Short-Term State Electricity Load Forecasting Based on Transfer-Informer

The worldwide COVID-19 pandemic has caused an enormous impact on the operation mode of human society. Such sudden events bring sharp fluctuations and data inadequacy in datasets of several areas, which leads to challenges in solving related problems. Traditional deep learning models like CNN have shown relatively poor performance with small datasets during the COVID-19 pandemic. This is because the data insufficiency and fluctuations lead to serious problems in the training process. In our work, an Informer framework combined with Transfer learning methods (Transfer-Informer) is proposed to solve the data insufficiency in emergency situations, as well as to provide a more efficient self-attention mechanism for deep feature mining, with two distinctive advantages: (1) The ProbSpares self-attention mechanisms, which enables the proposed model to highlight dominant information and extract more typical features from time-series datasets. (2) The Transfer learning framework improves the generalization capability of the model, by transferring basic knowledge from normal situations to emergency cases with fewer data. In our experiments, Transfer-Informer is applied to short-term load forecasting, which achieves better predicting accuracy than traditional models. The empirical results indicate that the proposed model has put forward a baseline for short-term load forecasting in emergency situations and provided a feasible method to tackle sudden fluctuations in real problem-solving. © 2022 IEEE.

A Computational Approach for Predicting the Termination of COVID-19

In 2019, there was an epidemic to the human society, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The virus causes coronavirus disease 2019 (COVID-19). It is an uncertain disease encountered in society for which the technology and human society had not prepared before. COVID-19 first spread over the Wuhan city of China. Since, the past two years of time-span, it has affected the citizen's life culture and expectancy. Now, most of the population are concern about when will be COVID-19 terminate. Basically, this paper aims to analyze the COVID-19 data with features as total confirmed cases, death rate, and vaccination rate around the world-wide region. On analyzing the data, with the help of Machine Learning (ML) algorithms, we estimate the termination of COVID-19. The rapid expansion of the COVID-19 epidemic has compelled the need for technology in this field. © 2022 IEEE.

Short-Term State Electricity Load Forecasting Based on Transfer-Informer

The worldwide COVID-19 pandemic has caused an enormous impact on the operation mode of human society. Such sudden events bring sharp fluctuations and data inadequacy in datasets of several areas, which leads to challenges in solving related problems. Traditional deep learning models like CNN have shown relatively poor performance with small datasets during the COVID-19 pandemic. This is because the data insufficiency and fluctuations lead to serious problems in the training process. In our work, an Informer framework combined with Transfer learning methods (Transfer-Informer) is proposed to solve the data insufficiency in emergency situations, as well as to provide a more efficient self-attention mechanism for deep feature mining, with two distinctive advantages: (1) The ProbSpares self-attention mechanisms, which enables the proposed model to highlight dominant information and extract more typical features from time-series datasets. (2) The Transfer learning framework improves the generalization capability of the model, by transferring basic knowledge from normal situations to emergency cases with fewer data. In our experiments, Transfer-Informer is applied to short-term load forecasting, which achieves better predicting accuracy than traditional models. The empirical results indicate that the proposed model has put forward a baseline for short-term load forecasting in emergency situations and provided a feasible method to tackle sudden fluctuations in real problem-solving. © 2022 IEEE.

Towards Automated Multiclass Severity Prediction Approach for COVID-19 Infections Based on Combinations of Clinical Data

The recent dramatic expansion of the COVID-19 outbreak is placing enormous strain on human society as a whole. Numerous biomarkers are being investigated in an effort to track the condition of the patient. This could interfere with signs of many other illnesses, making it more difficult for a specialist to diagnose or predict the severity level of the case. As a result, the focus of this research was on the development of a multiclass prediction system capable of dealing with three severity cases (severe, moderate, and mild). The lymphocyte to CRP ratio (C-reactive protein blood test) and SpO2 (blood oxygen saturation level) indicators were ranked and used as prediction system attributes. A machine learning model based on SVMs is created. A total of 78 COVID-19 patients were recruited from the Azizia primary health care sector/Wasit Health Directorate/Ministry of Health to form different combinations of COVID-19 clinical dataset. The outcomes demonstrate that the proposed approach had an average accuracy of 82%. The established prediction system allows for the early identification of three severity cases, which reduces deaths. © 2022 Ahmed M. Dinar et al.

