Open Access
BIO Web Conf.
Volume 86, 2024
International Conference on Recent Trends in Biomedical Sciences (RTBS-2023)
Article Number 01106
Number of page(s) 7
Published online 12 January 2024
  • K. Zheng Yang et al., “Application of coolants during tool-based machining – A review,” Ain Shams Engineering Journal, 2022, doi: 10.1016/J.ASEJ.2022.101830. [Google Scholar]
  • S. Subramaniam et al., “Artificial Intelligence Technologies for Forecasting Air Pollution and Human Health: A Narrative Review,” Sustainability (Switzerland), vol. 14, no. 16, Aug. 2022, doi: 10.3390/SU14169951. [Google Scholar]
  • V. S. Rana et al., “Assortment of latent heat storage materials using multi criterion decision making techniques in Scheffler solar reflector,” International Journal on Interactive Design and Manufacturing, 2023, doi: 10.1007/S12008-023-01456-9. [Google Scholar]
  • S. Bali et al., “A framework to assess the smartphone buying behaviour using DEMATEL method in the Indian context,” Ain Shams Engineering Journal, 2023, doi: 10.1016/J.ASEJ.2023.102129. [Google Scholar]
  • P. Singh et al., “Comparative Study of Concrete Cylinders Confined Using Natural and Artificial Fibre Reinforced Polymers,” Lecture Notes in Mechanical Engineering, pp. 79–91, 2023, doi: 10.1007/978-981-19-4147-4_8. [Google Scholar]
  • M. Z. ul Haq et al., “Eco-Friendly Building Material Innovation: Geopolymer Bricks from Repurposed Plastic Waste,” in E3S Web of Conferences, EDP Sciences, 2023, p. 01201. [Google Scholar]
  • J. Y. Jeon, H. I. Jo, and K. Lee, “Psycho-physiological restoration with audio-visual interactions through virtual reality simulations of soundscape and landscape experiences in urban, waterfront, and green environments,” Sustain Cities Soc, vol. 99, p. 104929, Dec. 2023, doi: 10.1016/j.scs.2023.104929. [CrossRef] [Google Scholar]
  • X. Xie, H. Zhou, and Z. Gou, “Dynamic real-time individual green space exposure indices and the relationship with static green space exposure indices: A study in Shenzhen,” Ecol Indic, vol. 154, Oct. 2023, doi: 10.1016/j.ecolind.2023.110557. [Google Scholar]
  • A. Stanitsa, S. H. Hallett, and S. Jude, “Investigating pedestrian behaviour in urban environments: A Wi-Fi tracking and machine learning approach,” Multimodal Transportation, vol. 2, no. 1, Mar. 2023, doi: 10.1016/j.multra.2022.100049. [CrossRef] [Google Scholar]
  • A. L. C. Ciribini et al., “Tracking Users’ Behaviors through Real-time Information in BIMs: Workflow for Interconnection in the Brescia Smart Campus Demonstrator,” Procedia Eng, vol. 180, pp. 1484–1494, 2017, doi: 10.1016/j.proeng.2017.04.311. [CrossRef] [Google Scholar]
  • “Real-Time Information Access in Urban Environments: A User Interaction Study Using the Real-Time Information Test - Search |” Accessed: Nov. 06, 2023. [Online]. Available: [Google Scholar]
  • D. Alves, L. M. Martinez, and J. M. Viegas, “Retrieving Real-time Information to users in Public Transport Networks: An Application to the Lisbon Bus System,” Procedia Soc Behav Sci, vol. 54, pp. 470–482, Oct. 2012, doi: 10.1016/j.sbspro.2012.09.765. [CrossRef] [Google Scholar]
  • P. M. Torrens and S. Gu, “Inverse augmentation: Transposing real people into pedestrian models,” Comput Environ Urban Syst, vol. 100, Mar. 2023, doi: 10.1016/j.compenvurbsys.2022.101923. [CrossRef] [Google Scholar]
  • D. Kumar Singh and R. Sobti, “Long-range real-time monitoring strategy for Precision Irrigation in urban and rural farming in society 5.0,” Comput Ind Eng, vol. 167, May 2022, doi: 10.1016/j.cie.2022.107997. [CrossRef] [Google Scholar]
  • G. Jinquan, H. Hongwen, L. Jianwei, and L. Qingwu, “Driving information process system-based real-time energy management for the fuel cell bus to minimize fuel cell engine aging and energy consumption,” Energy, vol. 248, Jun. 2022, doi: 10.1016/ [CrossRef] [Google Scholar]
  • D. Liu, J. Kim, and Y. Ham, “Multi-user immersive environment for excavator teleoperation in construction,” Autom Constr, vol. 156, Dec. 2023, doi: 10.1016/j.autcon.2023.105143. [Google Scholar]
  • Pimpale, Yogita, Rajeev Kanday, and Sachin Gupta. “Analysis of Parity-Based Search Algorithms for Execution of Target Node in Relation to Automation Applications.” 2021 IEEE 2nd International Conference On Electrical Power and Energy Systems (ICEPES). IEEE, 2021.. [Google Scholar]
