Gao, W., Ping, S. & Liu, X. Gender differences in depression, anxiety, and stress among college students: a longitudinal study from China. J. Affect. Disord. 263, 292–300. https://doi.org/10.1016/j.jad.2019.11.121 (2020).
Google Scholar
El Ansari, W. & Stock, C. Explaining the gender difference in self-rated health among university students in Egypt. Women Health 56, 731–744. https://doi.org/10.1080/03630242.2015.1118733 (2016).
Google Scholar
Hill, M. R. G. S. & Hill, M. L. J. In their own words: stressors facing medical students in the millennial generation. Med. Educ. Online 23, 1530558. https://doi.org/10.1080/10872981.2018.1530558 (2018).
Google Scholar
Gusy, B., Lesener, T. & Wolter, C. Time pressure and health-related loss of productivity in university students: the mediating role of exhaustion. Front. Public Health 9, 653440. https://doi.org/10.3389/fpubh.2021.653440 (2021).
Google Scholar
Kercher, J. Academic success and dropout among international students in Germany and other major host countries. DAAD Focus. Deutscher Akademischer Austauschdienst, 2018. https://www2.daad.de/medien/der-daad/analysen-studien/daad-focus_academic_success_and_dropout_among_international_students_2019.pdf
Hunt, J. & Eisenberg, D. Mental health problems and help-seeking behavior among college students. J. Adolesc. Health 46, 3–10. https://doi.org/10.1016/j.jadohealth.2009.08.008 (2010).
Google Scholar
Pereira, S. et al. University student mental health survey 2020: a large scale study into the prevalence of student mental illness within UK universities. Insight Network, London, UK, 2020. https://assets.website-files.com/602d05d13b303dec233e5ce3/60305923a557c3641f1a7808_Mental%20Health%20Report%202019%20(2020).pdf
Chen, W., Yu, S. & Xiong, D. Effects of Tai Chi intervention on perceived stress, anxiety, and sleep in college students. American College Health Association-National College Health Assessment II: Reference Group Executive Summary (2018). Available online at: https://www.scirp.org/journal/paperinformation?paperid=98388
Lin, Z.-Z. et al. Prevalence of depression among university students in China: a systematic review and meta-analysis. BMC Psychol. 13, 373. https://doi.org/10.1186/s40359-025-02688-y (2025).
Google Scholar
Auerbach, R. P. et al. WHO world mental health surveys international college student project: Prevalence and distribution of mental disorders. J. Abnorm. Psychol. 127, 623–638 (2018).
Google Scholar
Zare, N., Parvareh, M., Noori, B. & Namdari, M. Mental health status of Iranian university students using the GHQ-28: A meta-analysis. SJKU. 21, 1–16 (2016).
Chen, J., Shi, H., Pan, W. & Sun, D. Characterizing the supportive environment of informal spaces on cold region university campuses to enhance social interaction behavior. Buildings 14, 1529. https://doi.org/10.3390/buildings14061529 (2024).
Google Scholar
Alnusairat, S. A. Y. & Al-Sharif, Z. Towards meaningful university space: Perceptions of the quality of open spaces for students. Buildings 11, 555. https://doi.org/10.3390/buildings11110556 (2021).
Google Scholar
Lee, H. J. & Szinovacz, M. E. Positive, negative, and ambivalent interactions with family and friends: Associations with well-being. J. Marriage Fam. 78, 660–679. https://doi.org/10.1111/jomf.12302 (2016).
Google Scholar
Keith, W. B. Academic support, social support, and professional development of higher and lower achieving psychology majors. N. Am. J. Psychol. 17, 201–215 (2015).
Hasan, N. et al. Influence of campus outdoor spaces on students behavior: Enhancing social interaction and learning at An-Najah University. An-Najah Univ. J. Res. – (Nat. Sci.) 39, 2335 (2024). https://doi.org/10.35552/anujr.a.39.2.2335
Thwaites, K. & Simkins, I. Experiential Landscape: An Approach to People, Place and Space (Routledge, 2007).
Kaplan, R. & Kaplan, S. The Experience of Nature: A Psychological Perspective (Cambridge Univ, 1989).
Ulrich, R. S., Simons, R. F., Losito, B. D., Fiorito, E. & Miles, M. Stress recovery during exposure to natural and urban environments. J. Environ. Psychol. 11, 201–230. https://doi.org/10.1016/S0272-4944(05)80184-7 (1991).
