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   https://doi.org/10.1063/PT.3.5297  

   Miao, Bo; Shrock, Jaron; Milchberg, Howard (August 2023, Physics Today)

   To accelerate electrons to multi-GeV energies with lasers, keep the bright light tight. 

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2. Light Scalars and the Koto Anomaly

   https://doi.org/10.1103/PhysRevLett.124.191801  

   Egana-Ugrinovic, Daniel; Homiller, Samuel; Meade, Patrick (May 2020, Physical Review Letters)
3. Heavy–Light Susceptibilities in the sQGP

   https://doi.org/10.5506/APhysPolBSupp.16.1-A92  

   Liu, S.Y.F.; Rapp, R. (January 2023, Acta Physica Polonica B Proceedings Supplement)
4. The Two Lines Light Source (TLLS)

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5. Compensating the cell-induced light scattering effect in light-based bioprinting using deep learning

   https://doi.org/10.1088/1758-5090/ac3b92  

   Guan, Jiaao; You, Shangting; Xiang, Yi; Schimelman, Jacob; Alido, Jeffrey; Ma, Xinyue; Tang, Min; Chen, Shaochen (December 2021, Biofabrication)

   Abstract Digital light processing (DLP)-based three-dimensional (3D) printing technology has the advantages of speed and precision comparing with other 3D printing technologies like extrusion-based 3D printing. Therefore, it is a promising biomaterial fabrication technique for tissue engineering and regenerative medicine. When printing cell-laden biomaterials, one challenge of DLP-based bioprinting is the light scattering effect of the cells in the bioink, and therefore induce unpredictable effects on the photopolymerization process. In consequence, the DLP-based bioprinting requires extra trial-and-error efforts for parameters optimization for each specific printable structure to compensate the scattering effects induced by cells, which is often difficult and time-consuming for a machine operator. Such trial-and-error style optimization for each different structure is also very wasteful for those expensive biomaterials and cell lines. Here, we use machine learning to learn from a few trial sample printings and automatically provide printer the optimal parameters to compensate the cell-induced scattering effects. We employ a deep learning method with a learning-based data augmentation which only requires a small amount of training data. After learning from the data, the algorithm can automatically generate the printer parameters to compensate the scattering effects. Our method shows strong improvement in the intra-layer printing resolution for bioprinting, which can be further extended to solve the light scattering problems in multilayer 3D bioprinting processes. 

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