Transmission Dynamics and Optimal Control Strategy to Mitigate the Spread of Novel Coronavirus: The Case of Iran

The outbreak of the novel coronavirus (COVID-19) is currently considered a great challenge to the health of human society. In this study, since the COVID-19 vaccines are currently being developed, a vaccine allocation as an effective pharmaceutical strategy to immunize people against disease is being considered. To this end, a new extended SIR-type model including vaccination compartment and reinfection transmission to predict disease behavior in Iran has been formulated. The mathematical analysis of the model is investigated to verify that the proposed model is well-posed epidemiologically. The biological parameters are evaluated via a nonlinear least-square fitting approach. Finally, to investigate the impact of preventive pharmaceutical measures on flattening the curve of the COVID-19 incidence in Iran, the optimal control strategy is applied. The results of numerical simulations and the optimal control analysis illustrate that the combined implementation of time-dependent measures has a drastic impact on disease burden reduction. © 2022 IEEE.

Blood pressure measurement system visualized through a mobile application

COVID19 proved a devastating threat to human society in terms of health, economy, and lifestyle. It quickly spread around the world and caused many governments to close their borders and declare a general quarantine at the national level sending everyone home, with this they changed the lifestyle of many people because they lost the mobility of moving from one place to another. This has led to people somehow losing physical activity and the fear of moving on public roads. According to the World Health Organization (WHO), physical inactivity is the fourth risk factor for global mortality since it generates 3.2 million deaths annually, this is worrying since people do not perform any physical activity. In view of this problem, in this article a blood pressure measurement system was made visualized through a mobile application, in such a way that it can help to observe if they have a stable or high blood pressure, with this, it will be possible to diagnose if a person can present hypertension and prevent them from suffering from any cardiovascular disease. Through the design of the blood pressure measurement system, it was possible to observe that the operation was done correctly, the sensor makes the corresponding measurements and classifies it according to the measurement made, all this is visualized through a mobile application, showing if the person presents a normal or elevated pressure. © 2022 IEEE.

Gamification with Datathon: An Industry Case Study on COVID-19

Technology surrounds Human society, and through which trillion tons of Data are readily available in different forms. Gamification is becoming a part of the human lifestyle through entertainment, fun, or learning and growing immensely in various industries such as e-commerce marketing. Data has become available to a large extent but extracting valuable information is still a question in different sectors. Moreover, to handle big data, several organizations are making investments and trying to hire talent from around the globe. This paper discusses the industry case studies that reflect the importance of gamified event Datathon to solve such problems. During COVID-19, where Data plays a key factor, valuable case studies are discussed and analyzed in this paper. Gamification offers users different pleasures through achievement and competition, and Datathon is prominent in problem-solving for emerging data but lacks the gap in studies. This research paper highlights the various aspects of the true gamification benefit implemented across industries through Datathon to solve significant problems. The author shares detailed insights by discussing the case studies considering different regions and sectors and analyzing during the COVID-19 pandemic. © 2022 IEEE.