Google Scholar
Hadavi, S. & Sullivan, W. C. Environmental aesthetics. In Handbook of Behavioral and Cognitive Geography 307–321 (Edward Elgar Publishing, 2018). https://doi.org/10.4337/9781784717544.00027
Berghman, M. & Hekkert, P. Towards a unified model of aesthetic pleasure in design. New Ideas Psychol. 45, 136–144. https://doi.org/10.1016/j.newideapsych.2017.03.004 (2017).
Google Scholar
Tao, W., Wu, Y., Li, W. & Liu, F. Influence of classroom colour environment on college students’ emotions during campus lockdown in the COVID-19 post-pandemic era—A case study in Harbin, China. Buildings 12, 1873. https://doi.org/10.3390/buildings12111873 (2022).
Google Scholar
Li, X., Ni, G. & Dewancker, B. Improving the attractiveness and accessibility of campus green space for developing a sustainable university environment. Environ. Sci. Pollut. Res. 26, 33399–33415. https://doi.org/10.1007/s11356-019-06319-z (2019).
Google Scholar
Li, K., Zhai, Y., Dou, L. & Liu, J. A preliminary exploration of landscape preferences based on naturalness and visual openness for college students with different moods. Front. Psychol. 12, 629650. https://doi.org/10.3389/fpsyg.2021.629650 (2021).
Google Scholar
Wang, R., Jiang, W. & Lu, T. Landscape characteristics of university campus in relation to aesthetic quality and recreational preference. Urban For. Urban Green. 66, 127389. https://doi.org/10.1016/j.ufug.2021.127389 (2021).
Google Scholar
Mohamed, A. Everyday aesthetics and attractiveness of the university campus. In The Social Contexts of Young People – Engaging Youth and Young Adults (2022). https://doi.org/10.5772/intechopen.107873
Wang, R. & Zhao, J. Effects of evergreen trees on landscape preference and perceived restorativeness across seasons. Landsc. Res. 45, 649–661. https://doi.org/10.1080/01426397.2019.1699507 (2020).
Google Scholar
Yang, H. et al. Association between natural/built campus environment and depression among Chinese undergraduates: multiscale evidence for the moderating role of socioeconomic factors after controlling for residential self-selection. Front. Public Health 10, 844541. https://doi.org/10.3389/fpubh.2022.844541 (2022).
Google Scholar
Lau, S. S. Y., Gou, Z. & Liu, Y. Healthy campus by open space design: approaches and guidelines. Front. Archit. Res. https://doi.org/10.1016/j.foar.2014.06.006 (2014).
Google Scholar
Malekinezhad, F., Courtney, P., Bin Lamit, H. & Vigani, M. Investigating the mental health impacts of university campus green space through perceived sensory dimensions and the mediation effects of perceived restorativeness on restoration experience. Front. Public Health 8, 578241. https://doi.org/10.3389/fpubh.2020.578241 (2020).
Google Scholar
Jahani, A. & Saffariha, M. Aesthetic preference and mental restoration prediction in urban parks: an application of environmental modeling approach. Urban For. Urban Green. 54, 126775. https://doi.org/10.1016/j.ufug.2020.126775 (2020).
Google Scholar
Aboufazeli, S., Jahani, A. & Farahpour, M. Aesthetic quality modeling of the form of natural elements in the environment of urban parks. Evol. Intell. 12, (2024). https://doi.org/10.1007/s12065-022-00768-1
Mundher, R. et al. Aesthetic quality assessment of landscapes as a model for urban forest areas: a systematic literature review. Forests 13, 991. https://doi.org/10.3390/f13070991 (2022).
Google Scholar
Jahani, A. & Mohammadi, F. Aesthetic quality modeling of landscape in urban green space using artificial neural network. J. Nat. Environ. 69, 951–963 (2016).
Balasubramanian, S., Irulappan, C. & Kitchley, J. L. Aesthetics of urban commercial streets from the perspective of cognitive memory and user behavior in urban environments. Front. Archit. Res. 11, 949–962. https://doi.org/10.1016/j.foar.2022.03.003 (2022).
Google Scholar
Jahani, A. Forest landscape aesthetic quality model (FLAQM): A comparative study on landscape modelling using regression analysis and artificial neural networks. J. For. Sci. 65, 61–69. https://doi.org/10.17221/86/2018-JFS (2019).
