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学者姓名：丁铮
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Abstract ：
1940 年,萨一佛在福建永安地区创办《战时木刻画报》刊物已开始努力摆脱欧洲艺术作风,向着大众化和民族化的方向前进.本文以《战时木刻画报》切入,对萨一佛木刻艺术中的大众化和民族形式进行研究,集中探察当时福建木刻所面临的社会背景、十日漫画社时期萨一佛思想转捩、萨一佛对于早期木刻中民族形式存在的问题以及民族形式的探索手段这四个问题,借此引入萨一佛视角理解新兴木刻版画的大众化、民族化等核心议题,让学界对永安抗战文艺能有更为清晰、客观、完整的认识.
Keyword ：
抗战转型 抗战转型 新兴木刻 新兴木刻 民族化和大众化 民族化和大众化 萨一佛 萨一佛
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| GB/T 7714 | 陈少枫 , 苏晨茜 , 丁铮 . 转捩与宣传 [J]. | 美与时代 , 2025 , (3) : 77-84 . |
| MLA | 陈少枫 等. "转捩与宣传" . | 美与时代 3 (2025) : 77-84 . |
| APA | 陈少枫 , 苏晨茜 , 丁铮 . 转捩与宣传 . | 美与时代 , 2025 , (3) , 77-84 . |
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Abstract ：
1940年，萨一佛在福建永安地区创办《战时木刻画报》刊物已开始努力摆脱欧洲艺术作风，向着大众化和民族化的方向前进。本文以《战时木刻画报》切入，对萨一佛木刻艺术中的大众化和民族形式进行研究，集中探察当时福建木刻所面临的社会背景、十日漫画社时期萨一佛思想转捩、萨一佛对于早期木刻中民族形式存在的问题以及民族形式的探索手段这四个问题，借此引入萨一佛视角理解新兴木刻版画的大众化、民族化等核心议题，让学界对永安抗战文艺能有更为清晰、客观、完整的认识。
Keyword ：
抗战转型 抗战转型 新兴木刻 新兴木刻 民族化和大众化 民族化和大众化 萨一佛 萨一佛
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| GB/T 7714 | 陈少枫 , 苏晨茜 , 丁铮 . 转捩与宣传——福建萨一佛抗战主题探索及新兴木刻民族形象的塑造研究 [J]. | 美与时代(下) , 2025 , 8 (01) : 77-84 . |
| MLA | 陈少枫 等. "转捩与宣传——福建萨一佛抗战主题探索及新兴木刻民族形象的塑造研究" . | 美与时代(下) 8 . 01 (2025) : 77-84 . |
| APA | 陈少枫 , 苏晨茜 , 丁铮 . 转捩与宣传——福建萨一佛抗战主题探索及新兴木刻民族形象的塑造研究 . | 美与时代(下) , 2025 , 8 (01) , 77-84 . |
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绿道作为城市绿地生态系统的重要组成部分，城市绿道文化服务功能是实现以人为核心的城市发展的重要内容，也是联结人文社会与自然环境的重要方式。本文基于IPA模式，以福道为研究对象，收集公众对于福道生态系统文化服务功能的形象感知进行评价，并基于此提出3项提升文化服务功能的建议：1）活化周边文化遗产，展现当地本土文化实力；2）深度挖掘公众需求，针对性强化自然教育服务；3）结合不同游客需求，重点维持休闲娱乐、健康康养、旅游服务和环境生态等4项服务的长效发展。
Keyword ：
IPA模型 IPA模型 公众视角 公众视角 文化服务功能 文化服务功能 绿道 绿道
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| GB/T 7714 | 谢祉琦 , 丁铮 . 基于公众感知的福道生态系统文化服务评价 [J]. | 黑龙江环境通报 , 2025 , 38 (03) : 31-33 . |
| MLA | 谢祉琦 等. "基于公众感知的福道生态系统文化服务评价" . | 黑龙江环境通报 38 . 03 (2025) : 31-33 . |
| APA | 谢祉琦 , 丁铮 . 基于公众感知的福道生态系统文化服务评价 . | 黑龙江环境通报 , 2025 , 38 (03) , 31-33 . |
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在科技革新的背景下，技术驱动舞台空间设计的核心命题在于找到技术、艺术与观众之间的平衡。当下，舞台空间设计需解决的问题是技术应以何种方式增强观者的参与感。英国皇家莎士比亚剧团的《暴风雨》是以数字技术增强传统舞台作品，后来的《Dream》则是沉浸式技术孕育下的新戏剧形式，是线上交互戏剧的一次大胆尝试，既为观众带来了便捷、平等的戏剧艺术参与方式，也有效解决了传统戏剧吸引力匮乏的问题，成为探索解决技术、艺术与观众之间平衡的生动案例，可为中国戏曲舞台空间的数字演绎提供参考。
Keyword ：
《Dream》 《Dream》 数字演绎 数字演绎 沉浸式 沉浸式 英国皇家莎士比亚剧团 英国皇家莎士比亚剧团
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| GB/T 7714 | 谢佳晖 , 丁铮 . 英国皇家莎士比亚剧团舞台空间的数字演绎策略探析 [J]. | 四川戏剧 , 2025 , 5 (06) : 81-85 . |
| MLA | 谢佳晖 等. "英国皇家莎士比亚剧团舞台空间的数字演绎策略探析" . | 四川戏剧 5 . 06 (2025) : 81-85 . |
| APA | 谢佳晖 , 丁铮 . 英国皇家莎士比亚剧团舞台空间的数字演绎策略探析 . | 四川戏剧 , 2025 , 5 (06) , 81-85 . |
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晚明时期著名文人曹学佺在福建地区所建园林——石仓园，不仅展示了闽中地区的独特文化风情，更彰显出文人对风雅文化的执着追求。石仓园内所承载的景观空间不仅揭示了曹学佺个人的情感流变，还映射出晚明文人在园林中所呈现的情感寄托，因此从个人的思绪投射到景观的情绪，共筑成石仓园的情感逻辑。通过对石仓园的详细梳理，更能理解其在建成前后的情感流变以及深刻的文化内涵。
Keyword ：
