[1] Que, X., Huang, J., Ralph, J., Zhang, J., Prabhu, A., Morrison, S., Hazen, R. and Ma, X., 2024. Using adjacency matrix to explore remarkable associations in big and small mineral data. Geoscience Frontiers (中科院一区top), 15(5), p.101823. https://doi.org/10.1016/j.gsf.2024.101823
[2] Hong, Y., Que, X*., Wang, Z., Ma, X., Wang, H., Salati, S. and Liu, J., 2024. Mangrove extraction from super-resolution images generated by deep learning models. Ecological Indicators (中科院一区top), 159, p.111714.https://doi.org/10.1016/j.ecolind.2024.111714
[3] Que, X., Ma, X., Ma, C., and Chen, Q.: A spatiotemporal weighted regression model (STWR v1.0) for analyzing local nonstationarity in space and time, Geoscientific Model Development (中科院一区), 13, 6149–6164, 2020.https://doi.org/10.5194/gmd-13-6149-2020
[4] Wang, Z., Que, X*., Li, M., Liu, Z., Shi, X., Ma, X., Fan, C. and Lin, Y., 2024. Spatiotemporally weighted regression (STWR) for assessing Lyme disease and landscape fragmentation dynamics in Connecticut towns. Ecological Informatics (中科院二区), 84, p.102870. https://doi.org/10.1016/j.ecoinf.2024.102870
[5] Que, X., Ma, C., Ma, X. and Chen, Q., 2021. Parallel computing for fast spatiotemporal weighted regression. Computers & Geosciences (中科院二区), 150, p.104723. https://doi.org/10.1016/j.cageo.2021.104723
[6] Fan, C., Que, X.*, Wang, Z. and Ma, X., 2023. Land cover impacts on surface temperatures: evaluation and application of a novel spatiotemporal weighted regression approach. ISPRS International Journal of Geo-Information (SCI), 12(4), p.151.https://doi.org/10.3390/ijgi12040151
[7] Que, X., Zhuang, X., Ma, X., Lai, Y., Xie, J., Fei, T., Wang, H. and Yuming, W.U., 2024. Modeling the spatiotemporal heterogeneity and changes of slope stability in rainfall-induced landslide areas. Earth Science Informatics (SCI), 17(1), pp.51-61.https://doi.org/10.1007/s12145-023-01165-7
[8] Deng, S.Y. and Que, X*., 2019. Research on the teaching assessment of students of science and engineering teachers in a university. Computer Applications in Engineering Education (SCI), 27(1), pp.5-12.https://doi.org/10.1002/cae.22051
[9] Chen, L., Wang, Z., Ma, X., Zhao, J., Que, X.*, Liu, J., Chen, R. and Li, Y., 2024. Empirical analysis of a Super-SBM-based framework for wetland carbon stock safety assessment. Remote Sensing (中科院二区), 16(10), p.1678.https://doi.org/10.3390/rs16101678
[10] Lai, Y., Fei, T., Wang, C., Xu, X., Zhuang, X., Que, X*., Zhang, Y., Yuan, W., Yang, H. and Hong, Y., 2025. Energy Carbon Emission Reduction Based on Spatiotemporal Heterogeneity: A County-Level Empirical Analysis in Guangdong, Fujian, and Zhejiang. Sustainability (SCI/SSCI), 17(7), p.3218.https://doi.org/10.3390/su17073218
[11] Hong, Y., Zhou, R., Liu, J., Que, X., Chen, B., Chen, K., He, Z. and Huang, G., 2025. Monitoring Mangrove Phenology Based on Gap Filling and Spatiotemporal Fusion: An Optimized Mangrove Phenology Extraction Approach (OMPEA). Remote Sensing (中科院二区), 17(3), p.549.https://doi.org/10.3390/rs17030549
[12] Zhang, J., Que, X., Madhikarmi, B., Hazen, R.M., Ralph, J., Prabhu, A., Morrison, S.M. and Ma, X., 2024. Using a 3D heat map to explore the diverse correlations among elements and mineral species. Applied Computing and Geosciences (ESCI/EI), 21, p.100154.https://doi.org/10.1016/j.acags.2024.100154
