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This study conducts a bibliometric analysis using the Web of Science database on 1,950 articles published in the journal Mindfulness from 2012 to 2022. By constructing a knowledge graph, the research delineates the evolution, stages of development, and emerging trends in the field of mindfulness. Significant growth in the annual publication volume has been observed since 2012, with the research progression segmented into three distinct phases. The United States has emerged as a pivotal contributor to the field, dominating in terms of publication volume, researcher involvement, and institutional contributions. Through the application of keyword co-occurrence and reference co-citation analysis, five principal clusters were identified, focusing on mindfulness, meditation, depression, stress, and self-compassion, underscoring these as focal research areas. Furthermore, the exploration of mindfulness within the educational sphere in Taiwan is still nascent, signaling a critical need for bolstered research support in diverse thematic domains. [ABSTRACT FROM AUTHOR]

In the context of the growing emphasis on energy conservation and carbon reduction, the widespread deployment of smart meters in residential and commercial buildings is instrumental in promoting electricity savings. In Taiwan, local governments are actively promoting the installation of smart meters, empowering residents to monitor their electricity consumption and detect abnormal usage patterns, thus mitigating the risk of electrical fires. This safety-oriented approach is a significant driver behind the adoption of smart meters. However, the analysis of the substantial data generated by these meters necessitates pre-processing to address anomalies. Presently, these data primarily serve billing calculations or the extraction of power-saving patterns through big data analytics. To address these challenges, this study proposes a comprehensive approach that integrates a relational database for storing electricity consumption data with knowledge graphs. This integrated method effectively addresses data scarcity at various time scales and identifies prolonged periods of excessive electricity consumption, enabling timely alerts to residents for specific appliance shutdowns. Deep learning techniques are employed to analyze historical consumption data and real-time smart meter readings, with the goal of identifying and mitigating hazardous usage behavior, consequently reducing the risk of electrical fires. The research includes numerical values and text-based predictions for a comprehensive evaluation, utilizing data from ten Taiwanese households in 2022. The anticipated outcome is an improvement in household electrical safety and enhanced energy efficiency. [ABSTRACT FROM AUTHOR]

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Knowledge graphs are useful sources for various AI applications, however the basic paradigm to support pilot training is still unclear. In the paper, It is proposed to generate the customized knowledge graph of flight trainings using machine learning method for the flight training program. In order to provide the successful key to the further understanding of the learning problems between the students and the instructors. In this research, we collected data from an aeronautical academic in Taiwan that students were trained for Recreation Pilot License Program. We performed a test on 24 students at the first of each training course, 16 data of collected been used on building the module, 8 of them used to exam the module. There are 12 courses in the training program, and 30 hours total time were suggested by academic. The score which we applied on test were based on LCG method which is the sum of Maneuver and SRM Grades. For the indicators of course component in Learner Centered Grading, namely (a) CCS1: Operation & Effect of Controls; (b) CCS2: Straight & Level; (c) CCS3: Climbing & Descending; (d) CCS4: Turning; (e) CCS5: Stalling; (f) CCS6: Revision; (g) CCS7: Circuits; (h) CCS8: Cross-Wind Training; (i) CCS9: Circuit Emergency; (j) CCS10: Solo Circuit; (k) CCS11: Forced Landing; and (l) CCS12: Precautionary & Searching Landing. Through the method of Knowledge Graph, we deduct and predict the number of hours that need to be added for each student's learning. Using the dynamic knowledge graph to display the key issues of the course learning continuously, and make follow-up decisions for the students, instructors and airliners. [ABSTRACT FROM AUTHOR]

The most important parts of Taiwan's agriculture are animal husbandry and the pig industry, of which the output value reached NT$75.6 billion in 2017. Taiwan has a high technical level of pig raising. However, practical pig-raising skills rely mainly on the inheritance of mentoring experience. The livestock and pig breeding industry in Taiwan has no relevant pig breeding knowledge management information system and no intelligent knowledge question-answering system. Therefore, this study designs and implements an intelligent knowledge question-and-answer system for pig farming. To identify intelligent questions and answers for raising livestock pigs, this study addresses the following issues: (a) to determine the semantic meaning of a sentence, the system needs to accurately interpret the meaning of a question and to identify the expression of a knowledge entity. Therefore, this study applies the lattice long-short-term memory (LSTM) and structured perceptron methods to parse sentences accurately and correctly perform word segmentation. A set of stop words for pig raising in Taiwan is initially established to ensure accurate sentence parsing; (b) to understand the intent of each sentence, the bidirectional gated recurrent init (bi-GRU) method is adopted to realize the knowledge extraction of the livestock in the question and complete the intent detection and slot filling. The bi-GRU extracts the correct livestock knowledge and classifies it into suitable topics; (c) the wide range of knowledge sources in questions often leads to unrelated vocabulary, repeated words, and structural loss in potential answers. To establish an effective knowledge search graph, the method infers implicit knowledge from the existing knowledge and conducts related knowledge retrieval based on the inference results. The knowledge data model is defined in the open standard resource description framework Knowledge filtering and knowledge reasoning strategies are presented to produce candidate answers to the livestock problem; (d) to obtain the final answer, most related works search established knowledge graphs, often producing multiple ambiguous candidate answers. The Siamese neural network (SNN) is adopted to obtain accurate answers. An SNN compares candidate answers to a livestock problem as two input values, previously trained by the bidirectional LSTM neural network. The final answer is determined from the cosine similarity value, which represents the highest relevance to the question. Finally, this study implements the intelligent pig-raising knowledge question-answering system based on the proposed methodology. The evaluation results reveal that the proposed system is accurate and practicable. [ABSTRACT FROM AUTHOR]