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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Adaptive survey design has attracted great interest in recent years, but the number of case studies describing actual implementation is still thin. Reasons for this may be the gap between survey methodology and data collection, practical complications in differentiating effort across sample units and lack of flexibility of survey case management systems. Currently, adaptive survey design is a standard option in redesigns of person and household surveys at Statistics Netherlands and it has been implemented for the Dutch Health survey in 2018. In this article, the implementation of static adaptive survey designs is described and motivated with a focus on practical feasibility.
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Graphs are ubiquitous. Many graphs, including histograms, bar charts, and stacked dotplots, have proven tricky to interpret. Students’ gaze data can indicate students’ interpretation strategies on these graphs. We therefore explore the question: In what way can machine learning quantify differences in students’ gaze data when interpreting two near-identical histograms with graph tasks in between? Our work provides evidence that using machine learning in conjunction with gaze data can provide insight into how students analyze and interpret graphs. This approach also sheds light on the ways in which students may better understand a graph after first being presented with other graph types, including dotplots. We conclude with a model that can accurately differentiate between the first and second time a student solved near-identical histogram tasks.
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Light profoundly impacts many aspects of human physiology and behaviour, including the synchronization of the circadian clock, the production of melatonin, and cognition. These effects of light, termed the non-visual effects of light, have been primarily investigated in laboratory settings, where light intensity, spectrum and timing can be carefully controlled to draw associations with physiological outcomes of interest. Recently, the increasing availability of wearable light loggers has opened the possibility of studying personal light exposure in free-living conditions where people engage in activities of daily living, yielding findings associating aspects of light exposure and health outcomes, supporting the importance of adequate light exposure at appropriate times for human health. However, comprehensive protocols capturing environmental (e.g., geographical location, season, climate, photoperiod) and individual factors (e.g., culture, personal habits, behaviour, commute type, profession) contributing to the measured light exposure are currently lacking. Here, we present a protocol that combines smartphone-based experience sampling (experience sampling implementing Karolinska Sleepiness Scale, KSS ratings) and high-quality light exposure data collection at three body sites (near-corneal plane between the two eyes mounted on spectacle, neck-worn pendant/badge, and wrist-worn watch-like design) to capture daily factors related to individuals’ light exposure. We will implement the protocol in an international multi-centre study to investigate the environmental and socio-cultural factors influencing light exposure patterns in Germany, Ghana, Netherlands, Spain, Sweden, and Turkey (minimum n = 15, target n = 30 per site, minimum n = 90, target n = 180 across all sites). With the resulting dataset, lifestyle and context-specific factors that contribute to healthy light exposure will be identified. This information is essential in designing effective public health interventions.
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Het welbevinden van de Nederlandse jeugd staat onder druk. Jeugdprofessionals die dagelijks werken aan vorming, begeleiding, preventie en behandeling van jonge mensen, zien zich daarin geconfronteerd met de nodige uitdagingen. Aangezien kennis en protocollen ontoereikend zijn in het komen tot adequaat handelen, is het van belang te exploreren wat ‘praktische wijsheid’ van individuele professionals hierin kan betekenen. Data uit acht open interviews, met inzet van de rich pictures methode, zijn geanalyseerd om te onderzoeken wat praktische wijsheid in het jeugddomein behelst. Dit toonde aan dat professionals komen tot het juiste handelen in knellende situaties door het eigen morele kompas als belangrijke richting gever te gebruiken. Het handelingsrepertoire is rijk en loopt uiteen van het volgen van intuïtie en wendbaarheid tot het gebruikmaken van kennis en zelfkennis. Het is van belang dat organisaties ruimte maken voor wat professionals al doen hieromtrent, maar ook dat (gezamenlijke) reflectie wordt gefaciliteerd. Elementen uit onze onderzoeksmethode lijken hierbij behulpzaam te kunnen zijn.
