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学者姓名：丁铮
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Abstract ：
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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The rapid growth of the aging population, alongside functional decline and more older adults living independently, has increased demand for age-friendly infrastructure and walkable communities. This study proposes a quantitative framework to assess how multi-scale built environments influence older adults' walkability, addressing the scarcity of scalable and interpretable models in age-friendly urban research. By combining the cumulative opportunity method, street-scene semantic segmentation, XGBoost, and GeoSHapley-based spatial effect analysis, the study finds that (1) significant spatial disparities in walkability exist in Xiamen's central urban area. Over half of the communities (54.46%) failed to meet the minimum threshold (20 points) within the 15 min community life circle (15-min CLC), indicating inadequate infrastructure. The primary issue is low coverage of older adults' welfare facilities (only 16.26% of communities are within a 15 min walk). Despite renovations in Jinhu Community, walkability remains low, highlighting persistent disparities. (2) Communities with abundant green space are predominantly newly developed areas (64.06%). However, these areas provide fewer facilities on average (2.3) than older communities (5.7), resulting in a "green space-service mismatch", where visually appealing environments lack essential services. (3) Human perception variables such as safety, traffic flow, and closure positively influence walkability, while visual complexity, heat risk, exposure, and greenness have negative effects. (4) There is a clear supply and demand mismatch. Central districts combine high walkability with substantial older adults' service demand. Newly built residential areas in the periphery and north have low density and insufficient pedestrian facilities. They fail to meet daily accessibility needs, revealing delays in age-friendly development. This framework, integrating nonlinear modeling and spatial analysis, reveals spatial non-stationarity and optimal thresholds in how the built environment influences walkability. Beyond methodological contributions, this study offers guidance for planners and policymakers to optimize infrastructure allocation, promote equitable, age-friendly cities, and enhance the health and wellbeing of older residents.
Keyword ：
age-friendly urban renewal age-friendly urban renewal community-level built environment community-level built environment explainable machine learning explainable machine learning multi-scale living circles multi-scale living circles walkability walkability
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| GB/T 7714 | Su, Chenxi , Chen, Zhengyan , Cheng, Yuxuan et al. Decoding Multi-Scale Environmental Configurations for Older Adults' Walkability with Explainable Machine Learning [J]. | SUSTAINABILITY , 2025 , 17 (18) . |
| MLA | Su, Chenxi et al. "Decoding Multi-Scale Environmental Configurations for Older Adults' Walkability with Explainable Machine Learning" . | SUSTAINABILITY 17 . 18 (2025) . |
| APA | Su, Chenxi , Chen, Zhengyan , Cheng, Yuxuan , Chen, Shaofeng , Li, Wenting , Ding, Zheng . Decoding Multi-Scale Environmental Configurations for Older Adults' Walkability with Explainable Machine Learning . | SUSTAINABILITY , 2025 , 17 (18) . |
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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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Global climate change has intensified the regional linkage of the urban heat island effect (UHI), posing major challenges to urban sustainability. This study examines three major metropolitan areas in China to understand how thermal environments interact under different geographical conditions. It aims to improve the heat island network structure and enhance governance strategies. The study combines XGBoost machine learning with the GeoSHapley method to build a high-precision thermal resistance surface, overcoming the bias of traditional methods and capturing nonlinear effects, spatial variation, and factor interactions. The heat island network is modeled using circuit theory and evaluated and verified using structural indicators such as alpha closure, (3 line-to-point ratio and gamma connectivity. Key findings include: 1) Resistance factors vary significantly across regions. In Chongqing, topographic factors (DEM and SLOPE) account for 72 % of the resistance, dominating the network. In Xiamen-Zhangzhou-Quanzhou and Wuhan, NDBI contributes 0.37 and 0.38, respectively, as the main driver. 2)GeoSHapley analysis identified cooling thresholds for resistance factors. For example, NDVI thresholds are [0.75-0.85] in Xiamen-Zhangzhou-Quanzhou and [0.65-0.70] in Wuhan, offering a scientific basis for resistance classification. 3)The Chongqing network shows the highest connectivity (gamma = 0.81) and integrity (alpha = 0.70), with a model fit of R2 = 0.907. Xiamen-Zhangzhou-Quanzhou also performs well, proving the method works in complex terrain. This study improves upon past methods by introducing dynamic resistance classification and interaction analysis. It offers a scalable framework for managing urban thermal environments based on local geography, supporting ecological and sustainable urban development.
Keyword ：
GeoSHapely GeoSHapely Resistive surface Resistive surface Spatial network Spatial network Surface temperature Surface temperature Thermal environment Thermal environment XGBoost XGBoost
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| GB/T 7714 | Chen, Shaofeng , Qiu, Yuwei , Xu, Yuhan et al. Modeling and optimization of heat island networks based on machine learning and the perspective of spatial heterogeneity in metropolitan areas [J]. | URBAN CLIMATE , 2025 , 63 . |
| MLA | Chen, Shaofeng et al. "Modeling and optimization of heat island networks based on machine learning and the perspective of spatial heterogeneity in metropolitan areas" . | URBAN CLIMATE 63 (2025) . |
| APA | Chen, Shaofeng , Qiu, Yuwei , Xu, Yuhan , Huang, Jiafang , Ding, Zheng . Modeling and optimization of heat island networks based on machine learning and the perspective of spatial heterogeneity in metropolitan areas . | URBAN CLIMATE , 2025 , 63 . |
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Evaluating the restoration quality of university outdoor spaces is often constrained by subjective surveys and manual assessment, limiting scalability and objectivity. This study addresses this gap by applying explainable machine learning to predict restorative quality from campus imagery, enabling large-scale, data-driven evaluation and capturing complex nonlinear relationships that traditional methods may overlook. Using Fujian Agriculture and Forestry University as a case study, this study extracted road network data, generated 297 coordinates at 50-m intervals, and collected 1197 images. Surveys were conducted to obtain restorative quality scores. The Mask2Former model was used to extract landscape features, and decision tree algorithms (RF, XGBoost, GBR) were selected based on MAE, MSE, and EVS metrics. The combination of optimal algorithms and SHAP was employed to predict restoration quality and identify key features. This research also used a multivariate linear regression model to identify features with significant statistical impact but lower features importance ranking. Finally, the study also analyzed heterogeneity in scores for three restoration indicators and five campus zones using k-means clustering. Empirical results show that natural elements like vegetation and water positively affect psychological perception, while structural components like walls and fences have negative or nonlinear effects. On this basis, this study proposes spatial optimization strategies for different campus areas, offering a foundation for creating high-quality outdoor environments with restorative and social functions.
