Electric energy consumption is increasing much faster than the predicted growth in energy generation. Although the installed capacity of renewable energy sources is also expanding, grid congestion remains unavoidable without adopting smart energy management systems (EMS) and flexible power electronics structures. Given the significant installed capacity of photovoltaic (PV) systems in the residential sector, moving towards zero-emission buildings (ZEBs) through the use of storage systems and smart power electronics is essential. This article provides a detailed review of power electronics solutions for ZEBs and offers strategies to address related challenges. By exploring the promising future of the low-voltage dc (LVDC) industry in ZEBs, it presents and compares grid connection scenarios and evaluates their overall efficiencies across hybrid, dc, and ac technologies. Furthermore, it addresses the integration of dc and ac systems in energy resources (ER), proposing solutions for challenges related to protection, grounding, and leakage currents. Finally, it examines the latest EMS solutions, emphasizing the shift to full digitalization through a combination of cloud-based and edge-computing platforms.
Within article, peculiarities of the origin, formation, functioning and development of innovation ecosystems are examined. The main participants (research institutions and universities, business structures and corporations, investors and governments) are identified, and three main levels (business ecosystem, communities and platforms) are allocated. The comparison is made between open and closed, regional and sectoral innovation ecosystems. The global practice of clustering is considered and the rating of innovation clusters is analysed on this basis. Based on the study of the analysis of the best world practices of creation and implementation of innovation ecosystems, the authors propose the model of implementation of the existing experience in the practice of formation of innovation ecosystems in the region, which includes description of initial provisions of formation of the innovation ecosystem; identification of the main actors in formation of the innovation ecosystem; determination of the expected results of interaction of the main actors in formation of the innovation ecosystem. The article pays special attention to the study of involvement of artificial intelligence tools in strategic development of innovation ecosystems. The author emphasises the breadth of these opportunities-from market analytics and decision support under uncertainty to automated intellectual property management and investor search. To quantify the maturity of the regional innovation ecosystem, the authors propose the IEMI index, the analytical tool that takes into account five key components: science, human resources, cooperation, financing and regulatory environment.The authors investigate how artificial intelligence technologies manifest trends in changing approaches to formation and strategic development of regional innovative entrepreneurial ecosystems. The authors emphasise the key role of universities in these processes, which in the context of digital transformation act not only as centres of education and science, but also as active providers of the innovation economy. The article provides examples of how global and Ukrainian universities are implementing smart technologies in knowledge transfer and partnership building.
This paper demonstrates the effectiveness of deep learning algorithms compared to traditional mathematical and machine learning algorithms in forecasting stock price volatility. Specifically, a Long Short-Term Memory (LSTM) network, representing deep learning approaches, is evaluated against ARIMA and GARCH mathematical models, as well as Support Vector Machines and Random Forest machine learning algorithms. The research aims to identify the strengths and limitations of each model in terms of prediction accuracy and efficiency by analyzing daily stock prices and trading volumes of selected companies. The findings suggest that the LSTM model offers significant advantages in capturing the complex and dynamic nature of financial markets, providing more reliable forecasting tools for investors and policymakers. The study also highlights the trade-off between accuracy and computational requirements, concluding that the GARCH model may be a suitable option when accuracy is not the primary factor, and computation speed is of utmost importance.
The article is devoted to the multifaceted typology of American rap pseudonyms with the element money in their structure on the basis of the nominative, lexico-semantic, etymological, ontological, and structural features within the English-language pseudonymicon. A rap pseudonym is defined as an anthroponym deliberately created by a rapper for a new identification and unique characterization of their personality in order to emphasize a special status in show business and potentially transform into a successful and famous stage performer. Grammatically, it is characterized by a singular form, based on the referential meaning of the unit, which provides information exclusively about one rap performer. A rap pseudonym functions as a holistic nominative sign with either a morphological (word) or syntactic (phrase or sentence) structure. It was established that such units perform nominative, identificatory, esoteric, representative, communicative, emotive, stylistic, and self-expressive functions. In writing, such rap pseudonyms are capitalized, and the element "money" is fixed lexically and graphically through the dollar symbol "$". It was revealed that while the hierarchy of proper names, including these rap pseudonyms, appears as onym–anthroponym–pseudonym–artist-pseudonym–vocal-pseudonym–rap-pseudonym–rap-pseudonym-money, the hierarchy of nominative groupings to which these units belong is represented as language picture of the world–nominative space–onomasticon–anthroponymicon–pseudonymicon–rap-pseudonymicon. By analyzing various aspects of the rap pseudonyms, we identified and described a series of classifications within the American rap pseudonymicon. These include structural (according to morphological and syntactic structure), componential (according to the number of constituents), semantic (according to the type of meaning), stylistic (according to stylistic affiliation of units), etymological-componential (according to the origin and onomastic status), ontological (according to the real/fictional and original/modified criteria), and semantic-referential (according to the types of semantic classes of components) classifications.
Diversity management encompasses dimensions such as gender and sexual orientation, age, disabilities, nationality, ethnicity, religion, and socio-economic status (SES) and is fundamental to sustainable development, enabling higher education institutions (HEIs) to foster inclusive, equitable, and resilient academic environments. This study examines diversity management practices in seven Czech and five Ukrainian HEIs recognized as leaders in the Times Higher Education (THE) Impact Rankings. Data were obtained from university websites, SDG reports, or annual reports focusing on SDG 1, SDG 2, SDG 4, SDG 5, SDG 8, SDG 10, and SDG 16. The analysis shows that Czech universities demonstrate structured policies and stable resources, enabling them to offer comprehensive support for professional growth, gender equality, and inclusivity. Examples include sabbatical opportunities, gender-balanced organizational policies, and adaptive measures for individuals with disabilities. Conversely, Ukrainian universities exhibit remarkable resilience and adaptability, addressing challenges posed by the ongoing war. Key initiatives include supporting displaced students and veterans, restoring damaged infrastructure, and integrating inclusive education practices under wartime constraints. Despite differing contexts, both countries emphasize financial aid and scholarships as critical tools for ensuring equitable access to education. The findings underscore the importance of leveraging diversity dimensions to develop effective strategies for achieving SDGs while adapting to regional and institutional specificities. AcknowledgmentThis publication is based upon work from 24-PKVV-UM-002, ‘Strengthening the Resilience of Universities: Czech-Ukrainian Partnership for Digital Education, Research Cooperation, and Diversity Management,’ supported by the Czech Development Agency and the Ministry of Foreign Affairs under the initiative ‘Capacity Building of Public Universities in Ukraine 2024’.