文人心境 文人心境 晚明文化 晚明文化 曹学佺 曹学佺 石仓园 石仓园
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| GB/T 7714 | 庄晓雯 , 陈少枫 , 苏晨茜 et al. 失落的风雅——晚明福州石仓园中的情感逻辑探赜 [J]. | 建筑与文化 , 2025 , 4 (04) : 139-142 . |
| MLA | 庄晓雯 et al. "失落的风雅——晚明福州石仓园中的情感逻辑探赜" . | 建筑与文化 4 . 04 (2025) : 139-142 . |
| APA | 庄晓雯 , 陈少枫 , 苏晨茜 , 丁铮 . 失落的风雅——晚明福州石仓园中的情感逻辑探赜 . | 建筑与文化 , 2025 , 4 (04) , 139-142 . |
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Objective In response to the challenges posed by mental health issues among college students and the declining quality of campus environments, this study aims to reveal the complex mechanisms underlying the relationship between campus audiovisual environments and the quality of students' attention recovery. It further explores campus landscape optimization pathways driven by multi-source data, providing scientific basis for sustainable campus planning.Methods Taking Fuzhou University Town as a case study, this study integrates machine learning technology with multi-source data (street view images, social media text, and PRS-11 questionnaires) to construct a "multi-modal perception mechanism analysis-dynamic evaluation iteration" framework. The CNN-BiLSTM model was used to predict attention recovery quality, combined with HRNet semantic segmentation, GBRT soundscape prediction, and CSV-T4SA sentiment analysis models to quantify audiovisual elements. XGBoost models and SHAP interpretability analysis were employed to reveal the effects and interaction mechanisms of variables.Results (1) Attention recovery quality is significantly higher in liberal arts and agricultural/forestry universities than in science and engineering universities, with boundary effects and the synergistic design of humanistic soundscapes being key factors; (2) SHAP analysis identifies humanistic soundscapes, natural soundscapes, and color complexity as core influencing factors, with their effects exhibiting significant threshold characteristics; (3) Linear interaction mechanisms among audiovisual elements are discovered, such as the interaction between vegetation density and building enclosure degree enhancing recovery efficacy, and the synergistic design of musical soundscapes and paving materials can optimize perceptual experiences.Conclusion By innovatively integrating multi-source data and machine learning techniques, this study systematically analyzes the relationship between campus audiovisual environments and attention recovery, breaking through the limitations of traditional linear analysis. The proposed "threshold response design" and "cross-modal collaborative optimization" strategies provide a new paradigm for campus planning, validate the scientific value of multi-sensory interaction design for mental health promotion, and offer a transferable methodological framework for global university environmental upgrades.
