Search Results (60)

ToM (ToM) and empathy, integral components of children’s social cognitive development, are shaped by multifaceted factors. The developmental trajectories of ToM and empathy in kindergarten children have long been focal points of inquiry for researchers and educators. Among these determinants, environmental factors emerge as significant predictors of children’s ToM and empathetic abilities. In contemporary society, digital screens have transformed into a ubiquitous medium for kindergarten children, deeply embedded in their daily life, learning, and recreational activities. Consequently, screen exposure has become a novel and distinctive environmental context for childhood development, diverging from traditional settings. This shift raises critical questions that have become focal in recent developmental media research: Does screen exposure correlate with children’s ToM and empathy? And how do key dimensions of screen use (e.g., duration, content) influence the development of these social cognitive skills? To address these queries, this study employed a two-phase experimental approach. Initially, a total of 642 parental questionnaires were collected to comprehensively investigate the current status of digital screen usage among Chinese kindergarten children. Subsequently, the ToM and empathy levels of 126 children were systematically evaluated. The findings revealed that the average daily duration of children’s screen time exhibited a significant negative predictive effect on their ToM level, consistent with prior longitudinal studies that linked early excessive screen exposure to poorer later ToM performance. Conversely, engagement with child-friendly content (e.g., prosocial narratives) and parent–child discussions regarding character emotions during screen exposure (e.g., dialogic questioning while co-viewing) emerged as positive predictors of ToM. Notably, no significant predictive relationships were identified between various dimensions of screen exposure and children’s empathy. This research elucidates the impact of screen exposure on crucial aspects of children’s social cognition, offering practical implications for optimizing screen device utilization to foster children’s holistic development. Full article

This convergent mixed-methods investigation explores the changing place of Buddhism in Chinese youth lives in the post-pandemic era using data from a national survey (N = 2812) and semi-structured interviews (n = 24). Although traditional religious affiliation is still generally low among participants, Buddhism still serves as an important psychosocial and symbolic resource. In contrast to doctrinal commitments, youth connect with Buddhism through emotional identification, ritual adaptability, and virtual arenas. Results indicate a unique profile of symbolic-affective religiosity, whereby Buddhism is selectively taken up as an emotional regulation tool, moral guide, and existential reassurer. This form of engagement is frequently enabled by digital rituals, smartphone applications, and social media interactions, highlighting the mediatized character of modern spiritual engagement. Subgroup analysis reveals considerable heterogeneity among this population with differences by region, gender, level of education, and religion of family background, which implies that “Buddhist youth” in China must be conceived as a pluralistic and fluid category. The study contributes to scholarship on youth spirituality and post-institutional religion by emphasizing the functional rather than theological dimensions of religious engagement among East Asia’s younger generations. Full article

In Chinese character learning, distinguishing similar characters is challenging for learners regardless of their proficiency. This is due to the complex orthography (visual word form) linking symbol, pronunciation, and meaning. Multimedia learning is a promising approach to implement learning strategies for Chinese characters. However, the availability of multimodal resources specifically designed for distinguishing similar Chinese characters is limited. With the advanced development of generative artificial intelligence (GenAI), we propose a practical framework for constructing multimodal resources, enabling flexible and semi-automated resource generation for Chinese character learning. The framework first constructs image illustrations due to their broad applicability across various learning contexts. After that, other four types of multimodal resources implementing learning strategies for similar character learning can be developed in the future, including summary slide, micro-video, self-test question, and basic information. An experiment was conducted with one group receiving the constructed multimodal resources and the other receiving the traditional text-based resources for similar character learning. We explored the participants’ learning performance, motivation, satisfaction, and attitudes. The results showed that the multimodal resources significantly improved performance on distinguishing simple characters, but were not suitable for non-homophones, i.e., visually similar characters with different pronunciations. Micro-videos introducing character formation knowledge significantly increased students’ learning motivation for character evolution and calligraphy. Overall, the resources received high satisfaction, especially for micro-videos and image illustrations. The findings regarding the effective design of multimodal resources for implementing learning strategies (e.g., using visual mnemonics, character formation knowledge, and group reviews) and implications for different Chinese character types are also discussed. Full article

