ABSTRACT
The Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) causes Covid-19, an infectious illness. A methodology was created to track the vaccination history of people with the Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) that causes Covid-19, an infectious illness. The system operates on a Raspberry Pi processor that is designed to authenticate the vaccination records of individuals. The Vaccination Identification System consists of various components connected to the Raspberry Pi Zero 2W microprocessor, Pi camera, an LCD display, LED indicators, a buzzer, a DC servo motor, and a PCB converter. The proposed system grants access to vaccinated individuals and denies access to those who are not vaccinated.
ABSTRACT
When it comes to supplying oxygen, current standard hospitals in Iran have proven inadequate in the face of the COVID-19 pandemic, particularly during infection peaks. Power disruptions drastically reduce the oxygen pressure in hospitals, putting patients' health at risk. The present study is the first to attempt to power an oxygen concentrator with a solar-energy-based system. The HOMER 2.81 package was used for technical–economic–environmental–energy analysis. The most notable aspects of this work include evaluating different available solar trackers, using up-to-date equipment price data and up-to-date inflation rate, considering the temperature effects on solar cell performance, sensitivity analysis for the best scenario, considering pollution penalties, and using a three-time tariff system with price incentives for renewable power. The study has been carried out at Hajar Hospital, Shahrekord, Chaharmahal and Bakhtiari Province, Iran. The study showed that, by supplying 60% of the power demand, the dual-axis solar tracking system offered the highest annual power output (47,478 kWh). Furthermore, generating power at—$0.008/kWh due to selling power to the grid, the vertical-axis tracker was found to be the most economical design. Comparing the configuration with a vertical-axis tracker with the conventional scenario (relying on the power distribution grid), the investment is estimated to be recovered in three years with $234,300 in savings by the end of the 25th year. In the best economic scenario, 6137 kg CO2 is produced, and the analysis revealed the negative impact of a temperature rise on the performance and solar power output.
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From November 30 to December 02 2022, the IEEE Latin-American Conference on Communications (LATINCOM) returned to Brazil for its fourteenth edition. LATINCOM was held in the wonderful city of Rio de Janeiro, which had the privilege to offer attendees all its fantastic beauties comprising landscapes with a series of green mountains cascading down to the coast. LATINCOM's journey to Rio de Janeiro started in Medellín, Colombia, in 2009, Bogotá, also in Colombia, in 2010. It first appeared in Brazil, Belém, in 2011. Then it moved to Cuenca, Ecuador, in 2012, Santiago, Chile, in 2013, Cartagena de Indias, Colombia, in 2014, Arequipa, Peru, in 2015, Medellin, Colombia, in 2016, Guatemala City, Guatemala, in 2017, and Guadalajara, Mexico, in 2018. LATINCOM was held for a second time in Brazil, in Salvador, 2019. The conference was forced to go online in 2019, hybrid in 2021, in Santo Domingo, Dominican Republic, and then finally returning to the face-to-face format in 2022, in Rio de Janeiro. This brought to the 14th edition a remarkable characteristic, as it represented the return to in-presence conferences after the Covid-19 outbreak. LATIN-COM is held annually and attracts submissions and participants from around the globe. In 2022, the program was organized in three intensive days, including four keynote speeches, four tutorials, two workshops, and 16 technical sessions.
ABSTRACT
Covid19 has infected many individuals around the world, this virus is spreading rapidly. In the context of controlling and handling the spread of COVID-19, appropriate strategies and policies are needed, mathematics will play a very important role in this problem, especially to provide information about this with an understanding of the dynamics of the transmission of this covid virus. To suppress the spread of the Covid-19 virus which is currently hitting, several countries have implemented large-scale social restrictions. To identify the best approach to reduce of this Covid-19 disease spreading at minimal cost, we developed a mathematical model of the covid 19 virus by implementing large-scale social restrictions and applying optimal control theory. We provide two types of control: the first is in the form of an education campaign about covid-19 and an awareness program, and the second is in the form of a quarantine program. Compared with no optimal control, giving optimal control can provide a more significant reduction in the number of populations S, Sr, O, P, I and increase the number of individual populations that recover more significantly.
