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  • (-) Empa Authors ≠ Solai Raja Pandiyan, Vigneashwara
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Source term inversion of short-lived nuclides in complex nuclear accidents based on machine learning using off-site gamma dose rate
Ling, Y., Liu, C., Shan, Q., Hei, D., Zhang, X., Shi, C., … Wang, J. (2024). Source term inversion of short-lived nuclides in complex nuclear accidents based on machine learning using off-site gamma dose rate. Journal of Hazardous Materials, 465, 133388 (16 pp.). https://doi.org/10.1016/j.jhazmat.2023.133388
Enhanced deep-learning model for carbon footprints of chemicals
Zhang, D., Wang, Z., Oberschelp, C., Bradford, E., & Hellweg, S. (2024). Enhanced deep-learning model for carbon footprints of chemicals. ACS Sustainable Chemistry and Engineering, 12(7), 2700-2708. https://doi.org/10.1021/acssuschemeng.3c07038
Label-free digital holotomography reveals ibuprofen-induced morphological changes to red blood cells
Bergaglio, T., Bhattacharya, S., Thompson, D., & Nirmalraj, P. N. (2023). Label-free digital holotomography reveals ibuprofen-induced morphological changes to red blood cells. ACS Nanoscience Au, 3(3), 241-255. https://doi.org/10.1021/acsnanoscienceau.3c00004
A machine learning tool for future prediction of heat release capacity of in-situ flame retardant hybrid Mg(OH)2-Epoxy nanocomposites
Bifulco, A., Casciello, A., Imparato, C., Forte, S., Gaan, S., Aronne, A., & Malucelli, G. (2023). A machine learning tool for future prediction of heat release capacity of in-situ flame retardant hybrid Mg(OH)2-Epoxy nanocomposites. Polymer Testing, 127, 108175 (8 pp.). https://doi.org/10.1016/j.polymertesting.2023.108175
Facilitated machine learning for image-based fruit quality assessment
Knott, M., Perez-Cruz, F., & Defraeye, T. (2023). Facilitated machine learning for image-based fruit quality assessment. Journal of Food Engineering, 345, 111401 (9 pp.). https://doi.org/10.1016/j.jfoodeng.2022.111401
Approximating optimal building retrofit solutions for large-scale retrofit analysis
Thrampoulidis, E., Hug, G., & Orehounig, K. (2023). Approximating optimal building retrofit solutions for large-scale retrofit analysis. Applied Energy, 333, 120566 (22 pp.). https://doi.org/10.1016/j.apenergy.2022.120566
Machine learning of twin/matrix interfaces from local stress field
Troncoso, J. F., Hu, Y., della Ventura, N. M., Sharma, A., Maeder, X., & Turlo, V. (2023). Machine learning of twin/matrix interfaces from local stress field. Computational Materials Science, 228, 112322 (11 pp.). https://doi.org/10.1016/j.commatsci.2023.112322
Unlocking the potential of CuAgZr metallic classes: a comprehensive exploration with combinatorial synthesis, high-throughput characterization, and machine learning
Wieczerzak, K., Groetsch, A., Pajor, K., Jain, M., Müller, A. M., Vockenhuber, C., … Michler, J. (2023). Unlocking the potential of CuAgZr metallic classes: a comprehensive exploration with combinatorial synthesis, high-throughput characterization, and machine learning. Advanced Science, 10(31), 2302997 (15 pp.). https://doi.org/10.1002/advs.202302997
Regularised learning with selected physics for power system dynamics
Xie, H., Bellizio, F., Cremer, J. L., & Strbac, G. (2023). Regularised learning with selected physics for power system dynamics. In Power tech conference. 2023 IEEE Belgrade PowerTech (p. (7 pp.). https://doi.org/10.1109/PowerTech55446.2023.10202688
GIR dataset: a geometry and real impulse response dataset for machine learning research in acoustics
