The Causal Relationship between Cryptocurrencies and Other Major World Economic Assets: A Granger Causality Test

This study examines the causal relationship between cryptocurrencies and other major world economic assets, such as gold, stocks, oil, and bonds, using both Granger causality and correlation analyses. The study focuses on the period between 2018 and 2022, using a vector autoregressive model (VAR) to analyze data on cryptocurrencies and other major world economic assets, which collectively represent over 90% of the market during the observed period. Results show that correlation clearly identifies causal interdependency between cryptocurrencies and other major world economic assets and that the variation in cryptocurrencies increasingly explains other major world economic assets. The results reveal that there is Granger causality between the cryptocurrencies (Tether, USD Coin, and Binance USD) and the other major world economic assets (BOND, SP500, and GOLD). Additionally, the study finds evidence that market inefficiency in the cryptocurrency market increased between 2018 and 2022. The findings suggest that the properties of the cryptocurrency market are highly dynamic and that researchers should be hesitant to generalize the market properties observed during idiosyncratic periods. The relevant information is swiftly reflected in asset prices when investors are more interested in a news event, increasing volatility. Strong evidence suggests that volatility spill overs increase sharply at this time. The structure of these markets frequently changes, and a large number of cryptocurrencies appear and disappear every day. &nbsp

Knowledge Organization of Integrated Water Resources Management: A Case of Chi River Basin, Thailand

This study is a part of the research project on the Development of an Ontology-Based Semantic Search for Integrated Water Resources Management of the Chi River Basin (CRB), Thailand. The study aimed at developing the knowledge domain of water resources management for CRB. The research methods included document analysis and qualitative research by adopting Liou (1990)’s knowledge acquisition approach. Fifteen experts including ten experts in the areas of environmental engineering, water resources engineering, and GIS, and seven government officers who has been involving with water management in the CRB were interviewed. The experts also took part in the processes of developing the knowledge domain, classifying, andstructuring the knowledge for water resources management in the CRB. The results of this research were the knowledge domain of water resource management for CRB. An example of the water resources management knowledge domain which was structured by following concepts and processes of water resources management in Thailand is shown in this paper

Ontology-based big data analysis for orchid smart farming

Background. Precision agriculture or smart farming is becoming more and more important in modern orchid farming in Thailand. Sensing and communication technologies have witnessed explosive growth in the recent past. These technologies are empowering information systems from many domains such as health care, environmental monitoring and farming, to collect and store large volume of data. Objectives. The research aims to develop an ontology for big data analysis for the smart farming in Rajamangala University of Technology Srivijaya (RUTS), Nakhon Si Thammarat campus. Methods. The ontology design and development process comprises: (1) Ontology design: the domain ontology provide vocabularies for concepts and relations within the orchid domain, and information ontology which specifies the record structure of databases; (2) Ontology development, which consists of five processes: (i) defining the scope, (ii) investigating the existing ontologies and plan to reuse, (iii) defining terms and its relations, (iv) create instances, and (v) implementation and evaluation. Results. The research outcome is the domain ontology and information ontology wherein 11 concepts of smart farming were identified and classified into classes and sub-classes. Contributions.The system is designed for assisting orchid farmers by giving recommended measures and expected results based on the knowledge extracted from best practices.Published versio