  • I. Kaate, J. Salminen, J. Santos, S. G. Jung, R. Olkkonen, and B. Jansen, “The realness of fakes: Primary evidence of the effect of deepfake personas on user perceptions in a design task,” International Journal of Human Computer Studies, vol. 178, Oct. 2023, doi: 10.1016/j.ijhcs.2023.103096. [CrossRef] [Google Scholar]
  • C. Liu et al., “Supporting virtual power plants decision-making in complex urban environments using reinforcement learning,” Sustain Cities Soc, vol. 99, Dec. 2023, doi: 10.1016/j.scs.2023.104915. [Google Scholar]
  • J. D. Blanco Cadena, G. Salvalai, G. Bernardini, and E. Quagliarini, “Determining behavioural-based risk to SLODs of urban public open spaces: Key performance indicators definition and application on established built environment typological scenarios,” Sustain Cities Soc, vol. 95, Aug. 2023, doi: 10.1016/j.scs.2023.104580. [CrossRef] [Google Scholar]
  • M. Anedda, M. Fadda, R. Girau, G. Pau, and D. Giusto, “A social smart city for public and private mobility: A real case study,” Computer Networks, vol. 220, Jan. 2023, doi: 10.1016/j.comnet.2022.109464. [CrossRef] [Google Scholar]
  • Rai, Mritunjay Kumar, Rajeev Kanday, and Reji Thomas. “Current Issues and Challenges of Security in IoT Based Applications.” Advances in Intelligent Systems and Interactive Applications: Proceedings of the 4th International Conference on Intelligent, Interactive Systems and Applications (IISA2019) 4. Springer International Publishing, 2020. [Google Scholar]
  • A. Phillips, D. Plastara, A. Z. Khan, and F. Canters, “Integrating public perceptions of proximity and quality in the modelling of urban green space access,” Landsc Urban Plan, vol. 240, p. 104875, Dec. 2023, doi: 10.1016/j.landurbplan.2023.104875. [CrossRef] [Google Scholar]
  • R. Abe, “Preferences of urban rail users for first- and last-mile autonomous vehicles: Price and service elasticities of demand in a multimodal environment,” Transp Res Part C Emerg Technol, vol. 126, May 2021, doi: 10.1016/j.trc.2021.103105. [Google Scholar]
  • O. Mousavizadeh, M. Keyvan-Ekbatani, and T. M. Logan, “Real-time turning rate estimation in urban networks using floating car data,” Transp Res Part C Emerg Technol, vol. 133, Dec. 2021, doi: 10.1016/j.trc.2021.103457. [CrossRef] [Google Scholar]
  • V. Stange, A. Goralzik, S. Ernst, M. Steimle, M. Maurer, and M. Vollrath, “Please stop now, automated vehicle! – Passengers aim to avoid risk experiences in interactions with a crossing vulnerable road user at an urban junction,” Transp Res Part F Traffic Psychol Behav, vol. 87, pp. 164–188, May 2022, doi: 10.1016/j.trf.2022.04.001. [CrossRef] [Google Scholar]
  • V. Bakhtiari, F. Piadeh, K. Behzadian, and Z. Kapelan, “A critical review for the application of cutting-edge digital visualisation technologies for effective urban flood risk management,” Sustain Cities Soc, vol. 99, p. 104958, Dec. 2023, doi: 10.1016/J.SCS.2023.104958. [CrossRef] [Google Scholar]
  • O. P. Agboola, F. M. Bashir, Y. A. Dodo, M. A. S. Mohamed, and I. S. R. Alsadun, “The influence of information and communication technology (ICT) on stakeholders’ involvement and smart urban sustainability,” Environmental Advances, vol. 13, Oct. 2023, doi: 10.1016/j.envadv.2023.100431. [CrossRef] [Google Scholar]
  • P. Najafi, M. Mohammadi, P. van Wesemael, and P. M. Le Blanc, “A user-centred virtual city information model for inclusive community design: State-of-art,” Cities, vol. 134, Mar. 2023, doi: 10.1016/j.cities.2023.104203. [CrossRef] [Google Scholar]
  • J. Ninić, A. Gamra, and B. Ghiassi, “Real-time assessment of tunnelling-induced damage to structures within the building information modelling framework,” Underground Space (China), vol. 14, pp. 99–117, Feb. 2024, doi: 10.1016/j.undsp.2023.05.010. [CrossRef] [Google Scholar]
  • M. Yang, G. Oh, T. Xu, J. Kim, J. H. Kang, and J. Il Choi, “Multi-GPU-based real-time large-eddy simulations for urban microclimate,” Build Environ, vol. 245, Nov. 2023, doi: 10.1016/j.buildenv.2023.110856. [Google Scholar]
  • G. Bernardini, T. M. Ferreira, P. B. Julià, R. R. Eudave, and E. Quagliarini, “Assessing the spatiotemporal impact of users’ exposure and vulnerability to flood risk in urban built environments,” Sustain Cities Soc, p. 105043, Nov. 2023, doi: 10.1016/J.SCS.2023.105043. [Google Scholar]

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