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Abstract

The main objective of this study was to evaluate the intensity and character of the inflammatory reaction caused by an innovative polyester-polyurethane vascular prosthesis implanted into the abdominal aorta of 9 Beagle dogs aged 1-3 years. At 6 and 12 months post implantation the prostheses were removed and tissues samples were examined using 2 methods: histology and immunohistochemistry (IHC). Histology slides stained with hematoxylin and eosin (H&E) were evaluated for the intensity of inflammation by observing the density of inflammatory cells and graded 1 to 4 (1- light inflammation, 4 – severe inflammation). The pro-inflammatory mediator tumor necrosis factor-alpha (TNFα) and two anti-inflammatory mediators, interleukin 1 receptor antagonist (IL1ra), and interleukin 10 (IL10), were also assessed in the tissue samples by IHC methods. Mean (n=5) inflammation grade in H&E slides at 6 months post-implantation (6Mpost) was 2 and mean (n=4) inflammation grade at 12 months (12Mpost) was almost 3. IHC staining showed that TNFα and IL1ra in tissue samples obtained from 6Mpost dogs were expressed at the same intensity indicating equal pro- and anti-inflammatory cytokine levels. However, in the 12Mpost tissues TNFα was expressed more intensely than IL1ra and IL10. Moreover, in 2 dogs at 12Mpost, there were signs of infection assessed on the basis of neutrophil infiltration in the prostheses. In conclusion, the assessment of pro-inflammatory mediators such as TNFα and anti-inflammatory mediators, such as IL1ra and IL10, can help to interpret the intensity of the inflammatory process directed at synthetic prostheses.

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Authors and Affiliations

A. Mālniece
A. Auzāns
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Abstract

To improve power system reliability, a protection mechanism is highly needed. Early detection can be used to prevent failures in the power transmission line (TL). A classification system method is widely used to protect against false detection as well as assist the decision analysis. Each TL signal has a continuous pattern in which it can be detected and classified by the conventional methods, i.e., wavelet feature extraction and artificial neural network (ANN). However, the accuracy resulting from these mentioned models is relatively low. To overcome this issue, we propose a machine learning-based on Convolutional Neural Network (CNN) for the transmission line faults (TLFs) application. CNN is more suitable for pattern recognition compared to conventional ANN and ANN with Discrete Wavelet Transform (DWT) feature extraction. In this work, we first simulate our proposed model by using Simulink® and Matlab®. This simulation generates a fault signal dataset, which is divided into 45.738 data training and 4.752 data tests. Later, we design the number of machine learning classifiers. Each model classifier is trained by exposing it to the same dataset. The CNN design, with raw input, is determined as an optimal output model from the training process with 100% accuracy.

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Authors and Affiliations

S. Fuada
H.A. Shiddieqy
T. Adiono

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