[13] Zhang, J., Clairmont, C., Que, X., Li, W., Chen, W., Li, C. and Ma, X., 2025. Streamlining geoscience data analysis with an LLM-driven workflow. Applied Computing and Geosciences (ESCI/EI), 25, p.100218.https://doi.org/10.1016/j.acags.2024.100218
[14] Que, X., Zhang, J., Chen, W., Ralph, J. and Ma, X., 2024. OpenMindat v1. 0.0 R package: A machine interface to Mindat open data to facilitate data-intensive geoscience discoveries. EGUsphere, 2024, pp.1-31.https://doi.org/10.5194/egusphere-2024-1141
[15] Ma, X., Ralph, J., Zhang, J., Que, X., Prabhu, A., Morrison, S.M., Hazen, R.M., Wyborn, L. and Lehnert, K., 2024. OpenMindat: Open and FAIR mineralogy data from the Mindat database. Geoscience Data Journal (SCI), 11(1), pp.94-104.https://doi.org/10.1002/gdj3.204
[16] Chen, R., Wang, C., Que, X.*, Liao, F.H., Ma, X., Wang, Z., Li, Z., Wen, K., Lai, Y. and Xu, X., 2024. Exploring Urban Heat Distribution and Thermal Comfort Exposure Using Spatiotemporal Weighted Regression (STWR). Buildings (SCI), 14(6), p.1883.https://doi.org/10.3390/buildings14061883
[17] Zou, X., Wang, C., Que, X*., Ma, X., Wang, Z., Fu, Q., Lai, Y. and Zhuang, X., 2024. Spatiotemporal Heterogeneous Responses of Ecosystem Services to Landscape Patterns in Urban–Suburban Areas. Sustainability (SCI/SSCI), 16(8), p.3260.https://doi.org/10.3390/su16083260
[18] Cui, Z., Chen, Q., Liu, G., Ma, X. and Que, X., 2021. Multiple-point geostatistical simulation based on conditional conduction probability. Stochastic Environmental Research and Risk Assessment (中科院二区), 35(7), pp.1355-1368.https://doi.org/10.1007/s00477-020-01944-4
[19] Ralph, J., Von Bargen, D., Martynov, P., Zhang, J., Que, X., Prabhu, A., Morrison, S.M., Li, W., Chen, W. and Ma, X., 2024. Mindat. org–the open access mineralogy database to accelerate data-intensive geoscience research. American Mineralogist (SCI).https://doi.org/10.2138/am-2024-9486
[20] Wang, Z., Fan, C., Que, X., Liao, F.H., Ma, X. and Wang, H., 2024. Multi-scale analysis of urban forests and socioeconomic patterns in a desert city, Phoenix, Arizona. Scientific Reports (SCI, 中科院二区), 14(1), p.23864. https://doi.org/10.1038/s41598-024-74208-8
[21] Chen, Q., Zheng, X., Xu, B., Sun, M., Zhou, Q., Lin, J., Que, X., Zhang, X. and Xu, Y., 2024. Exploring the spatiotemporal relationship between influenza and air pollution in Fuzhou using spatiotemporal weighted regression model. Scientific Reports (SCI, 中科院二区), 14(1), p.4116. https://doi.org/10.1038/s41598-024-54630-8
[22] Ralph, J., Martynov, P., Ma, X., Von Bargen, D., Li, W., Huang, J., Golden, J., Profeta, L., Prabhu, A., Morrison, S. and Que, X., 2024. Identifier Service in the Mindat Database: Persistent and Structured Access to Massive Records of Minerals and Other Natural Materials.Data Intelligence (ESCI/EI), https://doi.org/10.3724/2096-7004.di.2024.0021
[23] Que X*., Su S. (2021) Geographically Weighted Regression. In: Daya Sagar B., Cheng Q., McKinley J., Agterberg F. (eds) Encyclopedia of Mathematical Geosciences. Encyclopedia of Earth Sciences Series. Springer, Cham. https://doi.org/10.1007/978-3-030-26050-7_141-1