Google Scholar
Havinga, I. et al. Social media and deep learning capture the aesthetic quality of the landscape. Sci. Rep. 11, 1. https://doi.org/10.1038/s41598-021-99282-0 (2021).
Google Scholar
Kerebel, A. et al. Landscape aesthetic modelling using Bayesian networks: conceptual framework and participatory indicator weighting. Landsc. Urban Plan. 185, 258–271. https://doi.org/10.1016/j.landurbplan.2019.02.001 (2019).
Google Scholar
Jahani, A. et al. Environmental modeling of landscape aesthetic value in natural urban parks using artificial neural network technique. Model. Earth Syst. Environ. 8, 163–172. https://doi.org/10.1007/s40808-020-01068-2 (2022).
Google Scholar
Działek, J. et al. (Re) greening transition of academic green spaces as a response to social and environmental challenges: the role of bottom-up initiatives. Urban For. Urban Green. https://doi.org/10.1016/j.ufug.2025.128692 (2025).
Google Scholar
Soydaner, D. & Wagemans, J. Unveiling the factors of aesthetic preferences with explainable AI. Br. J. Psychol. https://doi.org/10.1111/bjop.12707 (2024).
Google Scholar
Wen, H. et al. An assessment of the psychologically restorative effects of the environmental characteristics of university common spaces. Environ. Impact Assess. Rev. 110, 107645. https://doi.org/10.1016/j.eiar.2024.107645 (2025).
Google Scholar
Asadi, M. et al. Relationship of connected and separate knowing to individualism-collectivism among Iranian and American students. Adv. Cogn. Sci. 8, 17–22 (2006).
Rahnamayi, M. T., Talshy, M. & Moradi, N. Analytical study on necessity of decentralization of higher education (Iran–Tehran 2013). Int. J. Archit. Urban Dev. 3, 65–70 (2013).
Enwin, A. The influence of culture and heritage on interior aesthetics. Glob. J. Eng. Technol. Adv. 19, 113–122. https://doi.org/10.30574/gjeta.2024.19.1.0062 (2024).
Google Scholar
Ministry of Science, Research and Technology [MSRT]. Ministry of Science, Research and Technology of Iran. Available at: https://www.msrt.ir/fa (accessed 2024).
Rivera, C. et al. Developing public space and land values in cities and neighbourhoods. UN Habitat (2018). https://unhabitat.org/sites/default/files/download-manager-files/Discussion%20Paper%20-%20Developing%20Public%20Space%20and%20Land%20Values%20in%20Cities%20and%20Neighbourhoods.pdf
Asim, F. et al. Restoring the mind: A neuropsychological investigation of university campus built environment aspects for student well-being. Build. Environ. 244, 110810. https://doi.org/10.1016/j.buildenv.2023.110810 (2023).
Google Scholar
Wang, R. et al. Characteristics of urban green spaces in relation to aesthetic preference and stress recovery. Urban For. Urban Green. 14, 6–13. https://doi.org/10.1016/j.ufug.2019.03.005 (2019).
Google Scholar
Kaplan, S. The restorative benefits of nature: Toward an integrative framework. J. Environ. Psychol. 15, 169–182. https://doi.org/10.1016/0272-4944(95)90001-2 (1995).
Google Scholar
Sarmad, Z., Hejazi, E. & Bazargan, A. Research Methods in Behavioral Sciences (Agah Publ, 2014).
Jahani, A., Saffariha, M. & Barzegar, P. Landscape aesthetic quality assessment of forest lands: An application of machine learning approach. Soft Comput. 27, 6671–6686. https://doi.org/10.1007/s00500-022-07642-3 (2023).
Google Scholar
Jahani, A. & Rayegani, B. Forest landscape visual quality evaluation using artificial intelligence techniques as a decision support system. Stoch. Environ. Res. Risk Assess. 34, 1473–1486. https://doi.org/10.1007/s00477-020-01832-x (2020).
Google Scholar
Rout, A. & Galpern, P. Benches, fountains and trees: using mixed-methods with questionnaire and smartphone data to design urban green spaces. Urban For. Urban Green. 67, 127335. https://doi.org/10.1016/j.ufug.2021.127335 (2022).
Google Scholar
Gülçin, D. & Yalçınkaya, N. M. Correlating fluency theory-based visual aesthetic liking of landscape with landscape types and features. Geo-Spat. Inf. Sci. 27, 237–256. https://doi.org/10.1080/10095020.2022.2125836 (2024).
