Challenges and Solution Pathways in Water Use Through the Lens of COVID-19

Water withdrawal for public/private suppliers and public services (defined as public water use) and for irrigation (defined as irrigation water use) are essential components of agricultural water management as well as of the planning and management of domestic, commercial, and municipal water supplies. A significant fraction of the public and irrigation water use is consumptive (defined as the part of water withdrawn that is consumed) in nature, and it is primarily freshwater. Global climate change and variability have substantially impacted the large-scale drivers of freshwater resources across the globe, which include, for example, precipitation, temperature, evapotranspiration, soil moisture, and hydrologic extremes. Global environmental change has also influenced several local-scale freshwater availability drivers, such as water quality, municipal policies, and water taxation. Overall, the changes in freshwater resources have potentially stressed irrigation and public water use. Population growth has altered the supply–demand fronts of water balance, resulting in increased water supply stresses. Researchers have considered several soft-and hard-path solutions to augment the deficit in the supply–demand fronts;however, each solution has its own pros and cons. The ongoing COVID-19 pandemic has exacerbated the already existing critical issues related to sustainable future water use. New challenges have emerged, requiring both short-and long-term solutions. Hence, it is essential to understand the current public and irrigation water use changes resulting from the pandemic. An appropriate estimate of the future changes in water use would help develop/upgrade new/current water resource systems that can mitigate risks and show increased resiliency against global climate and environmental changes and unprecedented events like the COVID-19 pandemic. In this opinion chapter, we discuss some examples of the regional/local changes in water use during the ongoing pandemic and our increased preparedness or the lack of it. Additionally, the chapter focuses on the future risks and resilience of water resource systems to meet the future demands of water use as well as to face unprecedented events such as the COVID-19 pandemic. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer 2022.

Use of population health data to promote equitable recruitment for a primary care practice implementation trial addressing unhealthy alcohol use.

Background: Recruiting underrepresented people and communities in research is essential for generalizable findings. Ensuring representative participants can be particularly challenging for practice-level dissemination and implementation trials. Novel use of real-world data about practices and the communities they serve could promote more equitable and inclusive recruitment. Methods: We used a comprehensive primary care clinician and practice database, the Virginia All-Payers Claims Database, and the HealthLandscape Virginia mapping tool with community-level socio-ecological information to prospectively inform practice recruitment for a study to help primary care better screen and counsel for unhealthy alcohol use. Throughout recruitment, we measured how similar study practices were to primary care on average, mapped where practices' patients lived, and iteratively adapted our recruitment strategies. Results: In response to practice and community data, we adapted our recruitment strategy three times; first leveraging relationships with residency graduates, then a health system and professional organization approach, followed by a community-targeted approach, and a concluding approach using all three approaches. We enrolled 76 practices whose patients live in 97.3% (1844 of 1907) of Virginia's census tracts. Our overall patient sample had similar demographics to the state for race (21.7% vs 20.0% Black), ethnicity (9.5% vs 10.2% Hispanic), insurance status (6.4% vs 8.0% uninsured), and education (26.0% vs 32.5% high school graduate or less). Each practice recruitment approach uniquely included different communities and patients. Discussion: Data about primary care practices and the communities they serve can prospectively inform research recruitment of practices to yield more representative and inclusive patient cohorts for participation.

An Analytical Approach to Predict the COVID-19 Death Rate in Bangladesh Utilizing Multiple Regression and SEIR Model

The epidemic of COVID-19 has turned out to be a huge fear for the world. There is currently no advisable drug or cure available to treat this condition. According to WHO statistics, COVID-19 has become a progressive lung illness that is spread by respiratory droplets and other forms of contact. According to WHO, there is still no treatment or defensive plan that has risen till the period to encounter the COVID- 19 pandemic that was arisen in China in late 2019. The purpose of our study is to predict the COVID-19 situation by analyzing the death rate, recovery rate, and susceptibility rate with the help of the regression model and SEIR model. Two analytical models (SEIR and Regression) have been used. Our analysis has shown the prediction of the COVID-19 death rate in Bangladesh with the help of a Regression and SEIR model. We have analyzed the instances per million, number of death rates per million from the SEIR and Regression results and compared them with the real-time result. We have used a valid data set of Bangladesh, collected from the Institute of Epidemiology, Disease Control and Research (ICR) from 18 March 2020 to July 18, 2021. Our experimental result shows promising performance. Examples and descriptions are provided to explain the technique. © 2021 IEEE.

