[1] Aranda-Michel,E., Luketich, J. D., Rao, R., Morell, V. O., Arnaoutakis, G. J., Kilic, A., Dunn-Lewis, C., & Sultan, I.(2022). The effect of receiving an award from the American Association for Thoracic Surgery Foundation. JTCVS Open, 10, 282-289. https://doi.org/10.1016/j.xjon.2021.10.066.
[2] Azzopardi L., Girolami M., & Van Risjbergen K. (2003). Investigating the relationship between language model perplexity and IR precision-recall measures. In Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval, 369-370.https://doi.org/10.1145/860435. 860505.
[3] Baerwald T. J., Firth P.,& Ruth, S. L.(2016). The Dynamics of Coupled Natural and Human Systems Program at the U.S. National Science Foundation: lessons learned in interdisciplinary funding program development and management. Current Opinion in Environmental Sustainability, 19, 123-133.https://doi.org/10.1016/j.cosust.2016.02.001.
[4] Bailey, K. D. (2006). Systems theory. In Springer eBooks (pp. 379-401). https://doi.org/10.1007/0-387-36274-6_19.
[5] Bermudez-Edo,M., Barnaghi, P., & Moessner, K.(2018). Analysing real world data streams with spatio-temporal correlations: Entropy vs. Pearson correlation. Automation in Construction, 88, 87-100.https://doi.org/10.1016/j.autcon.2017.12.036.
[6] Blei D. M., Ng A. Y., & Jordan M.I. (2003). Latent Dirichlet Allocation.Journal of Machine Learning Research, 3, 993-1022. https://doi.org/10.1162/jmlr.2003.3.4-5.993.
[7] Blondel V. D., Guillaume J., Lambiotte R., & Lefebvre E. (2008). Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, 2008(10), P10008.https://doi.org/10.1088/1742-5468/2008/10/p10008.
[8] Bozeman B.,& Youtie, J.(2017). Socio-economic impacts and public value of government-funded research: Lessons from four US National Science Foundation initiatives. Research Policy, 46(8), 1387-1398.https://doi.org/10.1016/j.respol.2017.06.003.
[9] Ebadi A., Auger A.,& Gauthier, Y.(2022). Detecting emerging technologies and their evolution using deep learning and weak signal analysis. Journal of Informetrics, 16(4), 101344.https://doi.org/10.1016/j.joi.2022.101344.
[10] Fruchterman, T.M.J. and Reingold, E.M. (1991), Graph drawing by force-directed placement. Softw: Pract. Exper., 21: 1129-1164. https://doi.org/10.1002/spe.4380211102.
[11] Funkner A. A., Yakovlev A. N.,& Kovalchuk, S. V.(2022). Surrogate-assisted performance prediction for data-driven knowledge discovery algorithms: Application to evolutionary modeling of clinical pathways. Journal of Computational Science, 59, 101562. https://doi.org/10.1016/j.jocs.2022.101562.
[12] Giannopoulos, G., & Munro, J. (2019). Innovation Ecosystems—A Systems-Based Theory of Innovation. In Elsevier eBooks (pp. 19-41). https://doi.org/10.1016/b978-0-12-813804-5.00002-4.
[13] Gibson E., Daim T. U.,& Dabić, M.(2019). Evaluating university industry collaborative research centers. Technological Forecasting and Social Change, 146, 181-202. https://doi.org/10.1016/j.techfore.2019.05.014.
[14] Gozuacik N., Sakar C. O.,& Ozcan, S.(2023). Technological forecasting based on estimation of word embedding matrix using LSTM networks. Technological Forecasting and Social Change, 191, 122520.https://doi.org/10.1016/j.techfore.2023.122520
[15] Hagen, L. (2018). Content analysis of e-petitions with topic modeling: How to train and evaluate LDA models? Information Processing and Management, 54(6), 1292-1307. https://doi.org/10.1016/j.ipm.2018.05.006.
[16] Han J., Kamber M., & Pei J. (2012). Getting to know your data. In Elsevier eBooks (pp. 39-82).https://doi.org/10.1016/b978-0-12-381479-1.00002-2
[17] Hauptmann, E. (2022). Book review. Studies in History and Philosophy of Science, 94, 206-207.https://doi.org/10.1016/j.shpsa.2022.02.009
[18] Hu K., Luo Q., Qi K., Yang S., Mao J., Fu X., Zheng J., Wu H., Guo Y.,& Zhu, Q.(2019). Understanding the topic evolution of scientific literatures like an evolving city: Using Google Word2Vec model and spatial autocorrelation analysis. Information Processing and Management, 56(4), 1185-1203.https://doi.org/10.1016/j.ipm.2019.02.014
[19] Huang L., Chen X., Ni X., Liu J., Cao X.,& Wang, C.(2021). Tracking the dynamics of co-word networks for emerging topic identification. Technological Forecasting and Social Change, 170, 120944.https://doi.org/10.1016/j.techfore.2021.120944
[20] Liang Z., Mao J., Lu K., Ba Z.,& Li, G.(2021). Combining deep neural network and bibliometric indicator for emerging research topic prediction. Information Processing and Management, 58(5), 102611.https://doi.org/10.1016/j.ipm.2021.102611
[21] Liu Z., Xu H., Yue L. (2018). Research on Lagging Effect of Topic Diffusion Evolution Face to Prediction of Research Front. Journal of the China Society for Scientific and Technical Information, 37(10), 979-988.