Google Scholar
Tomitaka, M., Uchihara, S., Goto, A. & Sasaki, T. Species richness and flower color diversity determine aesthetic preferences of natural-park and urban-park visitors for plant communities. Environ. Sustain. Indic. 11, 100130. https://doi.org/10.1016/j.indic.2021.100130 (2021).
Google Scholar
Ma, H., Xu, Q. & Zhang, Y. High or low? Exploring the restorative effects of visual levels on campus spaces using machine learning and street view imagery. Urban For. Urban Green. 88, 128087. https://doi.org/10.1016/j.ufug.2023.128087 (2023).
Google Scholar
Erdinç, S. Y. A timeless journey of strength and beauty: The potentials of the use of stone in architecture. J. Des. Resil. Archit. Plan. 4, 100. https://doi.org/10.47818/DRArch.2023.v4i3100 (2023).
Google Scholar
Guo, W., Hongyan, W. & Liu, X. Research on the psychologically restorative effects of campus common spaces from the perspective of health. Front. Public Health 11, 1131180. https://doi.org/10.3389/fpubh.2023.1131180 (2023).
Google Scholar
Huang, Q., Yang, M., Jane, H.-A., Li, S. & Bauer, N. Trees, grass, or concrete? The effects of different types of environments on stress reduction. Landsc. Urban Plan. 193, 103654. https://doi.org/10.1016/j.landurbplan.2019.103654 (2020).
Google Scholar
Zhang, P., He, Q., Chen, Z., Li, X. & Ma, J. An empirical study on the promotion of students’ physiological and psychological recovery in green space on campuses in the post-epidemic era. Int. J. Environ. Res. Public Health 20, 151. https://doi.org/10.3390/ijerph20010151 (2022).
Google Scholar
Deng, L. et al. Empirical study of landscape types, landscape elements and landscape components of the urban park promoting physiological and psychological restoration. Urban For. Urban Green. 48, 126488. https://doi.org/10.1016/j.ufug.2019.126488 (2020).
Google Scholar
Ning, W., Yin, J., Chen, Q. & Sun, X. Effects of brief exposure to campus environment on students’ physiological and psychological health. Front. Public Health 11, 1051864. https://doi.org/10.3389/fpubh.2023.1051864 (2023).
Google Scholar
Mahrous, A., Dewidar, K., Refaat, M. & Nessim, A. The impact of biophilic attributes on university students’ level of satisfaction: Using virtual reality simulation. Ain Shams Eng. J. 15, 102304. https://doi.org/10.1016/j.asej.2023.102304 (2024).
Google Scholar
Twedt, E., Rainey, R. M. & Proffitt, D. R. Designed natural spaces: Informal gardens are perceived to be more restorative than formal gardens. Front. Psychol. 7, 88. https://doi.org/10.3389/fpsyg.2016.00088 (2016).
Google Scholar
Jahani, A. & Saffariha, M. Modeling of trees failure under windstorm in harvested Hyrcanian forests using machine learning techniques. Sci. Rep. 11, 1124. https://doi.org/10.1038/s41598-020-80426-7 (2021).
Google Scholar
Jahani, A., Feghhi, J., Makhdoum, M. F. & Omid, M. Optimized forest degradation model (OFDM): An environmental decision support system for environmental impact assessment using an artificial neural network. J. Environ. Plann. Manag. 59, 214–232. https://doi.org/10.1080/09640568.2015.1005732 (2016).
Google Scholar
Jahani, A. & Saffariha, M. Environmental decision support system for plane trees failure prediction: A comparison of multi-layer perceptron and random forest modeling approaches. Agrosyst. Geosci. Environ. 5, e20316. https://doi.org/10.1002/agg2.20316 (2022).
Google Scholar
Dewi, K., Adikara, P. P. & Adinugroho, S. Prediksi Indeks Harga Konsumen (IHK) kelompok perumahan, air, listrik, gas dan bahan bakar menggunakan metode support vector regression. J. Pengemb. Teknol. Inf. Ilmu Komput. 2, 3856–3862 (2018).
