Cтратегии и технологии инновационного развития корпораций

У монографії представлено результати дослідження й систематизації теоретичних, науково-методологічних і практичних положень та розробок щодо стратегій та технологій інноваційного розвитку корпорацій. Запропоновано і обґрунтовано технології управління інноваційним розвитком підприємств, стратегії розвитку бізнесу, визначено сучасні реалії та тенденції корпоративного маркетингу, культури та соціальної відповідальності бізнесу, запропоновано нові підходи у корпоративному управлінні, обґрунтовано доцільність використання краудтехнологій фінансування інноваційних проектів, визначено підхо-ди щодо управління персоналом корпорацій в поведінковій економіці. Для науковців та фахівців сфери економіки та управління підприємствам

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

Hydration of the Carboxylate Group in Anti-Inflammatory Drugs: ATR-IR and Computational Studies of Aqueous Solution of Sodium Diclofenac

Diclofenac (active ingredient of Voltaren) has a significant, multifaceted role in medicine, pharmacy, and biochemistry. Its physical properties and impact on biomolecular structures still attract essential scientific interest. However, its interaction with water has not been described yet at the molecular level. In the present study, we shed light on the interaction between the steric hindrance (the intramolecular N–H···O bond, etc.) carboxylate group (−CO₂^–) with water. Aqueous solution of sodium declofenac is investigated using attenuated total reflection-infrared (ATR-IR) and computational approaches, i.e., classical molecular dynamics (MD) simulations and density functional theory (DFT). Our coupled classical MD simulations, DFT calculations, and ATR-IR spectroscopy results indicated that the −CO₂^– group of the diclofenac anion undergoes strong specific interactions with the water molecules. The combined experimental and theoretical techniques provide significant insights into the spectroscopic manifestation of these interactions and the structure of the hydration shell of the −CO₂^– group. Moreover, the developed methodology for the theoretical analysis of the ATR-IR spectrum could serve as a template for the future IR/Raman studies of the strong interaction between the steric hindrance −CO₂^– group of bioactive molecules with the water molecules in dilute aqueous solutions

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