Decoding rural connections:A comparative insight into social network analysis in rural communities of China and beyond

Social Network Theory and methods have emerged as pivotal tools for dissecting intricate interdisciplinary issues in rural communities.This study aims to systematically delineate the application characteristics and trends of Social Network Analysis(SNA)in rural community research.Using a twofold approach,we integrate a traditional literature review and CiteSpace bibliometric analysis to assess the application status and evolutionary trends of SNA methods in this context.The key findings include the following:①Chinese research trends:scholars predominantly concentrate on the“three rural”issues(related to agriculture,rural areas,and small-scale farmers)and social support mechanisms for vulnerable rural populations.With policy shifts,rural revitalization,tourism,governance,social trust,and multi-dimensional poverty are poised to emerge as hot topics for the future.For further refinement,we suggest that the application of SNA in rural community research could benefit from content expansion,long-term studies,and innovative modelling techniques.②Research by international scholars has been primarily directed toward the physical and mental health of rural residents,as well as socioeconomic issues.Despite these studies covering a range of typical cases across various nations,a conspicuous lack of thorough,systematic,and prolonged efforts focused on rural community development in specific regions remains.Additionally,health issues affecting rural residents are expected to sustain long-standing and focused international academic attention.This study contributes to a more nuanced understanding of the current applications and potential future directions of SNA in rural community studies,both in China and internationally.

Ontology Dynamics in a Data Life Cycle: Challenges and Recommendations from a Geoscience Perspective

Ontologies are increasingly deployed as a computer-accessible representation of key semantics in various parts of a data life cycle and, thus, ontology dynamics may pose challenges to data management and re-use. By using examples in the field of geosciences, we analyze challenges raised by ontology dynamics, such as heavy reworking of data, semantic heterogeneity among data providers and users, and error propagation in cross-discipline data discovery and re-use. We also make recommendations to address these challenges： （1） communities of practice on ontologies to re- duce inconsistency and duplicated efforts; （2） use ontologies in the procedure of data collection and make them accessible to data users; and （3） seek methods to speed up the reworking of data in a Semantic Web context.

Machine learning in building energy management: A critical review and future directions

Over the past two decades,machine learning(ML)has elicited increasing attention in building energy management(BEM)research.However,the boundary of the ML-BEM research has not been clearly defined,and no thorough review of ML applications in BEM during the whole building life-cycle has been published.This study aims to address this gap by reviewing the ML-BEM papers to ascertain the status of this research area and identify future research directions.An integrated framework of ML-BEM,composed of four layers and a series of driving factors,is proposed.Then,based on the hype cycle model,this paper analyzes the current development status of ML-BEM and tries to predict its future development trend.Finally,five research directions are discussed:(1)the behavioral impact on BEM,(2)the integration management of renewable energy,(3)security concerns of ML-BEM,(4)extension to other building life-cycle phases,and(5)the focus on fault detection and diagnosis.The findings of this study are believed to provide useful references for future research on ML-BEM.