The research deals with metaphors as being one of the primary means used by Lyudmila Ulitskaya when writing the novel " Sincerely Yours Shurik ", to form diverse and multifaceted collective female images of representatives of the classic psychological patterns presented in the work. This research reflects the results of the study related to the work of Lyudmila Yevkinevna Ulitskaya, an actress of modern Russian prose. The novel "The Sincerely to You Shorek" is one of Ludmila Ulitskaya's creations (the year of writing - 2003), which, like her other works, is distinguished by a unique presentation style, rich vocabulary, lexical and semantic diversity, and a special style of writing. writer. The main objective of the research is to look at the metaphors in which the author forms the images of the protagonists in the text space, and with the development of the narrative, and their role in shaping the plot of the work. In the course of the study, the following tasks were accomplished: identifying the metaphors that make up the female images of the novel “Sincerely Yours Shurik”, which were classified during text analysis, and distributed into “heroine/psychotype” groups, as well as the analysis of the above-mentioned forms of speech along with the formation of The plot of the work in general.
In this research, cyclic compounds derived from 2- furfural mercaptan (oxazole, triazoles) were synthesized, and their biological efficacy was measured and compared with standard drugs. Also, their effectiveness as anti-oxidant was measured and compared with ascorbic acid as a standard substance. Some of the synthesized compounds were deduced with good efficacy. © 2021 Sami Publishing Company. All rights reserved
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Arum maculatum is traditionally used for the control of many diseases and illnesses such as kidney pain, liver injury, hemorrhoids. However, the detailed biomedical knowledge about this species is still lacking. This study reports on the bioactive components and the possible mechanisms underlying the antioxidant, anti-inflammatory and cytotoxic activity of A. maculatum leaf extract. Gas chromatography-mass spectrometry (GC-MS) was used for phytochemical analysis. Assay of 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide ) (MTT) was used to determine the cytotoxicity in the murine cell line L20B upon exposure to different extract concentrations for 24 h. Enzyme-linked immunosorbent assay (ELISA) was used to detect pro-in
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In this work, an inventive photovoltaic evaporative cooling (PV/EC) hybrid system was constructed and experimentally investigated. The PV/EC hybrid system has the prosperous advantage of producing electrical energy and cooling the PV panel besides providing cooled-humid air. Two cooling techniques were utilized: backside evaporative cooling (case #1) and combined backside evaporative cooling with a front-side water spray technique (case #2). The water spraying on the front side of the PV panel is intermittent to minimize water and power consumption depending on the PV panel temperature. In addition, two pad thicknesses of 5 cm and 10 cm were investigated at three different water flow rates of 1, 2, and 3 lpm. In Case #1,
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Gingival crevicular fluid (GCF) may reflect the events associated with orthodontic tooth movement. Attempts have been conducted to identify biomarkers reflecting optimum orthodontic force, unwanted sequallea (i.e. root resorption) and accelerated tooth movement. The aim of the present study is to find out a standardized GCF collection, storage and total protein extraction method from apparently healthy gingival sites with orthodontics that is compatible with further high-throughput proteomics. Eighteen patients who required extractions of both maxillary first premolars were recruited in this study. These teeth were randomly assigned to either heavy (225g) or light force (25g), and their site specific GCF was collected at baseline and aft
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Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class
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