This research delves into the urgent requirement for innovative agricultural methodologies amid growing concerns over sustainable development and food security. By employing machine learning strategies, particularly focusing on non-parametric learning algorithms, we explore the assessment of soil suitability for agricultural use under conditions of drought stress. Through the detailed examination of varied datasets, which include parameters like soil toxicity, terrain characteristics, and quality scores, our study offers new insights into the complexities of predicting soil suitability for crops. Our findings underline the effectiveness of various machine learning models, with the decision tree approach standing out for its accuracy, despite the need for comprehensive data gathering. Moreover, the research emphasizes the promise of merging machine learning techniques with conventional practices in soil science, paving the way for novel contributions to agricultural studies and practical implementations.

Regional differentiation in the Russian Federation is considered to be high in terms of gross regional product (GRP) per capita level, growth rate, and other indicators. Inefficient use of region-specific spaces entails redistribution processes in order to maximize positive agglomeration effects throughout the country. These encompass economic restructuring based on production value-added chain extension and expanding inter-regional collaborative linkages. Besides, it is vital to assess the opportunities of individual Russian territories for participation therein. The research goal is to develop a scientifically based methodology to determine promising sectoral composition of the regional economies and that of spatial interactions. Such methodology would consider the feasibility of combining “smart” industrial specializations, regional resource potential, prevailing contradictions in the economic, innovative, and technological development of the country’s internal space. The proposed methodological approach opens the way to exploit the existing regional economic potential to the full, firstly, via establishing sectoral priorities of the region regarding the regulatory factors for the territorial capital to have a major effect on the increased potential GRP level; secondly, through benchmarking performance of the available development reserves within leading regions from homogeneous groups having similar characteristics and factor potentials; thirdly, via developing inter-regional integration prospects in terms of regional potential redistribution to ensure growth in potential gross domestic product. An extensive analytical and applied investigation of the proposed methodological approach was carried out from 2014 to 2020. Diversified estimates were obtained for a wide range of indicators due to evidences from 85 Russian regions and 13 types of economic activity. Such an integrated approach allows revealing actual imbalances and barriers that impede regional development, ensures the efficient use of production factors, and enables to trace ways to implement transformation policies and design effective regulatory mechanisms. The results provide arguments in favor of strengthening inter-regional connectivity and supporting inter-regional cooperation. This insight not only contributes to the academic discourse on complex development of a territory but also holds practical implications for policymakers and regional planners aimed at ensuring comprehensiveness and robustness of the evaluation supporting the decision-making process.

This study developed a specific scale to measure the impact of extrinsic motivations on students’ decisions to pursue online graduate programs at business schools in Latin America. Using a mixed-methods approach, the research proceeded in three stages. In the first stage, the construct was defined by identifying key extrinsic factors motivating students to enroll in online graduate programs, followed by the creation and initial validation of the scale in Colombia. The second stage involved testing the scale in Chile to determine its cross-cultural applicability. In the third stage, the scale’s predictive validity was confirmed, demonstrating its effectiveness in explaining how extrinsic motivations influence students’ intentions to enroll in online graduate programs. The findings indicate that the scale, composed of five dimensions—Cost Reduction, Ability to Study from Any Location, Control Over Learning Pace, Flexibility to Balance Study and Work, and Avoiding Commuting Time—is a reliable predictor of student preferences and intentions in online graduate education. The final scale includes 25 items across these dimensions, measuring extrinsic factors through items related to flexibility, time savings, and global accessibility. Validation in two Latin American countries confirms the scale’s relevance across diverse cultural contexts, enhancing its applicability within the region. This study provides empirical evidence that extrinsic motivation is a key determinant of students’ intentions to enroll in online programs in developing countries. It confirms that extrinsic motivations reflect a preference for flexible learning options compatible with students’ lifestyles and professional needs, linked to their beliefs about time management, professional advancement, and career opportunities associated with earning a graduate degree.

The effective allocation of resources within police patrol departments is crucial for maintaining public safety and operational efficiency. Traditional methods often fail to account for uncertainties and variabilities in police operations, such as fluctuating crime rates and dynamic response requirements. This study introduces a fuzzy multi-state network (FMSN) model to evaluate the reliability of resource allocation in police patrol departments. The model captures the complexities and uncertainties of patrol operations using fuzzy logic, providing a nuanced assessment of system reliability. Virtual data were generated to simulate various patrol scenarios. The model’s performance was analyzed under different configurations and parameter settings. Results show that resource sharing and redundancy significantly enhance system reliability. Sensitivity analysis highlights critical factors affecting reliability, offering valuable insights for optimizing resource management strategies in police organizations. This research provides a robust framework for improving the effectiveness and efficiency of police patrol operations under conditions of uncertainty.