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In this article, ambassadors of the International Council for Open and Distance Education (ICDE) Open Educational Resources (OER) Advocacy Committee (OERAC) provide a snapshot of regional and global Open Educational Resources (OER)initiatives. This committee has been active since 2017 with membership renewed biannually. The ambassadors work to further OER awareness and understanding, to increase global recognition of OER, and provide policy support for the acceptance and application of OER. This overview highlights national and regional initiatives associated with the UNESCO OER recommendation and the five action areas that include: building capacity and leveraging OER; developing supporting policies; ensuring equity and effectiveness; encouraging sustainable OER model development; and, promoting and facilitating international collaboration. In addition, monitoring and evaluation of the action areas are suggested to be prioritized. This overview is not exhaustive, and much work remains to implement the OER Recommendation at scale, maximize its implementation, connect these recommendations to the United Nation's Sustainable Development Goals (SDGs), along with the futures of education with a new social contract for education, individuals, and the planet.

With the continuous development of the internet, an increasing number of images have been tampered with on the network, accompanied by a growing range of techniques to cover up tampering traces.However, most current detection models neglect the impact of image post-processing on tamper detection algorithms, limiting their real-life applications.To address these issues, a general image tampering location model based on enhanced samples and the pseudo-twin network was proposed.The pseudo-twin network enabled the model to learn tampering features in real images.On one hand, by applying convolution constraints, the image content was suppressed, allowing the model to focus more on residual trace information of tampering.The two-branch structure of the network facilitated the comprehensive utilization of image feature information.By utilizing enhanced samples, the model could dynamically generate the most crucial pictures for learning tamper types, enabling targeted training of the model.This approach ensured that the model converged in all directions, ultimately obtaining the global optimal model.The idea of data enhancement was employed to automatically generate abundant tampered images and corresponding masks, effectively resolving the limited tampering dataset issue.Extensive experiments were conducted on four datasets, demonstrating the feasibility and effectiveness of the proposed model in pixel-level tamper detection.Particularly on the Columbia dataset, the algorithm achieves a 33.5% increase in F1 score and a 23.3% increase in MCC score.These results indicate that the proposed model harnesses the advantages of deep learning models and significantly improves the effectiveness of tamper location detection.

Although the study of artificial intelligence (AI) used in language teaching and learning is increasingly prevailing, research on language two (L2) learner cognitive psychological factors about AI writing corrective feedback (WCF) is scarce. This paper explores L2 learner cognitive psychology of pigai, an AI evaluating system for English writings in China, from perspectives of perception, noticing, uptake, initiative, retention and emotion. It investigates the consistency between learner cognitive psychology about AI WCF and the expected one and probes into the correlation of learner cognitive psychological factors about AI WCF, aiming at bridging the gap between the research of AI WCF and that of L2 learner cognitive psychology. After a 5-point Likert anonymous questionnaire survey of 1952 undergraduate L2 learners in Anhui University of Finance and Economics (AUFE), the statistical data of Pearson correlation coefficient indicate that learner perception, noticing, uptake, initiative, retention and emotion are positively related in the context of AI WCF, which conforms to the early research of learner cognitive psychology about WCF. But one sample t-test reveals that learner cognitive psychology of AI WCF only occasionally or sometimes consists with the expected one. The subsequent random interviews with 15 respondents suggest that pigai WCF is beneficial to L2 writing, yet there is still much room for it to improve to be deeply integrated with human WCF.

Background Obstructive sleep apnea (OSA) has a high prevalence, and it has been shown to be an independent risk factor for various diseases. Therefore, it is important to strengthen screening for population at highrisk of OSA. OSA patients are prone to combine with lipid metabolism disorders, but it remains unclear whether the atherogenic index of plasma (AIP), visceral adiposity index (VAI), lipid accumulation product (LAP), cardiometabolic index (CMI), and Chinese visceral adiposity index (CVAI), which are used asmetabolic indexes, can be used to predict OSA. Objective To analyze the correlation between metabolic indexes and OSA, and evaluate the predictive efficacy of each metabolic index through a case-control study. Methods A total of 2 968 inpatients with suspected OSA and aged ≥18 years who completed polysomnography (PSG) in the First Affiliated Hospital of Xinjiang Medical University from March 2017 to June 2022 were selected, with 2 850 patients finally included based on the inclusion and exclusion criteria and divided into the OSA group 〔apnea-hypopnea index (AHI) ≥5 times/h, n=2 193〕 and non-OSA group (AHI0.05), but AIP and CVAI were associated with OSA (P