102,771 research outputs found
Covid-19 Vaccine Efficiency Calculator(Peter Chew Formula)
No full textAbstract
Background: The World Health Organization (WHO) said the situation in India was a "devastating reminder" of what the coronavirus could do. India shifts from mass vaccine exporter to importer, worrying the world. Every country needs to vaccinate its citizens faster , vaccination can reduce viral load. This results in vaccination that can reduce transmission , preventing serious illness and death'. Therefore, Countries with higher levels of vaccination can prevent them from becoming "Second India". Game Base Learning to Prevent Infection from COVID-19 (version 3) [Peter Chew, 2020 or ID: ppcovidwho-2401]. The app allows anyone to intuitively see the accumulation of a large number of asymptomatic carriers in some countries has led to the high infection rate of covid-19. This is what is happening in India now. Preprint study, App raise public awareness of the importance of Covid-19 vaccination(4) [Peter Chew, 2021] can visually see countries where vaccination is slow and it is difficult to control the spread of COVID-19. This is what is happening in India now. Preprint study, Vaccination Education App (1) [Peter Chew, 2021] shows that most people do not take the covid-19 vaccine because they question the safety and effectiveness of the vaccine . Preprint study, Peter Chew Formular for Calculate Covid-19 Vaccine Efficiency [Peter Chew, 2021] create a new simple formula for calculate the efficiency of the covid-19 vaccine. Based on considerations, Some members of the public have weak computing power or do not have enough time to perform calculations. Therefore, the new Peter Chew formula needs to be applied to the PCET calculator [Peter Chew, 2021].
Method: The home page of the application. The logo Covid-19 related formula has been added, and the user can go to the Covid-19 related formula page by simply pressing the logo. There will be 2 options on the Covid-19 related formula page, use the conventional formula to calculate the covid-19 vaccine efficiency or use the Peter Chew formula to calculate the covid-19 vaccine efficiency. For the Peter Chew formula page for calculating the efficiency of the Covid-19 vaccine, there are two more options, in which the percentage of the vaccinated group and the non-vaccinated group is compared, which is equal to k or k = 100.
Result: The advantage of Peter Chew's formula is that we can assume a high target vaccination population with k = 100, such as a target group of medical workers. When k = 100, the calculation of Peter Chew's formula is very simple, VE = (b - a )/b x 100%. Another advantage of Peter Chew formula is that public can easily obtain the required data from public news, which allows the public to easily calculate on their own. As a result, confidence in the effectiveness of the covid-19 vaccine has increased.
Conclusion : Although there are already online calculators for calculating relative risks, such as MedCalc statistical software (https://www.medcalc.org/calc/relative_risk.php), existing formulas require more data, and some data is difficult to obtain from public news, which makes it difficult for the public to calculate on their own. As a result, confidence in the effectiveness of the covid-19 vaccine has declined. Using the Covid-19 Vaccine Efficiency Calculator of Peter Chew's formula, you can easily obtain the required data from the public news. Therefore, the public can use the Covid-19 Vaccine Efficiency Calculator to easily calculate the covid-19 vaccine efficiency and confidence of the covid-19 vaccine, and to register the vaccine.
The app can be obtained for free via this link
i)Windows: https://peter-chew.itch.io/pcetcalculatorversion2window
ii)Android: https://peter-chew.itch.io/pcetcalculatorversion2android
Keywords: Covid-19 Vaccine Efficiency Calculator, Peter Chew formula, Covid-19
Covid-19 Vaccine Efficiency Calculator(Peter Chew Formula)
No full textAbstract
Background: The World Health Organization (WHO) said the situation in India was a "devastating reminder" of what the coronavirus could do. India shifts from mass vaccine exporter to importer, worrying the world. Every country needs to vaccinate its citizens faster , vaccination can reduce viral load. This results in vaccination that can reduce transmission , preventing serious illness and death'. Therefore, Countries with higher levels of vaccination can prevent them from becoming "Second India". Game Base Learning to Prevent Infection from COVID-19 (version 3) [Peter Chew, 2020 or ID: ppcovidwho-2401]. The app allows anyone to intuitively see the accumulation of a large number of asymptomatic carriers in some countries has led to the high infection rate of covid-19. This is what is happening in India now. Preprint study, App raise public awareness of the importance of Covid-19 vaccination(4) [Peter Chew, 2021] can visually see countries where vaccination is slow and it is difficult to control the spread of COVID-19. This is what is happening in India now. Preprint study, Vaccination Education App (1) [Peter Chew, 2021] shows that most people do not take the covid-19 vaccine because they question the safety and effectiveness of the vaccine . Preprint study, Peter Chew Formular for Calculate Covid-19 Vaccine Efficiency [Peter Chew, 2021] create a new simple formula for calculate the efficiency of the covid-19 vaccine. Based on considerations, Some members of the public have weak computing power or do not have enough time to perform calculations. Therefore, the new Peter Chew formula needs to be applied to the PCET calculator [Peter Chew, 2021].
