The Brisbane Heat have maintained their squad for tomorrow night’s KFC Big Bash League clash with the Adelaide Strikers at the Gabba.
The Heat have added left-arm…
The Brisbane Heat have maintained their squad for tomorrow night’s KFC Big Bash League clash with the Adelaide Strikers at the Gabba.
The Heat have added left-arm…
December 26, 2025 (MLN): The Government of Pakistan
and the Asian Development Bank (ADB) have signed two major financing agreements
worth a combined $730 million aimed at strengthening the country’s power
infrastructure and accelerating…
In just 20 days, the Ranveer Singh starrer has raked in a staggering Rs 944 crore worldwide, and the 21st day saw even bigger crowds than opening day, with early estimates putting the haul at Rs 26 crore—just shy of the film’s opening day of…
The Moon is now a few days into the new cycle, so there is plenty to see when you look up in the sky tonight.
As of Friday, Dec. 26, the moon phase is Waxing…
India’s economic growth will require a substantial expansion of its manufacturing base and infrastructure, with iron and steel playing a central role as an input to key sectors such as infrastructure, automobiles, and housing. While the sector has grown steadily in recent years, per capita steel consumption in India remains well below the global average, indicating significant growth potential. At the same time, the sector is a major source of employment and contributes meaningfully to the economy, particularly outside large urban centers.
India’s commitment to achieve net-zero emissions by 2070 adds urgency to addressing emissions from the iron and steel sector, one of the country’s largest emitters. Demand is expected to rise, yet commercially mature low-carbon technologies remain limited and costly. Against this backdrop, this policy brief assesses the policy levers needed to support low-carbon steel production in India, examining their implications for emissions reduction, employment, and project economics.
Mirror photo by Colette Costlow /
Glendale science teacher Ethan Maneval points to the lightbulb in the center of the projector system in the Glendale Junior Senior High School planetarium.
FLINTON — Each year, Glendale Junior Senior High School…
The National Database and Registration Authority (NADRA) offices in Islamabad will remain closed on Friday, December 26, due to a public holiday.
According to an official notice, all NADRA offices will resume operations at 8:00am on Saturday,…
Zadeh, L. A. Fuzzy sets. Inf. Control 8(3), 338–353. https://doi.org/10.1016/S0019-9958(65)90241-X (1965).
Atanassov, K. T. Intuitionistic fuzzy sets. Fuzzy Sets Syst. 20(1), 87–96. https://doi.org/10.1016/S0165-0114(86)80034-3 (1986).
B. C. Cuong, Picture fuzzy sets-first results. part 1, seminar neuro-fuzzy systems with applications Inst. Math. Hanoi (2013).
Atanassov, K. T. Circular intuitionistic fuzzy sets. J. Intell. Fuzzy Syst. 39(5), 5981–5986 (2020).
Xu, Y. & Zhang, D. Identifying AI-driven emerging trends in service innovation and digitalized industries using the circular picture fuzzy WASPAS approach. Symmetry 17(9), 1546 (2025).
Liu, S. & Zhao, D. Diffusion and economic growth fuzzy intelligent system based on DSGE model. J. Intell. Fuzzy Syst. 40(4), 5975–5983. https://doi.org/10.3233/JIFS-189437 (2021).
F. Subkhan, M. S. Maarif, N. T. Rochman, and Y. Nugraha, Digital economy: reinforcing competitive economy of smart cities, A Fuzzy-AHP Approach In 2024 International Conference on ICT for Smart Society (ICISS) IEEE 1–10 Accessed Oct 01 2025. Available: https://ieeexplore.ieee.org/abstract/document/10751367/ (2024)
R. Imamguluyev, A. Panahov, A. Jabbarov, A. Hajiyev, and K. Aghayeva, “The Role of Fuzzy Logic in the Digital Transformation of Economics: Innovative Analysis and Strategies,” in Intelligent and Fuzzy Systems, vol. 1530, C. Kahraman, S. Cebi, B. Oztaysi, S. Cevik Onar, C. Tolga, I. Ucal Sari, and I. Otay, Eds., in Lecture Notes in Networks and Systems, vol. 1530. , Cham: Springer Nature Switzerland, 2025, pp. 676–683. https://doi.org/10.1007/978-3-031-98565-2_73.
Zhang, X. & Wang, A. Enhancing the performance of vocational education in the digital economy with the application of fuzzy logic algorithm. Int. J. Comput. Intell. Syst. 17(1), 185. https://doi.org/10.1007/s44196-024-00591-9 (2024).