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Current awareness of epidemic threats and critical experiences of the COVID-19 pandemic require extension of the management model in the smart city, especially in the field of mobility and transport services, with monitoring of epidemic hazards. This paper addresses the issue of epidemic hazards, a new challenge in smart cities, and customer delivery services. The novel DHI methodology for epidemic hazards assessment is presented and applied to compare customer delivery services in aspects of SARS-CoV-2 epidemic hazards. The case studies presented a detailed analysis of epidemic hazards on the basis of process algorithms and dedicated quantitative scales to assess factors influencing the mechanisms of virus transmission. The developed DHI methodology and the results obtained for transport services constitute important cognitive knowledge for the administrative personnel in smart city.
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The COVID-19 pandemic has caused significant changes in many aspects of daily life, including learning, working, and communicating. As countries aim to recover their economies, there is an increasing need for smart city solutions, such as crowd monitoring systems, to ensure public safety both during and after the pandemic. This paper presents the design and implementation of a real-time crowd monitoring system using existing public Wi-Fi infrastructure. The proposed system employs a three-tiered architecture, including the sensing domain for data acquisition, the communication domain for data transfer, and the computing domain for data processing, visualization, and analysis. Wi-Fi access points were used as sensors that continuously monitored the crowd and uploaded data to the server. To protect the privacy of the data, encryption algorithms were employed during data transmission. The system was implemented in the Sri Chiang Mai Smart City, where nine Wi-Fi access points were installed in nine different locations along the Mekong River. The system provides real-time crowd density visualizations. Historical data were also collected for the analysis and understanding of urban behaviors. A quantitative evaluation was not feasible due to the uncontrolled environment in public open spaces, but the system was visually evaluated in real-world conditions to assess crowd density, rather than represent the entire population. Overall, the study demonstrates the potential of leveraging existing public Wi-Fi infrastructure for crowd monitoring in uncontrolled, real-world environments. The monitoring system is readily accessible and does not require additional hardware investment or maintenance. The collected dataset is also available for download. In addition to COVID-19 pandemic management, this technology can also assist government policymakers in optimizing the use of public space and urban planning. Real-time crowd density data provided by the system can assist route planners or recommend points of interest, while information on the popularity of tourist destinations enables targeted marketing.
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In this paper, we present CoFighter, a mobile application for prevention and management of COVID-19 and other related pandemics in the globalized world. We took advantage of the proliferation of mobile smart devices in every home to design and implement an Android application for COVID-19 and similar pandemics. Since the outbreak of COVID-19 pandemic in 2019, there has been even more serious pressures on governments and health institutions on the best way to provide appropriate and reliable guide to individuals on how to contain the virus and similar pandemics in the future. Citizens have not been adequately informed of the various provisions and guides by their governments and the wide usage of social media had led to the spread of fake news, misinformation and conspiracy theories. It therefore becomes very necessary to develop a dynamic information repository in the form of a mobile application to help combat the spread of any pandemic whenever the need arises. The application provides information on COVID-19, vaccine challenges, prevention guides and cases management and timely updates to keep citizens properly and adequately informed. It makes provision for future similar pandemics that could throw the world into chaos as the CORONA virus did in 2019. The weaknesses and challenges observed in most popularly existing COVID-19 applications were highlighted and implemented in CoFighter. CoFighter provides users, governments and health workers with a platform not only to manage COVID-19 and other similar pandemics in the future, but also helps frontline health workers to better manage the pandemics. The developed application runs on an Android device with Android version 4.2 or higher and can be used not only to manage COVID-19 pandemic, but also to manage economic crisis and similar future pandemics. CoFighter is available via the Repository: https://github.com/OkeyIsOkay/CoFighter-Project.
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In today's international society, a vast amount of information that no one could have imagined a while ago is circulating in the world. This trend is expected to be further accelerated in the "new normal” life with COVID-19, where online social activities have become commonplace. Although organic polymers are also used for short-distance in-equipment information-transmission optical fibers, only amorphous silica (SiO 2 ) is used for long distances. In other words, what supports the information society is the insulating material familiar to readers of this IEEE Electrical Insulation Magazine.
