Creativity – help or hindrance? The impact of product review creativity on perceived helpfulness
In: Computers in human behavior, Band 156, S. 108182
ISSN: 0747-5632
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In: Computers in human behavior, Band 156, S. 108182
ISSN: 0747-5632
January 27 2020, a day that will be remembered by the Indian people for a few decades, where a deadly virus peeped into a life of a young lady and till now it has been so threatening as it took up the life of 3.26 lakh people just in India. With the start of the virus government has made mandatory to wear masks when we go out in to crowded or public areas such as markets, malls, private gatherings and etc. So, it will be difficult for a person in the entrance to check whether everyone one are entering with a mask, in this paper we have designed a smart door face mask detection to check whether who are wearing or not wearing mask. By using different technologies such as Open CV, MTCNN, CNN, IFTTT, ThingSpeak we have designed this face mask detection. We use python to program the code. MTCNN using Viola- Jones algorithm detects the human faces present in the screen The Viola-Jones algorithm first detects the face on the grayscale image and then finds the location on the colored image. In this algorithm MTCNN first detects the face in grayscale image locates it and then finds this location on colored image. CNN for detecting masks in the human face is constructed using sample datasets and MobileNetV2 which acts as an object detector in our case the object is mask. ThingSpeak is an open-source Internet of things application used to display the information we get form the smart door. This deployed application can also detect when people are moving. So, with this face mask detection, as a part to stop the spread of the virus, we ensure that with this smart door we can prevent the virus from spreading and can regain our happy life.
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In: Intelligence and Security Informatics; Lecture Notes in Computer Science, S. 479-485
In: Journal of service research, Band 27, Heft 2, S. 250-267
ISSN: 1552-7379
Visual aesthetics play a pivotal role in attracting and retaining customers in service environments. Building on theories of environmental psychology, this study introduces a novel and comprehensive aesthetic measure for evaluating servicescape design, which is called as the "visual aesthetic quotient" (VAQ). This measure is presented as the ratio of the dimensions of order and complexity in servicescape's visual design, and it aims to provide an objective and holistic approach of servicescape design evaluation. In addition, we introduce and validate a pioneering method for quantifying order and complexity objectively using algorithmic models applied to servicescape images. We investigated and established the influence of the VAQ on the perceived attractiveness of servicescapes, developing its role further in this context. The entire approach was comprehensively and rigorously examined using four studies (social media analytics, eye-tracking, a field experiment, and an experimental design), contributing to conceptual advancement and empirical testing. This study provides a novel, computational, objective, and holistic aesthetic measure for effective servicescape design management by validating computational aesthetic measures and establishing their role in influencing servicescape attractiveness; testing the mediation of processing fluency and pleasure; and examining the moderating effects of service context.
Background: The COVID-19 pandemic has disrupted routine hospital services globally. This study estimated the total number of adult elective operations that would be cancelled worldwide during the 12 weeks of peak disruption due to COVID-19. Methods: A global expert response study was conducted to elicit projections for the proportion of elective surgery that would be cancelled or postponed during the 12 weeks of peak disruption. A Bayesian β-regression model was used to estimate 12-week cancellation rates for 190 countries. Elective surgical case-mix data, stratified by specialty and indication (surgery for cancer versus benign disease), were determined. This case mix was applied to country-level surgical volumes. The 12-week cancellation rates were then applied to these figures to calculate the total number of cancelled operations. Results: The best estimate was that 28 404 603 operations would be cancelled or postponed during the peak 12 weeks of disruption due to COVID-19 (2 367 050 operations per week). Most would be operations for benign disease (90·2 per cent, 25 638 922 of 28 404 603). The overall 12-week cancellation rate would be 72·3 per cent. Globally, 81·7 per cent of operations for benign conditions (25 638 922 of 31 378 062), 37·7 per cent of cancer operations (2 324 070 of 6 162 311) and 25·4 per cent of elective caesarean sections (441 611 of 1 735 483) would be cancelled or postponed. If countries increased their normal surgical volume by 20 per cent after the pandemic, it would take a median of 45 weeks to clear the backlog of operations resulting from COVID-19 disruption. Conclusion: A very large number of operations will be cancelled or postponed owing to disruption caused by COVID-19. Governments should mitigate against this major burden on patients by developing recovery plans and implementing strategies to restore surgical activity safely.
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