Schölkopf, B. & Smola, A. J. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. (MIT Press, 2002). https://mcube.lab.nycu.edu.tw/~cfung/docs/books/scholkopf2002learning_with_kernels.pdf
Géron, A. Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow. (O’Reilly Media, 2019). https://www.rasa-ai.com/wp-content/uploads/2022/02/Aur%C3%A9lien-G%C3%A9ron-Hands-On-Machine-Learning-with-Scikit-Learn-Keras-and-Tensorflow_-Concepts-Tools-and-Techniques-to-Build-Intelligent-Systems-O%E2%80%99Reilly-Media-2019.pdf
Goodfellow, I., Bengio, Y. & Courville, A. Deep learning. (MIT Press, 2016). http://alvarestech.com/temp/deep/Deep%20Learning%20by%20Ian%20Goodfellow,%20Yoshua%20Bengio,%20Aaron%20Courville%20(z-lib.org).pdf
LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521, 436–444. https://doi.org/10.1038/nature14539 (2015).
Google Scholar
Elgendy, M. Deep learning for vision systems. (Simon and Schuster, 2020). https://bayanbox.ir/view/5513061350247641016/Mohamed-Elgendy-Deep-Learning-for-Vision-Systems-Manning-Publications-2020.pdf
Chen, Z. et al. How vegetation colorization design affects urban forest aesthetic preference and visual attention: an eye-tracking study. Forests 14, 71491. https://doi.org/10.3390/f14071491 (2023).
Google Scholar
Rodríguez-Avellaneda, A. H., Rodriguez, R., Shafieezadeh, A. & Yilmaz, A. Socioeconomic disparities in hurricane-induced power outages: insights from multi-hurricane data in Florida using XGBoost. Sustain. Cities Soc. 106362 (2025). https://doi.org/10.1016/j.scs.2025.106362
Ao, Y., Li, H., Zhu, L., Ali, S. & Yang, Z. The linear random forest algorithm and its advantages in machine learning assisted logging regression modeling. J. Petrol. Sci. Eng. 174, 776–789. https://doi.org/10.1016/j.petrol.2018.11.067 (2019).
Google Scholar
Ouedraogo, I., Defourny, P. & Vanclooster, M. Application of random forest regression and comparison of its performance to multiple linear regression in modeling groundwater nitrate concentration at the African continent scale. Hydrogeol. J. 26, 1–14. https://doi.org/10.1007/s10040-018-1900-5 (2018).
Google Scholar
Warner, B., Ratner, E., Carlous-Khan, K., Douglas, C. & Lendasse, A. Ensemble learning with highly variable class-based performance. Mach. Learn. Knowl. Extr. 6, 2149–2160. https://doi.org/10.3390/make6040106 (2024).
Google Scholar
Safaei-Farouji, M. et al. Exploring the power of machine learning to predict carbon dioxide trapping efficiency in saline aquifers for carbon geological storage project. J. Clean. Prod. 372, 133778. https://doi.org/10.1016/j.jclepro.2022.133778 (2022).
Google Scholar
Ye, M. & Hill, M. C. Global sensitivity analysis for uncertain parameters, models, and scenarios. In Sensitivity Analysis in Earth Observation Modelling 177–210 (Elsevier, 2017). https://doi.org/10.1016/B978-0-12-803011-0.00010-0
Oparaji, U., Sheu, R.-J., Bankhead, M., Austin, J. & Patelli, E. Robust artificial neural network for reliability and sensitivity analyses of complex non-linear systems. Neural Netw. 96, 80–90. https://doi.org/10.1016/j.neunet.2017.09.003 (2017).
Google Scholar
Cortez, P. & Embrechts, M. J. Using sensitivity analysis and visualization techniques to open black box data mining models. Inf. Sci. 225, 1–17. https://doi.org/10.1016/j.ins.2012.10.039 (2013).
Google Scholar
Pianosi, F. et al. Sensitivity analysis of environmental models: a systematic review with practical workflow. Environ. Model. Softw. 79, 214–232. https://doi.org/10.1016/j.envsoft.2016.02.008 (2016).
Google Scholar
Thwaites, K., Mathers, A. & Simkins, I. Socially Restorative Urbanism: The Theory, Process and Practice of Experiemics (Routledge, 2013).
Peng, Z., Zhang, R., Dong, Y. & Liang, Z. A study on the relationship between campus environment and college students’ emotional perception: a case study of Yuelu Mountain National University Science and Technology City. Buildings 14, 2849. https://doi.org/10.3390/buildings14092849 (2024).
Google Scholar
Lee, L. K., Zakaria, N. A. & Foo, K. Y. Psychological restorative potential of a pilot on-campus ecological wetland in Malaysia. Sustainability 14, 246. https://doi.org/10.3390/su14010246 (2021).
