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.

This academic paper explores the impact of multi-entity cooperation on the effectiveness of public service provision in China. It examines the social governance pattern proposed by the 19th National Congress of the CCP and the emphasis on co-building, co-governing, and sharing. The paper highlights the need for collaboration among various entities and the transition from sole government provision to improve urban public services. It aims to investigate the moderating effects of institutions, policies, and public participation. The study will involve quantitative and qualitative phases in three cities in Guangdong Province and target governmental departments, commercial organizations, non-profit social organizations, and local residents. The research aims to provide policy recommendations, innovate institutional policies, enhance public engagement, and improve multi-party cooperation and urban public services. It seeks to contribute practical models and measures for effective government public management and service implementation.

The study aims to explore the impact of examination-oriented education on Chinese English learners and the importance of cultural intelligence in second language acquisition. Through a questionnaire administered to postgraduate students majoring in English in China, the research discovered that the emphasis on test scores and strategies in China’s higher English education system has led to a neglect of cultural backgrounds and cross-cultural communication. The findings underscore the necessity for reforms in English teaching within Chinese higher education to cultivate students’ intercultural intelligence and enhance their readiness for international careers in the era of globalization.

This study employs logistic regression to investigate determinants influencing active living among elderly individuals, with “Active Living” (1 = Active, 0 = Inactive) as the dependent variable. Analysing data from 500 participants, findings reveal significant associations between active living and variables such as chronic conditions (OR = 0.29, p < 0.001), mental well-being (OR = 1.57, p < 0.001), social support (OR = 5.75, p < 0.001), access to parks/recreational facilities (OR = 2.59, p < 0.001), income levels (OR = 1.82, p = 0.003), cultural attitudes (OR = 2.72, p < 0.001), and self-efficacy (OR = 2.01, p < 0.001). These findings highlight the complex interplay of factors influencing active living among elderly populations. Recommendations include implementing targeted interventions to manage chronic conditions, enhance mental well-being, strengthen social networks, improve access to recreational spaces, provide economic support for fitness activities, promote positive cultural attitudes towards aging, and empower older adults through self-efficacy programs. Such interventions are crucial for promoting healthier aging and fostering sustained engagement in physical activity among older adults.

The proportion of national logistics costs to Gross Domestic Product (NLC/GDP) serve as a valuable indicator for estimating a country’s overall macro-level logistics costs. In some developing nations, policies aimed at reducing the NLC/GDP ratio have been elevated to the national agenda. Nevertheless, there is a paucity of research examining the variables that can determine this ratio. The purpose of this paper is to offer a scientific approach for investigating the primary determinants of the NLC/GDP and to advice policy for the reduction of macro-level logistics costs. This paper presents a systematic framework for identifying the essential criteria for lowering the NLC/GDP score and employs co-integration analysis and error correction models to evaluate the impact of industrial structure, logistics commodity value, and logistics supply scale on NLC/GDP using time series data from 1991 to 2022 in China. The findings suggest that the industrial structure is the primary factor influencing logistics demand and a significant determinant of the value of NLC/GDP. Whether assessing long-term or short-term effects, the industrial structure has a substantial impact on NLC/GDP compared to logistics supply scale and logistics commodity value. The research offers two policy implications: firstly, the goals of reducing NLC/GDP and boosting the logistics industry’s GDP are inherently incompatible; it is not feasible to simultaneously enhance the logistics industry’s GDP and decrease the macro logistics cost. Secondly, if China aims to lower its macro-level logistics costs, it must make corresponding adjustments to its industrial structure.

This research article examines the relationship between the level of social welfare expenditure and economic growth rates, based on unbalanced panel data from 38 OECD countries covering the period from 1985 to 2022. Four hypotheses are formulated regarding the impact of social expenditure on economic growth rates. Through multiple iterations of regression model building, employing various combinations of dependent and independent variables, and conducting tests for stationarity and causality, compelling empirical evidence was obtained on the negative influence of social welfare spending on economic growth rates. The study takes into account both government and non-governmental expenditures on social welfare, a novelty in this field. This approach allows for a detailed examination of the effects of different components on economic growth and provides a more comprehensive understanding of the relationships. The findings indicate that countries with high levels of social welfare spending experience a slowdown in economic growth rates. This is associated with increasing demands on social security systems, their growing inclusivity, and the escalating required levels of financing, which are increasingly covered by debt sources. The research highlights the need to strike a balance between social expenditures and economic growth rates and proposes a set of measures to ensure economic growth outpaces the indexing of social expenditures. The abstract underscores the relevance of the study in light of the widespread recognition of the necessity to combat inequality, poverty, and destitution, and calls on OECD countries’ governments to pay increased attention to social policy in order to achieve sustainable and balanced economic growth.