[24] Que X*., Ma X., Ma C., Liu F., Chen Q. (2021) Spatiotemporal Weighted Regression. In: Daya Sagar B., Cheng Q., McKinley J., Agterberg F. (eds) Encyclopedia of Mathematical Geosciences. Encyclopedia of Earth Sciences Series. Springer, Cham. https://doi.org/10.1007/978-3-030-26050-7_307-1
[25] Chen, W., Ma, X., Wang, Z., Li, W., Fan, C., Zhang, J., Que, X. and Li, C., 2024. Exploring neuro-symbolic AI applications in geoscience: implications and future directions for mineral prediction. Earth Science Informatics (SCI), 17(3), pp.1819-1835. https://doi.org/10.1007/s12145-024-01278-7
[26] Wang, H., Chen, M., Wang, Z., Huang, L., Caudill, C.C., Qu, S. and Que, X., 2024. How does extreme point sampling affect non-extreme simulation in geographical random forest?. Earth Science Informatics (SCI), 17(3), pp.1983-1991.https://doi.org/10.1007/s12145-024-01268-9
[27] Li, C., Zhang, J., Kale, A., Que, X., Salati, S. and Ma, X., 2022. Toward trust-based recommender systems for open data: A literature review. Information, 13(7), p.334.https://doi.org/10.3390/info13070334
[28] Chen Q., Liu G., Ma X., Que X. (2021) Spatial Analysis. In: Daya Sagar B., Cheng Q., McKinley J., Agterberg F. (eds) Encyclopedia of Mathematical Geosciences. Encyclopedia of Earth Sciences Series. Springer, Cham. https://doi.org/10.1007/978-3-030-26050-7_300-1
[29] Hummer D, Ma X, Que X, Zhang S, Liu C, Hazen R, Golden J & Downs R, Towards Quantitative Scales of Lithophilicity, Chalcophilicity and Hydrophilicity Using Statistical Correlations Among the Mineral-Forming Elements, Goldschmidt2020, https://doi.org/10.46427/gold2020.1113
[30] Tian, Shanjun, Wu, Chonglong, Liu, G, Que, X., Chen, Qiyu. (2016). Spatiotemporal Data Model for Managing 3D Geological Solid Models in Coal Digging Simulation. Int. J. Earth Sci. Eng (SCI), 12, 2472-2479.
[31] G Liu, X Que, X Hu, S Tian, J Zhu. Spatiotemporal Data Model forMulti-Factor Geological Process Analysis with Case Study, Mathematics of PlanetEarth, Springer Berlin Heidelberg,2014:319-323. https://doi.org/10.1007/978-3-642-32408-6_71
[32] Que, X., Wu, C., Chen, R., Liu, J. and Lu, C., 2016, July. Spatiotemporal data model for geographical process analysis with case study. In 2016 15th International Symposium on Parallel and Distributed Computing (ISPDC) (pp. 390-394). IEEE.https://doi.org/10.1109/ISPDC.2016.66
[33] Que, X., Liu, G., He, Z. and Qi, G., 2014. Realistic 3D terrain roaming and real-time flight simulation. 3D Research (EI), 5, pp.1-11. https://doi.org/10.1007/s13319-014-0027-2
[34] 费婷婷,丁晓婷,阙翔*,等.基于SBM-DEA与STWR模型的中国能源碳排放效率时空异质性分析[J].环境工程 (CSCD), 2024, 42(10):188-200.
[35] 朱映辰,谭芳林,阙翔*, 洪宇,潘爱芳,刘金福. 多时间尺度下森林公园负离子变化特征及与温湿度关系研究[J].西北林学院学报(CSCD),2023,38(06):211-218+227. doi:10.3969/j.issn.1001-7461.2023.06.28
[36] 龚声燕,连海峰, 阙翔*, 刘金福,谭芳林.基于APEI的2013—2018年福建省大气污染评价[J].环境工程(CSCD),2023,41(02):73-81.
[37] 苏少强, 阙翔* , 严宣辉,何中声,刘金福,李梦航,黄朝法,刘海,基于STWR模型的森林病虫影响因素研究[J].西北农林科技大学学报(自然科学版)(CSCD),2022, 50(11).
[38] 阙翔,陈日清,刘必雄,罗超,李梦航,苏少强.福建省农业技术推广信息化展示平台设计与实现[J].农业工程,2021,11(03):43-49.
[39] 林津, 洪宇, 林志玮, 阙翔, 刘金福, 连海峰. 福建泉州湾河口湿地时空动态及其驱动机理[J]. 北京林业大学学报(CSCD).2021,43(6):75-82.
[40] 温康民,任国玉,阙翔,孙秀宝.福州城市热岛精细化时空结构特征研究[J].环境监测管理与技术(CSCD) , 2023,35(01):14-19.