Kernel density based linear regression estimate

For linear regression models with non normally distributed errors, the least squares estimate (LSE) will lose some efficiency compared to the maximum likelihood estimate (MLE). In this article, we propose a kernel density-based regression estimate (KDRE) that is adaptive to the unknown error distribution. The key idea is to approximate the likelihood function by using a nonparametric kernel density estimate of the error density based on some initial parameter estimate. The proposed estimate is shown to be asymptotically as efficient as the oracle MLE which assumes the error density were known. In addition, we propose an EM type algorithm to maximize the estimated likelihood function and show that the KDRE can be considered as an iterated weighted least squares estimate, which provides us some insights on the adaptiveness of KDRE to the unknown error distribution. Our Monte Carlo simulation studies show that, while comparable to the traditional LSE for normal errors, the proposed estimation procedure can have substantial efficiency gain for non normal errors. Moreover, the efficiency gain can be achieved even for a small sample size

A GIS Partial Discharge Defect Identification Method Based on YOLOv5

The correct identification of partial discharge types is of great significance to the stable operation of GIS. In order to improve the recognition accuracy and result of partial discharge, and to meet the requirements of real-time monitoring of GIS equipment, this paper proposes a GIS partial discharge defect recognition model based on YOLOv5. First, the GIS partial discharge simulation experiment is established to create the dataset of partial discharge PRPD map. Then, a YOLOv5-based GIS partial discharge defect recognition model is constructed, and different training methods are used to optimize the parameters of the model. By comparing with target detection models based on other deep learning methods, such as Faster-RCNN and YOLOv4, the YOLOv5 model discussed in the paper has significantly improved the recognition efficiency and recognition accuracy, in which mAP value is 95.89% and FPS is 28.89. In addition, the model can realize the distinction and identification of multiple PD types in a single PRPD map. At last, the YOLOv5-based GIS partial discharge defect identification model is applied to the test in a 500 kV substation. The model accurately determines the type of GIS partial discharge, which verifies the accuracy and validity of the model

A GIS Partial Discharge Defect Identification Method Based on YOLOv5

The correct identification of partial discharge types is of great significance to the stable operation of GIS. In order to improve the recognition accuracy and result of partial discharge, and to meet the requirements of real-time monitoring of GIS equipment, this paper proposes a GIS partial discharge defect recognition model based on YOLOv5. First, the GIS partial discharge simulation experiment is established to create the dataset of partial discharge PRPD map. Then, a YOLOv5-based GIS partial discharge defect recognition model is constructed, and different training methods are used to optimize the parameters of the model. By comparing with target detection models based on other deep learning methods, such as Faster-RCNN and YOLOv4, the YOLOv5 model discussed in the paper has significantly improved the recognition efficiency and recognition accuracy, in which mAP value is 95.89% and FPS is 28.89. In addition, the model can realize the distinction and identification of multiple PD types in a single PRPD map. At last, the YOLOv5-based GIS partial discharge defect identification model is applied to the test in a 500 kV substation. The model accurately determines the type of GIS partial discharge, which verifies the accuracy and validity of the model

Extracellular Membrane Vesicles of <i>Escherichia coli</i> Induce Apoptosis of CT26 Colon Carcinoma Cells

Escherichia coli (E. coli) is commonly utilized as a vehicle for anti-tumor therapy due to its unique tumor-targeting capabilities and ease of engineering modification. To further explore the role of E. coli in tumor treatment, we consider that E. coli outer membrane vesicles (E. coli-OMVs) play a crucial role in the therapeutic process. Firstly, E. coli-OMVs were isolated and partially purified by filtration and ultracentrifugation, and were characterized using techniques such as nanoparticle tracking analysis (NTA), transmission electron microscopy (TEM) and Western Blot (WB). The obtained extracellular nanoparticles, containing OMVs, were found to inhibited the growth of CT26 tumor in mice, while the expression of Bax protein was increased and the expression of Bcl-2 protein decreased. In vitro experiments showed that E. coli-OMVs entered CT26 cells and inhibited cell proliferation, invasion and migration. In addition, in the presence of E. coli-OMVs, we observed an increase in apoptosis rate and a decrease in the ratio of Bcl-2/Bax. These data indicate that E. coli-OMVs inhibits the growth of CT26 colon cancer by inducing apoptosis of CT26 cells. These findings propose E. coli-OMVs as a promising therapeutic drug for colorectal cancer (CRC), providing robust support for further research in related fields

Phase Structure and Electrical Properties of Sm-Doped BiFe_0.98Mn_0.02O₃ Thin Films

Bi1−xSmxFe0.98Mn0.02O3 (x = 0, 0.02, 0.04, 0.06; named BSFMx) (BSFM) films were prepared by the sol-gel method on indium tin oxide (ITO)/glass substrate. The effects of different Sm content on the crystal structure, phase composition, oxygen vacancy content, ferroelectric property, dielectric property, leakage property, leakage mechanism, and aging property of the BSFM films were systematically analyzed. X-ray diffraction (XRD) and Raman spectral analyses revealed that the sample had both R3c and Pnma phases. Through additional XRD fitting of the films, the content of the two phases of the sample was analyzed in detail, and it was found that the Pnma phase in the BSFMx = 0 film had the lowest abundance. X-ray photoelectron spectroscopy (XPS) analysis showed that the BSFMx = 0.04 film had the lowest oxygen vacancy content, which was conducive to a decrease in leakage current density and an improvement in dielectric properties. The diffraction peak of (110) exhibited the maximum intensity when the doping amount was 4 mol%, and the minimum leakage current density and a large remanent polarization intensity were also observed at room temperature (2Pr = 91.859 μC/cm2). By doping Sm at an appropriate amount, the leakage property of the BSFM films was reduced, the dielectric property was improved, and the aging process was delayed. The performance changes in the BSFM films were further explained from different perspectives, such as phase composition and oxygen vacancy content