Kaur, P., Verma, R. & Mahanti, N. C. Selection of vendor using analytical hierarchy process based on fuzzy preference programming. Opsearch 47(1), 16–34 (2010).
Kaur, P. Selection of vendor based on intuitionistic fuzzy analytical hierarchy process. Adv. Oper. Res. 2014, 1–10. https://doi.org/10.1155/2014/987690 (2014).
Hussain, A., Ullah, K., Yang, M.-S. & Pamucar, D. Aczel-Alsina aggregation operators on T-spherical fuzzy (TSF) information with application to TSF multi-attribute decision making. Ieee Access 10, 26011–26023 (2022).
Wang, R., Wang, J., Gao, H. & Wei, G. Methods for MADM with picture fuzzy muirhead mean operators and their application for evaluating the financial investment risk. Symmetry 11(1), 6 (2018).
Kaur, P., Dutta, V., Pradhan, B. L., Haldar, S. & Chauhan, S. A pythagorean fuzzy approach for sustainable supplier selection using TODIM. Math. Probl. Eng. 2021, 1–11. https://doi.org/10.1155/2021/4254894 (2021).
P. Kaur and A. Priya, Selection of inventory policy under pythogrean fuzzy environment Sci. Technol. Asia 62–71 (2020).
M. R. Seikh and U. Mandal, Some picture fuzzy aggregation operators based on frank t-norm and t-conorm: application to MADM process Informatica 45(3):https://doi.org/10.31449/inf.v45i3.3025 (2021)
Hussain, A., Mahmood, T., Smarandache, F. & Ashraf, S. TOPSIS approach for MCGDM based on intuitionistic fuzzy rough Dombi aggregation operations. Comput. Appl. Math. 42(4), 176. https://doi.org/10.1007/s40314-023-02266-1 (2023).
Abbas, F., Ali, J. & Mashwani, W. K. Partitioned Hamy mean aggregation for multi-criteria group decision-making in the MAIRCA framework with q-rung orthopair fuzzy 2-tuple linguistic information. Granul. Comput. 9(3), 62 (2024).
Wang, H. Sustainable circular supplier selection in the power battery industry using a linguistic T-spherical fuzzy MAGDM model based on the improved ARAS method. Sustainability 14(13), 7816 (2022).
Zavadskas, E. K., Turskis, Z., Antucheviciene, J. & Zakarevicius, A. Optimization of weighted aggregated sum product assessment. Elektron. Ir Elektrotechnika 122(6), 3–6 (2012).
Rani, P., Mishra, A. R. & Pardasani, K. R. A novel WASPAS approach for multi-criteria physician selection problem with intuitionistic fuzzy type-2 sets. Soft Comput. 24(3), 2355–2367 (2020).
Ayyildiz, E., Erdogan, M. & Taskin Gumus, A. A Pythagorean fuzzy number-based integration of AHP and WASPAS methods for refugee camp location selection problem: a real case study for Istanbul, Turkey. Neural Comput. Appl. 33(22), 15751–15768. https://doi.org/10.1007/s00521-021-06195-0 (2021).
Senapati, T. & Chen, G. Picture fuzzy WASPAS technique and its application in multi-criteria decision-making. Soft Comput. 26(9), 4413–4421. https://doi.org/10.1007/s00500-022-06835-0 (2022).
Albaity, M., Mahmood, T. & Ali, Z. Impact of machine learning and artificial intelligence in business based on intuitionistic fuzzy soft WASPAS method. Mathematics 11(6), 1453 (2023).
Abbas, F., Ali, J., Mashwani, W. K., Gündüz, N. & Syam, M. I. q-Rung orthopair fuzzy 2-tuple linguistic WASPAS algorithm for patients’ prioritization based on prioritized Maclaurin symmetric mean aggregation operators. Sci. Rep. 14(1), 10659 (2024).
Xu, Z. Intuitionistic fuzzy aggregation operators. IEEE Trans. Fuzzy Syst. 15(6), 1179–1187 (2007).
Zhao, H., Xu, Z., Ni, M. & Liu, S. Generalized aggregation operators for intuitionistic fuzzy sets. Int. J. Intell. Syst. 25(1), 1–30. https://doi.org/10.1002/int.20386 (2010).
Fahmi, A., Khan, A., Maqbool, Z. & Abdeljawad, T. Circular intuitionistic fuzzy Hamacher aggregation operators for multi-attribute decision-making. Sci. Rep. 15(1), 5618 (2025).
