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  • SAGE Publications  (2)
  • Shu, Hua  (2)
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  • SAGE Publications  (2)
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  • 1
    Online Resource
    Online Resource
    What Factors Contribute to Reading in ADHD: Examining Reading-Related Skills in Children With ADHD Only/Comorbid Developmental Dyslexia
    SAGE Publications ; 2024
    In:  Journal of Attention Disorders Vol. 28, No. 2 ( 2024-01), p. 201-210
    Details
    In: Journal of Attention Disorders, SAGE Publications, Vol. 28, No. 2 ( 2024-01), p. 201-210
    Abstract: ADHD and developmental dyslexia (DD) frequently co-occur. However, it is unclear why some children with ADHD acquire DD while others do not. Methods: A total of 830 children (including typically developing controls, ADHD only, DD only, and ADHD + DD groups) of two ages (younger: first–third grade; older: fourth–sixth grade) were assessed on measures of reading ability and reading-related skills. Results: The clinical groups had different degrees of impairment in each reading-related skill. Regression results found that the four groups had different skills in predicting reading ability in younger and older grades. Especially, rapid automatized naming (RAN) was the only predictor of reading ability in children with ADHD only. Conclusions: The study highlights that RAN plays an important role in the reading development of children with ADHD only, reflecting the possible protective role of RAN in reading development.
    Type of Medium: Online Resource
    ISSN: 1087-0547 , 1557-1246
    URL: Article
    DOI: 10.1177/10870547231210287
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2024
    detail.hit.zdb_id: 2188086-4
    SSG: 5,2
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    Online Resource
  • 2
    Online Resource
    Online Resource
    Inferring gender and age of customers in shopping malls via indoor positioning data
    SAGE Publications ; 2020
    In:  Environment and Planning B: Urban Analytics and City Science Vol. 47, No. 9 ( 2020-11), p. 1672-1689
    Details
    In: Environment and Planning B: Urban Analytics and City Science, SAGE Publications, Vol. 47, No. 9 ( 2020-11), p. 1672-1689
    Abstract: Customer profiles that include gender and age information are important to businesses and can be used to promote sales and provide personalized services. This information is gathered in e-commerce by analyzing customer visit records in virtual web space. However, such practice is difficult in brick-and-mortar businesses because the data that can be utilized to infer customer profiles are limited in physical spaces. In this paper, we attempt to infer the gender and age of customers using indoor positioning data generated by the Wi-Fi engine. To achieve this, we first construct a synthesized features vector to distinguish different profiles. This vector contains both customer spatial–temporal mobility characteristics and interest preferences. A hidden Markov model group detection method is then applied to detect customers who shop together because they usually show the same shopping behavior and it is difficult to distinguish their profiles. Finally, a random forest inference model is proposed to infer profiles of customers who shop alone. The indoor positioning data collected in the Longhu Tianjie Plaza in Chongqing were used as a case study. The result shows that customer profiles are indeed inferable from indoor positioning data. The accuracy of the gender inference model reaches 73.9%, while that of the age inference model is 67.9%. This demonstrates the potential value of new “big data” for promoting precision marketing and customer management in brick-and-mortar businesses.
    Type of Medium: Online Resource
    ISSN: 2399-8083 , 2399-8091
    URL: Article
    DOI: 10.1177/2399808319841910
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2020
    detail.hit.zdb_id: 2879402-3
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    Online Resource
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