Xydis, A., Perraudin, N., Rust, R., Heutschi, K., Casas, G., Grognuz, O. R., … Perez-Cruz, F. (2023). GIR dataset: a geometry and real impulse response dataset for machine learning research in acoustics. Applied Acoustics, 208, 109333 (12 pp.). https://doi.org/10.1016/j.apacoust.2023.109333
Physically consistent neural ODEs for learning multi-physics systems
Zakwan, M., Di Natale, L., Svetozarevic, B., Heer, P., Jones, C. N., & Trecate Ferrari, G. (2023). Physically consistent neural ODEs for learning multi-physics systems. In H. Ishii, Y. Ebihara, Jichi Imura, & M. Yamakita (Eds.), IFAC PapersOnLine: Vol. 56. 22nd IFAC world congress, Yokohama, Japan, July 9-14, 2023 (pp. 5855-5860). https://doi.org/10.1016/j.ifacol.2023.10.079
Machine learning-based screening for biomarkers of psoriasis and immune cell infiltration
Zhou, Y., Wang, Z., Han, L., Yu, Y., Guan, N., Fang, R., … Li, J. (2023). Machine learning-based screening for biomarkers of psoriasis and immune cell infiltration. European Journal of Dermatology, 33(2), 147-156. https://doi.org/10.1684/ejd.2023.4453
A data-driven approach for window opening predictions in non-air-conditioned buildings
Fu, Y., Zhou, T., Lun, I., Khayatian, F., Deng, W., & Su, W. (2022). A data-driven approach for window opening predictions in non-air-conditioned buildings. Intelligent Buildings International, 14(3), 329-345. https://doi.org/10.1080/17508975.2021.1963651
3D printable soft sensory fiber networks for robust and complex tactile sensing
Hardman, D., George Thuruthel, T., Georgopoulou, A., Clemens, F., & Iida, F. (2022). 3D printable soft sensory fiber networks for robust and complex tactile sensing. Micromachines, 13(9), 1540 (16 pp.). https://doi.org/10.3390/mi13091540
Reconstructing radial stem size changes of trees with machine learning
Luković, M., Zweifel, R., Thiry, G., Zhang, C., & Schubert, M. (2022). Reconstructing radial stem size changes of trees with machine learning. Journal of the Royal Society Interface, 19(194), 20220349 (13 pp.). https://doi.org/10.1098/rsif.2022.0349
Higher-order accurate neural network for real-time fluid velocimetry
Manickathan, L., Mucignat, C., & Lunati, I. (2022). Higher-order accurate neural network for real-time fluid velocimetry. In Proceedings of the 20th international symposium on the application of laser and imaging techniques to fluid mechanics 2022 (p. (13 pp.). Sine nomine.
Kinematic training of convolutional neural networks for particle image velocimetry
Manickathan, L., Mucignat, C., & Lunati, I. (2022). Kinematic training of convolutional neural networks for particle image velocimetry. Measurement Science and Technology, 33(12), 124006 (16 pp.). https://doi.org/10.1088/1361-6501/ac8fae
Estimating BOS image deformation with a lightweight CNN
Mucignat, C., Manickathan, L., Shah, J., Rösgen, T., & Lunati, I. (2022). Estimating BOS image deformation with a lightweight CNN. In Proceedings of the 20th international symposium on the application of laser and imaging techniques to fluid mechanics 2022 (p. (14 pp.). Sine nomine.
Machine learning-based characterization of the nanostructure in a combinatorial Co-Cr-Fe-Ni compositionally complex alloy film
Nagy, P., Kaszás, B., Csabai, I., Hegedűs, Z., Michler, J., Pethö, L., & Gubicza, J. (2022). Machine learning-based characterization of the nanostructure in a combinatorial Co-Cr-Fe-Ni compositionally complex alloy film. Nanomaterials, 12(24), 4407 (14 pp.). https://doi.org/10.3390/nano12244407
Quantifying the climate and human-system-driven uncertainties in energy planning by using GANs
Perera, A. T. D., Khayatian, F., Eggimann, S., Orehounig, K., & Halgamuge, S. (2022). Quantifying the climate and human-system-driven uncertainties in energy planning by using GANs. Applied Energy, 328, 120169 (12 pp.). https://doi.org/10.1016/j.apenergy.2022.120169