Research that discusses the impact of implementing Green Human Resource Management and environmentally friendly behavior, especially in sustainable tourism, is limited. It becomes crucial to understand how implementing good green human resource management practices in tourism sector organizations. To achieve the objectives of this research, a qualitative approach was used where the data and information collected were obtained through direct observation and interviews with tourism informants. The findings show the importance of environmentally friendly behavior as the implementation of green human resource management is able to improve tourism management. The uniqueness of this research is developing a model of human resource readiness in implementing environmentally friendly behavior towards sustainable tourism. This resource readiness will be reflected in the GHRM model in supporting sustainable tourism. The results of this research offer a model of sustainable Green Tourism which includes antecedents, implementation and results achieved. These antecedents come from internal and external (environmental ethics and management commitment) managers which will result in good GHRM implementation. This model will be the basis for implementing sustainable tourism in human resource management practices based on literature reviews and also tourism management practices.

The paper proposes a methodology for the analysis and evaluation of the traffic scheme of Bulgarian cities. The authors combine spatial, network, and socio-economic analyses of cities with transport operators’ financial-economic evaluation, sociological studies of transport habits, and the possibilities of new information technologies for transport modeling (such as geographic information systems). The model proposes several approaches to optimize the municipality’s transport scheme. It results from a new need to improve urban traffic, the quality of transport services, and the integration of urban transport into the regional economy of Stara Zagora municipality. It presents a description, analysis, and outline of the opportunities for developing urban transport connectivity and mobility in Stara Zagora municipality. The research results show a deficit of transport connectivity between the different parts of the city, reflecting on the regional economy’s development and the efficiency of the environment and the population.

The purpose of this study was to investigate the published literature on human resource management and school performance from January 2012 to December 2022. Numerous literature evaluations have been conducted on human resource management and organizational performance, but school or teacher performance has received less attention than organizational performance. The PICOC (population, intervention, comparison, outcome, and context) technique is integrated into each stage of the PSALSAR framework to assure the study’s objective and comparability. This in-depth research is conducted in three stages: identifying pertinent keywords, screening pertinent papers, and selecting pertinent publications for review utilizing the PRISMA (Preferred Reporting Items for Systematic Reviews and Mata Analysis) technique. This made a final database with 44 publications that met the study’s requirements for inclusion. This study reveals that HRM practices and school performance are correlated. The results of the research identify the eight most essential HRM practices for improving school performance, which included planning, organizing, recruitment and selection, training and development, performance management, employee relations and involvement, reward and compensation, health, safety, and work-life balance. Leadership style, motivation, satisfaction, productivity and task performance, competency, culture and climate, empowerment, and commitment were among the performance-influencing elements.

The country has vigorously promoted the work of school aesthetic education, which is a great opportunity and also a major challenge for the art major of Yili Normal University. At present, the quality of teaching in the art major is not very optimistic. How to ensure and improve the teaching quality of the art major is worth pondering. Based on the investigation of the current situation of teaching quality in the art college, this article identifies the factors that hinder the improvement of teaching quality, analyzes the reasons, and ultimately provides strategies to improve the teaching quality of the art major, providing effective reference suggestions to ensure the teaching quality of the major.

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.

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.