Bozyigit, M. C., Olgun, M. & Ünver, M. Circular pythagorean fuzzy sets and applications to multi-criteria decision making. Informatica 34(4), 713–742. https://doi.org/10.15388/23-INFOR529 (2023).
Mahmood, T. & Ali, Z. Multi-attribute decision-making methods based on Aczel-Alsina power aggregation operators for managing complex intuitionistic fuzzy sets. Comput. Appl. Math. 42(2), 87. https://doi.org/10.1007/s40314-023-02204-1 (2023).
Hussain, A., Ullah, K., Al-Quran, A. & Garg, H. Some T-spherical fuzzy dombi hamy mean operators and their applications to multi-criteria group decision-making process. J. Intell. Fuzzy Syst. 45(6), 9621–9641 (2023).
M. Riaz, K. Naeem, and D. Afzal, Pythagorean m-polar fuzzy soft sets with TOPSIS method for MCGDM Punjab Univ. J. Math. 52 (3) Accessed Feb 25 2025 Available http://journals.pu.edu.pk/journals/index.php/pujm/article/viewArticle/3469 (2020)
Hussain, A., Wang, H., Ullah, K. & Pamucar, D. Novel intuitionistic fuzzy Aczel Alsina Hamy mean operators and their applications in the assessment of construction material. Complex Intell. Syst. 10(1), 1061–1086. https://doi.org/10.1007/s40747-023-01116-1 (2024).
Park, S. & Lee, J. K. A study on the technological connections between BIM (building information modeling) and interior architecture design – focusing on the applications of Spatial object and its properties. J. Korean Des. Knowl. 34, 35–44. https://doi.org/10.17246/jkdk.2015.34.004 (2015).
Eastman, C. Automated assessment of early concept designs. Architectural Des. 79 (2), 52–57. https://doi.org/10.1002/ad.851 (2009).
Lee, J. K. et al. Development of space database for automated Building design review systems. Autom. Constr. 24, 203–212. https://doi.org/10.1016/j.autcon.2012.03.002 (2012).
Alawadhi, M. & Yan, W. Deep learning from parametrically generated virtual buildings for real-world object recognition. ArXiv Preprint. arXiv:2302.05283. (2023). https://doi.org/10.48550/arXiv.2302.05283
Fisher, M., Ritchie, D., Savva, M., Funkhouser, T. & Hanrahan, P. Example-based synthesis of 3D object arrangements. ACM Trans. Graphics. 31.6, 1–11. https://doi.org/10.1145/2366145.2366154 (2012).
Ustyugov, A. How will artificial intelligence change our living space? Forbes. (2021). https://www.forbes.com/sites/forbestechcouncil/2021/09/30/how-will-artificial-intelligence-change-our-living-spaces/?sh=753fa54e20b6
Kim, J. & Lee, J. K. Stochastic detection of interior design styles using a deep-learning model for reference images. Appl. Sci. 10, 20, 7299. https://doi.org/10.3390/app10207299 (2020).
Baduge, S. et al. Artificial intelligence and smart vision for Building and construction 4.0: machine and deep learning methods and applications. Autom. Constr. 141, 104440. https://doi.org/10.1016/j.autcon.2022.104440 (2022).
Onur, N. A review of the use of examples for automating architectural design tasks. Comput. Aided Des. 96, 13–30. https://doi.org/10.1016/j.cad.2017.10.005 (2018).
Pan, Y., Zhang, L. & Integrating BIM and AI for smart construction management: current status and future directions. Arch. Comput. Methods Eng. 30, 1081–1110. https://doi.org/10.1007/s11831-022-09830-8 (2023).
Pizarro, P. N., Hitschfeld, N., Sipiran, I. & Saavedra, J. M. Automatic floor plan analysis and recognition. Autom. Constr. 140, 104348. https://doi.org/10.1016/j.autcon.2022.104348 (2022).
Regona, M. et al. Opportunities and adoption challenges of AI in the construction industry: A PRISMA review. J. Open. Innovation: Technol. Market Complex. 8 (1), 45. https://doi.org/10.3390/joitmc8010045 (2022).
