Environmental behavior has become one of the most important research areas in the field of sustainable development in recent years. Based on 818 papers on environmental behavior in the Web of Science database from 2002 to 2020, this paper uses CiteSpace software to analyze the trends in publication, subject categories, influential authors and journals, countries, and institutional collaborations. The results show that environmental behavior research has steadily increased over the past 19 years and has gradually achieved diversity and intersection in research subjects. The research on environmental behavior is mainly distributed in the United States, China, and European countries, with the United States being the largest contributor in the field and at the center of the institutional collaboration network. The present research hotspots are as follows: the concept of environmental behavior, factors affecting environmental behavior, the dimension division of environmental behavior, and the construction of a sustainable environmental behavior model. The sustainable development, predictive environmental behavior indicators, factors that affect environmental behavior, and the construction of theoretical models of environmental behavior will become future research trend.
Rice (Oryza sativa) is one of the most important crops in the world and serves as a staple food source for more than half of the world's population. Research into when, where, and how rice was initially cultivated and eventually domesticated is essential. Research on these questions has been greatly advanced recently, along with nearly continuous research in both genetics and archaeology using newly developed analytical techniques. Here, we review the scientific understanding of rice domestication in the Yangtze River valley from both an archaeological and genetic perspective, and discuss the relationship between rice domestication and the Yangtze River civilization. Recent genetic research suggests that domesticated rice (O. sativa ssp. japonica) first occurred in southern China, including the Yangtze River valley and the Pearl River Basin. Current findings from archaeology support the view that O. sativa ssp. japonica was firstly domesticated in the Yangtze River valley ca.10,000-8,000 BP, and rice cultivation and agricultural development triggered the Yangtze River civilization. These findings enhance our understanding of rice domestication and related cultivation culture and also have implications for conservation of plant resources in the Yangtze River valley.
In traditional historical research, interpreting historical documents subjectively and manually causes problems such as one-sided understanding, selective analysis, and one-way knowledge connection. In this study, we aim to use machine learning to automatically analyze and explore historical documents from a text analysis and visualization perspective. This technology solves the problem of large-scale historical data analysis that is difficult for humans to read and intuitively understand. In this study, we use the historical documents of the Qing Dynasty Hetu Dangse,preserved in the Archives of Liaoning Province, as data analysis samples. China’s Hetu Dangse is the largest Qing Dynasty thematic archive with Manchu and Chinese characters in the world. Through word frequency analysis, correlation analysis, co-word clustering, word2vec model, and SVM (Support Vector Machines) algorithms, we visualize historical documents, reveal the relationships between functions of the government departments in the Shengjing area of the Qing Dynasty, achieve the automatic classification of historical archives, improve the efficient use of historical materials as well as build connections between historical knowledge. Through this, archivists can be guided practically in historical materials’ management and compilation.
Government archives comprise a series of documents or important information resources produced or used by government departments to meet the needs of economic and social governance, fulfill government functions, and perform official activities. They have inestimable value in economic development, social operations, and strategy. However, due to the lack of effective application of knowledge management technology, government information resources are locked in archive records, making it difficult to achieve knowledge organization and interoperability. Based on the theories and methods of the semantic web and semantic ontology construction, we summarize the relevant domain knowledge of government archives and construct the Chinese Government Archive Ontology (GAO) with the collection of government archives as the data drive. The ontology model implements the semantic interoperability between government archives and performs knowledge representation and reasoning on their content. We define the classes and properties of GAO, describe its creation process in detail, and demonstrate its applicability in practice through SPARQL query and event logical representation based on the 5W1H framework. GAO is capable of realizing linked data and discovering the knowledge hidden behind the policies of various agencies and departments, which can serve scientific decision-making and the economic society, regulate institutional powers, and further promote the research and practice of archival science in the digital age.
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