Google Scholar
Rinaldi, B. M. Botanic nations: the aesthetic of the forest in Chandigarh and Singapore. J. Landsc. Archit. 18, 40–53. https://doi.org/10.1080/18626033.2023.2258724 (2023).
Google Scholar
Guo, L.-N., Zhao, R.-L., Ren, A.-H., Niu, L.-X. & Zhang, Y.-L. Stress recovery of campus street trees as visual stimuli on graduate students in autumn. Int. J. Environ. Res. Public Health 17, 148. https://doi.org/10.3390/ijerph17010148 (2020).
Google Scholar
Markevych, I. et al. Exploring pathways linking greenspace to health: theoretical and methodological guidance. Environ. Res. 158, 301–317. https://doi.org/10.1016/j.envres.2017.06.028 (2017).
Google Scholar
Du, Y., Zou, Z., He, Y., Zhou, Y. & Luo, S. Beyond blue and green spaces: identifying and characterizing restorative environments on Sichuan Technology and Business University campus. Int. J. Environ. Res. Public Health 19, 13500. https://doi.org/10.3390/ijerph192013500 (2022).
Google Scholar
Xu, W., Zhao, J., Huang, Y. & Hu, B. Design intensities in relation to visual aesthetic preference. Urban For. Urban Green. 34, 305–310. https://doi.org/10.1016/j.ufug.2018.07.011 (2018).
Google Scholar
Fedorovskaya, N. A., Kravchenko, I. A. & Chernova, A. V. Aestheticization of the park environment around utilitarian objects of university campuses (by the example of the design concept of reconstruction of the park parking area of the FEFU campus). Urban Stud. 4, 10–19. https://doi.org/10.7256/2310-8673.2022.4.37485 (2022).
Google Scholar
Xu, W., Jiang, B. & Zhao, J. Effects of seasonality on visual aesthetic preference. Landsc. Res. 47, 388–399. https://doi.org/10.1080/01426397.2022.2039110 (2022).
Google Scholar
Swami, V., Barron, D. & Furnham, A. Exposure to natural environments, and photographs of natural environments, promotes more positive body image. Body Image 24, 82–94. https://doi.org/10.1016/j.bodyim.2017.12.006 (2018).
Google Scholar
Higuera-Trujillo, J. L., Maldonado, J.L.-T. & Millán, C. L. Psychological and physiological human responses to simulated and real environments: a comparison between photographs, 360 panoramas, and virtual reality. Appl. Ergon. 65, 398–409. https://doi.org/10.1016/j.apergo.2017.05.001 (2017).
Google Scholar
Moscoso, C., Matusiak, U. B., Svensson, P. & Orleanski, K. Analysis of stereoscopic images as a new method for daylighting studies. ACM Trans. Appl. Percept. 11, 1–13. https://doi.org/10.1145/2665078 (2015).
Google Scholar
Catusse, M., Corda, E. & Aebischer, N. Winter habitat selection and food choice of the capercaillie (Tetrao urogallus) in the French Pyrenees. Game Wildl. Sci. 19, 261–280 (2002).
Karimimoshaver, M., Parsamanesh, M., Aram, F. & Mosavi, A. The impact of the city skyline on pleasantness; state of the art and a case study. Heliyon 7, e07009. https://doi.org/10.1016/j.heliyon.2021.e07009 (2021).
Google Scholar
Peng, Y.-L., Li, Y., Cheng, W.-Y. & Wang, K. Evaluation and optimization of sense of security during the day and night in campus public spaces based on physical environment and psychological perception. Sustainability 16, 1256. https://doi.org/10.3390/su16031256 (2024).
Google Scholar
Zeng, X., Zhang, B., Chen, S., Lin, Y. & Haans, A. Exploring the impact of daytime and nighttime campus lighting on emotional responses and perceived restorativeness. Buildings 6, 872. https://doi.org/10.3390/buildings15060872 (2025).
Google Scholar
Moazamnia, M., Hassanzadeh, Y., Nadiri, A., Khatibi, R. & Sadeghfam, S. Formulating a strategy to combine artificial intelligence models using Bayesian model averaging to study a distressed aquifer with sparse data availability. J. Hydrol. 571, 765–781. https://doi.org/10.1016/j.jhydrol.2019.02.011 (2019).
Google Scholar