A Hybrid Approach to Find COVID-19 Related Lung Infection Utilizing 2-Bit Image Processing

This study describes the deployment of an image processing approach for finding COVID-19 affected lungs. Medical scans are useful in diagnosing illnesses and determining if organs are working normally. Medical image processing is an ongoing research subject in where numerous ways are used to help diagnosis, as well as different image processing techniques that may be used. Picture processing was used in this work, which includes image pretreatment, histogram leveling, smothering, eroding, and dilation. The usage of 2-bit picture is selected since this characteristic is well-known and there are several resources accessible. The Open CV library, which includes a plethora of image processing functions, is likewise free to use. Our experiment has shown how COVID-19 affected lung disorders can easily be identified with the help of a 2-bit image segmentation technique. The plan comprises (1) using a deep robust acquisition access to portion proper regions of interest from bleak medical examination image sizes of 903 total, (2) using a propagative neural network to improve contrast, sharpness, and illuminance of image contents, and (3) from the beginning to the conclusion, a regression strategy plan was used to accomplish medical picture categorization by material design in deep neural networks.

An Efficient Approach to Validate COVID-19 Related Vaccine Myths Utilizing LDA Algorithm

Scientists from the whole world have been working their heart and soul to invent the COVID-19 vaccine. When they are succeed to make the vaccine, various rumors are spread. COVID-19 situation has made our world standstill. When the vaccine came out for the first time, people were enthusiastic to take a shot. But the myth, rumors about vaccination also followed the success. In this paper, we have tried to validate the COVID-19 related vaccine myth and rumors with the help of the LDA algorithm. We have used data mining, text mining and sentiment analysis for the experiment. The outcome of our experiment has shown that most people are positive about vaccination but the negative impact is also there. Our experiment has found that most of the people are talking about "vaccine", "people","moron"and "ever". We have proposed a technique to validate this kind of vaccine myth. LDA algorithms have been able to predict and validate the myth up to 70% compared to other frameworks out there. Promising efficiency is exhibited by our experimental result. © 2022 ACM.

A Semester Like No Other: Use of Natural Language Processing for Novice-Led Analysis on End-of-Semester Responses on Students' Experience of Changing Learning Environments Due to COVID-19

In response to campus closures due to COVID-19, the learning environment in a foundational engineering course unexpectedly shifted from hands-on, collaborative work to remote delivery, accomplished within a short period of time. Through end-of-semester course surveys, students were asked open-ended questions to get feedback about their experience with the goal of using student feedback for curriculum planning and improvement should there be continued need to facilitate the course remotely in subsequent semesters. However, with 1,170 responses, the volume of data made it challenging to analyze, interpret and use the feedback for decision-making for following semesters. To address this challenge, we utilized Natural Language Processing (NLP) based techniques - algorithmic ways to analyze, interpret, and present words and sentiments from student responses visually, to inform a novice-led analysis to ultimately help with course planning for future semesters. © American Society for Engineering Education, 2021

Evaluation of COVID-19 Restrictions on Distance Runners' Training Habits Using Wearable Trackers

The COVID-19 pandemic caused widespread disruption to many individuals' lifestyles. Social distancing restrictions implemented during this global pandemic may bring potential impact on physical activity habits of the general population. However, running is one of the most popular forms of physical activity worldwide and one in which it could be maintained even during most COVID-19 restrictions. We aimed to determine the impact of COVID-19 restrictions on runners' training habits through analyzing the training records obtained from their GPS enabled wearable trackers. Retrospective and prospective data were collected from an online database (https://wetrac.ucalgary.ca). Runners' training habits, including frequency, intensity and duration of training, weekly mileage and running locations were analyzed and compared 9 months before and after the start of COVID-19 restrictions in March 2020. We found that runners ran 3 km per week more (p = 0.05, Cohen's d = 0.12) after the start of COVID-19 restrictions, and added 0.3 training sessions per week (p = 0.03, Cohen's d = 0.14). Moreover, runners ran an additional 0.4 sessions outdoors (p < 0.01, Cohen's d = 0.21) but there was no significant change in the intensity or duration of training sessions. Our findings suggested that runners adopted slightly different training regimen as a result of COVID-19 restrictions. Our results described the collective changes, irrespective of differences in response measures adopted by various countries or cities during the COVID-19 pandemic.

