Search

Purpose: The paper aims to propose a virtual reality (VR) wisdom teaching model in open university English course from the perspective of "Metaverse". The study aims to testify the stimulation for English learning and the effectiveness of English-expressing with VR tools for adult learners from the practice in a pilot reform project. Design/methodology/approach: The paper opted for an exploratory study using ICARE Design Model as the framework, under the grounded theories of constructivism and multi-modal teaching. The study compared the evaluation data of one-semester English learning performance between the experimental class (67 students) with VR practice and the controlled class (67 students), including speaking test score, qualitative feedback and in-depth experience analysis. The data were complemented by reflection paper analysis, including manual evaluation (the criteria of semantics, pronunciation, fluency and completeness), questionnaire survey (in the form of five-point Likert scale) and semi-structured interview. Findings: The paper provides empirical insights about the VR wisdom teaching model in English language teaching and learning in a Chinese Open University. The empirical results suggest that "3I" features of VR technology could make up for the shortcomings of traditional English classes in open universities in China, and VR resources designed with curriculum teaching materials could also be helpful for students' command of knowledge points and language skills. What's more, the sense of authentic experience in virtual could promote the teaching and learning effect in college English classes. Research limitations/implications: The present study focuses on a wisdom mode of foreign language teaching and learning for adult learners in open education, so the research results may lack generalizability. Therefore, researchers are encouraged to further explore the deep integration of VR/artificial intelligence in foreign language teaching and learning. Originality/value: This paper fulfills an identified need to study how VR tools provide an engaging, fun and immersive language learning environment, to enhance autonomous learning and learning engagement.

This paper proposes using video-supported reflection to facilitate EFL students' reflective practice of oral presentations. Based on reflective learning theory, four teaching stages are designed and used in an intensive oral presentation training project: creating reflection scenarios, providing reflection scaffolds, guiding collaborative reflection, and summarizing learning gains. Data are collected through students' reflective journals and focus group interviews. Findings indicate that the project has a positive impact on students' reflective abilities and their perceived learning gains. Problems encountered by the students are identified and discussed in light of research and practice on how to support and enhance reflective learning.

Cyber Threat Intelligence (CTI) plays a crucial role in cybersecurity. However, traditional information extraction has low accuracy due to the specialization of CTI and the concealment of relations. To improve the performance of CTI relation extraction in the knowledge graph, we propose a relation extraction architecture called Adversarial Training for Cyber Threat Intelligence Relation Extraction (AT4CTIRE). Additionally, we developed a large-scale cybersecurity dataset for CTI analysis and evaluation called Cyber Threat Intelligence Analysis (CTIA). Inspired by Generative Adversarial Networks, we integrate contextual semantics to refine our study. Firstly, we use some wrong triples with incorrect relations to train the generator and produce high-quality generated triples as adversarial samples. Secondly, the discriminator used actual and generated samples as training data. Integrating the discriminator and the context-embedding module facilitates a deeper understanding of contextual CTI within threat triples. Finally, training a discriminator identified the relation between the threat entities. Experimentally, we set two CTI datasets and only one baseline that we could find to test the effect in the cybersecurity domain. We also took general knowledge graph completion tests. The results demonstrate that AT4CTIRE outperforms existing methods with improved extraction accuracy and a remarkable expedited training convergence rate. [ABSTRACT FROM AUTHOR]

Astragali Radix (AR) is one of the monarch drugs of Fangji Huangqi decoction and has the effects of inducing diuresis to alleviate edema, tonifying and strengthening the body. However, there is a paucity of research regarding the effective fraction and the underlying metabolic mechanism of AR on nephrotic syndrome (NS). This work aims to elucidate the potential mechanisms of AR treating NS, as well as to identify effective part and components. Firstly, body weight, kidney index, 24‐h urea protein, and biochemical parameters were used to confirm the kidney injury. The most effective part of AR was determined based on the indicators above. Then, 1H NMR, UHPLC‐QTOF/MS, and GC‐MS‐based metabolomic approaches were used to investigate differential metabolites closely associated with the effective part against NS. A "C‐T‐P‐D" network (a network diagram of "TCM prescription–herbs–components–targets–metabolites–pathways–disease") was constructed by intersecting the targets of differential metabolites with those of AR treating NS. The efficacy indicators determined the n‐butanol part of AR as the best effective part. Multiplatform metabolomics and network pharmacology study indicated that the potential mechanism for treating NS may be related to targets (MIF, SRC, and GBA) and metabolic pathways (citrate cycle, glyoxylate and dicarboxylate metabolism, alanine, aspartate and glutamate metabolism, and glycolysis/gluconeogenesis). [ABSTRACT FROM AUTHOR]

Due to the limitations of various conditions, rt-PA intravenous thrombolysis has been rarely applied in grass-roots hospitals to treat acute cerebral infarction in elderly patients. In this article, two female patients, aged over 90 years old, diagnosed with acute massive cerebral infarction were reported. According to relevant inclusion and exclusion criteria, standard rt-PA intravenous thrombolysis was delivered, which saved the life of patients. Two patients were discharged after symptom remission. Subsequent follow-up demonstrated that two patients obtained favorable long-term efficacy.

Cyberspace is regarded as the fifth largest activity space following land, sea, air, and space. Protecting cyberspace security is a major issue related to national security, national sovereignty, and the legitimate rights and interests of the people. With the rapid development of artificial intelligence (AI) technology and its application in various fields, cyberspace security has been facing new challenges. This study analyzes the new risks of cyberspace security in the era of AI, such as more intelligent network attacks, more frequent large-scale network attacks, higher concealment of network attacks, stronger confrontation game of network attacks, and easier exposure to stealing of important data. AI technology has significant advantages in dealing with massive data, multi-source heterogeneous data, and real-time dynamic data, which can significantly improve the defense capability of cyberspace. This study introduces some key problems and technologies of AI-enabled cyberspace security defense, particularly the construction of a cyberspace security knowledge brain and the detection of network attacks. Furthermore, we propose the corresponding countermeasures and suggestions from three aspects: the construction of a dynamic and scalable network security knowledge brain, the promotion of intelligent detection against network attacks, and the evaluation of AI technologies’ security.