Volume 5 Issue 1: March 2026 (in progress)

This study examines the visible thematic patterns of artificial intelligence (AI)–related research in Mainland China, Taiwan, Hong Kong, and Macao from 2021 to 2025. The analysis is based on Scopus-indexed journal articles and uses keyword frequency counts, VOSviewer density visualizations, and KH Coder co-occurrence networks. These methods are applied to describe how AI-related keywords appear across the four regions and how their distributions change during the five-year period. Across all datasets, terms such as machine learning, deep learning, and neural network appear frequently and occupy central positions in the visual outputs. The VOSviewer heatmaps show that regions with larger publication volumes display wider areas of keyword density, while regions with smaller datasets present more compact clusters. Beginning in 2024 and 2025, generative AI–related terms, including large language model and ChatGPT, become visible across all regions. The KH Coder networks illustrate that the four regions contain multiple clusters of co-occurring keywords, with differences in cluster size and distribution reflecting the underlying dataset scale and the topics present in each regional corpus. Overall, the results offer a descriptive account of how AI-related terms appear in the collected datasets and how their visible distributions vary among the four regions during the study period. The findings are intended to summarize observable patterns without inferring causal explanations or evaluating the significance of regional differences.

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Metaphor translation plays a key role in cross-cultural communication. Among metaphor types, animal metaphors stand out for their rich cultural connotations and cognitive complexity, making them a valuable testing ground for translation strategies. Despite growing interest, existing research has yet to fully clarify the cultural adaptation mechanisms involved in rendering animal metaphor translation across languages. In particular, how different translation agents dynamically process these culturally loaded expressions remains underexplored, which complicates efforts to optimize human-machine collaboration. This study adopts conceptual metaphor theory and an integrated methodology combining qualitative and quantitative analysis with theoretical interpretation. Drawing on three English translations of Mo Yan’s Frog—by Howard Goldblatt, ChatGPT-4.0, and ChatGLM—this study conducts a systematic comparison of how human and machine translators handle animal metaphors. The analysis shows that effective rendering requires more than literal transfer: it depends on activating culture-specific frames and maintaining evaluative stance, not merely preserving surface imagery. While recent advances in artificial intelligence yield relatively high rates of literal retention, machine translations tend to remain surface-bound when metaphors are culturally or politically charged. By contrast, the cultural awareness and interpretive craft evident in the human translation more consistently preserve metaphorical nuance and ideological force. This study offers new evidence for research on metaphor translation and provides practical guidance for improving human-machine collaborations in literary contexts—e.g., using machine outputs to secure surface mapping while human translators recalibrate cultural frames and stance.

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Cancellations on the Dutch Railway network are a common and unpredictable occurrence; however, little research has focused on predicting these cancellations. Previous studies on the Dutch railway system have primarily concentrated on delay prediction. For this regression task, models such as XGBoost, Random Forest, Long Short-Term Memory (LSTM), and Gradient Boosting Decision Tree have been shown to perform well. Graph neural network-based models have been used for regression tasks on other transportation networks. We propose a Dynamic Graph Neural Network (DGNN) combined with an LSTM network for binary classification of cancelled trajectories. We compare the model with baseline models on a seasonal split to compare the feature importance across different seasons. Model performance is gauged using paired t-tests on bootstrapped F1 scores. Additionally, Precision, Recall, Balanced Accuracy, and AUC are considered metrics for further comparison. The newly proposed features achieve mostly positive feature importance scores across the models. Amongst the evaluated models, the proposed DGNN and XGBoost outperform the baseline models. Overall, the models underperform with F1 scores no higher than 0.4. This paper provides insight into the influence of various weather and operational features on cancellations on the Dutch railway network, with the operational features proving insightful.

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