Olena Nifatova

In the post-war reconstruction of Ukraine, agricultural universities will provide the state with specialists capable of implementing the seamless integration of “green” benchmarks. The full-scale war has not only threatened the sustainable operation of all spheres of higher education institutions but also forced them to look for new ways of adaptation and development. This study aims to assess the readiness of higher education institutions in the agrarian sector to meet the personnel needs of the state in the medium-term of post-war reconstruction. The paper used a group of parameters with specific indicators: education (change in the number of graduates at the Bachelor’s and Master’s educational levels, the ratio of publicly-funded and fee-based education seekers, changes in the average entrance score), science (change in the number of articles published in Scopus and the corresponding h-index, the number of Erasmus grants received, the amount of budget funding for science), recognition (academic ranking of higher education institutions by the Webometrics and Top-200 Ratings), and financing (estimated amounts of funding from the general and special fund, and their ratio). The study offers a unique approach to calculating the Integrated Sustainability Index of higher education institutions. This index clearly illustrates how efficiently 15 agricultural higher education institutions have adapted to wartime transformations. For example, Sumy National Agrarian University and Lviv National Natural Resource Management University have this index exceeding 30%. The study draws attention to the problematic areas of university activity during the war and suggests extending the valuable experience of Sumy National Agrarian University.

Acknowledgment
The authors are grateful to the Armed Forces of Ukraine, who are liberating the territory of Ukraine at the cost of their own lives, as well as to Ukrainian farmers, who, despite the war, are doing everything possible to prevent a food crisis in the world.
This study is co-funded by the European Union through the European Education and Culture Executive Agency (EACEA) within the project “Europeanisation of Doctoral Studies in Line with the Innovative Doctoral Training Principles in Europe: Towards a Common Future” 101083493 - EDOCS - ERASMUS-JMO-2022-HEI-TCH-RSCH https://edocs.snau.edu.ua/en/

Type of the article: Theoretical Article

Abstract
The shift towards the “product as a service” paradigm requires adaptive pricing tools. The article aims to conceptualize AI-driven algorithmic pricing methods for short-term rentals and to validate them through simulation modelling as a synthetic proof of concept. The methodology distinguishes conceptual evaluation – structural-functional and SWOT analyses – from empirical simulation. The proposed framework of the actual demand-probability model uses a logistic function as a deep learning-based predictive model (MLP) within an explicit revenue-maximization procedure. While the MLP is trained to minimize prediction error in latent demand, the forecast WTP serves as a critical input to a bounded search module that calculates the revenue-maximising price. The algorithmic model demonstrates a significant uplift in simulated gross booking revenue relative to the Static, Rule-Based, and Random Forest benchmarks, based on 100 Monte Carlo repetitions. A deep learning-based predictive model with an average cumulative revenue of USD 7,307.42 outperforms the Random Forest model with high statistical significance and achieves a high win rate across simulated scenarios. Probability-based algorithmic pricing mitigates the risks of resource downtime. The sensitivity analysis confirms MLP model stability across varying consumer price elasticities. AI-driven pricing is positioned as a scalable, systemic digital technology for coordinating economic processes in modern platform industries.

Acknowledgments
This research was funded by a grant from the state budget of Ukraine, “Fundamental grounds for Ukraine’s transition to a digital economy based on the implementation of Industries 3.0; 4.0; 5.0” (No. 0124U000576) and “Digital Economy and Renewable Energy: Building Sustainable Business Models for Ukraine’s Post-War Reconstruction” (No. 0126U000877).