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Bedrijven bevinden zich tegenwoordig vaak in een keten. Een keten kan worden beschouwd als een verzameling organisaties die een virtueel netwerk delen waar informatie, diensten, goederen of geld doorheen stroomt. Hierbij staan ICT-systemen veelal centraal. Deze afhankelijkheid werkt in de hand dat cyber-gerelateerde risico’s een opmars maken binnen ketens. Niet elke ketenorganisatie beschikt echter over de middelen en kennis om zichzelf te beschermen: om tot sterke ketens te komen is informatiedeling tussen ketenorganisaties over actuele dreigingen en incidenten van belang. Een doel van dit verkennend onderzoek, dat is uitgevoerd in opdracht van het Nationaal Cyber Security Centrum (NCSC), is om inzicht te bieden in de succesfactoren van informatiedeling-initiatieven op het gebied van cyberveiligheid. Met deze kennis kan het NCSC haar accounthouders en adviseurs helpen om de doelgroepen positief te motiveren om actie te nemen ter versterking van ketenweerbaarheid. Tevens wordt met dit onderzoek beoogd om aanknopingspunten voor vervolgonderzoek te identificeren. Het identificeren van succesfactoren vond plaats op basis van een literatuurstudie en gestructureerde interviews met in totaal zes leden uit drie verschillende bestaande informatiedeling-initiatieven rondom cybersecurity: het Managed Service Provider (MSP) Information Sharing and Analysis Centre (ISAC), Energie ISAC en de securitycommissie van de Nederlandse Energie- Data Uitwisseling (NEDU). Alle respondenten zijn informatiebeveiligingsexperts die hun organisatie vertegenwoordigen in de samenwerkingsverbanden. In totaal zijn 20 succesfactoren geïdentificeerd. Deze factoren zijn vervolgens gecategoriseerd tot vier thema’s die bijdragen aan een succesvolle informatiedeling. De thema’s zijn samen te vatten als teamfactoren, individuele factoren, managementfactoren en faciliterende factoren. De vier meest genoemde succesfactoren zijn: ● Expertise: Leden met onderscheidende en gespecialiseerde kennis bevorderen de informatiedeling en zijn ondersteunend aan het individuele leerdoel van de leden. ● Vertrouwen: Vertrouwen is een essentiële voorwaarde voor de bereidheid om samen te werken en informatie te delen. Tijd is hierin een cruciale factor: tijd is nodig voor vertrouwen om te ontstaan. ● Lidmaatschapseisen: Expliciete en impliciete lidmaatschapseisen zorgen voor een selectie op geschikte deelnemers en faciliteren daarmee het onderling vertrouwen. ● Structurele opzet: Een samenwerking dient georganiseerd te zijn volgens een structuur en met een stabiele bezetting van voldoende omvang. Vervolgonderzoek zou zich kunnen richten op het identificeren van strategieën voor het opstarten van samenwerkingsverbanden en het over de tijd behouden van enthousiasme onder de leden in de informatiedeling-initiatieven rondom cybersecurity. Ook onderzoek naar de eigenschappen of kwaliteiten van de voorzitter en hoe deze bijdragen aan het succesvol initiëren en onderhouden van een samenwerkingsverband zijn genoemd. Ook is nog onvoldoende duidelijk hoe gedeelde of juist onderscheidende expertise van de leden bijdraagt aan succes van de informatiedeling-initiatieven. Verder is er behoefte aan kennis over hoe de samenwerking tussen ketenpartners op het gebied van cyberveiligheid buiten bestaande samenwerkingsverbanden is ingericht. Denk hierbij aan een uitbreiding van de huidige studie, maar met een focus op kleinere bedrijven die deel uitmaken van ketens, maar waarbij IT niet de corebusiness is, aangezien die volgens respondenten als risicovol worden gezien voor de keten.
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Like many countries, the COVID-19 pandemic has forced Statistics Netherlands to make changes in its fieldwork strategy. Since mid-March 2020, there have been limited opportunities to conduct face-to-face interviews. Therefore, from September 2020, CAPI sampled people are offered the opportunity to respond by telephone. For this purpose, face-to-face interviewers are instructed to persuade the potential respondent at the doorway. When people refuse a face-to-face interview, interviewers ask for a telephone number and try to make an appointment to conduct the interview by telephone. The aim of our study was to investigate the effects of conducting the interview by telephone instead of face-to-face on important survey outcome variables. We were particularly interested in whether differences are due to selection effects or caused by mode-specific measurement errors. Because we did not have the time or capacity to set up a controlled experiment, we performed regression analyses to decompensate the differences between selection effects and mode-specific measurement errors. We used data of the Labour Force Survey (LFS) and the Housing Survey (WoON). Our analysis showed that there were differences in important target variables, for both LFS and WoON. These differences were, however, mainly caused by selection effects – which can be taken into account for during weighting – and were less likely to be caused by mode specific measurement errors. Although there are important limitations and caveats, these findings are supportive to further implement this field strategy.
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Estimation of the factor model by unweighted least squares (ULS) is distribution free, yields consistent estimates, and is computationally fast if the Minimum Residuals (MinRes) algorithm is employed. MinRes algorithms produce a converging sequence of monotonically decreasing ULS function values. Various suggestions for algorithms of the MinRes type are made for confirmatory as well as for exploratory factor analysis. These suggestions include the implementation of inequality constraints and the prevention of Heywood cases. A simulation study, comparing the bootstrap standard deviations for the parameters with the standard errors from maximum likelihood, indicates that these are virtually equal when the score vectors are sampled from the normal distribution. Two empirical examples demonstrate the usefulness of constrained exploratory and confirmatory factor analysis by ULS used in conjunction with the bootstrap method.
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