Under the growing demand for processing multimodal and cross-lingual information, traditional retrieval systems have encountered substantial limitations when handling heterogeneous inputs such as images, textual layouts, and multilingual language expressions. To address these challenges, a unified retrieval framework has been proposed, which integrates visual features from images, layout-aware optical character recognition (OCR) text, and bilingual semantic representations in Chinese and English. This framework aims to construct a shared semantic embedding space that mitigates semantic discrepancies across modalities and resolves inconsistencies in cross-lingual mappings. The architecture incorporates three main components: a visual encoder, a structure-aware OCR module, and a multilingual Transformer. Furthermore, a joint contrastive learning loss has been introduced to enhance alignment across both modalities and languages. The proposed method has been evaluated on three core tasks: a single-modality retrieval task from image → OCR, a cross-lingual retrieval task between Chinese and English, and a joint multimodal retrieval task involving image, OCR, and language inputs. Experimental results demonstrate that, in the joint multimodal setting, the proposed model achieved a Precision@10 of 0.693, Recall@10 of 0.684, nDCG@10 of 0.672, and F1@10 of 0.685, substantially outperforming established baselines such as CLIP, LayoutLMv3, and UNITER. Ablation studies revealed that removing either the structure-aware OCR module or the cross-lingual alignment mechanism resulted in a decrease in mean reciprocal rank (MRR) to 0.561, thereby confirming the critical role of these components in reinforcing semantic consistency across modalities. This study highlights the powerful potential of large language models in multimodal semantic fusion and retrieval tasks, providing robust solutions for large-scale semantic understanding and application scenarios in multilingual and multimodal contexts. Full article

Ne Zha 2 (Ne Zha: Mo Tong Nao Hai, 哪吒之魔童闹海, 2025) is a prime example of the modernization of traditional literary intellectual property (IP). It has achieved the highest box office gross in Chinese cinematic history and ranks among the top five highest-grossing films globally. This article uses a case study approach to examine the adaptation strategies of Ne Zha 2 (2025), offering strategic insights for future film adaptations. The analysis focuses on four key dimensions—character, plot, theme, and esthetics—to explore how these elements contribute to the film’s adaptation. The findings reveal that the film strikes a balance between intertextuality and innovation, achieved through multidimensional character development, narrative subversion, contemporary thematic reinterpretation, and sophisticated esthetic techniques. By maintaining the emotional connection to the classical IP, the adaptation not only delivers stunning visual spectacles but also provides a culturally immersive experience, revitalizing traditional mythology with contemporary relevance. Full article

In the field of music notation recognition, while the recognition technology for common notation systems such as staff notation has become quite mature, the recognition techniques for traditional Chinese notation systems like guqin tablature (jianzipu) and Kunqu opera gongchepu remain relatively underdeveloped. As an important carrier of China’s thousand-year musical culture, the digital preservation and inheritance of Kunqu opera’s Gongche notation hold significant cultural value and practical significance. By addressing the unique characteristics of Gongche notation, this study overcomes the limitations of Western staff notation recognition technologies. By constructing a deep learning model adapted to the morphology of Chinese character-style notation symbols, it provides technical support for establishing an intelligent processing system for Chinese musical documents, thereby promoting the innovative development and inheritance of traditional music in the era of artificial intelligence. This paper has constructed the LGRC2024 (Gong-che notation based on Lilu Qu Pu) dataset. It has also employed data augmentation operations such as image translation, rotation, and noise processing to enhance the diversity of the dataset. For the recognition of Gong-che notation, the YOLOv8 model was adopted, and the network performances of its lightweight (n) and medium-weight (m) versions were compared and analyzed. The superior-performing YOLOv8m was selected as the basic model. To further improve the model’s performance, SimAM, Triplet Attention, and Multi-scale Convolutional Attention Module (MSCAM) were introduced to optimize the model. The experimental results show that the accuracy of the basic YOLOv8m model increased from 65.9% to 78.2%. The improved models based on YOLOv8m achieved recognition accuracies of 80.4%, 81.8%, and 83.6%, respectively. Among them, the improved model with the MSCAM module demonstrated the best performance in all aspects. Full article

