to investigate optimal vaccination strategies in different phases of a pandemic. Background of the study is that many countries have started their vaccination procedures against the COVID-19 disease in the beginning of 2021, but supply shortages for the vaccines prevented that everyone could be vaccinated immediately. At the beginning of 2022, in contrast, the vaccine supply was ample, but the effectiveness of different existing vaccines to avoid infection by new virus variants was in doubt, as well as the acceptance of booster doses decreased over time. To account for these effects, two formulations of optimization tasks based on different epidemic models are proposed in this paper. The solution of these tasks determines optimal distribution strategies for available vaccines, and optimized vaccination schemes to reduce the need of booster doses for later phase. Effectiveness of these strategies compared with other popular strategies (as applied in practice) is demonstrated through a series of simulations © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
Marketing involves assembling, storing, processing, packaging, grading, transportation and distribution of agricultural commodities from farmers to consumers. Marketing plays an important role in accelerating the pace of economic development of farmers. The present study mainly deals with the impact of COVID-19 on marketing behaviour of mango growers in Prakasam district of Andhra Pradesh. The paper deals with changes that occurred in marketing behavior before and during pandemic, problems faced by the mango growers during pandemic. The study area was selected by purposive sampling and 50 respondents were selected randomly from three villages. The impact of pandemic on marketing behaviour was analyzed with the help of frequency, percentage and paired t-test using SPSS software. Before pandemic 78.40 per cent of mango growers sold their produce when prices are attractive,80.00 per cent sold to the export organization,86.00 per cent to nearby town,78.00 per cent by means of tractor and 82.00 per cent received their market information through fellow farmers. During pandemic, 80.00 per cent of the respondents sold their produce immediately after the harvest, 84.00 per cent of mango growers sold directly to the consumers, 64.00 per cent sold to nearby villages, 64.00 per cent sold by means of other transport sources, 78.00 percent received their market information through social media. Indicators of marketing behaviour such as time of sale, mode of sale, place of sale, mode of transport, source of market information had significant mean difference and showed decrease in mean values during pandemic when compared to before pandemic. The findings of the study revealed that there is a significant impact of pandemic on marketing behaviour of mango growers. Major problem faced by the mango growers during pandemic is inadequate transport facilities (Garrett score 69.32) followed by export organizations remain closed(67.68) and fluctuations in price(60.54).
ABSTRACT
COVID-19, the novel coronavirus that has disrupted lives around the world, continues to challenge how humans interact in public and shared environments. Repopulating the micro-spatial setting of an office building, with virus spread and transmission mitigation measures, is critical for a return to normalcy. Advice from public health experts, such as maintaining physical distancing from others and well-ventilated spaces, are essential, yet there is a lack of sound guidance on configuring office usage that allows for a safe return of workers. This paper highlights the potential for decision-making and planning insights through location analytics, particularly within an office setting. Proposed is a spatial analytic framework addressing the need for physical distancing and limiting worker interaction, supported by geographic information systems, network science, and spatial optimization. The developed modeling approach addresses dispersion of assigned office spaces as well as associated movement within the office environment. This can be used to support the design and utilization of offices in a manner that minimizes the risk of COVID-19 transmission. Our proposed model produces two main findings: (1) that the consideration of minimizing potential interaction as an objective has implications for the safety of work environments, and (2) that current social distancing measures may be inadequate within office settings. Our results show that leveraging exploratory spatial data analyses through the integration of geographic information systems, network science, and spatial optimization, enables the identification of workspace allocation alternatives in support of office repopulation efforts. © 2022 held by the owner/author(s).
ABSTRACT
COVID-19 is a rapidly spreading illness around the globe, yet healthcare resources are limited. Timely screening of people who may have had COVID-19 is critical in reducing the virus's spread considering the lack of an effective treatment or medication. COVID-19 patients should be diagnosed as well as isolated as early as possible to avoid the infection from spreading and levelling the pandemic arc. To detect COVID-19, chest ultrasound tomography seems to be an option to the RT-PCR assay. The Ultrasound of the lung is a very precise, quick, relatively reliable surgical assay that can be used in conjunction with the RT PCR (Reverse Transcription Polymerase Chain Reaction) assay. Differential diagnosis is difficult due to large differences in structure, shape, and position of illnesses. The efficiency of conventional neural learning-based Computed tomography scans feature extraction is limited by discontinuous ground-glass and acquisitions, as well as clinical alterations. Deep learning-based techniques, primarily Convolutional Neural Networks (CNN), had successfully proved remarkable therapeutic outcomes. Moreover, CNNs are unable to capture complex features amongst images examples, necessitating the use of huge databases. In this paper semantic segmentation method is used. The semantic segmentation architecture U-Net is applied on COVID-19 CT images as well as another method is suggested based on prior semantic segmentation. The accuracy of U-Net is 87% and by using pre-trained U-Net with convolution layers gives accuracy of 89.07%. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
Healthcare sectors such as hospitals, nursing homes, medical offices, and hospice homes encountered several obstacles due to the outbreak of Covid-19. Wearing a mask, social distancing and sanitization are some of the most effective methods that have been proven to be essential to minimize the virus spread. Lately, medical executives have been appointed to monitor the virus spread and encourage the individuals to follow cautious instructions that have been provided to them. To solve the aforementioned challenges, this research study proposes an autonomous medical assistance robot. The proposed autonomous robot is completely service-based, which helps to monitor whether or not people are wearing a mask while entering any health care facility and sanitizes the people after sending a warning to wear a mask by using the image processing and computer vision technique. The robot not only monitors but also promotes social distancing by giving precautionary warnings to the people in healthcare facilities. The robot can assist the health care officials carrying the necessities of the patent while following them for maintaining a touchless environment. With thorough simulative testing and experiments, results have been finally validated. © 2022 IEEE.
ABSTRACT
A stochastic model for individual immune response is developed. This model is then incorporated in a larger simulation model for the spread of COVID-19 in a population. The simulator allows random transitions between being susceptible, exposed, having mild or severe symptoms, as well as random non-exponential sojourn times in those states. The model is more efficient than others based on geographical location, where the virus spreads according to actual distance between individuals. We are able to simulate much larger populations and vary parameters such as time between vaccinations, probability of infection, and so on. We present an application to study the effects on healthcare as a function of vaccination policies. © 2022 IEEE.
ABSTRACT
The coronavirus, commonly known as SARS COVID-19, is causing a pandemic that is affecting individuals all over the world. The spread of the virus compelled the authorities to impose a rigorous lockdown on its citizens. Every person in society may experience a variety of issues as a result of this. According to WHO (World Health Organization) regulations, the sole method to halt the virus's spread is to wear a face mask. Therefore, the suggested approach makes sure that everyone appropriately wears a face mask in public locations. The objective of this approach is to detect people without face masks and people who wear facemasks incorrectly in social environments. This system consists of multiple face detection modules to find the area of interest within the video frames. In the next level, using the trained Deep Learning model, the presence of a mask is detected and faces without mask and faces wearing masks incorrectly are highlighted. The dataset for face mask identification comprises of 8190 photos with unique facial annotations from the Kaggle and RMFD datasets that come into two categories: "with mask” and "without mask”. © 2022 IEEE