We present a simple analytical formalism based on the Lorentz-Scherrer equation and Bernoulli statistics for estimating the fraction of crystallites (and the associated uncertainty parameters) contributing to all finite Bragg peaks of a typical powder pattern obtained from a static polycrystalline sample. We test and validate this formalism using numerical simulations, and show that they can be applied to experiments using monochromatic or polychromatic (pink-beam) radiation. Our results show that enhancing the sampling efficiency of a given powder diffraction experiment for such samples requires optimizing the sum of the multiplicities of reflections included in the pattern along with the wavelength used in acquiring the pattern. Utilizing these equations in planning powder diffraction experiments for sampling efficiency is also discussed.
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Thank you for sharing this story! However, please do so in a way that respects the copyright of this text. If you want to share or reproduce this full text, please ask permission from Innovation Origins (partners@innovationorigins.com) or become a partner of ours! You are of course free to quote this story with source citation. Would you like to share this article in another way? Then use this link to the article: https://innovationorigins.com/en/silicon-sampling-ai-powered-personas-offer-new-insights-for-market-research-but-have-limitations/ n the rapidly evolving field of marketing and communication, staying ahead means embracing technological innovations. The latest breakthrough, silicon sampling, leverages AI to revolutionize market research by creating synthetic personas that mimic human responses. This method, which utilizes large language models (LLMs) like GPT-4o, offers a cost-efficient and less time-consuming alternative to traditional market research. Roberta Vaznyte and Marieke van Vliet (Fontys University of Applied Science) have explored the promise and challenges of silicon sampling, highlighting key findings from recent experiments and the implications for the future of market research.
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Challenges that surveys are facing are increasing data collection costs and declining budgets. During the past years, many surveys at Statistics Netherlands were redesigned to reduce costs and to increase or maintain response rates. From 2018 onwards, adaptive survey design has been applied in several social surveys to produce more accurate statistics within the same budget. In previous years, research has been done into the effect on quality and costs of reducing the use of interviewers in mixed-mode surveys starting with internet observation, followed by telephone or face-to-face observation of internet nonrespondents. Reducing follow-ups can be done in different ways. By using stratified selection of people eligible for follow-up, nonresponse bias may be reduced. The main decisions to be made are how to divide the population into strata and how to compute the allocation probabilities for face-to-face and telephone observation in the different strata. Currently, adaptive survey design is an option in redesigns of social surveys at Statistics Netherlands. In 2018 it has been implemented in the Health Survey and the Public Opinion Survey, in 2019 in the Life Style Monitor and the Leisure Omnibus, in 2021 in the Labour Force Survey, and in 2022 it is planned for the Social Coherence Survey. This paper elaborates on the development of the adaptive survey design for the Labour Force Survey. Attention is paid to the survey design, in particular the sampling design, the data collection constraints, the choice of the strata for the adaptive design, the calculation of follow-up fractions by mode of observation and stratum, the practical implementation of the adaptive design, and the six-month parallel design with corresponding response results.
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