Processing ancient text images presents significant challenges due to severe visual degradation, missing glyph structures, and various types of noise caused by aging. These issues are particularly prominent in Chinese historical documents and stone inscriptions, where diverse writing styles, multi-angle capturing, uneven lighting, and low contrast further hinder the performance of traditional OCR techniques. In this paper, we propose a unified neural framework, UniText, for the detection, recognition, and glyph restoration of Chinese characters in images of historical documents and inscriptions. UniText operates at the character level and processes full-page inputs, making it robust to multi-scale, multi-oriented, and noise-corrupted text. The model adopts a multi-task architecture that integrates spatial localization, semantic recognition, and visual restoration through stroke-aware supervision and multi-scale feature aggregation. Experimental results on our curated dataset of ancient Chinese texts demonstrate that UniText achieves a competitive performance in detection and recognition while producing visually faithful restorations under challenging conditions. This work provides a technically scalable and generalizable framework for image-based document analysis, with potential applications in historical document processing, digital archiving, and broader tasks in text image understanding. Full article

The spread of Tibetan Buddhism in Mongolia brought with it a wealth of Buddhist knowledge. Over time, Mongolian scholars and practitioners engaged with this knowledge and produced numerous works encompassing Buddhist learning, particularly in medicine. A prominent figure in this intellectual landscape is the Fourth Mindol Nomun Khan, Jambalchoijidanzanperenley (1789–1839), commonly known as Mindol Qutuɣtu (or Mindol Hutugtu). Despite being recognized for his remarkable contributions to the development of Mongolian medicine, considerable uncertainty has surrounded Mindol Qutuɣtu’s ethnic identity. This article aims to clarify Mindol Qutuɣtu’s ethnic origin and examine the broader medical context of his seminal work, The Treasury of All Precious Instructions (Man ngag rin chen ’byung gnas), highlighting the visionary concepts he presented. While the basis of Mindol Qutuɣtu’s work lies in Tibetan medicine, he boldly introduced treatment methodologies from other medical traditions, including Indian (Ayurvedic), Chinese, and European medicine, into the realm of Mongolian medicine. His insightful work reflects both intellectual ambition and practical occupation on increasing healing efficacy, as evidenced by his influential contributions to a combined and multicultural approach to medicine. Today, his innovative and inceptive contributions remain essential in understanding the historical development and current diverse character of Mongolian traditional medical practices. Full article

Ancient paintings are an intangible window to the economy, politics, and customs of the past. Their characteristics have evolved or were made obsolete, with only limited contemporary connections remaining. This research aims to preserve and to interact with characters in 2D paintings to evolve their cultural identity through combining AI and AR. The scope of this research covers traditional Chinese paintings archived by the National Palace Museum in digital collections, mainly “New Year’s Market in a Time of Peace”. About 25 characters were used for training and 3D reconstruction in RODIN^®. The models were converted into Augment^® and Sketchfab^® platforms as reactivated AR characters to interact with new urban fabrics and landscapes. Stable Diffusion^® and RODIN^® were successfully integrated to perform image training and reconstruct 3D AR models of various styles. As a result, interactions were conducted in two ways: in a mixed context with mixed characters in a painting and in a familiar context in the real world with mixed characters. It was found that AR facilitated the interpretation of how the old urban fabric was arranged. Using AI and AR is a current issue. Combining AI and AR can activate ubiquitous preservation to perform recursive processing from diffused images in order to reconstruct 3D models. This activated heritage preservation method is a reasonable alternative to redefining intangible subjects with a new and evolved contemporary cultural identity. Full article

