Open Access
Issue |
E3S Web Conf.
Volume 596, 2024
International Conference on Civil, Materials, and Environment for Sustainability (ICCMES 2024)
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Article Number | 01022 | |
Number of page(s) | 16 | |
Section | Civil, Materials and Environment for Sustainability ICCMES 2024 | |
DOI | https://doi.org/10.1051/e3sconf/202459601022 | |
Published online | 22 November 2024 |
- Godbole, Aditi S., Tyagi, Kanishka, and Manry, Michael T. “Neural Decision Directed Segmentation of Silicon Defects.” In The 2013 International Joint Conference on Neural Networks (IJCNN), 1–8. IEEE, (2013). [Google Scholar]
- Tyagi, Kanishka, Kwak, Nojun, and Manry, Michael. “Optimal Conjugate Gradient Algorithm for Generalization of Linear Discriminant Analysis Based on L1 Norm.” In International Conference on Pattern Recognition, (2014). [Google Scholar]
- Cai, Xun, Tyagi, Kanishka, and Manry, Michael. “An Efficient Conjugate Gradient Based Multiple Optimal Learning Factors Algorithm of Multilayer Perceptron Neural Network.” In International Joint Conference on Neural Networks, (2014). [Google Scholar]
- Cai, Xun, Tyagi, Kanishka, Manry, Michael T., and Chen, Zhi. “An Efficient Conjugate Gradient Based Learning Algorithm for Multiple Optimal Learning Factors of Multilayer Perceptron Neural Network.” In 2014 International Joint Conference on Neural Networks (IJCNN), 1093–1099. IEEE, (2014). [CrossRef] [Google Scholar]
- Jeong, Il-Young, Tyagi, Kanishka, and Lee, Kyogu. “MIREX 2013: An Efficient Paradigm for Audio Tag Classification Using Sparse Autoencoder and Multi-Kernel SVM.” 2013 [Google Scholar]
- Tyagi, Kanishka. “Second Order Training Algorithms For Radial Basis Function Neural Networks.” Department of Electrical Engineering, The University of Texas at Arlington, (2012). [Google Scholar]
- Cai, Xun, Chen, Zhi, Tyagi, Kanishka, Yu, Kuan, Li, Ziqiang, and Zhu, Bo. “Second Order Newton’s Method for Training Radial Basis Function Neural Networks.” Journal of Computer Research and Development 52, no. 7 (2015): 1477. [Google Scholar]
- Auddy, Soumitro Swapan, Tyagi, Kanishka, Nguyen, Son, and Manry, Michael. “Discriminant Vector Transformations in Neural Network Classifiers.” In 2016 International Joint Conference on Neural Networks (IJCNN), 1780-1786. IEEE, (2016). [Google Scholar]
- Nguyen, Son, Tyagi, Kanishka, Kheirkhah, Parastoo, and Manry, Michael. “Partially Affine Invariant Back Propagation.” In 2016 International Joint Conference on Neural Networks (IJCNN), 811–818. IEEE, (2016). [CrossRef] [Google Scholar]
- Hao, Yilong, Tyagi, Kanishka, Rawat, Rohit, and Manry, Michael. “Second Order Design of Multiclass Kernel Machines.” In 2016 International Joint Conference on Neural Networks (IJCNN), 3233–3240. IEEE, (2016). [CrossRef] [Google Scholar]
- Tyagi, Kanishka, and Lee, Kyogu. “Applications of Deep Learning Network on Audio and Music Problems.” IEEE Computational Intelligence Society Walter Karplus Summer Research Grant 2013, (2013). [Google Scholar]
- Tyagi, N., & Suresh, S. “Production of cellulose from sugarcane molasses using Gluconacetobacter intermedius SNT-1: optimization & characterization.” Journal of Cleaner Production 112 (2016): 71- 80. [CrossRef] [Google Scholar]
- Tyagi, N., Mathur, S., & Kumar, D. “Electrocoagulation process for textile wastewater treatment in continuous upflow reactor.” NISCAIR-CSIR, India (2014). [Google Scholar]
