Nitric oxide (NO) is a critical indicator of energy deposition in the lower thermosphere because of its formational pathways. Thus, it is important to constrain sources of NO, such as meteoroid generated hypersonic flows below 95 km altitude. This paper aims to examine the process of and place the upper estimate on NO production in high temperature flow fields of strongly ablating meteoroids. For centimeter-sized meteoroids, the production of NO is bound within the dynamically stable volume of bright meteor plasma trains in the region of 80-95 km. Our estimate of the upper limit of the cumulative mass of NO produced annually by centimeter-sized meteoroids is significantly lower than that reported in previous early studies. In the context of shock waves, we explored the reasons why centimeter-sized meteoroids are the most efficient producers of NO. Effects of nonlinear processes on meteoric NO production are discussed.
The emergence of anti-social behaviour in online environments presents a serious issue in today’s society. Automatic detection and identification of such behaviour are becoming increasingly important. Modern machine learning and natural language processing methods can provide effective tools to detect different types of anti-social behaviour from the pieces of text. In this work, we present a comparison of various deep learning models used to identify the toxic comments in the Internet discussions. Our main goal was to explore the effect of the data preparation on the model performance. As we worked with the assumption that the use of traditional pre-processing methods may lead to the loss of characteristic traits, specific for toxic content, we compared several popular deep learning and transformer language models. We aimed to analyze the influence of different pre-processing techniques and text representations including standard TF-IDF, pre-trained word embeddings and also explored currently popular transformer models. Experiments were performed on the dataset from the Kaggle Toxic Comment Classification competition, and the best performing model was compared with the similar approaches using standard metrics used in data analysis.
Machine learning techniques have been increasingly used in astronomical applications and have proven to successfully classify objects in image data with high accuracy. The current work uses archival data from the Faint Images of the Radio Sky at Twenty Centimeters (FIRST) to classify radio galaxies into four classes: Fanaroff-Riley Class I (FRI), Fanaroff-Riley Class II (FRII), Bent-Tailed (BENT), and Compact (COMPT). The model presented in this work is based on Convolutional Neural Networks (CNNs). The proposed architecture comprises three parallel blocks of convolutional layers combined and processed for final classification by two feed-forward layers. Our model classified selected classes of radio galaxy sources on an independent testing subset with an average of 96% for precision, recall, and F1 score. The best selected augmentation techniques were rotations, horizontal or vertical flips, and increase of brightness. Shifts, zoom and decrease of brightness worsened the performance of the model. The current results show that model developed in this work is able to identify different morphological classes of radio galaxies with a high efficiency and performance.
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