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The deadly Corona virus that first appeared in a seafood market in the Wuhan city of China in December 2019 has been causing global distress by claiming lives and collapsing economies. Given its serious nature, there is an urgent need to understand the virus's future trajectory. The current study predicts the next day confirmed, death and recovery cases of COVID-19 pandemic for India, Italy, Spain, and the USA by using a modified multilayer neural network (MMLNN) model. The spread of the COVID-19 data is collected from the Kaggle website for the period of 22nd January 2020 to 20th April 2020 (i.e., for 90 days). The predicted figures of the spread of the disease have been estimated and compared with the actual values. Higher precision of the estimates has been observed from the MMLNN model compared to the conventional multilayer neural network (MLANN) model. Specifically, the MMLNN model does faster and more efficient training of the data resulting in less error. The paper forecasts the next day figures (i.e., for 21st April) for all the three cases and does the comparison of the results with the actual values reported. A deviation of 6% is obtained for India, and for the other three countries the deviation is below 3.5%. Given the high accuracy predictive power, the authors recommend that the MMLNN model can be integrated into the health policy of the countries that are struggling with the spread of the virus. Specifically, a decision on health policies such as restrictions on movement can be based on the short-range predictions of the spread of the virus infection.
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Faced with COVID-19 and the trend of aging, it is demanding to develop an online health metrics sensing solution for sustainable healthcare. An edge radio platform owning the function of integrated sensing and communications is promising to address the challenge. Radar demonstrates the capability for noncontact healthcare with high sensitivity and excellent privacy protection. Beyond conventional radar, this article presents a unique silicon-based radio platform for health status monitoring supported by coherent frequency-modulated continuous-wave (FMCW) radar at Ku-band and communication chip. The radar chip is fabricated by a 65-nm complementary metal–oxide–semiconductor (CMOS) process and demonstrates a 1.5-GHz chirp bandwidth with a 15-GHz center frequency in 220-mW power consumption. A specific small-volume antenna with modified Vivaldi architecture is utilized for emitting and receiving radar beams. Biomedical experiments were implemented based on the radio platform cooperating with the antenna and system-on-chip (SoC) field-programmable gate array (FPGA) edge unit. An industrial, scientific, and medical (ISM)-band frequency-shift keying (FSK) communication chip in 915-MHz center frequency with microwatt-level power consumption is used to attain communications on radar-detected health information. Through unified integration of radar chip, management software, and communication unit, the integrated radio platform featuring −72-dBm sensitivity with a 500-kb/s FSK data rate is exploited to drastically empower sustainable healthcare applications.
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From August 21 to 25, 2022, the sixth annual IEEE DEIS-sponsored Summer School on Extra-High Voltage DC (EHVDC) Transmission took place in Monmouth, located on the border between England and Wales, UK. Unfortunately, the summer school was not spared from the COVID-19 pandemic, which, as many of us know, has become a constant companion in our everyday lives. As a result of some cases of illness among the moderators and speakers, the summer school was quickly adapted into an all-too-familiar hybrid event. However, this made it possible for all participants to attend all presentations, network, and exchange ideas in the best way possible, while also staying safe.
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The acute respiratory distress syndrome patients largely need a mechanical ventilator intervention. There are procedures that have been developed to guide the physicians during the ventilation of the patient. Berlin definition of the acute respiratory distress syndrome has been developed with ventilator adjustment settings/procedures. The procedures may however be a challenge for some physicians to remember during the intense ventilator intervention. Physicians are found to make human errors that may lead to the death of the patient. This, therefore, calls for the need of a logic system that will reason for the physician, that is, guide the physician. A fuzzy logic system was used to build the fuzzy set rules based on the Berlin definition. The MATLAB Simulink was used to simulate the system. The results show that the fuzzy-based ARDS Berlin definition can guide the physician on the adjustments to be made during the ventilation.
