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Your search keyword '"Gong, Ruohan"' showing total 21 results
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21 results on '"Gong, Ruohan"'

6. A Method for Hot Spot Temperature Prediction of a 10 kV Oil-Immersed Transformer

Author

Cihan Duan, Gong Ruohan, Yiming Xie, Yu Quan, Yongqing Deng, Jiangjun Ruan, and Daochun Huang

Subjects
General Computer Science, 020209 energy, 02 engineering and technology, 01 natural sciences, law.invention, law, 0103 physical sciences, 0202 electrical engineering, electronic engineering, information engineering, General Materials Science, support vector regression, multi-physical field analysis, Transformer, Mathematics, 010302 applied physics, Maximum temperature, business.industry, oil-immersed transformer, General Engineering, Structural engineering, Support vector machine, Mean absolute percentage error, Hot spot temperature, lcsh:Electrical engineering. Electronics. Nuclear engineering, Orthogonal array, business, lcsh:TK1-9971
Abstract

This paper proposed a prediction method to predict a 10-kV oil-immersed transformer hot spot temperature (HST). A set of feature temperature points on the transformer iron shell is proposed based on fluid-thermal field calculation. These feature points, as well as transformer load rate, are taken as the input parameters of a machine learning model established by support vector regression (SVR), thus to describe their relationships with the HST. This model is trained by nine samples selected by L9(34) orthogonal array and applied to predict the HST of 20 test samples. The training samples are all obtained by simulation, and the test samples have consisted of simulation and transformer temperature rise test results. With effective parameter optimization of the SVR model, the predicted results agree well with the experimental and simulation data, the mean absolute percentage error (MAPE) is 1.55%, and the maximum temperature difference is less than 3 °C. The results validated the validity and the generalization performance of the prediction model.

Published
2019

21. Lactoferrin/CD133 antibody conjugated nanostructured lipid carriers for dual targeting of blood-brain-barrier and glioblastoma stem cells.

Author

Zhao C, Zhu X, Yang H, Tan J, Gong R, Mei C, Cai X, Su Z, and Kong F

Subjects
Humans, Cell Line, Tumor, Animals, Mice, Drug Delivery Systems, Antibodies chemistry, Blood-Brain Barrier metabolism, Glioblastoma drug therapy, Glioblastoma metabolism, Glioblastoma pathology, Lactoferrin chemistry, AC133 Antigen metabolism, Neoplastic Stem Cells drug effects, Neoplastic Stem Cells metabolism, Temozolomide pharmacology, Nanostructures chemistry, Drug Carriers chemistry, Lipids chemistry, Brain Neoplasms drug therapy, Brain Neoplasms metabolism, Brain Neoplasms pathology
Abstract

The main reasons for the difficulty in curing and high recurrence rate of glioblastoma multiforme (GBM) include: 1. The difficulty of chemotherapy drugs in penetrating the blood-brain barrier (BBB) to target tumor cells; 2. The presence of glioma stem cells (GSCs) leading to chemotherapy resistance. Therefore, breaking through the limitations of the BBB and overcoming the drug resistance caused by GSCs are the main strategies to address this problem. This study presents our results on the development of lactoferrin (Lf)/CD133 antibody conjugated nanostructured lipid carriers (Lf/CD133-NLCS) for simultaneously targeting BBB and GSCs. Temozolomide (TMZ) loaded Lf/CD133-NLCS (Lf/CD133-NLCS-TMZ) exhibited high-efficiency in vitro anti-tumor effects toward malignant glioma cells (U87-MG) and GSCs, while demonstrating no significant toxicity to normal cells at concentrations lower than 200 μg ml -1 . The results of the in vitro targeting GBM study revealed a notably higher cellular uptake of Lf/CD133-NLCS-TMZ in U87-MG cells and GSCs in comparison to Lf/CD133 unconjugated counterpart (NLCS-TMZ). In addition, increased BBB permeability were confirmed for Lf/CD133-NLCS-TMZ compared to NLCS-TMZ both in vitro and in vivo . Taking together, Lf/CD133-NLCS-TMZ show great potential for dual targeting of BBB and GSCs, as well as GBM therapy based on this strategy., (© 2024 IOP Publishing Ltd. All rights, including for text and data mining, AI training, and similar technologies, are reserved.)

Published
2024
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