Одержання поліметакрилатних присадок та дослідження експлуатаційних властивостей легованої індустріальної оливи

The study explored the influence of temperatures and the impact of concentrations of the initiator and the monomer on the kinetics of lauryl methacrylate homopolymerization in benzene. The reaction sequence with respect to the initiator (1.38±0.07), the monomer (1.69±0.02) and also the activation energy (Еа=96.4±0.506 kJ/mol) was determined according to the experimental points of the first fixed area in the polymerization process (S ≤ 10 %). The numerical values of the reaction order for this system were high due to the high structuring of the LMA and a high viscosity of the system. The optimal conditions for obtaining polymethacrylate additives were determined on the basis of kinetic studies and the following parameters: the temperature of 80 ± 1°C, the concentration of benzoyl peroxide of 0.5 wt.% based on the total monomers’ weight, the ratio of lauryl methacrylate : benzene = 1:1, and the reaction time of 3 to 4 hours. The qualitative composition of the additives was confirmed with the infrared spectrometry. According to a thermogravimetric analysis, it has been found that synthesized (co)polymers are thermally stable up to the temperatures of 255–265°C.The influence of polymethacrylate additives in the oil I-20A on the rheological, depressor and antiwear properties was also studied. The viscosity curves for the obtained systems were described. The viscosity index of the obtained alloyed oil was determined according to the kinematic viscosity values at the temperatures of 50°C and 100°C. The depressor and antiwear properties of the lubricant were investigated at the optimal concentration of the additive in the amount of 2 wt.% in the oil. The operational properties of the industrial oil with an additive in the amount of 1.4 wt.% were also summarized in the study. The antiwear properties of the alloyed oil I–20A were tested in friction on a four ball machine. It has been found that the PMA20 additive with concentration of 2 wt.% in the oil can be used to obtain an alloyed industrial lubricant as a commodity with desirable operational properties (VI = 140 at TFr = –19°C).The obtained lubricant can be used for friction reduction and wear protection of equipment elements of power plants.Исследовано кинетику гомополимеризации лаурилметакрилата в бензоле. Получены полиметакрилатные присадки путем сополимеризации лаурилметакрилата с метилакрилатом в бензоле и исследованы их физико–химические свойства. Показано влияние концентрации и состава присадки на реологические свойства индустриального масла И–20А. Установлена оптимальная концентрация присадки в масле И–20А, при которой улучшаются вязкостно–температурные, депрессорные и противоизносные свойства.Досліджено кінетику гомополімеризації лаурилметакрилату в бензолі. Одержано поліметакрилатні присадки кополімеризацією лаурилметакрилату з метилакрилатом у бензолі та досліджено їх фізико-хімічні властивості. Показано вплив концентрації та складу присадки на реологічні властивості індустріальної оливи І–20А. Встановлено оптимальну концентрацію присадки в оливі І–20А, за якої покращуються в’язкісно–температурні, депресорні та протизношувальні властивості

UniMorph 4.0:Universal Morphology

The Universal Morphology (UniMorph) project is a collaborative effort providing broad-coverage instantiated normalized morphological inflection tables for hundreds of diverse world languages. The project comprises two major thrusts: a language-independent feature schema for rich morphological annotation and a type-level resource of annotated data in diverse languages realizing that schema. This paper presents the expansions and improvements made on several fronts over the last couple of years (since McCarthy et al. (2020)). Collaborative efforts by numerous linguists have added 67 new languages, including 30 endangered languages. We have implemented several improvements to the extraction pipeline to tackle some issues, e.g. missing gender and macron information. We have also amended the schema to use a hierarchical structure that is needed for morphological phenomena like multiple-argument agreement and case stacking, while adding some missing morphological features to make the schema more inclusive. In light of the last UniMorph release, we also augmented the database with morpheme segmentation for 16 languages. Lastly, this new release makes a push towards inclusion of derivational morphology in UniMorph by enriching the data and annotation schema with instances representing derivational processes from MorphyNet

Negotiating language attitudes on TikTok : computational challenges

This poster discusses the technical aspects of compiling a corpus of TikTok comments documenting (dynamics in) language attitudes towards Ukrainian, Russian and Surzhyk, especially problems in dealing with trilingual data. On preparing the data for our study of language attitude, several computational linguistics problems had to be solved, namely: working with multilingual texts and recognizing sarcasm (which can also be multilingual)

Negotiating language attitudes on TikTok : a sociolinguistic content analysis

This poster discusses the preliminary results of a corpus-based study on (changes in) language attitudes towards Ukrainian, Russian and Surzhyk between June 2022 and June 2023

SIGMORPHON–UniMorph 2022 Shared Task 0: Generalization and Typologically Diverse Morphological Inflection

The 2022 SIGMORPHON–UniMorph shared task on large scale morphological inflection generation included a wide range of typologically diverse languages: 33 languages from 11 top-level language families: Arabic (Modern Standard), Assamese, Braj, Chukchi, Eastern Armenian, Evenki, Georgian, Gothic, Gujarati, Hebrew, Hungarian, Itelmen, Karelian, Kazakh, Ket, Khalkha Mongolian, Kholosi, Korean, Lamahalot, Low German, Ludic, Magahi, Middle Low German, Old English, Old High German, Old Norse, Polish, Pomak, Slovak, Turkish, Upper Sorbian, Veps, and Xibe. We emphasize generalization along different dimensions this year by evaluating test items with unseen lemmas and unseen features separately under small and large training conditions. Across the five submitted systems and two baselines, the prediction of inflections with unseen features proved challenging, with average performance decreased substantially from last year. This was true even for languages for which the forms were in principle predictable, which suggests that further work is needed in designing systems that capture the various types of generalization required for the world’s languages