“Restorative-Repressive” perception on post-industrial parks based on artificial and natural scenarios: Difference and mediating effect

The development of post-industrial landscapes at industrial sites plays an important role to fill urban green spaces. However, current research on the use and redevelopment of post-industrial sites has mainly focused on ecological restoration, and studies combined with objective and subjective data to quantify public preferences remain poorly understood. In this study, deep learning was used to semantically segment the post-industrial landscape, and a multiple stepwise regression model was used to analyze the non-linear correlation between quantitative indicators and public “restorative-repressive” perception, and structural equation model (SEM) between quantitative indicators and public perception data were established. We investigated and found (1) Semantic segmentation models for machine learning combined with principal component analysis (PCA) and non-metric multidimensional scaling (NMDS) analysis can categorize post-industrial parks into two groups dominated by artificial elements and natural elements. (2) Public perceptions varied more in the natural element-dominated group and less in the industrial element-dominated group. In addition, waterbody in the post-industrial landscape existed as a destabilizing factor. (3) There was a difference in the correlation between quantitative indicators and subjective perceptions in the two categories of parks. (4) Height of industrial building (HIB), function of industrial building(FIB), vegetation succession(VS) were significantly influenced public satisfaction. These findings informed that public satisfaction with post-industrial landscapes can be enhanced by taking full account of the different uses of natural and artificial elements and enabling researchers to analyze the redevelopment of post-industrial landscapes from a new perspective of evidence-based design.     

基于线上评价文本的广州海珠广场公共空间形象感知研究*

利用ROST-CM6软件的文本挖掘技术，并基于大众点评网、携程旅行网上海珠广场的评价文本数据，分析广州市民对海珠广场的形象感知情况。通过对高频词、语义结构网络和文本情绪进行分析，并结合问卷调查进一步论证，得到海珠广场在市民心中已有交通便捷、文化氛围浓郁2个主要的形象特征；且其获得的大众评价较高，积极情绪评论占比最多。然而，海珠广场在人群活动类型和环境绿化方面仍有优化空间，应构建更多空间形式来丰富活动类型，并通过改善绿化环境打造特色景观。     

基于接触状态感知的羊胴体后腿自适应分割控制方法

针对羊胴体后腿骨肉边界未知、尺寸多变和可见性约束限制造成的机器人自主分割精确度低与易受阻卡住的问题，提出一种羊胴体后腿自适应分割控制方法，并开展羊胴体后腿分割试验进行验证。该方法以接触状态感知为核心，有效提取接触类型特征、接触异常度特征和接触方向特征，通过构建深度时空神经网络识别接触类型，构建深度自编码网络估计接触异常度，采用主成分分析方法检测主要接触方向，实现接触状态多模态感知，机器人通过动态运动基元模仿学习人类操作技能，并结合接触状态感知信息实现关节运动的自适应调节。试验结果表明：深度时空网络模型在羊胴体后腿分割验证集上的识别准确率为98.44%;深度自编码网络模型能够较好地估计验证集样本的接触异常度，区分不同的接触状态。机器人基于自适应分割控制方法开展实际分割试验，与对照组相比，最大分割力下降幅度为29 N,最大力矩下降幅度为7 N·m,证明该方法的有效性；平均最大残留肉厚度为3.6 mm,平均分割残留率为4.9%,分割残留率与羊胴体质量呈现负相关，证明该方法具有良好的泛化性和准确性，并且整体分割效果较好，满足羊胴体后腿分割要求。     

风险态度、制度信任对失地农民风险感知的影响

剖析农民失地后感知的各类风险，深刻揭示失地农民的风险态度和制度信任对其风险感知的影响，对于政府征地安置工作具有重要参考意义。本文基于沪、赣、桂失地农民的问卷调查，通过构建失地农民风险感知评价体系，对失地农民的风险感知与风险态度进行测度，并采用有序Logit模型对失地农民的风险态度和制度信任对社会保障风险感知、经济收入风险感知、居住环境风险感知和社会心理风险感知的影响进行了实证分析。研究结果表明，失地农民的风险态度对于上述四类风险感知均存在显著正向影响，其中风险规避型的失地农民风险感知程度更为显著。此外，对制度的信任能够有效地降低失地农民的风险感知。异质性分析表明，风险规避态度对中低收入失地农民和中西部地区失地农民的风险感知具有强化作用；制度信任对高收入失地农民和东部地区失地农民的风险感知具有抑制作用。因此，在推进征地工作并保障失地农民的权益时，应纾解农民的风险规避态度、提升政府征地制度的信任度，以降低并有效控制失地农民所面临的潜在风险。     

乡村振兴战略背景下农业职业教育角色定位研究

实施乡村振兴战略是全面建设社会主义现代化强国的重大战略决策和历史任务.北京农业职业学院充分发挥职业教育和农业行业复合功能及区位优势,构建以"政校行企"为载体,服务乡村产业振兴、组织振兴、生态振兴、文化振兴、人才振兴为核心的"五位一体"教育培训服务体系,全面服务首都乡村振兴,乡村人才培养质量、服务乡村振兴水平显著提升,产生巨大社会影响.     

