The issues related to the need to improve the environmental safety of internal combustion engines and the use of alternative fuels in them are considered. A method has been proposed to reliably reduce the content of toxic components in the exhaust gases of a diesel engine by replacing standard diesel fuel with methanol and methyl ester of rapeseed oil. The results of experimental studies of the environmental parameters of a diesel engine running on methanol and methyl ether of rapeseed oil are presented, the dependences of the influence of load and speed modes of its operation on them are established, and their numerical characteristics are determined. Based on experimental data, concentrations of toxic components in the exhaust gases of a diesel engine are presented, the process of formation of total hydrocarbons CHx, carbon dioxide CO2, nitrogen oxides NOx, carbon monoxide CO, soot C in the combustion chamber is considered and analyzed in detail, and the causal relationship between the formation of toxic components in the cylinder of a diesel engine is considered., powered by methanol and methyl ester of rapeseed oil, and indicators of its workflow.
У статті досліджено теоретичні та практичні засади цифрової трансформації сільськогосподарського дорадництва в умовах цифровізації аграрного сектору. Проаналізовано сутність агродорадництва як інструменту сталого розвитку сільських територій. Висвітлено передумови та переваги впровадження інформаційно-комунікаційних технологій у дорадчу практику. Наведено практичні приклади цифровізації галузі через розгортання регіональних екосистем. На основі аналізу сучасних цифрових рішень розкрито особливості еволюційного переходу від технологічної парадигми «Agriculture 4.0» до людиноцентричної моделі «Agriculture 5.0». Розглянуто використання штучного інтелекту у форматі спеціалізованих чат-ботів. Особливу увагу приділено застосуванню семантичних технологій та онтологічного моделювання на базі Semantic MediaWiki. У подальших дослідженнях передбачається обґрунтувати технологічний стек для розробки такого інтелектуального дорадчого середовища.
This systematic literature review examines how Large Language Models (LLMs) have transformed financial statement analysis by integrating narrative (textual) and quantitative data. Focusing on publications from 2017 to the present, we identified peer-reviewed articles, working papers, and conference proceedings from leading databases (Scopus, Web of Science, SSRN, and Google Scholar). Our review highlights four principal areas where LLMs have shown particular promise: risk and fraud detection, narrative summarization and sentiment analysis, Environmental, Social, and Governance (ESG) and sustainability reporting, and the integration of textual disclosures with traditional accounting metrics. These models – ranging from general-purpose Transformers (e.g., GPT, BERT) to specialized financial variants (e.g., FinBERT) – often outperform earlier machine learning approaches in tasks requiring nuanced linguistic understanding, but face challenges such as domain adaptation, interpretability, and potential model biases. Synthesizing existing studies, we observe a growing trend toward domain-specific LLMs that process both unstructured narrative text (e.g., annual reports, footnotes) and structured financial data, providing richer insights for auditors, analysts, and investors. However, empirical findings highlight concerns about data availability, reproducibility, and regulatory compliance. We suggest future research on standardized financial corpora for training robust LLMs, improved explainability tools for high-stakes decisions, and ethical frameworks to mitigate algorithmic bias. This review underscores LLMs’ transformative potential in economic and accounting domains while cautioning against uncritical deployment in sensitive settings.
The study substantiates the transformation of the global economic environment, which requires food enterprises to introduce innovative tools for foreign economic expansion. Entering international markets in the frozen foods segment requires a highly adaptive system based on Integrated Marketing Communications (IMC). The object of research is the export-oriented activity of the “Askania Frozen Foods” network. Systematic and comparative analysis, and matrix modeling are applied. The business model requires strict differentiation of communication flows. Forming export potential correlates with transmitting three blocks of advantages: IQF technology (FSSC 22000, ISO), cold chain logistics, and production adaptability. In the B2B market, personal and expert channels dominate. Future evolution of the IMC system must progress toward digitalization, predictive AI analytics, and ESG principles.
