This study aimed to determine the socio-economic poverty status of those living in rural areas using data surveys obtained from household expenditure and income. Machine learning-based classification and clustering models were proven to provide an overview of efforts to determine similarities in poverty characteristics. Efforts to address poverty classification and clustering typically involve comprehensive strategies that aim to improve socio-economic conditions in the affected areas. This research focuses on the combined application of machine learning classification and clustering techniques to analyze poverty. It aims to investigate whether the integration of classification and clustering algorithms can enhance the accuracy of poverty analysis by identifying distinct poverty classes or clusters based on multidimensional indicators. The results showed the superiority of machine learning in mapping poverty in rural areas; therefore, it can be adopted in the private sector and government domains. It is important to have access to relevant and reliable data to apply these machine learning techniques effectively. Data sources may include household surveys, census data, administrative records, satellite imagery, and other socioeconomic indicators. Machine learning classification and clustering analyses are used as a decision support tool to gain an understanding of poverty data from each village. These strategies are also used to describe the profile of poverty clusters in the community in terms of significant socio-economic indicators present in the data. Village clusters based on an analysis of existing poverty indicators are grouped into high, moderate, and low poverty levels. Machine learning can be a valuable tool for analyzing and understanding poverty by classifying individuals or households into different poverty categories and identifying patterns and clusters of poverty. These insights can inform targeted interventions, policy decisions, and resource allocation for poverty reduction programs.

Purpose: To reveal the impact mechanism of rural museum intervention on the construction of local identity of rural community residents, and provide practical reference for the protection and utilization of rural cultural identity. Methods: This study takes the Weijiapo Rural Museum in Luoyang, China as the research object, uses participatory observation and in-depth interview methods, and explains the specific characteristics of rural community resident identity construction through identity process theory (IPT). Results: (1) The impact of the intervention of rural museums on rural areas is reflected in four aspects: local spatial reconstruction, transformation of livelihood methods, reconstruction of social relationships, and evolution of cultural customs; (2) under the influence of rural museum construction, the representation of community residents’ identity has shown complex characteristics, with both positive and negative impacts coexisting; (3) the local identity of community residents affects their perception and attitude towards the construction of rural museums.

This qualitative research aimed to study the effectiveness of the local health constitution in controlling the spread of COVID-19. It reports the role of local communities, government agencies, and healthcare providers in implementing and enforcing local health constitutions and how their engagement can be improved to enhance surveillance. We also reported factors that influence compliance and strategies for improving compliance. We also evaluated the long-term sustainability of local health institutions beyond the pandemic. The population and sample group consisted of key members of the local health constitution teams at the provincial, sub-district, and village levels in the rural area of Ubon Ratchathani. Participants were purposively selected and volunteered to provide information. It included health science professionals, public health volunteers, community leaders, and local government officials, totaling 157 individuals. The study was conducted from December 2022 to September 2023. Our research shows that local health constitutions can better engage and educate communities to actively participate in pandemic surveillance and prevention. This approach is a learning experience for responding to emergencies, such as new infectious diseases that may arise in the future. This simplifies the work of officials, as everyone understands the guidelines for action. Relevant organizations contribute to disease prevention efforts, and there is sustainable improvement in work operations.