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Despite the rapid development of virtual exchange in foreign language education, its use in the field of Chinese language education is still rather nascent. Adopting a mixed-methods research approach, this study focuses on examining the pedagogical benefits of a Chinese-American virtual exchange as mediated by students' self-reported data. Two Chinese language classes (i.e. one at the elementary level and one at the intermediate level) at a small liberal arts college in the United States and a group of Chinese-speaking English majors at a Chinese university in Shanghai participated in the exchange. Analyses of four sources of qualitative data (i.e. WeChat group cultural discussion transcripts, Skype conversations, reflection journals, and end-of-program interviews) gave rise to four benefits of the exchange: promotion of cultural learning, improvement of Chinese language skills, enhancement of learning motivation, and establishment of a language learning community. Quantitative analyses of the end-of-semester questionnaire items show that the benefit regarding cultural learning, on average, received the highest rating, followed by community building and motivation enhancement, with improvement of Chinese skills being rated the lowest. Moreover, intermediate-level students evaluated all four benefits with higher ratings than elementary-level students. In particular, the two groups' quantitative evaluations in terms of Chinese skills and motivation differed significantly. However, both groups enthusiastically endorsed the benefits of cultural learning and community building. The pedagogical implications of these results are also discussed.

Machine learning-based modeling technology has recently become a powerful technique and tool for developing models for explaining, predicting, and describing system/human behaviors. In developing intelligent education systems or technologies, some research has focused on applying unique machine learning algorithms to build the ad-hoc student models for specific educational systems. However, systematically developing the data-driven student models from the educational data collected over prior educational experiences remains a challenge. We proposed a systematic and comprehensive machine learning-based modeling methodology to develop high-performance predictive student models from the historical educational data to address this issue. This methodology addresses the fundamental modeling issues, from data processing, to modeling, to model deployment. The said methodology can help developing student models for intelligent educational systems. After a detailed description of the proposed machine learning-based methodology, we introduce its application to an intelligent navigation tutoring system. Using the historical data collected in intelligent navigation tutoring systems, we conduct large-scale experiments to build the student models for training systems. The preliminary results proved that the proposed methodology is useful and feasible in developing the high-performance models for various intelligent education systems.

Point cloud-based detection focuses on land traffic, rarely marine, facing issues with ships: it struggles in bad weather due to reliance on adverse weather data and fails to detect ships effectively due to overlooking size and appearance differences. Addressing the above challenges, our work introduces point cloud data of marine scenarios under realistically simulated adverse weather conditions and a dedicated Ship Detector tailored for marine environments. To adapt to various maritime weather conditions, we simulate realistic rain and fog in collected marine scene point cloud data. Additionally, addressing the issue of losing geometric and height information during feature extraction for large objects, we propose a Ship Detector. It employs a dual-branch sparse convolution layer for extracting multi-scale 3D feature maps, effectively minimizing height information loss. Additionally, a multi-scale 2D convolution module is utilized, which encodes and decodes feature maps and directly employs 3D feature maps for target prediction. To reduce dependency on existing data and enhance model robustness, our training dataset includes simulated point cloud data representing adverse weather conditions. In maritime point cloud ship detection, our Ship Detector, compared to adjusted small object detectors, demonstrates the best performance. [ABSTRACT FROM AUTHOR]

Multiple sclerosis (MS) was defined as a rare disease in China due to its low prevalence. For a long time, interferon β was the only approved disease-modifying therapy (DMT). Since the first oral DMT was approved in 2018, DMT approval accelerated, and seven DMTs were approved within 5 years. With an increasing number of DMTs being prescribed in clinical practice, it is necessary to discuss the standardized MS treatment algorithms depending on the disease activity and DMT availability. In this review paper, more than 20 Chinese experts in MS have reviewed the therapeutic progress of MS in China and worldwide and discussed algorithms for treating relapsing MS (RMS) based on the available DMTs in China, providing insights for establishing the standardized RMS treatment algorithms in this country. Plain language summary: Treatment algorithms of relapsing multiple sclerosis in China In this review paper, more than 20 Chinese experts in MS have reviewed the therapeutic progress of MS in China and worldwide and discussed algorithms for treating relapsing MS (RMS) based on the available DMTs in China, providing insights for establishing the standardized RMS treatment algorithms in this country: 1) CIS and RRMS account for more than 90% of the MS patients and most of them are mild to moderate; 2) MS patients should initiate DMT treatments as soon as the disease has been diagnosed in order to reduce the risk of disease progression; 3) Patients who have been diagnosed with MS should start treatment with fundamental DMTs unless the disease course has been highly active; 4) MAGNIMS score may be a suitable and simplified assessment tool for measuring treatment response to DMTs; 5) Patients treated with corticosteroids and NSIS should be switched to the standardized DMT treatment during remission in accordance with disease activity. [ABSTRACT FROM AUTHOR]

In an attempt to promote telecollaborative exchanges in the field of teaching Chinese as a foreign language, this paper conducts a comprehensive review of telecollaborative practice from the past twenty years and focuses on five important themes emerging from the literature on telecollaboration, i.e. models, tasks, challenges, technologies, and new trends. Special attention is given to how each theme's findings can be applied to telecollaborative projects for Chinese language learners. Based on the model of competencies for telecollaborative teachers developed by O'Dowd (2013), the implications of twenty years of telecollaborative practice for teaching Chinese as a foreign language are discussed in four areas, i.e. organizational, pedagogical, digital competences, and attitudes and beliefs.

This article, titled "On the Perception of a Chinese American English Accent," is written by Chunsheng Yang, Gao Niu, and Han Luo. Chunsheng Yang is an associate professor of Chinese and applied linguistics at the University of Connecticut, with research areas including Chinese phonetics, phonology, second language acquisition, Chinese sociolinguistics, and Chinese pedagogy. Gao Niu is an assistant professor in actuarial science at Bryant University, specializing in big data analytics application in the insurance industry. Han Luo is an associate professor of Chinese at Lafayette College, focusing on Chinese linguistics, cognitive linguistics, second language acquisition, and foreign language pedagogy. Unfortunately, the full text of the article is not available in the provided source. [Extracted from the article]