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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Comment on “A positivity preserving and well-balanced DG scheme using finite volume subcells in almost dry regions”
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IL-10 gene-modified dendritic cells-induced type 1 T regulatory cells inhibit graft-versus-host disease while preserving graft-versus-leukemia effect
To explore the role of type 1 regulatory T (Tr1) cells in allogeneic bone marrow transplantation (allo-BMT), we generated Tr1 cells from naïve CD4{sup +} T cells in donor mice that were subjected to stimulation of recipient interleukin (IL)-10 gene-modified dendritic cells (DCs). In animal models, the severity of graft-versus-host disease (GVHD) in the group co-infused with DC-induced Tr1 cells was markedly milder than that in the groups without infusion of Tr1 cells. Moreover, the co-infusion Tr1 cells significantly prolonged the leukemia-free survival in tumor-bearing mice. Besides, Tr1-induced cells mediated the shift from T helper (Th)1 to Th2 cytokines in allo-BMT recipients. In Conclusions, our findings suggest that IL-10 gene-modified DC-induced Tr1 cells could inhibit GVHD while maintaining Graft-versus-leukemia (GVL) through mediating the shift of Th1/Th2 cytokines in an allo-BMT mice model.
Second-invariant-preserving Remap of the 2D deviatoric stress tensor in ALE methods
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Linearity-preserving monotone local projection stabilization schemes for continuous finite elements
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Preserved DNA Damage Checkpoint Pathway Protects against Complications in Long-Standing Type 1 Diabetes
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Symmetry-preserving WENO-type reconstruction schemes in Lagrangian hydrodynamics
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Second-order invariant-domain preserving approximation to the multi-species Euler equations
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Privacy-preserving federated learning: Application to behind-the-meter solar photovoltaic generation forecasting
Here, the growing usage of decentralized renewable energy sources has made accurate estimation of their aggregated generation crucial for maintaining grid flexibility and reliability. However, the majority of distributed photovoltaic (PV) systems are behind-the-meter (BTM) and invisible to utilities, leading to three challenges in obtaining an accurate forecast of their aggregated output. Firstly, traditional centralized prediction algorithms used in previous studies may not be appropriate due to privacy concerns. There is therefore a need for decentralized forecasting methods, such as federated learning (FL), to protect privacy. Secondly, there has been no comparison between localized, centralized, and decentralized forecasting methods for BTM PV production, and the trade-off between prediction accuracy and privacy has not been explored. Lastly, the computational time of data-driven prediction algorithms has not been examined. This article presents a FL power forecasting method for PVs, which uses federated learning as a decentralized collaborative modeling approach to train a single model on data from multiple BTM sites. The machine learning network used to design this FL-based BTM PV forecasting model is a multi-layered perceptron, which ensures privacy and security of the data. Comparing the suggested FL forecasting model to non-private centralized and entirely private localized models revealed that it has a high level of accuracy, with an RMSE that is 18.17% lower than localized models and 9.9% higher than centralized models.
Privacy-preserving obfuscation for distributed power systems
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