Keyword ：
attention recovery attention recovery Fuzhou Fuzhou healthy campus healthy campus machine learning machine learning spatial perception spatial perception
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| GB/T 7714 | Chen, Shaofeng , Chen, Zhengyan , Hong, Jiawen et al. Exploring the relationship between audio-visual perception in Fuzhou universities and college students' attention restoration quality using machine learning [J]. | FRONTIERS IN PSYCHOLOGY , 2025 , 16 . |
| MLA | Chen, Shaofeng et al. "Exploring the relationship between audio-visual perception in Fuzhou universities and college students' attention restoration quality using machine learning" . | FRONTIERS IN PSYCHOLOGY 16 (2025) . |
| APA | Chen, Shaofeng , Chen, Zhengyan , Hong, Jiawen , Zhuang, Xiaowen , Su, Chenxi , Ding, Zheng . Exploring the relationship between audio-visual perception in Fuzhou universities and college students' attention restoration quality using machine learning . | FRONTIERS IN PSYCHOLOGY , 2025 , 16 . |
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As universities become increasingly open, campuses are no longer only places for study and daily life for students and faculty, but also essential spaces for public visits and cultural identity. Traditional perception evaluation methods that rely on manual surveys are limited by sample size and subjective bias, making it challenging to reveal differences in experiences between groups (students/visitors) and the complex relationships between spatial elements and perceptions. This study uses a comprehensive open university in China as a case study to address this. It proposes a research framework that combines street-view image semantic segmentation, perception survey scores, and interpretable machine learning with sample augmentation. First, full-sample modeling is used to identify key image semantic features influencing perception indicators (nature, culture, aesthetics), and then to compare how students and visitors differ in their perceptions and preferences across campus spaces. To overcome the imbalance in survey data caused by group-space interactions, the study applies the CTGAN method, which expands minority samples through conditional generation while preserving distribution authenticity, thereby improving the robustness and interpretability of the model. Based on this, attribution analysis with an interpretable decision tree algorithm further quantifies semantic features' contribution, direction, and thresholds to perceptions, uncovering heterogeneity in perception mechanisms across groups. The results provide methodological support for perception evaluation of campus functional zones and offer data-driven, human-centered references for campus planning and design optimization.
Keyword ：
campus functional zones campus functional zones campus space campus space image semantic segmentation image semantic segmentation SHAP SHAP student-visitor differences student-visitor differences
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| GB/T 7714 | Zhuang, Xiaowen , Cai, Yi , Tang, Zhenpeng et al. Optimizing University Campus Functional Zones Using Landscape Feature Recognition and Enhanced Decision Tree Algorithms: A Study on Spatial Response Differences Between Students and Visitors [J]. | BUILDINGS , 2025 , 15 (19) . |
| MLA | Zhuang, Xiaowen et al. "Optimizing University Campus Functional Zones Using Landscape Feature Recognition and Enhanced Decision Tree Algorithms: A Study on Spatial Response Differences Between Students and Visitors" . | BUILDINGS 15 . 19 (2025) . |
| APA | Zhuang, Xiaowen , Cai, Yi , Tang, Zhenpeng , Ding, Zheng , Gan, Christopher . Optimizing University Campus Functional Zones Using Landscape Feature Recognition and Enhanced Decision Tree Algorithms: A Study on Spatial Response Differences Between Students and Visitors . | BUILDINGS , 2025 , 15 (19) . |
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As urbanization accelerates, the growing needs of children have led to a significant imbalance between supply and demand in urban spaces. Creating child-friendly environments is crucial for enhancing urban resilience and promoting sustainable development. However, there is currently a lack of sufficient quantitative methods to assess child-friendliness and analyze the complex interactions between children's perceptions and spatial factors. This study uses the central area of Xiamen as a case study to explore how different street environment characteristics influence perceptions of child-friendliness. This study integrates empathy-based stories (MEBS), street scene image analysis, XGBoost machine learning, and GeoSHapley spatial analysis to explore children's perceptions of urban spaces. The study reveals that: (1) The child-friendly resources in the central urban area of Xiamen are concentrated in the northeastern and Huli districts, while a supply-demand mismatch exists in Siming District, which has a higher population density; (2) Greenness and pavement coverage are critical in shaping child-friendliness, with greenness having the greatest positive impact; (3) Some areas with child-friendly renovations have a lower child-friendliness index, whereas regions like Guanyinshan, which did not undergo renovations, scored higher; (4) The interaction between greenness and openness positively influences perceptions, while enclosure and visual complexity have a negative effect. Building on the need for child-friendly environments, this study develops a spatial analysis framework to quantify the alignment of child-friendly supply and demand in Xiamen's central urban area, identify regions with mismatched supply and demand, and offer spatial decision support to improve urban environmental quality and promote sustainable development.