Samuel, A., Mahanta, N. R. & Technologies, N. Casel Vitug, A. Computational technology and artificial intelligence (AI) revolutionizing interior design graphics and modelling. 13th International Conference on Computing Communication and (ICCCNT), 1–6. (2022). https://doi.org/10.1109/ICCCNT54827.2022.9984232
Waayenberg, E. The future of design: Technology, automation, and how we should respond. Interior Design. (2021). https://interiordesign.net/designwire/the-future-of-design-technology-automation-and-how-we-should-respond/
Wang, H. et al. BIM-based automated design for HVAC system of office buildings—An experimental study. Build. Simul. 15, 1177–1192. https://doi.org/10.1007/s12273-021-0883-7 (2022).
Zhang, F., Chan, A. P., Darko, A., Chen, Z. & Li, D. Integrated applications of Building information modeling and artificial intelligence techniques in the AEC/FM industry. Autom. Constr. 139, 104289. https://doi.org/10.1016/j.autcon.2022.104289 (2022).
Hong, S. et al. Evaluation of practical requirements for automated detailed design module of interior finishes in architectural Building information model. Korean J. Constr. Eng. Manage. 23 (5), 87–97. https://doi.org/10.6106/KJCEM.2022.23.5.087 (2022).
Wang, K. et al. Planning and instantiating indoor scenes with relation graph and Spatial prior networks. ACM Trans. Graphics. 38 (4), 1–15. https://doi.org/10.1145/3306346.3322941 (2019).
Lee, J. K. & Kim, M. BIM-enabled conceptual modelling and representation of Building circulation. Int. J. Adv. Rob. Syst. https://doi.org/10.5772/58440 (2014). 11.127.
Lee, Y., Eastman, C. & Lee, J. K. Validations for ensuring the interoperability of data exchange of a building information model. Autom. Constr. 58, 176–195. https://doi.org/10.1016/j.autcon.2015.07.010 (2015).
Uhm, M. et al. Requirements for computational rule checking of requests for proposals (RFPs) for Building designs in South Korea. Adv. Eng. Inform. 29.3, 602–615. https://doi.org/10.1016/j.aei.2015.05.006 (2015).
Park, D. & Cha, H. A developing a machine leaning-based defect data management system for multi-family housing unit. Korean J. Constr. Eng. Manage. 24 (5), 35–43. https://doi.org/10.6106/KJCEM.2023.24.5.035 (2023).
Seoul Housing Portal. Housing policy for urban living housing. (2023). https://housing.seoul.go.kr/site/main/content/sh01_030800
Choi, S., Kim, Y., Nam, T., Hong, S. W. & Lee, J. K. Generative architectural plan drawings for early design decisions: Data grounding and additional training for specific use cases. Architectural Eng. Des. Manage., 1–21. https://doi.org/10.1080/17452007.2024.2445033 (2024).
Park, Y. & Oh, H. Interior design: Theory and for housing plan and realityDaseossure,. (1993).
Baek, H. S., Choi, S. H., Choi, D. S. & Joo, J. Y. Planning design guidelines for LH unit plan. Land. Hous. Inst. 16 (12), 71–82 (2012).
Autodesk. Revit for architecture & building design. https://www.autodesk.com/products/revit/overview
Autodesk. Dynamo for Revit. https://www.dynamobim.org/dynamo-for-revit/
Merrell, P., Schkufza, E., Li, Z., Agrawala, M. & Koltun, V. Interactive furniture layout using interior design guidelines. ACM Trans. Graphics. 30 (4), 1–10. https://doi.org/10.1145/1964921.1964982 (2011).
Jiang, S., Wang, M. & Ma, L. Gaps and requirements for applying automatic architectural design to Building renovation. Autom. Constr. 147, 104742. https://doi.org/10.1016/j.autcon.2023.104742 (2023).
Sydora, C. & Stroulia, E. Rule-based compliance checking and generative design for Building interiors using BIM. Autom. Constr. 120, 103368. https://doi.org/10.1016/j.autcon.2020.103368 (2020).
Yu, L. et al. Make it home: automatic optimization of furniture arrangement. ACM Trans. Graphics. 30 (4), 1–12. https://doi.org/10.1145/2010324.1964981 (2011).
Kán, P. & Kaufmann, H. Automated interior design using a genetic algorithm. 23rd ACM Symposium on Virtual Reality Software and Technology 25, 1–10. (2017). https://doi.org/10.1145/3139131.3139135
Lee, J. K., Eastman, C. M., Lee, J., Kannala, M. & Jeong, Y. S. Computing walking distances within buildings using the universal circulation network. Environ. Plan. 37 (4), 628–645. https://doi.org/10.1068/b35124 (2010).