Method: The home page of the application. The logo Covid-19 related formula has been added, and the user can go to the Covid-19 related formula page by simply pressing the logo. There will be 2 options on the Covid-19 related formula page, use the conventional formula to calculate the covid-19 vaccine efficiency or use the Peter Chew formula to calculate the covid-19 vaccine efficiency. For the Peter Chew formula page for calculating the efficiency of the Covid-19 vaccine, there are two more options, in which the percentage of the vaccinated group and the non-vaccinated group is compared, which is equal to k or k = 100.
Result: The advantage of Peter Chew's formula is that we can assume a high target vaccination population with k = 100, such as a target group of medical workers. When k = 100, the calculation of Peter Chew's formula is very simple, VE = (b - a )/b x 100%. Another advantage of Peter Chew formula is that public can easily obtain the required data from public news, which allows the public to easily calculate on their own. As a result, confidence in the effectiveness of the covid-19 vaccine has increased.
Conclusion : Although there are already online calculators for calculating relative risks, such as MedCalc statistical software (https://www.medcalc.org/calc/relative_risk.php), existing formulas require more data, and some data is difficult to obtain from public news, which makes it difficult for the public to calculate on their own. As a result, confidence in the effectiveness of the covid-19 vaccine has declined. Using the Covid-19 Vaccine Efficiency Calculator of Peter Chew's formula, you can easily obtain the required data from the public news. Therefore, the public can use the Covid-19 Vaccine Efficiency Calculator to easily calculate the covid-19 vaccine efficiency and confidence of the covid-19 vaccine, and to register the vaccine.
The app can be obtained for free via this link
i)Windows: https://peter-chew.itch.io/pcetcalculatorversion2window
ii)Android: https://peter-chew.itch.io/pcetcalculatorversion2android
Keywords: Covid-19 Vaccine Efficiency Calculator, Peter Chew formula, Covid-19
Joshua Davis: Author of Spare Parts
Full text linkCitation: K-State First (2016). Joshua Davis: Author of Spare Parts [Flier]. Manhattan, Kansas: K-State First.Flyer advertising Joshua Davis's author talk at Kansas State University
Steven Johnson Author Talk Poster
No full textK-State Book NetworkA poster advertising an author talk by Steven Johnson at Kansas State University on September 3, 2014. Steven Johnson's book "The Ghost Map" was the 2014-2015 common book
Ada Nield Chew: England’s forgotten suffragist
Full text linkAn open access essay on Ada Nield Chew, author, activist, suffragist, examining her work and her legacy, published in this popular online Magazine, an extension of the BBC History Magazine
Chew
Full text linkChew is a series of playful interventions involving multi-sensory, participatory approaches that were interspersed throughout the session ‘Distracted Pedagogy' at the conference. The interventions encourage focus to oscillate from the mind to the body and from thought to action. The interventions draw attention to materials and bring ‘process’ into the space of theory through embodied cognition.
Chew aims to tackle the impact of material processes on pedagogical practices; developing research through embodied learning and critical methodologies in relation to subversion, diversion and repetition.
Chew asks whether material engagement can help to divert passive thinking into research. It aims to provide a space in which material can draw attention to the individual’s sensual, intellectual, and subconscious thinking through embodiment, subversion, diversion and repetitive actions. It involves four x 3-minute prompts of multi-sensory approaches that are designed to encourage focus to shift from the mind to the body, and from thought to action. Participants are given gum to chew and Playdough to manipulate throughout the interventions.
Chew highlights issues of student-centred learning within hybrid and material pedagogical methodologies. The activities allow a chewing through making. The interactions are intended to enable a material processing of theory; a constant oscillation between making and language
Optimizing Chew and Chen's Pitch-Spelling Algorithm
No full textPitch-spelling algorithms attempt to compute the
correct pitch names (e.g., C#4, Bb5) of the notes in a
passage of tonal music, when given only the onset
time, MIDI note number, and possibly the duration
and voice of each note. This article reports on a
study in which Chew and Chen’s (2003a, 2003b,
2005) pitch-spelling algorithm was re-implemented
and then optimized by running it with a range of
different parameter value combinations on a test
corpus containing 195,972 notes and consisting of
216 movements from works by eight Baroque and
Classical composers. The results of this evaluation
cast doubt upon some of the claims made by Chew
and Chen that were based on results obtained by
running their algorithm on a much smaller test corpus
containing only 4,462 notes and consisting of
just two movements from sonatas by Beethoven and
You-Di Huang’s Song of Ali-Shan. The results presented
here suggest that Chew and Chen’s algorithm
could be simplified in various ways without
compromising its performance
Supplemental Material - Big Data Analysis of Terror Management Theory’s Predictions in the COVID-19 Pandemic
No full textSupplemental Material for Big Data Analysis of Terror Management Theory’s Predictions in the COVID-19 Pandemic by Peter K. H. Chew in Journal of Death and Dying</p
Introduction to musculoskeletal diagnostic ultrasound, part II: Examination of the lower limb
No full textGoing Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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