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The analysis of the high volume of data spawned by web search engines on a daily basis allows scholars to scrutinize the relation between the user's search preferences and impending facts. This study can be used in a variety of economics contexts. The purpose of this study is to determine whether it is possible to anticipate the unemployment rate by examining behavior. The method uses a cross-correlation technique to combine data from Google Trends with the World Bank's unemployment rate. The Autoregressive Integrated Moving Average (ARIMA), Autoregressive Integrated Moving Average with eXogenous variables (ARIMAX) and Vector Autoregression (VAR) models for unemployment rate prediction are fit using the analyzed data. The models were assessed with the various evaluation metrics of mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), median absolute error (MedAE), and maximum error (ME). The average outcome of the various evaluation metrics proved the significant performance of the models. The ARIMA (MSE = 0.26, RMSE = 0.38, MAE = 0.30, MAPE = 7.07, MedAE = 0.25, ME = 0.77), ARIMAX (MSE = 0.22, RMSE = 0.25, MAE = 0.29, MAPE = 6.94, MedAE = 0.25, ME = 0.75), and VAR (MSE = 0.09, RMSE = 0.09, MAE = 0.20, MAPE = 4.65, MedAE = 0.20, ME = 0.42) achieved significant error margins. The outcome demonstrates that Google Trends estimators improved error reduction across the board when compared to model without them.
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Convergence, and its various configurations, is an established topic in technology and innovation management literature. This article contributes to the extant literature about industrial convergence by conducting an explorative analysis of the industrial crisis caused by the Covid-19 pandemic. In this article, we aim to explore how industrial convergence affects the business dynamics of the healthcare markets. To fulfill our research purpose, we perform a qualitative study by exploring retrospectively the case study of precision medicine. Thus, we use a case study approach based on the triangulation of the multiple sources of evidence gathered. We propose a conceptual framework attesting to the continued recourse to digitalization for the need for data integration and the creation of hybrid figures within healthcare markets during the industrial crisis. This article proposes various implications for researchers and practitioners dealing with the management and development of innovation in convergent sectors.
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Intervention policies against COVID-19 have caused large-scale disruptions globally, and led to a series of pattern changes in the power system operation. Analyzing these pandemic-induced patterns is imperative to identify the potential risks and impacts of this extreme event. For this purpose, we developed an open-access data hub (COVID-EMDA+), an open-source toolbox (CoVEMDA), and a few evaluation methods to explore what the U.S. power systems are experiencing during COVID-19. These resources could be broadly used for research, public policy, and educational purposes. Technically, our data hub harmonizes a variety of raw data such as generation mix, demand profiles, electricity price, weather observations, mobility, confirmed cases and deaths. Typical methods are reformulated and standardized in our toolbox, including baseline estimation, regression analysis, and scientific visualization. Here the fluctuation index and probabilistic baseline are proposed for the first time to consider data fluctuation and estimation uncertainty. Furthermore, we conduct three empirical studies on the U.S. power systems, and share new solutions and findings to address several issues of public concerns. This conveys a more complete picture of the COVID-19 impact and also opens up several attractive topics for future work. Python, Matlab source codes, and user manuals are all publicly shared on a Github repository.
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BackgroundInformation is essential for growth;without it, little can be accomplished. Data gathering has seen significant changes throughout the previous few centuries because of the certain transitory medium. The look and style of information transference are affected by the employment of new and emerging technologies, some of which are efficient, others are reliable, and many more are quick and effective, but a few were disappointing for various reasons. AimsThis study aims at using TextBlob and VADER analyser with historical tweets, to analyse emotional responses to the coronavirus pandemic (COVID-19). It shows us how much of a sociological, environmental, and economic impact it has in Nigeria, among other things. This study would be a tremendous step forward for students, researchers, and scholars who want to advance in fields like data science, machine learning, and deep learning.MethodologyThe hashtag ‘COVID-19' was used to collect 1,048,575 tweets from Twitter. The tweets were pre-processed with a Twitter tokenizer, while TextBlob and Valence Aware Dictionary for Sentiment Reasoning (VADER) were used for text mining and sentiment analysis, respectively. Topic modelling was done with Latent Dirichlet Allocation and visualized with Multidimensional scaling.ResultsThe result of the VADER sentiment returned 39.8%, 31.3%, and 28.9%, positive, neutral, and negative sentiment, respectively, while the result of the TextBlob sentiment returned 46.0%, 36.7%, and 17.3%, neutral, positive, and negative sentiment, respectively.ConclusionWith all of this, information from social media may be used to help organizations, governments, and nations around the world make smart and effective decisions about how to restrict and limit the negative effects of COVID-19. Also, know the opinion and challenges of people, then deal with the problem of misinformation. It is concluded that with popular belief a significant number of the populace regards COVID-19 as a virus that has come to stay, some believe it will eventually be conquered.
