State support for agriculture is a crucial tool for adjusting the competitive advantages of agricultural producers to a volatile market environment. In countries with diverse natural conditions for agriculture, however, the allocation of subsidies often focuses on bridging spatial development gaps rather than maximizing the return on inputs. To improve the efficiency of resource use in agriculture, it is essential to tailor subsidy criteria to regional disparities in agricultural potential. Using the example of Russia’s 81 administrative regions, the authors have tested a five-stage methodology for determining the support-generated parameters of output, efficiency, impact, revenue, and profitability. This methodology takes into account both natural and economic factors that contribute to the competitive advantages of each region. The study aims to identify the parts of the performance indicators, such as gross agricultural output and revenue, that are influenced by the amount of subsidies in five different types of territories, which are categorized by the cadastral value of their farmland. It has been found that the allocation of subsidies is not entirely based on the return on the funds allocated. There is a discrepancy between the competitive advantages of these territories in agricultural production and the amount of funds they receive through government support programs. The efficiency of government support differs significantly depending on the type of agricultural product produced in each territory. The approach developed by the authors provides a tool that policy makers can use when tuning the allocation of subsidies based on the differences in the agricultural potential of each territory.

This paper explores the integration of Large Language Models (LLMs) and Software-Defined Resources (SDR) as innovative tools for enhancing cloud computing education in university curricula. The study emphasizes the importance of practical knowledge in cloud technologies such as Infrastructure as a Service (IaaS), Platform as a Service (PaaS), Software as a Service (SaaS), DevOps, and cloud-native environments. It introduces Lean principles to optimize the teaching framework, promoting efficiency and effectiveness in learning. By examining a comprehensive educational reform project, the research demonstrates that incorporating SDR and LLMs can significantly enhance student engagement and learning outcomes, while also providing essential hands-on skills required in today’s dynamic cloud computing landscape. A key innovation of this study is the development and application of the Entropy-Based Diversity Efficiency Analysis (EDEA) framework, a novel method to measure and optimize the diversity and efficiency of educational content. The EDEA analysis yielded surprising results, showing that applying SDR (i.e., using cloud technologies) and LLMs can each improve a course’s Diversity Efficiency Index (DEI) by approximately one-fifth. The integrated approach presented in this paper provides a structured tool for continuous improvement in education and demonstrates the potential for modernizing educational strategies to better align with the evolving needs of the cloud computing industry.

An alternative for sustainable management in the cultivation of Capsicum annuum L. has focused on the use of plant growth promoting rhizobacteria (PGPR) and arbuscular mycorrhizal fungi (AMF). This research selected PGPRPGPR and AMF based on their effect on Bell Pepper and Jalapeño bell pepper plants. Five bacterial strains isolated from different localities in the state of Mexico (P61 [Pseudomonas tolaasii], A46 [P. tolaasii], R44 [Bacillus pumilus], BSP1.1 [Paenibacillus sp.] and OLs-Sf5 [Pseudomonas sp.]) and 3 AMF treatments (H1 [consortium isolated from Chile rhizosphere in the state of Puebla], H2 [Rhizophagus intraradices] and H3 [consortium isolated from lemon rhizosphere from the state of Tabasco]). In addition, a fertilized treatment (Steiner solution 25%) and an absolute control were included. Jalapeño bell pepper “Caloro” and Bell Pepper “California Wonder” seedlings were inoculated with AMF at sowing and with CPB 15 days after emergence, and grown under controlled environment chamber conditions. In Jalapeño bell pepper, the best bacterial strain was P61 and the best AMF treatment was H1; in Bell Pepper the best strain was R44 and the best AMF were H3 and H1. These microorganisms increased the growth of jalapeño bell pepper and Bell Pepper seedlings compared to the unfertilized control. Likewise, P61 and R44 positively benefited the photosynthetic capacity of PSII.

To evaluate the efficiency of decision-making units, researchers continually develop models simulating the production process of organizations. This study formulates a network model integrating undesirable outputs to measure the efficiency of Vietnam’s banking industry. Employing methodologies from the data envelopment analysis (DEA) approach, the efficiency scores for these banks are subsequently computed and comparatively analyzed. The empirical results indicate that the incorporation of undesirable output variables in the efficiency evaluation model leads to significantly lower efficiency scores compared to the conventional DEA model. In practical terms, the study unveils a deterioration in the efficiency of banking operations in Vietnam during the post-Covid era, primarily attributed to deficiencies in credit risk management. These findings contribute to heightening awareness among bank managers regarding the pivotal importance of credit management activities.