Estimation of Basic Reproduction Number and Herd Immunity for COVID-19 in India

In recent times, the rapid rise of the COVID-19 has imparted a devastating effect on human society. India has been perceiving the significant impacts of the COVID-19 in many ways. Estimation of basic reproduction number and herd immunity has become an important question which might support policy makers to take decisions for the improvement of the current scenario. In this chapter, the autoregressive integrated moving average (ARIMA) tool has been used to estimate confirm cases, discharge, deaths, and case fatality rate due to COVID-19 in India during March 1st–May 6th, 2020. The sequential bayesian (SB) method, Wallinga and Teunis approach (TD), exponential growth (EG), and maximum likelihood (ML) techniques are used to estimate the basic reproduction number and herd immunity due to COVID-19 in India. The findings are: basic reproduction number in earlier method as follows, 1.6998 (95% CI, 1.4595–1.9210), 1.8043 (95% CI, 1.6287–1.9894), 1.4685 (95% CI, 1.4672–1.4698) and 1.8931 (95% CI, 1.8655–1.9210) in SB, TD, EG, and ML, respectively. The estimations of herd immunity as follows for SB, TD, EG, and ML such as, 0.4116 (95% CI, 0.3148–0.4794), 0.4457 (95% CI, 0.3860–0.4973), 0.3190 (95% CI, 0.3184–0.3196), and 0.4717 (95% CI, 0.4639–0.4794), respectively. Results demonstrate the significant impact of epidemic dynamics of COVID-19 in India. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

The application of Markov random walk model on the epidemic spread of Covid-19 via SEIR and SEIRS model

Since the outbreak of the Covid-19 pandemic in 2020, most countries are still suffering from the virus, and human society has been greatly changed. As the new virus is highly contagious, many people are still infected with the virus every day, and even face death in serious cases. However, there are still a lot of people who do not realize the harm of the virus, in order to make people more intuitive feel the spread of the virus in a certain period, this paper will use two classic epidemiological mathematical models based on the Markov chain called SEIR and SEIRS model for simulating the virus spread in New York City in 180 days. In both models, there are four states: Susceptible, Exposed, Infected, and Recovered. At first, Markov chain was used to randomly generate a populous population, and only one person in the population was infected, and then the changes in the number of people in these four states were observed over time. In addition, by incorporating certain coefficients in the models into a formula, an index for measuring infectious diseases called Reproduction number (R0) will be obtained. The R0 of Covid-19 in New York City is about 5.93, much greater than 1. Indicating that on average one person can infect about six people, which is highly contagious, so measures need to be taken to reduce this number. Finally, the SEIRS model is more suitable by comparing these two models since people do get re-infected over time. © COPYRIGHT SPIE.

Evaluation and Study of Danao Flood Canal and BaiShou Bay Sediment in Huizhou under COVID -19 Epidemic

The COVID-19 epidemic has had a huge impact on human society, providing an opportunity for human beings to reflect on environmental governance. The sediment samples were collected from the Diversion Channel and Baishou Bay in Huizhou to analyze the element speciation distribution and pollution status. By graphite furnace atomic absorption spectrometry, atomic fluorescence spectrophotometry, flame atomic absorption Spectrophotometric methods to determine the content of the bottom sediments. The single factor index method, the Nemero comprehensive index method, the pollution load index method and the coefficient of variation analysis method were used to analyze. This study on the river bottom sediments of Huizhou is significant environmental effects of harmful elements. © 2021 Kriedt Enterprises Ltd. All right reserved.

Monitoring of Social distancing Face mask Using CNN

The outbreak of the Pandemic in last few months, rapid increase in the transmission of the virus and also the new emerging various strains of COVID-19 corona virus has led to complete Iockdown in the entire world. Meanwhile Iockdown imposed on various countries for longer duration has affected almost every sector of the society causing loss leading to hunger and poverty in the world. By considering all the situations and difficulties underwent by the human society a clear scenario where country not only needs Iockdown as it cannot be the effective solution in slowing down the rate of disease affecting people, So Society is Constantly looking for the alternatives that could help every sector in their business without loss is the topic of the hour. An alternative which could satisfy the above conditions is by Social Distancing and Wearing the Face mask. There by proposing our Real Time System which will detect whether required distance is maintained between two people and detect whether the face mask is worn or not by people with the aid of Web Camera using the most trending technologies Artificial Intelligence, Machine Learning Algorithms, Deep Learning, CNN and few more. © 2021 IEEE.

