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With courses being moved online because of COVID-19, there is a need for exploring innovative and effective online teaching models. This article firstly introduces the pedagogical practice of the fully online flipped instruction (FOFI), an adaptation of the flipped classroom approach that is employed completely online, which has been adopted for EFL undergraduates in China during COVID-19 based on video conferencing and prior experience in Small Private Online Course (SPOC)-based flipped classrooms. A questionnaire and two focus group discussions were conducted to investigate students' perceptions of this model. The data were collected and analysed quantitatively and qualitatively, which reveal their positive perception concerning teacher instruction, varied interaction, and multifunctional platforms. However, less motivated students hold contrasting opinions on learning platforms and online interaction. This study also demonstrates that students' interests, self-regulation, and learning environment significantly affect learner experience and learning outcomes. The implications for the implementation of FOFI are further discussed.

A conversational system is an artificial intelligence application designed to interact with users in natural language, providing accurate and contextually relevant responses. Building such systems for low-resource languages like Swahili presents significant challenges due to the limited availability of large-scale training datasets. This paper proposes a Retrieval-Augmented Generation-based system to address these challenges and improve the quality of Swahili conversational AI. The system leverages fine-tuning, where models are trained on available Swahili data, combined with external knowledge retrieval to enhance response accuracy and fluency. Four models—mT5, GPT-2, mBART, and GPT-Neo—were evaluated using metrics such as BLEU, METEOR, Query Performance, and inference time. Results show that Retrieval-Augmented Generation consistently outperforms fine-tuning alone, particularly in generating detailed and contextually appropriate responses. Among the tested models, mT5 with Retrieval-Augmented Generation demonstrated the best performance, achieving a BLEU score of 56.88%, a METEOR score of 72.72%, and a Query Performance score of 84.34%, while maintaining relevance and fluency. Although Retrieval-Augmented Generation introduces slightly longer response times, its ability to significantly improve response quality makes it an effective approach for Swahili conversational systems. This study highlights the potential of Retrieval-Augmented Generation to advance conversational AI for Swahili and other low-resource languages, with future work focusing on optimizing efficiency and exploring multilingual applications. [ABSTRACT FROM AUTHOR]

Euphorbia jolkinii dominates the subalpine meadows in Shangri-La (Southwest China) owing to its potent allelopathic effects. However, the effects underlying its allelopathy require further characterization at the physiological and molecular levels. In this study, the physiological, biochemical, and metabolic mechanisms underlying E. jolkinii allelopathy were investigated using Arundinella hookeri as a receptor plant. The treatment of A. hookeri seedlings with E. jolkinii aqueous extract (EJAE) disrupted their growth by inhibiting photosynthesis, disrupting oxidation systems, and increasing soluble sugar accumulation and chlorophyll synthesis. Collectively, this causes severe impairment accompanied by abnormal photosynthesis and reduced biomass accumulation. Moreover, EJAE treatment suppressed gibberellin, indoleacetic acid, zeatin, salicylic acid, and jasmonic acid levels while promoting abscisic acid accumulation. Further metabolomic analyses identified numerous differentially abundant metabolites primarily enriched in the α-linolenic, phenylpropanoid, and flavonoid biosynthesis pathways in EJAE-treated A. hookeri seedlings. This study demonstrated that E. jolkinii exhibits potent and comprehensive allelopathic effects on receptor plants, including a significant disruption of endogenous hormone synthesis, the inhibition of photosynthesis, an impairment of membrane and oxidation systems, and changes in crucial metabolic processes associated with α-linolenic, phenylpropanoid, and flavonoid biosynthesis. Thus, our study provides a solid theoretical foundation for understanding the regulatory mechanisms underlying E. jolkinii allelopathy. [ABSTRACT FROM AUTHOR]