- Tyagi, N., & Suresh, S. “Isolation and characterization of cellulose producing bacterial strain from orange pulp.” Advanced Materials Research 626 (2013): 475–479. [Google Scholar]
- Chittoori, Bhaskar, Anand J. Puppala, Rajinikanth Reddy, and David Marshall. “Sustainable Reutilization of Excavated Trench Material.” In GeoCongress 2012: State of the Art and Practice in Geotechnical Engineering, 4280–4289. 2012. [Google Scholar]
- Karduri, Rajini Kanth Reddy. “Sustainable Reutilization of Excavated Trench Material.” Master’s thesis, Civil & Environmental Engineering, University of Texas at Arlington, 2012. [Google Scholar]
- Karduri, Rajini K. R. “The Feasibility of Carbon Neutral Synthetic Fuels.” International Journal of Advanced Research in Innovative Discoveries in Engineering and Applications (IJARIDEA) (Dec 2017). [Google Scholar]
- Jia Z, Lin B (2020) Rethinking the choice of carbon tax and carbon trading in China. Technol Forecast Soc Chang 159:120187. https://doi.org/10.1016/j.techfore.2020.120187 [CrossRef] [Google Scholar]
- Kane SP, Matthias K (2023) Docker: Up & Running. O’Reilly Media, Inc. Retrieved from https://www.oreilly.com/library/view/docker-up/9781098131814/ [Google Scholar]
- Ke, G, Meng Q, Finley T, Wang T, Chen W, Ma W, Ye Q, Liu T-Y (2017) Lightgbm: A highly efcient gradient boosting decision tree. Advances in neural information processing systems [Google Scholar]
- Klaaßen L, Stoll C (2021) Harmonizing corporate carbon footprints. Nature. Communications 12(1):6149. https://doi.org/10.1038/s41467-021-26349-x [CrossRef] [Google Scholar]
- Kreibich N, Hermwille L (2021) Caught in between: credibility and feasibility of the voluntary carbon market post-2020. Climate Policy 21(7):939–957. https://doi.org/10.1080/14693062.2021.1948384 [CrossRef] [Google Scholar]
- Li M, Wiedmann T, Hadjikakou M (2019) Enabling full supply chain corporate responsibility: scope 3 emissions targets for ambitious climate change mitigation. Environ Sci Technol 54(1):400–411. [Google Scholar]
- Li Y, Wang Y, Chong D, Xu Z, Li L, Hu Y (2024) Carbon taxation in Singapore’s semiconductor sector: a mini-review on GHG emission metrics and reporting. Carbon Res 2(1):1–15. [Google Scholar]
- Martin RC (2003) Agile software development: principles, patterns, and practices. Prentice Hall PTR. Martin RC (2008) Clean code: a handbook of agile software craftsmanship. Pearson Education. [Google Scholar]
- Nguyen Q, Díaz-Rainey I, Kitto A, McNeil BI, Pittman NA, Zhang R (2023) Scope 3 emissions: Data quality and machine learning prediction accuracy. PLOS Climate 2(11):e0000208. [CrossRef] [Google Scholar]
- Puschmann T, Quattrocchi D (2023) Decreasing the impact of climate change in value chains by leveraging sustainable fnance. Journal of Cleaner Production 429:139575. https://doi.org/10.1016/j.jclepro.2023.139575 [CrossRef] [Google Scholar]
- Liu, B.; Xu, Y.; Yang, Y.; Lu, S. How public cognition influences public acceptance of CCUS in China: Based on the ABC (affect, behavior, and cognition) model of attitudes. Energy Policy 2021, 156, 112390. [CrossRef] [Google Scholar]
- Sitinjak, C.; Ebennezer, S.; Ober, J. Exploring Public Attitudes and Acceptance of CCUS Technologies in JABODETABEK: A Cross- Sectional Study. Energies 2023, 16, 4026. [CrossRef] [Google Scholar]
- Nielsen, J.A.E.; Stavrianakis, K.; Morrison, Z. Community acceptance and social impacts of carbon capture, utilization and storage projects: A systematic meta-narrative literature review. PLoS ONE 2022, 17, e0272409. [PubMed] [Google Scholar]
