以接收者为中心的行人噪声烦恼映射：一种使用随机森林、心理声学指标和开源数据的经济高效方法

研究提出了一种以接收者（行人）为中心的交通诱导噪声烦恼映射方法，通过心理声学指标、随机森林建模和开源数据，避免了昂贵的交通流量统计和专有软件的使用，其五步工作流程（数据收集、提取心理声学值、预测建模、测试优化、传播建模）生成了高分辨率的 GIS 噪声烦恼地图，结果显示大都会尺度的烦恼映射在技术上可行且经济实惠，能为步行质量评估、公共空间设计和主动交通政策提供决策支持，同时指出该方法虽未经过主观评估验证，但为城市健康和噪声污染影响研究提供了新工具
A traffic-induced noise annoyance mapping method centered on the recipient (peeder) is proposed. Through psychoacoustic indicators, random forest modeling and open source data, expensive traffic flow statistics and proprietary software are avoided. Its five-step workflow (data collection, psychoacoustic value extraction, prediction modeling, test optimization, and propagation modeling) generates a high-resolution GIS noise annoyance map. The results show that the metropolitan scale annoyance mapping is technically feasible and economical, and can provide decision-making support for walking quality assessment, public space design and active transportation policy. It also points out that although this method has not been subjectively verified, it provides a new tool for urban health and noise pollution impact research.

在虚拟现实中利用智能非玩家角色（NPC）：背景知识对理解和体验城市环境设计的影响

该研究以北京首钢公园 VR 模拟为场景，探讨智能 NPC 提供的历史文化背景知识对用户体验的影响。30 名参与者分两组，实验组接受 NPC 引导，对照组无，通过眼动、生理数据及问卷评估发现，实验组在兴趣区注视次数、时长更多，生理唤醒和认知理解得分更高，认知网络更复杂；结构方程模型证实 NPC 互动通过 gaze 行为和生理反应影响情感与认知，表明 NPC 背景知识可增强 VR 中的用户认知与情感体验，为相关领域应用提供参考。
This study uses the VR simulation of Beijing Shougang Park as a scenario to explore the impact of historical and cultural background knowledge provided by intelligent NPCs on user experience. The 30 participants were divided into two groups. The experimental group was guided by NPC, and the control group was not. Through eye movement, physiological data and questionnaire evaluation, it was found that the experimental group had more gazes and durations in the area of ​​interest, had higher scores of physiological arousal and cognitive understanding, and more complex cognitive networks; the structural equation model confirmed that NPC interaction influenced emotions and cognitive cognition through gaze behavior and physiological response, indicating that NPC background knowledge can enhance user cognition and emotional experience in VR, providing reference for applications in related fields.

对米兰和北京地铁站不安全感的看法的比较分析

本研究通过对比米兰（渐进式城市化）与北京（快速扩张）新旧地铁站的视觉环境要素，系统分析了地下公共空间的安全感知机制。采用360度图像采集、ADE20K与YOLOv8结合的语义分割技术，结合志愿者评分及XGBoost-SHAP机器学习模型，研究发现：人工照明、地面开放度和人群密度是提升安全感的共性核心要素，而墙壁封闭性、基础设施老化（如北京旧站的裸露管道、米兰旧站的破损设施）显著降低安全感。城市差异上，米兰新站的站台门因技术创新成为关键正向因素，北京新站虽现代化却因照明不均暴露隐患；旧站改造需针对性策略（如米兰增设商业设施、北京隐藏管道）。研究创新性提出“视觉-空间-感知”三元分析框架，为地下空间设计及城市交通系统优化提供了跨文化方法论支持，强调城市化模式差异下安全感知的动态适配需求。
This study systematically analyzes the security perception mechanism of underground public spaces by comparing the visual environmental elements of new and old subway stations in Milan (gradual urbanization) and Beijing (rapid expansion). Using semantic segmentation technology combined with 360-degree image acquisition, ADE20K and YOLOv8, combined with volunteer scores and XGBoost-SHAP machine learning model, the study found that artificial lighting, ground openness and population density are common core elements to enhance the sense of security, while wall enclosure and infrastructure aging (such as exposed pipelines in old Beijing stations and damaged facilities in old Milan stations) significantly reduce the sense of security. In terms of urban differences, the platform doors of Milan's new station have become a key positive factor due to technological innovation. Although the new station in Beijing is modernized, hidden dangers are exposed due to uneven lighting; the renovation of old stations requires targeted strategies (such as the addition of commercial facilities in Milan and hidden pipelines in Beijing). Research and innovatively propose the "visual-space-perception" ternary analysis framework, providing cross-cultural methodological support for underground space design and urban transportation system optimization, emphasizing the dynamic adaptation needs of security perception under the differences in urbanization modes.

