- 学术相关
- A collective intelligence assembly approach to informing responsive net zero policy design: a greenhouse gas removal UK case study
- The Best Decisions Are Not the Best Advice: Making Adherence-Aware Recommendations
- Boundary Effects in the Diffusion of New Products on Cartesian Networks
- Decision uncertainty as a context for motor memory
- Dynamic product competitive analysis based on online reviews
- Human-robot interactions in investment decisions
- Generativity and profitability on B2B innovation platforms: a simulation-based theory development
- Engaging Users on Social Media Business Pages: The Roles of User Comments and Firm Responses
- A survey on diffusion models for time series and spatio-temporal data
- 技术技巧
学术相关
A collective intelligence assembly approach to informing responsive net zero policy design: a greenhouse gas removal UK case study1
The Best Decisions Are Not the Best Advice: Making Adherence-Aware Recommendations2
Boundary Effects in the Diffusion of New Products on Cartesian Networks3
Decision uncertainty as a context for motor memory4
Dynamic product competitive analysis based on online reviews5
Human-robot interactions in investment decisions6
Generativity and profitability on B2B innovation platforms: a simulation-based theory development7
Engaging Users on Social Media Business Pages: The Roles of User Comments and Firm Responses8
A survey on diffusion models for time series and spatio-temporal data9
技术技巧
AntV支持WebXR
采用JavaScript语言使用WebXR API，结合WebGL绘制3D场景创建交互式VR/AR Web应用
用户可以通过手机相机查看
主要语法与常规制图语法一致
更主流的是使用three.js
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Hardisty, A., & Workman, M. (2024). A collective intelligence assembly approach to informing responsive net zero policy design: A greenhouse gas removal UK case study. Collective Intelligence, 3(2), 26339137241254099. https://doi.org/10.1177/26339137241254099 ↩
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Grand-Clément, J., & Pauphilet, J. (2024). The Best Decisions Are Not the Best Advice: Making Adherence-Aware Recommendations. Management Science. https://doi.org/10.1287/mnsc.2023.01851 ↩
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Fibich, G., Levin, T., & Gillingham, K. T. (2024). Boundary Effects in the Diffusion of New Products on Cartesian Networks. Operations Research. https://doi.org/10.1287/opre.2022.0004 ↩
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Ogasa, K., Yokoi, A., Okazawa, G., Nishigaki, M., Hirashima, M., & Hagura, N. (2024). Decision uncertainty as a context for motor memory. Nature Human Behaviour, 1–14. https://doi.org/10.1038/s41562-024-01911-x ↩
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Zheng, L., Sun, L., He, Z., & He, S. (2024). Dynamic product competitive analysis based on online reviews. Decision Support Systems, 114268. https://doi.org/10.1016/j.dss.2024.114268 ↩
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Bianchi, M., & Brière, M. (2024). Human-robot interactions in investment decisions. Management Science. https://doi.org/10.1287/mnsc.2022.03886 ↩
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Haki, K., Tanriverdi, H., Safaei, D., Schmid, M., Aier, S., & Winter, R. (2024). Generativity and profitability on B2B innovation platforms: A simulation-based theory development. Mis Quarterly, 48(2), 583–612. https://doi.org/10.25300/MISQ/2023/17710 ↩
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Cheng, X., Bala, H., & Yang, M. (2024). Engaging Users on Social Media Business Pages: The Roles of User Comments and Firm Responses. MIS Quarterly, 48(2), 731–748. https://doi.org/10.25300/MISQ/2023/17621 ↩
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Yang, Y., Jin, M., Wen, H., Zhang, C., Liang, Y., Ma, L., Wang, Y., Liu, C., Yang, B., Xu, Z., Bian, J., Pan, S., & Wen, Q. (2024). A survey on diffusion models for time series and spatio-temporal data (No. arXiv:2404.18886). arXiv. http://arxiv.org/abs/2404.18886 ↩