[22] Lu K., Yang G.,& Wang, X.(2022). Topics emerged in the biomedical field and their characteristics. Technological Forecasting and Social Change, 174, 121218. https://doi.org/10.1016/j.techfore.2021.121218
[23] Mejia C.,& Kajikawa, Y.(2020). Emerging topics in energy storage based on a large-scale analysis of academic articles and patents. Applied Energy, 263, 114625. https://doi.org/10.1016/j.apenergy.2020.114625.
[24] Moehrle M. G.(2019). Similarity measurement in times of topic modelling. World Patent Information, 59, 101934. https://doi.org/10.1016/j.wpi.2019.101934
[25] Newman D., & Lau J., & Grieser K., & Baldwin T. (2010). Automatic Evaluation of Topic Coherence. Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics. InAssociation for Computational Linguistics, 100-108.
[26] Nichols, L. (2014). A topic model approach to measuring interdisciplinarity at the National Science Foundation. Scientometrics, 100(3), 741-754. https://doi.org/10.1007/s11192-014-1319-2
[27] Popper K. R.(1972). Objective knowledge: An Evolutionary Approach. Oxford: Clarendon Press.
[28] Porter, A. L., & Detampel, M. J. (1995). Technology opportunities analysis. Technological Forecasting and Social Change, 49(3), 237-255. https://doi.org/10.1016/0040-1625(95)00022-3
[29] Prabhaa S. S., Bindu N., Manoj P.,& Kumar, K. S.(2020). Citation network analysis of plastic electronics: Tracing the evolution and emerging research fronts. Materials Today: Proceedings.https://doi.org/10.1016/j.matpr.2020.04.236
[30] Salleh F. H.M., Arif, S. M., Zainudin, S., & Firdaus-Raih, M.(2015). Reconstructing gene regulatory networks from knock-out data using Gaussian Noise Model and Pearson Correlation Coefficient. Computational Biology and Chemistry, 59, 3-14. https://doi.org/10.1016/j.compbiolchem.2015.04.012.
[31] Savin, I. (2023). Evolution and recombination of topics in Technological Forecasting and Social Change. Technological Forecasting and Social Change, 194, 122723. https://doi.org/10.1016/j.techfore.2023.122723
[32] Shibata N., Kajikawa Y., Takeda Y., Sakata I.,& Matsushima, K.(2011). Detecting emerging research fronts in regenerative medicine by the citation network analysis of scientific publications. Technological Forecasting and Social Change, 78(2), 274-282. https://doi.org/10.1016/j.techfore.2010.07.006
[33] Small H., Boyack K. W.,& Klavans, R.(2014). Identifying emerging topics in science and technology. Research Policy, 43(8), 1450-1467. https://doi.org/10.1016/j.respol.2014.02.005
[34] Tamakloe R.,& Park, D.(2023). Discovering latent topics and trends in autonomous vehicle-related research: A structural topic modelling approach. Transport Policy, 139, 1-20. https://doi.org/10.1016/j.tranpol.2023.06.001
[35] Wang X., He J., Huang H.,& Wang, H.(2022). MatrixSim: A new method for detecting the evolution paths of research topics. Journal of Informetrics, 16(4), 101343. https://doi.org/10.1016/j.joi.2022.101343
[36] Xu H., Winnink J., Yue Z., Zhang H.,& Pang, H.(2021). Multidimensional Scientometric indicators for the detection of emerging research topics. Technological Forecasting and Social Change, 163, 120490.https://doi.org/10.1016/j.techfore.2020.120490
[37] Xu S., Hao L., Yang G., Lu K.,& An, X.(2021). A topic models based framework for detecting and forecasting emerging technologies. Technological Forecasting and Social Change, 162, 120366.https://doi.org/10.1016/j.techfore.2020.120366
[38] Yang J., Lu W., Hu J.,& Huang, S.(2022). A novel emerging topic detection method: A knowledge ecology perspective. Information Processing and Management, 59(2), 102843. https://doi.org/10.1016/j.ipm.2021.102843
[39] Ye G., Wang C., Wu C., Peng Z., Wei J., Song X., Tan Q.,& Wu, L.(2023). Research frontier detection and analysis based on research grants information: A case study on health informatics in the US. Journal of Informetrics, 17(3), 101421. https://doi.org/10.1016/j.joi.2023.101421
[40] Zhang Y., Guo Y., Wang X., Zhu D.,& Porter, A. L.(2013). A hybrid visualisation model for technology roadmapping: bibliometrics, qualitative methodology and empirical study. Technology Analysis & Strategic Management, 25(6), 707-724. https://doi.org/10.1080/09537325.2013.803064
[41] Zhang Y., Zhang G., Chen H., Porter A. L., Zhu D.,& Lu, J.(2016). Topic analysis and forecasting for science, technology and innovation: Methodology with a case study focusing on big data research. Technological Forecasting and Social Change, 105, 179-191. https://doi.org/10.1016/j.techfore.2016.01.015