There is a large literature on public-private-partnership, covering many different areas and aspects. This article deals with a specific but important aspect: the decision-making mechanisms to choose the management of PPP enterprises. In this sector, a suitable choice of managers is of particular importance because the persons chosen must balance the public and private interests. This is often difficult to achieve. Two new procedures are discussed, “Directed Random Choice” and “Rotating CEOs”. In each case, the advantages and disadvantages of the procedure of choosing the managers of PPP enterprises are discussed and evaluated. It is concluded that the two novel mechanisms should be seriously considered when choosing the managers of PPP enterprises.

The research aims to map environmental protection strategies and the related control tools and to identify the links among companies with the largest number of employees and sites in Hungary. The research questions were answered using a questionnaire survey method. The authors used cluster analysis to classify the 205 company strategies into the identified strategy clusters: Leaders, Awakeners, and Laggards. Then, the examined 21 environmental management control tools in the sample were divided into four groups: strategic, administrative, methodological and economic. Economic and strategic methods were the most common in the sample. The authors used cross-tabulation analysis to examine whether there is a statistically proven relationship between belonging to environmental strategy clusters and specific control tools. The analysis showed significant but weak to moderate relationships. According to Cramer's V and the contingency coefficient, the closest relationship between the tested environmental management control tools and membership in environmental strategy clusters is shown by evaluating investments, assessing the economic viability of environmental strategies, and running an environmental training program for employees. In case of the robust lambda indicator, a significant relationship was found by examining the economics of environmental strategies and identifying environmental success factors and eco-balances. It can be concluded that the companies under examination follow a set of environmental goals, which they have incorporated into their strategic objectives. They use the available environmental management control toolbox to develop their strategies and to monitor their implementation to varying degrees.

Managing business development related to the innovation of intelligent supply chains is an important task for many companies in the modern world. The study of management mechanisms, their content and interrelations of elements contributes to the optimization of business processes and improvement of efficiency. This article examines the experience of China in the context of the implementation of intelligent supply chains. The study uses the methods of thematic search and systematic literature review. The purpose of the article is to analyze current views on intelligent supply chain management and identify effective business management practices in this area. The analysis included publications devoted to various aspects of supply chain management, innovation, and the implementation of digital technologies. The main findings of the article are as follows: Firstly, the key elements of intelligent supply chain management mechanisms are identified, secondly, successful experiences are summarized and the main challenges that companies face in their implementation are identified. In addition, the article focuses on the gaps in research related to the analysis of successful experiences and the reasons for achieving them.

The use of plant viruses as bioherbicides represents a fascinating and promising frontier in modern agriculture and weed management. This review article delves into the multifaceted world of harnessing plant viruses for herbicidal purposes, shedding light on their potential as eco-friendly, sustainable alternatives to traditional chemical herbicides. We begin by exploring the diverse mechanisms through which plant viruses can target and control weeds, from altering gene expression to disrupting essential physiological processes. The article highlights the advantages of utilizing plant viruses, such as their specificity for weed species, minimal impact on non-target plants, and a reduced environmental footprint. Furthermore, we investigate the remarkable versatility of plant viruses, showcasing their adaptability to various weed species and agricultural environments. The review delves into the latest advancements in genetic modification techniques, which enable the engineering of plant viruses for enhanced herbicidal properties and safety. In addition to their efficacy, we discuss the economic and ecological advantages of using plant viruses as bioherbicides, emphasizing their potential to reduce chemical herbicide usage and decrease the development of herbicide-resistant weeds. We also address the regulatory and safety considerations associated with the application of plant viruses in agriculture. Ultimately, this review article underscores the immense potential of plant viruses as bioherbicides and calls for further research, development, and responsible deployment to harness these microscopic agents in the ongoing quest for sustainable and environmentally friendly weed management strategies.

The article analyzes the process of formation of research universities as one of the elements of a strong innovation economy. The formation of a new university model is a global trend, successfully implemented in English-speaking countries. In Russia, the educational system is not yet ready to ensure the country’s effective competition in the innovation market. The Strategic Academic Leadership Program “Priority-2030” is designed to carry out the functional transformation of the entire infrastructure of human capital reproduction in a short period of time in Russia. The article presents an analysis of the main conditions for the development of a university with a research strategy, as well as an assessment of the implementation of this strategy by Moscow Polytechnic University. The methodological basis of the study was formed by qualitative methods: included observation and benchmarking of universities’ activities, which allowed to generalize the current global trends and best practices in the field of education. For the analysis we used the data of monitoring the activities of higher education organizations, data of official statistics, as well as data from reports and presentation materials of universities and online publications participating in the “5-100” and “Priority-2030” programs. The results of the study may be useful for researchers and practitioners engaged in the transformation of the Russian higher education system.

This study applies machine learning methods such as Decision Tree (CART) and Random Forest to classify drought intensity based on meteorological data. The goal of the study was to evaluate the effectiveness of these methods for drought classification and their use in water resource management and agriculture. The methodology involved using two machine learning models that analyzed temperature and humidity indicators, as well as wind speed indicators. The models were trained and tested on real meteorological data to assess their accuracy and identify key factors affecting predictions. Results showed that the Random Forest model achieved the highest accuracy of 94.4% when analyzing temperature and humidity indicators, while the Decision Tree (CART) achieved an accuracy of 93.2%. When analyzing wind speed indicators, the models’ accuracies were 91.3% and 93.0%, respectively. Feature importance revealed that atmospheric pressure, temperature at 2 m, and wind speed are key factors influencing drought intensity. One of the study’s limitations was the insufficient amount of data for high drought levels (classes 4 and 5), indicating the need for further data collection. The innovation of this study lies in the integration of various meteorological parameters to build drought classification models, achieving high prediction accuracy. Unlike previous studies, our approach demonstrates that using a wide range of meteorological data can significantly improve drought classification accuracy. Significant findings include the necessity to expand the dataset and integrate additional climatic parameters to improve models and enhance their reliability.