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With the advancement of digitalization and blended learning, the integration of internationalization and technology has become increasingly significant in the context of English-medium Instruction (EMI). Both in-person and live online EMI courses delivered by foreign teachers (EMI-FT) have emerged as key components of Chinese universities' internationalization strategies. EMI refers to the use of English to teach academic subjects in non-English-speaking countries, exposing students to the challenges of intercultural learning and communication. Prior research indicates that such environments often provoke emotional disturbances, especially in virtual settings where interpersonal engagement is limited. As an essential component of intercultural competence, intercultural sensitivity (IS) is believed to play a crucial role in enhancing students' ability to adapt and succeed in EMI-FT environments. Specifically, IS may influence deep learning (DL), which promotes a deeper understanding and the application of knowledge. However, existing studies have rarely examined how IS affects students' learning experiences and outcomes in both in-person and online EMI-FT contexts. Guided by Biggs' 3P (Presage-Process-Product) model, our study constructs a mediation framework to investigate the relationships among IS, DL, and learning outcomes (LO) across both delivery modes. Data were collected using self-reported instruments from 1192 students across five universities in southern China. The results revealed that: (1) students perceived live online EMI-FT as less effective than in-person EMI-FT in promoting DL and LO; (2) IS was positively associated with both DL and LO; (3) DL mediated the relationship between IS and LO, with a more substantial mediation effect in the online setting; and (4) multi-group analysis revealed significant differences in the IS-DL-LO pathways between in-person and online samples. These findings offer actionable insights for universities seeking to tailor EMI-FT strategies to enhance student engagement and learning across diverse instructional formats.
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http://dx.doi.org/10.1016/j.actpsy.2025.105410 | DOI Listing |
Driven by eutrophication and global warming, the occurrence and frequency of harmful cyanobacteria blooms (CyanoHABs) are increasing worldwide, posing a serious threat to human health and biodiversity. Early warning enables precautional control measures of CyanoHABs within water bodies and in water works, and it becomes operational with high frequency in situ data (HFISD) of water quality and forecasting models by machine learning (ML). However, the acceptance of early warning systems by end-users relies significantly on the interpretability and generalizability of underlying models, and their operability.
View Article and Find Full Text PDFPLoS One
September 2025
Department of Computer Science, COMSATS University Islamabad, Sahiwal, Pakistan.
The widespread dissemination of fake news presents a critical challenge to the integrity of digital information and erodes public trust. This urgent problem necessitates the development of sophisticated and reliable automated detection mechanisms. This study addresses this gap by proposing a robust fake news detection framework centred on a transformer-based architecture.
View Article and Find Full Text PDFPLoS One
September 2025
College of Business Administration, Northern Border University (NBU), Arar, Kingdom of Saudi Arabia.
The increasing dependence on cloud computing as a cornerstone of modern technological infrastructures has introduced significant challenges in resource management. Traditional load-balancing techniques often prove inadequate in addressing cloud environments' dynamic and complex nature, resulting in suboptimal resource utilization and heightened operational costs. This paper presents a novel smart load-balancing strategy incorporating advanced techniques to mitigate these limitations.
View Article and Find Full Text PDFBioinformatics
September 2025
Novo Nordisk Foundation Center for Protein Research, Department of Cellular and Molecular Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, 2200, Denmark.
Motivation: Representation learning has revolutionized sequence-based prediction of protein function and subcellular localization. Protein networks are an important source of information complementary to sequences, but the use of protein networks has proven to be challenging in the context of machine learning, especially in a cross-species setting.
Results: We leveraged the STRING database of protein networks and orthology relations for 1,322 eukaryotes to generate network-based cross-species protein embeddings.
IEEE Trans Biomed Eng
September 2025
Objective: Diffusion magnetic resonance imaging (dMRI) often suffers from low spatial and angular resolution due to inherent limitations in imaging hardware and system noise, adversely affecting the accurate estimation of microstructural parameters with fine anatomical details. Deep learning-based super-resolution techniques have shown promise in enhancing dMRI resolution without increasing acquisition time. However, most existing methods are confined to either spatial or angular super-resolution, disrupting the information exchange between the two domains and limiting their effectiveness in capturing detailed microstructural features.
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