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The internet of things (IoT) is quickly evolving, allowing for the connecting of a wide range of smart devices in a variety of applications including industry, military, education, and health. Coronavirus has recently expanded fast across the world, and there are no particular therapies available at this moment. As a result, it is critical to avoid infection and watch signs like fever and shortness of breath. This research work proposes a smart and robust system that assists patients with influenza symptoms in determining whether or not they are infected with the coronavirus disease (COVID-19). In addition to the diagnostic capabilities of the system, the system aids these patients in obtaining medical care quickly by informing medical authorities via Blynk IoT. Moreover, the global positioning system (GPS) module is used to track patient mobility in order to locate contaminated regions and analyze suspected patient behaviors. Finally, this idea might be useful in medical institutions, quarantine units, airports, and other relevant fields.
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The world's agricultural needs are growing with the pace of increase in its population. Agricultural farmers play a vital role in our society by helping us in fulfilling our basic food needs. So, we need to support farmers to keep up their great work, even in difficult times such as the coronavirus disease (COVID-19) outbreak, which causes hard regulations like lockdowns, curfews, and social distancing procedures. In this article, we propose the development of a recommender system that assists in giving advice, support, and solutions for the farmers' agricultural related complaints (or queries). The proposed system is based on the latent semantic analysis (LSA) approach to find the key semantic features of words used in agricultural complaints and their solutions. Further, it proposes to use the support vector machine (SVM) algorithm with Hadoop to classify the large agriculture dataset over Map/Reduce framework. The results show that a semantic-based classification system and filtering methods can improve the recommender system. Our proposed system outperformed the existing interest recommendation models with an accuracy of 87%.
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Chronic obstructive pulmonary disease (COPD) is a general clinical issue in numerous countries considered the fifth reason for inability and the third reason for mortality on a global scale within 2021. From recent reviews, a deep convolutional neural network (CNN) is used in the primary analysis of the deadly COPD, which uses the computed tomography (CT) images procured from the deep learning tools. Detection and analysis of COPD using several image processing techniques, deep learning models, and machine learning models are notable contributions to this review. This research aims to cover the detailed findings on pulmonary diseases or lung diseases, their causes, and symptoms, which will help treat infections with high performance and a swift response. The articles selected have more than 80% accuracy and are tabulated and analyzed for sensitivity, specificity, and area under the curve (AUC) using different methodologies. This research focuses on the various tools and techniques used in COPD analysis and eventually provides an overview of COPD with coronavirus disease 2019 (COVID-19) symptoms.
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Even though coronavirus disease 2019 (COVID-19) vaccination has been done, preparedness for the possibility of the next outbreak wave is still needed with new mutations and virus variants. A near real-time surveillance system is required to provide the stakeholders, especially the public, to act in a timely response. Due to the hierarchical structure, epidemic reporting is usually slow particularly when passing jurisdictional borders. This condition could lead to time gaps for public awareness of new and emerging events of infectious diseases. Online news is a potential source for COVID-19 monitoring because it reports almost every infectious disease incident globally. However, the news does not report only about COVID-19 events, but also various information related to COVID-19 topics such as the economic impact, health tips, and others. We developed a framework for online news monitoring and applied sentence classification for news titles using deep learning to distinguish between COVID-19 events and non-event news. The classification results showed that the fine-tuned bidirectional encoder representations from transformers (BERT) trained with Bahasa Indonesia achieved the highest performance (accuracy: 95.16%, precision: 94.71%, recall: 94.32%, F1-score: 94.51%). Interestingly, our framework was able to identify news that reports the new COVID strain from the United Kingdom (UK) as an event news, 13 days before the Indonesian officials closed the border for foreigners.