Zhang, S. H., Zhang, S. K., Xie, W. Y., Luo, C. Y. & Fu, H. B. Fast 3D indoor scene synthesis with discrete and exact layout pattern extraction. ArXiv Preprint. arXiv:2002.00328. (2020). https://doi.org/10.48550/arXiv.2002.00328
Zhang, Z. et al. Deep generative modeling for scene synthesis via hybrid representations. ACM Trans. Graphics. 39 (2), 1–21. https://doi.org/10.1145/3381866 (2020).
Lee, K. J. & Kim, S. G. Housing Planning Theory (Bosungak, 2001).
Kim, H. D. Housing Plan Design (Kimoondang, 1999).
Kang, B. S. et al. History of Korean Apartment Housing Planning (Sejinsa, 1999).
Pile, J. F. Interior design (Harry N. Abrams, 1988).
Panero, J. & Zelnik, M. Human dimension & interior space: a source book of design reference standards (Whitney Library of Design, 1979).
Tilley, A. R. & Henry Dreyfuss Associates. &. The measure of man and woman: human factors in design, revised edition. (John Wiley & Sons, (2001).
Neufert, E. & Neufert, P. Architects’ data (Wiley-Blackwell, 2012).
Peterson, C. Home office solutions: how to set up an efficient workspace anywhere in your house (Fox Chapel Publishing, 2020).
Stability, A. I. Stable Diffusion. (2022). https://stability.ai/blog/stable-diffusion-public-release
Finch Finch 3D. (2020). https://finch3d.com/.
Autodesk Revit Generative Design. (2020). https://www.autodesk.com/solutions/generative-design/architecture-engineering-construction
Testfit TestFit. (2017). https://testfit.io/.
Autodesk. Spacemaker, A. I. (2018). https://www.autodesk.com/products/spacemaker/overview
Jo, H., Lee, J. K., Lee, Y. C. & Choo, S. Generative artificial intelligence and Building design: early photorealistic render visualization of façades using local identity-trained models. J. Comput. Des. Eng. 11 (2), 85–105. https://doi.org/10.1093/jcde/qwae017 (2024).
Robert, McNeel & Associates Rhinoceros. https://www.rhino3d.com/
Robert, McNeel & Associates Grasshopper. https://www.grasshopper3d.com/
buildingSMART & International ISO 16739-1:2018 Industry foundation classes (IFC) for data sharing in the construction and facility management industries — Part 1: Data schema. (2018). https://technical.buildingsmart.org/standards/ifc/
Kim, K. & Yu, J. Integrated information management for composite object properties in BIM. Korea Inst. Constr. Eng. Manage. 16 (2), 97–105. https://doi.org/10.6106/KJCEM.2015.16.2.097 (2015).
Ma, R. et al. Language-driven synthesis of 3D scenes from scene databases. ACM Trans. Graphics. 37 (6), 1–16. https://doi.org/10.1145/3272127.3275035 (2018).
Zhang, S., Teizer, J., Lee, J. K., Eastman, C. M. & Venugopal, M. Building information modeling (BIM) and safety: automatic safety checking of construction models and schedules. Autom. Constr. 29, 183–195. https://doi.org/10.1016/j.autcon.2012.05.006 (2013).
Zhao, Q., Zhou, L., Lv, G. & Computers A 3D modeling method for buildings based on LiDAR point cloud and DLG. Environ. Urban Syst. 102, 101974. https://doi.org/10.1016/j.compenvurbsys.2023.101974 (2023).
Karan, E. & Asadi, S. Intelligent designer: A computational approach to automating design of windows in buildings. Autom. Constr. 102, 160–169. https://doi.org/10.1016/j.autcon.2019.02.019 (2019).
Song, J. & Lee, J. K. An approach to implementing automated modeling of interior design object using Spatial information training model – focused on an implementation example of automated layout system for ceiling light object. J. Korean Inst. Interior Des. 29 (2), 12–20. https://doi.org/10.14774/JKIID.2020.29.2.012 (2020).
Sanguinetti, P. et al. General system architecture for BIM: an integrated approach for design and analysis. Adv. Eng. Inform. 26.2, 317–333. https://doi.org/10.1016/j.aei.2011.12.001 (2012).
Zhao, P., Liao, W., Xue, H. & Lu, X. Intelligent design method for beam and slab of shear wall structure based on deep learning. J. Building Eng. 57, 104838. https://doi.org/10.1016/j.jobe.2022.104838 (2022).