- Anderson, J.J.; Rode, D.; Zhai, H.; Fischbeck, P. Transitioning to a carbon-constrained world: Reductions in coal-fired power plant emissions through unit-specific, least-cost mitigation frontiers. Appl. Energy 2021, 288, 116599. [CrossRef] [Google Scholar]
- Yang, H.; Chen, W. Retailer-driven carbon emission abatement with consumer environmental awareness and carbon tax: Revenue-sharing versus Cost-sharing. Omega 2018, 78, 179–191. [CrossRef] [Google Scholar]
- Andersson, F.N.G. Effects on the manufacturing, utility and construction industries of decarbonization of the energy-intensive and natural resource-based industries. Sustain. Prod. Consum. 2020, 21, 1–13. [CrossRef] [Google Scholar]
- Zhang, Q.; Tang, W.; Zhang, J. Green supply chain performance with cost learning and operational inefficiency effects. J. Clean. Prod. 2016, 112, 3267–3284. [CrossRef] [Google Scholar]
- Guo, J.-X.; Tan, X.; Gu, B.; Qu, X. The impacts of uncertainties on the carbon mitigation design: Perspective from abatement cost and emission rate. J. Clean. Prod. 2019, 232, 213–223. [CrossRef] [Google Scholar]
- Wang, Z.; Chen, H.; Huo, R.; Wang, B.; Zhang, B. Marginal abatement cost under the constraint of carbon emission reduction targets: An empirical analysis for different regions in China. J. Clean. Prod. 2020, 249, 119362. [CrossRef] [Google Scholar]
- Cui, L.-B.; Fan, Y.; Zhu, L.; Bi, Q.-H. How will the emissions trading scheme save cost for achieving China’s 2020 carbon intensity reduction target? Appl. Energy 2014, 136, 1043–1052. [CrossRef] [Google Scholar]
- Thakur, Ina, Anoop Verma, and Banu Ormeci. “Fe–TiO2 Composite Mediated the Hybrid Effect of Photocatalysis and Photo-Fenton for the Inactivation of Escherichia coli Using a Continuous Flow Recirculation Reactor.” Industrial & Engineering Chemistry Research 60, no. 20 (2021): 7558–7571. [CrossRef] [Google Scholar]
- Thakur, Ina, Anoop Verma, Banu Örmeci, and Vikas Sangal. “Applications of waste-derived visibly active Fe-TiO2 composite incorporating the hybrid process of photocatalysis and photo- Fenton for the inactivation of E. coli.” Environmental Science and Pollution Research 29, no. 48 (2022): 72247–72259. [CrossRef] [PubMed] [Google Scholar]
- Thakur, Ina, Anoop Verma, and Banu Örmeci. “Solar photocatalytic disinfection of real municipal wastewater using highly durable TiO2-coated composite in a pilot scale once through reactor.” Environmental Science and Pollution Research 30, no. 15 (2023): 43654–43664. [CrossRef] [Google Scholar]
- Thakur, Ina, Anoop Verma, and Banu Örmeci. “Visibly active Fe-TiO2 composite: A stable and efficient catalyst for the catalytic disinfection of water using a once-through reactor.” Journal of Environmental Chemical Engineering 9, no. 6 (2021): 106322. [CrossRef] [Google Scholar]
- Thakur, Ina, and Banu Örmeci. “Inactivation of E. coli in water employing Fe-TiO2 composite incorporating in-situ dual process of photocatalysis and photo-Fenton in fixed-mode.” Journal of Water Process Engineering 33 (2020): 101085. [CrossRef] [Google Scholar]
- Thakur, Ina, Anoop Verma, and Banu Örmeci. “Inactivation of bacteria present in secondary municipal wastewater effluent using the hybrid effect of Fe–TiO2 catalyst.“ Journal of Cleaner Production 352 (2022): 131575. [CrossRef] [Google Scholar]
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