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As the COVID-19 pandemic has reshaped consumers' perceptions of brand messaging, advertisers are revisiting their messages and investing more heavily to strengthen brand identity alignment. Brand identity alignment is the configuration of all semiotic components— brand name, logo, and slogan—used to support a company's desired brand image. With semiotic theory and congruence theory used as foundations, this paper examines attributes of slogans that affect their alignment with brand identity. Using data from a large-scale field study, followed by a validation study using senior advertising managers, the authors find that message clarity and creativity enhance, whereas a jingle in a slogan message reduces, the slogan–brand alignment. © 2023, World Advertising Research Center. All rights reserved.
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Background: The Public Health Emergency of International Concern by the World Health Organization (WHO) declared novel Coronavirus (nCoV-2019) outbreaks in 2019 as pandemic. Method(s): This research work made an analysis of the nCoV-2019 outbreak in India solely based on a mathematical model. Result(s): The historical epidemics in the world are plague, AIDS, Swine flu, ebola, zika virus, Black Death and SARS. Considering the model used for SARS 2003, the present research on COVID-2019 estimates characteristics of the rate of infections (I) and rate of recovery(R), which leads to the estima-tion of the I and R leading to predict the number of infections and recovery. Through ruling out the un-predictable and unreasonable data, the model predicts that the number of the cumulative 2019-nCoV cases may reach from 3398458 (mid of May) to 15165863, with a peak of the unrecovered infection (2461434-15165863) occurring in late April to late July. In this paper, we predicate how the confirmed infected cases would rapidly decrease until late March to July in India. We also focus on how the Gov-ernment of Odisha (a state of India) creates history in the protective measures of COVID-19. Conclusion(s): The growing infected cases may get reduced by 70-79% by strong anti-epidemic measures. The enforcement of shutdown, lockdown, awareness, and improvement of medical and health care could also lead to about one-half transmission decrease and constructively abridge the duration of the 2019 n-CoV.Copyright © 2021 Bentham Science Publishers.
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The current century has astonished us in transiting through the gravest epochs of human tragedy. COVID-19 is a wakeup call for the humanity. This present scenario challenges the demand of a technology that can analyze the entire world’s fatality rate as well as the situation in our country India and forecast the next few years so that the human lives can take necessary steps to overcome from the prolonged pain. So our objective is to show how machine learning has added its great impact in future forecasting and analyzing the challenges regarding the current pandemic. Machine learning is a subset of artificial intelligence where we are acknowledging the different figured data from the repository and further the information technology system helps to predict the spread of the disease in future by implementing different models and algorithms. Our research methodology mostly emphasizes on few standard forecasting models like linear regression (LR), support vector machine (SVM), time series analysis (we used the Holt’s linear model) and ARIMA model and SARIMA model. Predictions are done based upon the confirmed cases, and it has been found that time series model is providing the best result. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Objective: To estimate COVID-19 seropositivity among contacts of cases and to compare the seropositivity among different types of contact for assessing the differential risk & transmission dynamics. Material and Methods: A large-scale population-based serosurvey was carried out among the general population of Ahmedabad during the second half of October 2020. The contacts of cases were selected based on the population proportion and enrolled as an additional category. The seropositivity among the contacts was estimated using the enzyme-linked immunosorbent assay and compared with different types of contact and available demographic factors. Results: As of October 2020, the seropositivity against Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV2) among contacts of cases in Ahmedabad was 26.0% [95% confidence interval 24.2–28.0]. The seropositivity among family contacts was significantly higher (28.8%) compared to other contacts (24.4%) (Z=2.19, p-value=0.028). This trend was seen across all age groups and both sexes. The seropositivity was higher among females (27.7%) compared to males (24.5%) but the difference was statistically not significant (Z=1.64, p-value=0.101). In terms of age groups, the positivity had an increasing trend up to 60 years but declined after that. Conclusion: A seropositivity of 26.0% among contacts indicates that a large proportion of contacts demonstrated Immunoglobulin-G antibodies. This highlights asymptomatic transmission and/or low sensitivity of the diagnostic tests. The current strategy for contact tracing and testing among contacts is justified based on the significantly higher seropositiviamong family contacts. © 2021 JHSMR.
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Introduction: Delaying contraceptive access may contribute to unintended pregnancies, and during the covid-19 pandemic the appointment wait time for contraceptive care in free or low cost clinics in Georgia is unknown. Pharmacist prescribed contraception does not require an appointment and is used to improve access in other states. Research Question or Hypothesis: What is the appointment wait time in federally-funded family planning clinics, and are there differences between metropolitan (M) and non-metropolitan (NM) counties in Georgia? Study Design: Prospective, cross sectional, telephone-based survey. Methods: The Office of Population Affairs provided a list of Georgia clinics receiving Title X federal funding to provide free or low cost family planning services. Clinic location was defined as M or NM per 2013 National Center for Health Statistics Code. Using a prewritten script, researchers called clinics between Jan-May 2021 to inquire about the earliest available contraceptive appointment. Descriptive statistics, t-test, and chi square tests were completed using SPSS. Results: Of the 180 clinics called, 163 (90.6%) answered (89.6% M vs. 91.7% NM, p=0.63). Metropolitan clinics had longer average wait times to first available appointment (M 14.3 +/-16.1 vs. NM 6.0 +/-9.5 days, p<0.01), but no difference in availability of same day (50.8% M vs. 69.8% NM, p=0.07), walk-in (52.3% M vs. 68.3% NM, p=0.13), or telehealth appointments (43.1% M vs. 36.5% NM, p=0.47). Conclusion: During covid-19, average contraceptive appointment wait time in Georgia Title X federally-funded family planning clinics was 1 to 2 weeks. This is longer than the "walk-in" model that would likely be used if pharmacist prescribed contraception was permitted in Georgia. Patients seeking to minimize contraceptive appointment wait times, particularly in metropolitan areas, could potentially benefit if pharmacist prescribed contraception were permitted.
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The entire globe is going through a severe health crisis due to the rapid spread of Covid-19 Disease, which in turn creating a deep impact on the human lives and their day-to-day livelihood. Only hope of preventing it from further spread of disease is by following all the precautionary measures provided by the WHO. One of the most important safety measures is to wear face mask and maintain social distancing. Hence, we have proposed to develop a system can monitor whether a person is wearing a facemask correctly/not in real time. This will help to reduce the rapid spread of the disease in public places and various other organizations. We have proposed a solution that uses Artificial Intelligence and has a capability to detect the violation of wearing a face mask in real-time using Image Recognition and Video Techniques. The main intention of the proposed work is to provide the front-end software (Web or mobile application) for administrators to monitor the violation. The system will capture the violated instance and store the data. Our system will also include a User registration form where the users have to register their information along with their face images. This will be an input to the face recognition model, which will train and alert the user whenever violation occurs. © 2021 IEEE.