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Chengyue Zhang

Dados Biográficos

ID4417868
NOMEChengyue Zhang
PRENOMESChengyue
SOBRENOMEZhang
ASSINATURAZHANG C
AFILIAÇÕESPhillips Exeter Academy
ORCID0009-0007-4466-8845
VERIFICADOSim
TOTAL DE OBRAS3
TOTAL DE CITAÇÕES0
TOTAL COMO AUTOR3
TOTAL COMO EDITOR0
PRIMEIRO ANO DE PUBLICAÇÃO2023
ANO MAIS RECENTE DE PUBLICAÇÃO2025
ÍNDICE H0
  • Revisiting urban scaling through street-based hotspots across 360 Chinese cities

    Open Access•Ding Ma, Lili Deng et al.•ARTICLE•Cities•2025•Referências: 3

  • Disease burden comparison and associated risk factors of early- and late-onset neonatal sepsis in China and the USA, 1990–2019

    Open Access•Chengyue Zhang, Lianfang Yu et al.•ARTICLE•Global Health Action•2024

    BACKGROUND: The morbidity and mortality rates of neonatal sepsis are high, with significant differences in risk factors and disease burden observed between developing and developed countries. OBJECTIVE: To provide evidence to support recommendations on improving public health policies using a comparative systematic analysis of the disease burden. METHODS: Using data from the Global Burden of Disease Study 2019, the prevalence and incidence of ear…

  • Characterizing gender stereotypes in popular fiction

    Open Access•Chengyue Zhang, Ben Wu•ARTICLE•Online Journal of Communication…•2023

    Gender representation portrayed in popular mass media is known to reflect and reinforce societal gender stereotypes. This research uses two methods of natural language processing–Word2Vec and bidirectional encoder representations from transformers (BERT) model–to analyze gender representation in popular fiction and quantify gender bias with gender bias score. Word2Vec, which represents the words in vectorized format, can capture implicit human ge…

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  • Characterizing gender stereotypes in popular fiction

    Open Access•Chengyue Zhang, Ben Wu•ARTICLE•Online Journal of Communication…•2023

    Gender representation portrayed in popular mass media is known to reflect and reinforce societal gender stereotypes. This research uses two methods of natural language processing–Word2Vec and bidirectional encoder representations from transformers (BERT) model–to analyze gender representation in popular fiction and quantify gender bias with gender bias score. Word2Vec, which represents the words in vectorized format, can capture implicit human ge…

  • Disease burden comparison and associated risk factors of early- and late-onset neonatal sepsis in China and the USA, 1990–2019

    Open Access•Chengyue Zhang, Lianfang Yu et al.•ARTICLE•Global Health Action•2024

    BACKGROUND: The morbidity and mortality rates of neonatal sepsis are high, with significant differences in risk factors and disease burden observed between developing and developed countries. OBJECTIVE: To provide evidence to support recommendations on improving public health policies using a comparative systematic analysis of the disease burden. METHODS: Using data from the Global Burden of Disease Study 2019, the prevalence and incidence of ear…

  • Revisiting urban scaling through street-based hotspots across 360 Chinese cities

    Open Access•Ding Ma, Lili Deng et al.•ARTICLE•Cities•2025•Referências: 3

Geography (3 obras) · China (2 obras) · Archaeology (1 obras) · Artificial Intelligence (1 obras) · Burden of disease (1 obras) · Computer Science (1 obras) · Context (archaeology (1 obras) · Data science (1 obras) · Developing country (1 obras) · Disease (1 obras)

Ethnos_APP • Projeto Open Source • Licença MIT • Frontend v2.0.0 • Privacidade e Cookies • Documentação da API: api.ethnos.app/docs • Código da API: GitHub • DOI: 10.5281/zenodo.17049435 • Código do Frontend: GitHub • DOI: 10.5281/zenodo.17050053 • cruz.rio.br • Expectantes Misericordiae