Non-isothermal trimerization kinetics of 1,6-hexamethylene diisocyanate (HMDI) in the reaction mixture with epoxy resin (ER) at a fixed ratio HMDI/ER = 85/15 (mass) catalyzed by differential concentrations of triethylene amine (TEA) was studied by differential calorimetry at four heating rates. In contrast to the simple single-step models, the isoconversional approach proved to describe quantitatively the reaction kinetics over the entire range of conversions, The apparent activation energies of the curing process and the corresponding pre-exponential factors tended to decrease smoothly, the higher the conversion, whereas a definite excess of the amine component turned out to be necessary to arrive at the maximum reaction rate.
The effect of the time of preliminary storage at room temperature on the kinetics of HMDI trimerization at the fixed HMDI/ER ratio (85/25), and different concentrations of the catalyst, TEA, and the evolution of glass transition parameters of the end products in function of the curing reaction regime, were studied by calorimetry. It was established that both the activation energy Eα and the pre-exponential factor ln A increased, the longer the storage time, and both tended to decrease with the conversion α1 The time for the onset of gelation tgel decreased, the higher the catalyst concentration and/or the longer the preliminary storage time the former proved to be a major factor controlling tgel|. The glass transition temperatures Tg for post-cured samples of series HT “internally diluted” with stiff oxazolidone heterocycles were systematically higher than those for samples of series LT cured at 393 K whatever the catalyst concentration and/or the storage time.
The demand for the creation of information systems that simplifies and accelerates work has greatly increased in the context of the rapidinformatization of society and all its branches. It provokes the emergence of more and more companies involved in the development of softwareproducts and information systems in general. In order to ensure the systematization, processing and use of this knowledge, knowledge managementsystems are used. One of the main tasks of IT companies is continuous training of personnel. This requires export of the content from the company'sknowledge management system to the learning management system. The main goal of the research is to choose an algorithm that allows solving theproblem of marking up the text of articles close to those used in knowledge management systems of IT companies. To achieve this goal, it is necessaryto compare various topic segmentation methods on a dataset with a computer science texts. Inspec is one such dataset used for keyword extraction andin this research it has been adapted to the structure of the datasets used for the topic segmentation problem. The TextTiling and TextSeg methods wereused for comparison on some well-known data science metrics and specific metrics that relate to the topic segmentation problem. A new generalizedmetric was also introduced to compare the results for the topic segmentation problem. All software implementations of the algorithms were written inPython programming language and represent a set of interrelated functions. Results were obtained showing the advantages of the Text Seg method incomparison with TextTiling when compared using classical data science metrics and special metrics developed for the topic segmentation task. Fromall the metrics, including the introduced one it can be concluded that the TextSeg algorithm performs better than the TextTiling algorithm on theadapted Inspec test data set.
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