Search

This study investigates career trajectory and work locations of doctoral students trained in Macao and analyses how their career paths are shaped by perceived macro-level factors. Respondents from four applied disciplinary areas were selected for semi-structured in-depth interviews. Research results show that doctoral students who graduated from Macao higher education institutions enjoy good career prospects in Mainland China. Their competitiveness in the research-related job market benefits from having a multi-level support system and a training mode that promotes government–university–industry collaboration. Policies and demand from industrial sectors are involved in students' learning experience through channels such as financial support, project collaboration and networks. Doctoral students in Macao are strategic planners and actors in leveraging their human capital. As Macao becomes an emerging destination for cultivating high-level research labour, findings from this study capture a model of human capital formation in China's cross-system context.

结合实用案例讲解Java语法、面向对象程序设计技术和核心API。 全书共10章，内容涵盖Java概述、Java语法基础、面向对象基础、面向对象高级技术、 Java API、异常处理机制、Java I/O流、多线程、Java GUI编程和Java网络编程等知识要点。案例丰富，以JDK 17和IntelliJ IDEA等流行的开发环境为依托，力求让读者通过案例 掌握Java编程技术。另一个特色是在阐释专业内容的同时自然融入思政元素， 具有鲜明的时代性和引领性。 可作为普通高等院校计算机、软件工程、人工智能等专业“面向对象程序设计”“Java程序设计”课程的教材，也适 合编程爱好者自学和培训使用。

Investor sentiment and emotions have a strong impact on financial markets. In recent years there has been increasing interest in analyzing the sentiment of investors for stock price prediction using machine learning. Existing prediction models mostly depend on the analysis of trading data and company profit. few prediction theories have been built based on individual investors' sentiments. The fundamental reason is the difficulty to measure individual investors' sentiment.

In view of the complex marine environment of navigation, especially in the case of multiple static and dynamic obstacles, the traditional obstacle avoidance algorithms applied to unmanned surface vehicles (USV) are prone to fall into the trap of local optimization. Therefore, this paper proposes an improved artificial potential field (APF) algorithm, which uses 5G communication technology to communicate between the USV and the control center. The algorithm introduces the USV discrimination mechanism to avoid the USV falling into local optimization when the USV encounter different obstacles in different scenarios. Considering the various scenarios between the USV and other dynamic obstacles such as vessels in the process of performing tasks, the algorithm introduces the concept of dynamic artificial potential field. For the multiple obstacles encountered in the process of USV sailing, based on the International Regulations for Preventing Collisions at Sea (COLREGS), the USV determines whether the next step will fall into local optimization through the discrimination mechanism. The local potential field of the USV will dynamically adjust, and the reverse virtual gravitational potential field will be added to prevent it from falling into the local optimization and avoid collisions. The objective function and cost function are designed at the same time, so that the USV can smoothly switch between the global path and the local obstacle avoidance. The simulation results show that the improved APF algorithm proposed in this paper can successfully avoid various obstacles in the complex marine environment, and take navigation time and economic cost into account.

The number of tourist attractions reviews, travel notes and other texts has grown exponentially in the Internet age. Effectively mining users’ potential opinions and emotions on tourist attractions, and helping to provide users with better recommendation services, which is of great practical significance. This paper proposes a multi-channel neural network model called Pre-BiLSTM combined with a pre-training mechanism. The model uses a combination of coarse and fine- granularity strategies to extract the features of text information such as reviews and travel notes to improve the performance of text sentiment analysis. First, we construct three channels and use the improved BERT and skip-gram methods with negative sampling to vectorize the word-level and vocabulary-level text, respectively, so as to obtain more abundant textual information. Second, we use the pre-training mechanism of BERT to generate deep bidirectional language representation relationships. Third, the vectors of the three channels are input into the BiLSTM network in parallel to extract global and local features. Finally, the model fuses the text features of the three channels and classifies them using SoftMax classifier. Furthermore, numerical experiments are conducted to demonstrate that Pre-BiLSTM outperforms the baselines by 6.27%, 12.83% and 18.12% in average in terms of accuracy, precision and F1-score.

Text classification is an important topic in natural language processing, with the development of social network, many question-and-answer pairs regarding health-care and medicine flood social platforms. It is of great social value to mine and classify medical text and provide targeted medical services for patients. The existing algorithms of text classification can deal with simple semantic text, especially in the field of Chinese medical text, the text structure is complex and includes a large number of medical nomenclature and professional terms, which are difficult for patients to understand. We propose a Chinese medical text classification model using a BERT-based Chinese text encoder by N-gram representations (ZEN) and capsule network, which represent feature uses the ZEN model and extract the features by capsule network, we also design a N-gram medical dictionary to enhance medical text representation and feature extraction. The experimental results show that the precision, recall and F1-score of our model are improved by 10.25%, 11.13% and 12.29%, respectively, compared with the baseline models in average, which proves that our model has better performance.

This study explores the relationship between student teachers' beliefs and practices in early Chinese literacy instruction. Semi-structured interviews, classroom observation, and document analysis were conducted with six student teachers during their teaching practices. Findings indicated that the student teachers believed explicitly teaching literacy skills and imperceptible acquisition of literacy abilities through communication and meaning-making processes are essential in Chinese early literacy learning. However, they mainly taught Chinese literacy skills in their practices, which means the student teachers still needed to practice what they preached fully. The study suggests that possible reasons for the discrepancies include 'direct teaching' and 'rote learning' might be much easier for student teachers to design and conduct a lesson. Student teachers have limited abilities and experiences in conducting an ideal lesson, and the kindergarten curriculum and onsite supervisors highly influenced their teaching practices. The findings from this study suggested that more operational activities (such as designing lesson plans and conducting micro-teaching) should be used during pre-service training. Furthermore, the communication of educational beliefs between the university supervisor and the onsite supervisor should be strengthened.

This book is a study of Eric Voegelin (1901-1985) and of the role played by metaxy in his vision of political philosophy. Metaxy, already defined by ...