Effectiveness of Healthcare and Fitness Influencers during COVID Times

Influencer marketing has played a significant role in marketing to present its products on a digital platform. In this study, we are trying to gauge the effectiveness of healthcare and fitness influencers on Instagram. More specifically, the researchers are trying to find how the one-way interaction and the influencer's credibility are related to purchasing intention. Also, the researchers are trying to find the indirect relationship between purchase intention and social attractiveness, physical attractiveness, and attitude homophily. The researchers have used a linear regression process to analyse the data. This research paper studied the effect on purchase intention due to both source credibility and PSI. We also found the indirect relationship between purchase intention and attitude homophily, physical attractiveness and social attractiveness. Again, these outcomes will benefit both the companies and the bloggers to gain a competitive advantage over other market players. In this research paper, we have studied the consumer buying behaviour of a particular segment (healthcare and fitness) on Instagram. Also, our respondents are located in various parts of India. Due to these reasons, the researcher cannot generalize their results to other media platforms and other segments. The authors find that the source's credibility has a greater impact on purchase intention than a single source of interaction between the user and the influencer. Both credibility and Para social interaction displayed a positive relationship to purchase intention.

Effect of H2O2 Antiseptic on Dispersal of Cavitation-Induced Microdroplets.

The persisting outbreak of SARS-CoV-2 has posed an enormous threat to global health. The sustained human-to-human transmission of SARS-CoV-2 via respiratory droplets makes the medical procedures around the perioral area vulnerable to the spread of the disease. Such procedures include the ultrasonic dental cleaning method, which occurs within the oral cavity and involves cavitation-induced sprays, thus increasing the risk of pathogen transmission via advection. To understand the associated health and safety risks for patients and clinicians, it is critical to understand the flow pattern of the spray cloud around the operating region, the size and velocity distribution of the emitted droplets, and the extent of fluid dispersion until ultimate deposit on surfaces or escape through air vents. In this work, the droplet size and velocity distributions of the spray emerging from the tip of a free-standing common ultrasonic dental cleaning device were characterized via high-speed imaging. Deionized water and 1.5% and 3% aqueous hydrogen peroxide (H2O2) solutions were used as working fluids, with the H2O2-an established oxidizing agent-intended to curb the survival of virus released in aerosols generated from dental procedures. The measurements reveal that the presence of H2O2 in the working fluid increases the mean droplet size and ejection velocity. Detailed computational fluid dynamic simulations with multiphase flow models reveal benefits of adding small amounts of H2O2 in the feed stream of the ultrasonic cleaner; this practice causes larger droplets with shorter residence times inside the clinic before settling down or escaping through air vents. The results suggest optimal benefits (in terms of fluid spread) of adding 1.5% H2O2 in the feed stream during dental procedures involving ultrasonic tools. The present findings are not specific to the COVID-19 pandemic but should also apply to future outbreaks caused by airborne droplet transmission.

Recent developments and future scopes of electrical vehicles in power market on COVID-19 pandemic situation

The largest source of climate pollution in the world is transportation. To solve the climate crisis, we need to make the vehicles on our roads as clean as possible. We have only a decade left to change the way we use energy to avoid the worst impacts of climate change. Emissions from cars and trucks are not only bad for our planet;they’re bad for our health. Air pollutants from gasoline- and diesel-powered vehicles cause asthma, bronchitis, cancer, and premature death. The long-term health impacts of localized air pollution last a lifetime, with the effects borne out in asthma attacks, lung damage, and heart conditions. As the COVID-19 pandemic — a respiratory disease — continues to spread, a study by Harvard University found “a striking association between long-term exposure to harmful fine particulate matter and COVID-19 mortality in the United States” One of the primary causes of fine particulate matter pollution (PM2.5) is combustion from gasoline and diesel car engines. So in this paper we are mainly focused on development of Electrical Vehicles and what are problems to implementation in India. We have discussed different types of Government policies and future scope policies which have been taken by government. From this paper researchers will get clear idea of future of Non Pollutant Vehicles. So this paper is very important in Covid-19 pandemic situations because we will safe and secure from these types of pandemic disease only if our environment will be free from air pollution which creates by conventional vehicles. © 2021 Institute of Physics Publishing. All rights reserved.

COVID-19 Detection using Transfer Learning with Convolutional Neural Network

The Novel Coronavirus disease 2019 (COVID-19) is a fatal infectious disease, first recognized in December 2019 in Wuhan, Hubei, China, and has gone on an epidemic situation. Under these circumstances, it became more important to detect COVID-19 in infected people. Nowadays, the testing kits are gradually lessening in number compared to the number of infected population. Under recent prevailing conditions, the diagnosis of lung disease by analyzing chest CT (Computed Tomography) images has become an important tool for both diagnosis and prophecy of COVID-19 patients. In this study, a Transfer learning strategy (CNN) for detecting COVID-19 infection from CT images has been proposed. In the proposed model, a multilayer Convolutional neural network (CNN) with Transfer learning model Inception V3 has been designed. Similar to CNN, it uses convolution and pooling to extract features, but this transfer learning model contains weights of dataset Imagenet. Thus it can detect features very effectively which gives it an upper hand for achieving better accuracy. © 2021 IEEE.