Yinhua Pinggan Granules (YPG) is a patented traditional Chinese medicine (TCM) compound prescription, with wide clinical application against cold, cough, and relevant diseases. However, the chemical profiles of YPG in vivo are still unknown, hindering further pharmacological and quality control (QC) researches. This study presents an ultra-high-performance liquid chromatography coupled with high-resolution orbitrap mass spectrometry (UHPLC-MS)-based method. Using the Compound Discoverer platform and a self-built ‘in-house’ compound database, the metabolic profiles and pharmacokinetics characters of YPG were investigated. Consequently, a total of 230 compounds (including 39 prototype components and 191 metabolites) were tentatively identified, in which the parent compounds were mainly flavonoids, alkaloids, and terpenoids, and the main metabolic pathways of metabolites include hydration, dehydration, and oxidation. The serum concentration of seven major representative compounds, including quinic acid, chlorogenic acid, amygdalin, 3′-methoxypuerarin, puerarin, glycyrrhizic acid, and polydatin, were also measured, to elucidate their pharmacokinetics behaviors in vivo. The pharmacokinetic study showed that the seven representative compounds were quantified in rat plasma within 5 min post-administration, with T_max of less than 2 h, followed by a gradual decline in concentration over a 10 h period. The method demonstrated excellent linearity (R² > 0.998), precision, and recovery (RSD < 15%). As the first systematic characterization of YPG’ s in vivo components and metabolites using UHPLC-MS, this study may contribute to comprehensively elucidate the metabolic profiles of the major components in YPG, and provide a critical foundation for further investigation on the QC and bioactivity research of YPG. Full article

This paper examines the Nanatsu-dera manuscript of the Dafangguang Rulai Xingqi Weimizang Jing (RXWJ) through the lens of scribal practices, with a focus on variant characters (yitizi, 異體字) and textual transmission. As a “separately produced scripture” (bie sheng jing, 別生經), the RXWJ was not included in the woodblock-printed editions of the Chinese Buddhist canon, which limited its circulation and made manuscript copies—such as the Nanatsu-dera manuscript—critical for reconstructing its textual evolution, transmission, and scribal modifications. A detailed paleographic investigation reveals scribal variants, orthographic fluidity, and phonetic substitutions, illustrating both intentional adaptations and unintentional errors in textual transmission. Comparative analysis with Dunhuang fragments and the Taishō Canon further contextualizes these variations, shedding light on the interpretive challenges scribes and readers face. The findings suggest that the Nanatsu-dera manuscript underwent three stages of transmission: (1) it originated from the Fifty-Fascicle edition circulating in China, (2) it was used as a base text (diben, 底本) for manuscript copying in Japan, and (3) it was subsequently re-copied and preliminarily collated by Japanese scribes. By tracing scribal variants and textual transmission through a paleographic approach, this research underscores the critical role of manuscript culture in preserving texts outside the canonical tradition, offering new insights into the mechanisms of Buddhist textual transmission and adaptation in medieval East Asia. Full article

With the widespread application of Android devices in the energy sector, an increasing number of applications rely on SDKs to access privacy-sensitive data, such as device identifiers, location information, energy consumption, and user behavior. However, these data are often stored in different formats and naming conventions, which poses challenges for consistent extraction and identification. Traditional taint analysis methods are inefficient in identifying these entities, hindering the realization of accurate identification. To address this issue, we first propose a high-quality data construction method based on privacy protocols, which includes sentence segmentation, compression encoding, and entity annotation. We then introduce CPS-LSTM (Character-level Privacy-sensitive Entity Adaptive Recognition Model), which enhances the recognition capability of privacy-sensitive entities in mixed Chinese and English text through character-level embedding and word vector fusion. The model features a streamlined architecture, accelerating convergence and enabling parallel sentence processing. Our experimental results demonstrate that CPS-LSTM significantly outperforms the baseline methods in terms of accuracy and recall. The accuracy of CPS-LSTM is 0.09 higher than Lattice LSTM, 0.14 higher than WC-LSTM, and 0.05 higher than FLAT. In terms of recall, CPS-LSTM is 0.07 higher than Lattice LSTM, 0.12 higher than WC-LSTM, and 0.02 higher than FLAT. Full article