社会资本、价值认知对农户参与农药包装回收受偿意愿的影响——基于安徽的证据

为保护农村环境、保障公众健康，农药包装回收成为重要举措。基于安徽省305份农户调研数据，运用多元有序logit模型，实证研究社会资本和价值认知对农户农药包装回收受偿意愿的影响。结果表明：社会网络和社会规范对农户参与农药包装回收的受偿意愿有正向影响；农户的社会价值认知在一定程度上提高了有偿补贴条件下农户的参与意愿；农户参与农药包装回收的受偿意愿受到农户个体和家庭特征的影响，如年龄、收入对农药包装回收有正向影响，而劳动力数量则有负向影响。     

基于语义分割的非结构化田间道路场景识别

环境信息感知是智能农业装备系统自主导航作业的关键技术之一。农业田间道路复杂多变，快速准确地识别可通行区域，辨析障碍物类别，可为农业装备系统高效安全地进行路径规划和决策控制提供依据。该研究以非结构化农业田间道路场景为研究对象，根据环境对象动、静态属性进行类别划分，提出一种基于通道注意力结合多尺度特征融合的轻量化语义分割模型。首先采用Mobilenet V2轻量卷积神经网络提取图像特征，将混合扩张卷积融入特征提取网络最后2个阶段，在保证特征图分辨率的基础上增加感受野并保持信息的连续性与完整性；然后引入通道注意力模块对特征提取网络各阶段特征通道依据重要程度重新标定；最后通过空间金字塔池化模块将多尺度池化特征进行融合，获取更加有效的全局场景上下文信息，增强对复杂道路场景识别的准确性。语义分割试验表明，不同道路环境下本文模型可以对场景对象进行有效识别解析，像素准确率和平均像素准确率分别为94.85%、90.38%，具有准确率高、鲁棒性强的特点。基于相同测试集将本文模型与FCN-8S、SegNet、DeeplabV3+、BiseNet模型进行对比试验，本文模型的平均区域重合度为85.51%，检测速度达到8.19帧/s，参数数量为，相比于其他模型具有准确性高、推理速度快、参数量小等优点，能够较好地实现精度与速度的均衡。研究成果可为智能农业装备在非结构化道路环境下安全可靠运行提供技术参考。     

环境规制政策下养殖户环境风险感知对粪污资源化处理意愿的影响——以肉鸭养殖户为例

为了解畜禽产业环境污染治理与养殖户低碳循环生产方式，基于肉鸭生产大省的694份养殖户调查数据，采用二元Logit模型和调节效应分析的研究方法，研究了养殖户环境风险感知对粪污资源化处理意愿的影响及环境规制政策对养殖户环境风险感知—粪污资源化处理意愿关系的调节作用。结果表明:肉鸭养殖户对肉鸭养殖废弃物水体污染感知、养殖户进行粪污资源化处理能减少环境污染感知能够显著影响养殖户粪污资源化处理意愿；养殖户对肉鸭养殖废弃物土壤污染感知、空气污染感知、肉鸭养殖户环境风险损失感知对肉鸭养殖户粪污资源化处理意愿并不显著；政府的命令型、激励型和自愿型环境规制政策对养殖户风险感知—粪污资源化处理意愿存在一定的调节作用。为促进畜禽养殖户环境风险感知，加强畜禽环境污染防治，应在提升政策有效实施的前提下加强相关培训和宣传，因地制宜的实施养殖补贴激励政策，促进环境资源的优化配置，激励养殖户积极治理污染，提升养殖户的养殖绩效。     

基于多维感知的玉米大田巡检平台设计与试验

为了提高对大田种植玉米生长动态、干旱胁迫和病虫害等方面的智能巡检监测能力，设计了一种基于多维感知的移动巡检平台。首先，对底盘总成的转向系统、驱动系统和控制系统进行设计，并基于Arduino UNO控制器实现了巡检平台的转向及行驶功能。其次，搭建了包括GNSS/INS(Global navigation satellite system/inertial navigation system)组合导航系统、激光雷达(Light detection and ranging, LiDAR)和工业相机的多维感知系统，对感知系统时间同步方案、数据通信结构和信息采集软件进行设计，实现了巡检平台对环境的感知和数据可视化。最后，在玉米大田对巡检平台进行了底盘行驶性能试验和感知系统环境感知试验。试验结果表明：巡检平台左转平均最小转弯半径为2 922 mm,右转平均最小转弯半径为2 736 mm,最大爬坡度大于26.7%,位置PID控制下直线行驶平均速度为0.523 m/s,与期望速度0.5 m/s的误差为4.6%,行驶15 m平均偏移量为0.636 m,平均偏移率为4.24 cm/m,满足田间行驶性能要求...     

The relationship between urban green space and social health of individuals: A scoping review

Urban green space is an important resource to effectively improve the urban environment and human health. A growing body of evidence highlights their positive impact on physical, mental and social health. Less focus has been paid to the social health benefits of urban green space. Designing urban green space to improve the social health of various groups has become a crucial problem, given the lack of uniformity in defining and measuring social health, as well as the varying social health needs of different individuals. This review aims to explore the relationship between urban green space and social health of individuals, which is defined as how one gets along with other people, how other people react to him/her, and how one reacts with the social institutions and societal mores. According to the PRISMA extension for Scoping Review (PRISMA-ScR) guidelines, 60 articles published between 1998 and 2022 met the inclusion criteria were reviewed. The results demonstrated that different types of urban green space significantly influenced the social health of individuals. The dimensions of social health of individuals based on urban green space could be divided into four groups: social contacts, social relations, social support and social connections, which were mainly affected by the physical characteristics, perceptions, and usages of urban green space to varying degrees. Socio-demographic characteristics could moderate the strength and orientation of the relationship between urban green space and social health of individuals. As the first scoping review to analyze the relationship between urban green space and individual social health, this study not only provides evidence for the social health benefits of urban green space, but also offers information and tools for future research and policymakers.