Keyword ：
child-friendly city child-friendly city explainable machine learning explainable machine learning perception of street space perception of street space spatial effects spatial effects
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| GB/T 7714 | Su, Chenxi , Cheng, Yuxuan , Chen, Shaofeng et al. Perception of Child-Friendly Streets and Spatial Planning Responses in High-Density Cities Amidst Supply-Demand Disparities [J]. | BUILDINGS , 2025 , 15 (21) . |
| MLA | Su, Chenxi et al. "Perception of Child-Friendly Streets and Spatial Planning Responses in High-Density Cities Amidst Supply-Demand Disparities" . | BUILDINGS 15 . 21 (2025) . |
| APA | Su, Chenxi , Cheng, Yuxuan , Chen, Shaofeng , Li, Wenting , Nie, Kaining , Ding, Zheng . Perception of Child-Friendly Streets and Spatial Planning Responses in High-Density Cities Amidst Supply-Demand Disparities . | BUILDINGS , 2025 , 15 (21) . |
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A university campus is a composite built environment integrating research, daily life, culture, and ecological green space. Its landscape elements shape environmental perception and overall spatial quality. This study assesses spatial quality by identifying key features and optimizing their joint effects across three perceptions: safety, comfort, and belonging. Using a Chinese campus, we captured street-view images, applied semantic segmentation to quantify elements (grass, trees, buildings, roads, sidewalks), and used explainable machine learning with data augmentation to identify the features most relevant to these perceptions. This study then employed fuzzy-set Qualitative Comparative Analysis (fsQCA) to reveal configuration pathways that enhance spatial quality. Results show that data augmentation mitigates class imbalance and improves prediction accuracy. Key features include sky, river, bridge, people, grass, and sidewalks, and path analysis indicates that greater sky openness and higher densities of people, roads, sidewalks, and grass, together with fewer buildings, cars, and bare earth, enhance safety, comfort, and belonging. This study delivers globally transferable design rules and a replicable, policy-ready workflow that enables evidence-based campus upgrades across diverse regions.
Keyword ：
campus buildings campus buildings combined effects combined effects data augmentation data augmentation landscape features landscape features
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| GB/T 7714 | Zhuang, Xiaowen , Cai, Yi , Tang, Zhenpeng et al. Identification and Configuration Optimization of Key Campus Landscape Features Using Augmentation-Based Machine Learning and Configuration Analysis [J]. | BUILDINGS , 2025 , 15 (21) . |
| MLA | Zhuang, Xiaowen et al. "Identification and Configuration Optimization of Key Campus Landscape Features Using Augmentation-Based Machine Learning and Configuration Analysis" . | BUILDINGS 15 . 21 (2025) . |
| APA | Zhuang, Xiaowen , Cai, Yi , Tang, Zhenpeng , Ding, Zheng , Gan, Christopher . Identification and Configuration Optimization of Key Campus Landscape Features Using Augmentation-Based Machine Learning and Configuration Analysis . | BUILDINGS , 2025 , 15 (21) . |
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Natural landscapes are crucial resources for enhancing visitor experiences in ecotourism destinations. Previous research indicates that high temperatures may impact tourists' perception of landscapes and emotions. Still, the potential value of natural landscape perception in regulating tourists' emotions under high-temperature conditions remains unclear. In this study, we employed machine learning models such as LSTM-CNN, Hrnet, and XGBoost, combined with hotspot analysis and SHAP methods, to compare and reveal the potential impacts of natural landscape elements on tourists' emotions under different temperature conditions. The results indicate: (1) Emotion prediction and spatial analysis reveal a significant increase in the proportion of negative emotions under high-temperature conditions, reaching 30.1%, with negative emotion hotspots concentrated in the downtown area, whereas, under non-high temperature conditions, negative emotions accounted for 14.1%, with a more uniform spatial distribution. (2) Under non-high temperature conditions, the four most influential factors on tourists' emotions were Color complexity (0.73), Visual entropy (0.71), Greenness (0.68), and Aquatic rate (0.6). In contrast, under high-temperature conditions, the most influential factors were Greenness (0.6), Openness (0.56), Visual entropy (0.55), and Color complexity (0.55). (3) Compared to non-high temperature conditions, high temperatures enhanced the positive effects of environmental perception on emotions, with Greenness (0.94), Color complexity (0.84), and Enclosure (0.71) showing stable positive impacts. Additionally, aquatic elements under high-temperature conditions had a significant emotional regulation effect (contribution of 1.05), effectively improving the overall visitor experience. This study provides a data foundation for optimizing natural landscapes in ecotourism destinations, integrating the advantages of various machine learning methods, and proposing a framework for data collection, comparison, and evaluation of natural landscape perception under different temperature conditions. It thoroughly explores the potential of natural landscapes to enhance visitor experiences under various temperature conditions and provides sustainable planning recommendations for the sustainable conservation of natural ecosystems and ecotourism.
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| GB/T 7714 | Ye, Cuicui , Chen, Zhengyan , Ding, Zheng . How does high temperature weather affect tourists' nature landscape perception and emotions? A machine learning analysis of Wuyishan City, China [J]. | PLOS ONE , 2025 , 20 (5) . |
| MLA | Ye, Cuicui et al. "How does high temperature weather affect tourists' nature landscape perception and emotions? A machine learning analysis of Wuyishan City, China" . | PLOS ONE 20 . 5 (2025) . |
| APA | Ye, Cuicui , Chen, Zhengyan , Ding, Zheng . How does high temperature weather affect tourists' nature landscape perception and emotions? A machine learning analysis of Wuyishan City, China . | PLOS ONE , 2025 , 20 (5) . |
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