Cultural Tightness Speeds up the Effects of Anti-epidemic Policies on Controlling COVID-19 Prevalence

COVID-19 has been becoming a vital challenge for human society. With the outbreaks of the COVID-19 epidemic, the governments of multiple countries announced and enacted various anti-epidemic policies to deal with it. The present research proposed that cultural tightness, which is a basic cultural dimension to evaluate how strictly the social norms are abided by people in a society, could accelerate the effects of the anti-epidemic policies on constraints of the transmission of COVID-19. In specific, the new confirmed cases would be reduced by anti-epidemic policies in the tighter societies much earlier than the looser ones. In this work, we used cross-correlation analysis to investigate and analyze the leading and lagging associations between the stringency of anti-epidemic policies and the number of new confirmed cases among the usual tight and loose countries. The findings revealed that the severity of anti-epidemic policies is negatively correlated with the number of new confirmed cases in general. Moreover, cultural tightness does impact the effectiveness of the anti-epidemic policies on the constraints of COVID-19;that is, the lag weeks of new confirmed cases predicted by the stringency of anti-epidemic policies in the tight countries are significantly shorter than that of the loose countries. The control and prevention of COVID-19 around the world is far from optimistic, meanwhile the findings of the current research highlighted the role of cultural factors in the encounter with the century epidemic of human mankind. © 2021 IEEE

ON THE SUSTAINABILITY OF MOBILE TELECOMMUNICATIONS SYSTEMS

It is widely acknowledged that human society is transcending through the era of Society 5.0 which is powered by the rapidly evolving technologies of the fourth industrial revolution. The era is characterized by unprecedented volatility, uncertainty, complexity, and ambiguity in a highly globalised world. There is also a general understanding that sustainability is the paramount paradigm for the Society 5.0 era. Subsequently, and due to increasing concerns about the effects of climate change, the predominant context has been the environmental dimension of the sustainability paradigm. However, in recent times, economic, business, technology and even socio-political aspects have emerged as other dimensions to study and operationalize the sustainability paradigm. This preliminary paper arises from an on-going examination of the technological dimension of the sustainability paradigm. The study focuses on the sustainability of mobile telecommunications systems, especially given the significance of these systems as highlighted by the impacts of the ongoing Covid-19 pandemic. Copyright © 2021 by Naudé Scribante. Permission granted to IAMOT to publish and use.

Modeling the containment behavior of interacting populations in response to an epidemic

Epidemic disease outbreaks are among the major threats to the sustenance and health of human societies, as evidenced by the crises caused by the COVID-19 pandemic. Many people have lost their lives because of this pandemic, and the impact of it on the global economy has also been severe. Modeling the infectious disease outbreak in search of the set of optimal strategies to control the epidemics can help the public health policy makers to better decide and design relevant policies. In this study, spatial games under public goods policies are used to model the social response of different interacting populations to a new epidemic, where the decision makers are not individuals but societies. This approach is of great importance for policy evaluation, since there are usually not just individuals who decide to change their behavior in response to an epidemic, but societies who affect the change of individuals behaviors by setting relevant health policies, standards and regulations. © 2021 IISE Annual Conference and Expo 2021. All rights reserved.

Coupled human-environment system amid COVID-19 crisis: A conceptual model to understand the nexus.

The world today is dealing with a havoc crisis due to the pervasive outbreak of COVID-19. As a preventive measure against the pandemic, government authorities worldwide have implemented and adopted strict policy interventions such as lockdown, social distancing, and quarantine to curtail the disease transmission. Consequently, humans have been experiencing several ill impacts, while the natural environment has been reaping the benefits of the interventions. Therefore, it is imperative to understand the interlinked relationship between human society and the natural environment amid the current crisis. Herein, we performed a meta-analysis of existing literature reporting the various impacts of COVID-19 on human society and the natural environment. A conceptual model was developed to portray and address how the interaction of the existing elements of both sub-components of the coupled human-environment system (CHES) - human society and natural environment - are impacted by the government interventions. Results revealed a suite of positive and negative impacts of COVID-19 on both the sub-components. Our model provides an explicit impression of the complex nexus of CHES amid the current crisis. The proposed conceptual model could help in understanding the complex nexus by identifying the route of short-term impacts of COVID-19 measures and thus may aid in identifying priority areas for discussion and planning in similar crises as well.