SARS-COV-2传输上的街道构建环境：香港的研究

文章聚焦2020年1月至2022年3月香港五波疫情，研究街道层面建成环境（SLBE）对SARS-CoV-2空间传播风险（SSTR）的影响。研究重点包括街道环境要素分析、空间异质性及动态疫情响应。研究方法有数据收集与处理，收集病例相关建筑数据并通过Google街景获取图像分割街道环境要素，关联街道环境特征与建筑传播风险；空间分析，用全局和局部空间自相关识别疫情空间聚集模式并划分建筑传播风险集群；机器学习建模，用随机森林（RF）模型分析SLBE与SSTR的非线性关系，通过SHAP模型解释特征重要性及交互作用并对比其他模型性能。研究结果显示，高传播区域街道人行道、卫生设施、人工结构与传播风险正相关，低传播区域交通控制设施、未铺设道路与风险负相关；街道基础设施在第三、四波疫情影响更显著，自然要素在第一波作用明显；SHAP模型揭示环境特征阈值效应及交互作用；RF模型在高低传播风险集群中表现均最优，显著优于传统GWR模型。
The article focuses on five waves of epidemics in Hong Kong from January 2020 to March 2022, and studies the impact of street-level construction environment (SLBE) on SARS-CoV-2 spatial transmission risk (SSTR). Research focuses include street environmental factor analysis, spatial heterogeneity and dynamic epidemic response. Research methods include data collection and processing, collecting case-related building data and obtaining images through Google Street View to segment street environmental elements, and correlating street environmental characteristics and building transmission risks; spatial analysis, using global and local spatial autocorrelation to identify the spatial aggregation mode of the epidemic and divide the building transmission risk cluster; machine learning modeling, using random forest (RF) model to analyze the nonlinear relationship between SLBE and SSTR, explaining the importance of features and interactions through the SHAP model and comparing the performance of other models. The research results show that street sidewalks, sanitary facilities, and artificial structures in high-propagation areas are positively correlated with transmission risks, while traffic control facilities and unpaved roads are negatively correlated with risks; street infrastructure is more significant in the third and fourth waves of the epidemic, and natural factors play a significant role in the first wave; the SHAP model reveals the threshold effect and interaction of environmental characteristics; the RF model performs best in high and low transmission risk clusters, significantly better than the traditional GWR model.

在建筑设计教育中整合扩展现实（XR）：东南大学（中国）的系统评价和案例研究

本文聚焦扩展现实（XR）技术在建筑设计教育中的整合应用，探讨其在专业实践与教学间的衔接潜力。研究通过系统综述梳理XR在建筑行业现有应用，结合东南大学建筑学院本科毕业设计案例分析其在教学中的实际需求与适配场景，重点关注XR对传统设计方法的优化、设计范围的扩展及多方利益相关者参与的促进。研究方法包括系统文献综述、案例研究及提出技术整合框架。研究结果表明，XR在教学中有视觉化、交互、协作等价值，提出课程优化建议，也指出需解决技术成本、硬件兼容性及伦理问题等挑战，呼吁开发跨文化适用的XR教学框架并拓展其在城市规划等领域的应用
This paper focuses on the integrated application of extended reality (XR) technology in architectural design education and explores its connection potential between professional practice and teaching. The research systematically reviews the existing application of XR in the construction industry, combines the undergraduate graduation design cases of Southeast University School of Architecture to analyze its actual needs and adaptation scenarios in teaching, focusing on the optimization of traditional design methods by XR, expanding the design scope and promoting multi-stakeholder participation. Research methods include systematic literature reviews, case studies and proposed technical integration frameworks. The research results show that XR has the value of visualization, interaction, collaboration and other values ​​in teaching. It puts forward course optimization suggestions, and also points out that challenges such as technical cost, hardware compatibility and ethical issues need to be solved. It calls for the development of a cross-cultural applicable XR teaching framework and expand its application in urban planning and other fields.