Keyword ：
image semantic segmentation image semantic segmentation interpretable machine learning interpretable machine learning landscape optimization landscape optimization university campus university campus
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| GB/T 7714 | Zhuang, Xiaowen , Tang, Zhenpeng , Lin, Shuo et al. Prediction and Optimization of the Restoration Quality of University Outdoor Spaces: A Data-Driven Study Using Image Semantic Segmentation and Explainable Machine Learning [J]. | BUILDINGS , 2025 , 15 (16) . |
| MLA | Zhuang, Xiaowen et al. "Prediction and Optimization of the Restoration Quality of University Outdoor Spaces: A Data-Driven Study Using Image Semantic Segmentation and Explainable Machine Learning" . | BUILDINGS 15 . 16 (2025) . |
| APA | Zhuang, Xiaowen , Tang, Zhenpeng , Lin, Shuo , Ding, Zheng . Prediction and Optimization of the Restoration Quality of University Outdoor Spaces: A Data-Driven Study Using Image Semantic Segmentation and Explainable Machine Learning . | BUILDINGS , 2025 , 15 (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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【目的】随着城市化进程的加速，城市热岛效应的跨区域传导问题愈发凸显，成为亟待解决的环境问题。然而，传统阻力面构建方法在研究城市热岛效应时，存在主观性较强以及难以充分考虑空间异质性等问题，限制了对城市群间热环境关联机制的深入理解。因此，本研究以厦漳泉大都市圈为研究对象构建冷热岛空间网络，旨在揭示城市群间的热环境关联机制，并提出多尺度协同优化策略，为城市热环境的优化提供科学依据，助力区域气候适应性规划的推进，有效应对城市热岛效应带来的挑战。【方法】为实现上述目标，本研究创新性地整合XGBoost-GeoSHapley可解释机器学习模型与电路理论。通过XGBoost-GeoSHapley加性解释分析方法，结合地表温度分级结果，精准构建冷热岛阻力面，并深入量化阻力因子的空间效应、非线性关系以及交互作用。在此基础上，借助电路理论，精准识别冷热岛网络，进而识别关键的夹点、障碍点和中心性区域。最终，结合区域特征，提出针对性的优化策略，为厦漳泉大都市圈的热环境优化提供具体指导。【结果】冷岛网络呈现出“西北山地主导，沿海湿地补充”的格局，而热岛网络则主要集中在东部建成区。本研究共识别出43条热岛廊道（总长度412.99 km）和85条冷岛廊道（总长度1 693.08 km），并划定了热岛优先整改区1 067.25 km
Keyword ：
冷热岛网络 冷热岛网络 机器学习 机器学习 极端天气 极端天气 热岛效应 热岛效应 热环境 热环境 电路理论 电路理论 空间效应 空间效应 阻力面 阻力面
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| GB/T 7714 | 陈少枫 , 翁飞帆 , 邱羽薇 et al. 都市圈冷热岛空间网络构建与优化——可解释机器学习与电路理论的融合 [J]. | 北京林业大学学报 , 2025 , 47 (07) : 152-166 . |
| MLA | 陈少枫 et al. "都市圈冷热岛空间网络构建与优化——可解释机器学习与电路理论的融合" . | 北京林业大学学报 47 . 07 (2025) : 152-166 . |
| APA | 陈少枫 , 翁飞帆 , 邱羽薇 , 李泓杰 , 丁铮 . 都市圈冷热岛空间网络构建与优化——可解释机器学习与电路理论的融合 . | 北京林业大学学报 , 2025 , 47 (07) , 152-166 . |
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| GB/T 7714 | Yang, Litian , Yang, Lixin , Ding, Zheng et al. Study on effects of different land use types on heat island effect and its potential effects on residents' mental health in main city of Zhengzhou City [J]. | INTERNATIONAL JOURNAL OF MENTAL HEALTH NURSING , 2025 , 34 : 44-45 . |
| MLA | Yang, Litian et al. "Study on effects of different land use types on heat island effect and its potential effects on residents' mental health in main city of Zhengzhou City" . | INTERNATIONAL JOURNAL OF MENTAL HEALTH NURSING 34 (2025) : 44-45 . |
| APA | Yang, Litian , Yang, Lixin , Ding, Zheng , Lu, Dongfang . Study on effects of different land use types on heat island effect and its potential effects on residents' mental health in main city of Zhengzhou City . | INTERNATIONAL JOURNAL OF MENTAL HEALTH NURSING , 2025 , 34 , 44-45 . |
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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 et al. "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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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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