Chinese named entity recognition (NER) is a fundamental natural language processing (NLP) task that involves identifying and categorizing entities in text. It plays a crucial role in applications such as information extraction, machine translation, and question-answering systems, enhancing the efficiency and accuracy of text processing and language understanding. However, existing methods for Chinese NER face challenges due to the disruption of character-level semantics in traditional data augmentation, leading to misaligned entity labels and reduced prediction accuracy. Moreover, the reliance on English-centric fine-grained annotated datasets and the simplistic concatenation of label semantic embeddings with original samples limits their effectiveness, particularly in addressing class imbalances in low-resource scenarios. To address these issues, we propose a novel Chinese NER model, LGDA, which leverages Label-Guided Data Augmentation to mitigate entity label misalignment and sample distribution imbalances. The LGDA model consists of three key components: a data augmentation module, a label semantic fusion module, and an optimized loss function. It operates in two stages: (1) the enhancement of data with a masked entity generation model and (2) the integration of label annotations to refine entity recognition. By employing twin encoders and a cross-attention mechanism, the model fuses sample and label semantics, while the optimized loss function adapts to class imbalances. Extensive experiments on two public datasets, OntoNotes 4.0 (Chinese) and MSRA, demonstrate the effectiveness of LGDA, achieving significant performance improvements over baseline models. Notably, the data augmentation module proves particularly effective in few-shot settings. Full article

Few-shot font generation seeks to create high-quality fonts using minimal reference style images, addressing traditional font design’s labor-intensive and time-consuming nature, particularly for languages with large character sets like Chinese and Korean. Existing methods often require multi-stage training or predefined components, which can be time-consuming and limit generalizability. This paper introduces Patch-Font, a novel single-stage method that overcomes the limitations of prior approaches, such as multi-stage training or reliance on predefined components, by integrating a patch-based attention mechanism and a multitask encoder. Patch-Font jointly captures global style elements (e.g., overall font family characteristics) and local style details (e.g., serifs, stroke shapes), ensuring high fidelity to the target style while maintaining computational efficiency. Our approach incorporates triplet margin loss with hard positive/negative mining to disentangle style from content and a style fidelity loss to enhance local style consistency. Experiments on Korean (printed and handwritten) and Chinese fonts demonstrate that Patch-Font outperforms state-of-the-art methods in style accuracy, perceptual quality, and generation speed while generalizing robustly to unseen characters and font styles. By simplifying the font creation process and delivering high-quality results, Patch-Font represents a significant step forward in making font design more accessible and scalable for diverse languages. Full article

Traditional methods of assessing handwritten characters are often too subjective, inefficient, and lagging in feedback, which makes it difficult for educators to achieve fully objective writing assessments and for writers to receive timely suggestions for improvement. In this paper, we propose a convolutional neural network (CNN) architecture that combines the attention mechanism with multi-scale feature fusion; specifically, the features are weighted by designing a bottleneck layer that combines the Squeeze-and-Excitation (SE) attention mechanism to highlight the important information and by applying a multi-scale feature fusion method to enable the network to capture both the global structure and the local details of Chinese characters. Finally, a high-quality dataset containing 26,800 images of handwritten Chinese characters is constructed based on the application scenario of the writing grade test, covering the common Chinese characters in the writing grade exam; The experimental results show that the proposed method achieves 98.6% accuracy on the writing grade exam dataset and 97.05% on the ICDAR-2013 public dataset, significantly improving recognition accuracy. The constructed dataset and improved model are suitable for application scenarios such as writing grade exams, which helps to improve marking efficiency and accuracy. Full article