Effect of mulch on initial coverage of four groundcover species for low input landscaping in a Mediterranean climate

Groundcovers play a very important role in the creation of urban green spaces. They provide many agronomic and environmental benefits while simplifying the urban landscape management.In the areas with a Mediterranean-type climate, the development of low-input landscapes, with the ability to maintain their functions and aesthetical appeal, is pivotal. The aim of this study was to identify groundcover species that could be suited to the local environmental conditions and to evaluate the effect of different mulching systems on plant growth and weed suppression.Digital image analysis was used to measure the green cover percentage of four species (Hedera helix L., Hypericum calycinum L., Rosmarinus officinalis L., Vinca minor L.) grown on two different types of mulch (EcoCover^® and wood-chips) and on bare soil. Changes in plant coverage, NDVI (Normalized Difference Vegetation Index) and weed biomass were recorded over a two year period.The prostrate R. officinalis established rapidly and reached more than 80% of coverage within 3 months after planting. Also, it was not affected by weed competition. Non-native H. calycinum also exhibited a quick spread, but it was unsuitable for low-input Mediterranean landscapes because it died during late summer from fungal disease. H. helix and V. minor were also adversely affected by pathogens and, due to their slow initial growth and low percent cover in the second year, they did not control weeds during the study. With the exception of R. officinalis, mulching materials did not significantly improve the establishment and cover ability of the tested species, but had significant effects on weed suppression. Digital image analysis and spectral reflectance proved to be reliable and objective techniques to assess groundcover performance.     

中国制造业劳动力成本上升的正负效应分析

作为物质“形象”的第一层面被人们所感知的“物象”是不依赖于人之意识的客观存在。基于对传统村落人文物象的实证研究，认为其能够体现系统的传统造物文化思想，将其概括为“规则、参照、功能与技术、核心”四个层面，并从中揭示了“天、地、人、物”之间的关系，进一步阐释了造物文化思想对传统村落人文物象具有的现代启示。     

K步稳定的鞋印花纹图像自动聚类

目的 鞋印是刑事侦查的重要物证之一,如何对积累的大量鞋印花纹图像进行自动归类管理是刑事技术迫切需要解决的问题之一。与其他类图像不同,鞋印花纹图像具有种类多但数目未知、同类花纹分布不均匀且同类花纹数目少的特点。基于鞋印花纹图像的这些特点,用目前典型的聚类算法对鞋印花纹图像集进行聚类,并不能取得很好的效果。在对鞋印花纹图像进行分析的基础上,提出一种K步稳定的鞋印花纹图像自动聚类算法。方法 对已标记的鞋印花纹图像进行统计发现,各类鞋印花纹之间在特征空间上存在互不相交的区域(本文称为隔离带)。算法的核心思想是寻找各类鞋印花纹之间的隔离带,来将各类分开。过程为:以单调递增或递减的方式调整特征空间中判定两点为一类的阈值,得到数据集的多次划分;若在连续K次划分的过程中,某一类的成员不发生变化,则说明这K次调整是在隔离带中进行的,即聚出一类,并从数据集中删除已标记的数据;选择下一个阈值对剩余的数据集进行划分,输出K步不变的类;依此类推,直到剩余数据集为空,聚类完成。结果 在两类公开测试数据集和实际鞋印花纹数据集上进行实验,本文算法的主要性能指标都超过典型算法,其中在包含5792枚实际鞋印花纹数据集上的聚类准确率和F-Measure值分别达到了99.68%和95.99%。结论 针对鞋印花纹图像特点,提出了一种通过寻找各类之间的隔离带进行自动聚类的算法,并在实际应用中取得了很好的效果。且算法性能受参数的变化以及类的形状影响较小。本文算法同样适用于具有类似特点的其他数据集的自动聚类。     

梯形明渠水力学特征水深的解析计算式研究

通过数学理论分析,推出了梯形明渠的临界水深和正常水深高精度解析计算式,并与现有文献相关计算式进行了对比分析。结果表明,新推出的临界水深计算式相对误差小于0.3%;当梯形明渠坡比0.1~7时,新推出的正常水深初值计解析算公式相对误差一般小于3%;另外,给出了收敛速度更快的正常水深迭计算公式,且在m1时正常水深计算公式在初值迭代一次后其误差均小于0.5%。新推出的梯形明渠临界水深和正常水深解析计算式方便简捷、精度可靠。     

《唐物语》对朱买臣故事的摄取与创新

《唐物语》第十九段对《汉书·朱买臣传》中朱买臣故事进行改编，因受众群体、文化差异、民族向性的不同，在改编过程中必然产生对母国文化信息的过滤与人物形象变异。以朱买臣故事东渐日本为线索，运用比较文学流传学与变异学，探究该文学作品对朱买臣故事的摄取与创新、朱买臣故事日本化的实现条件等，以期增进文学文化交流中的互读互解，避免因文学流变中信息过滤增删、形象失落变形而产生的民族心理与意识形态的误读。     

基于卷积神经网络马铃薯叶片病害识别和病斑检测*

针对自然环境下马铃薯叶片病害识别率低和晚疫病斑定位难的问题,基于大田环境中采集的马铃薯叶片图像,首先对马铃薯叶片病害进行识别,对比AlexNet、VGG16、InceptionV3、ResNet50、MobileNet五种神经网络模型,结果表明InceptionV3模型的识别效果准确率最高,可达98.00%。其次对马铃薯叶片的晚疫病斑进行检测,提出一种改进型的CenterNet-SPP模型,该模型通过特征提取网络获取对象的中心点,再通过中心点回归获得中心点偏移量、目标大小等图像信息,训练后的模型在验证集下的mAP可达90.03%,以F₁为评价值分析对比其它目标检测模型,CenterNet-SPP模型的效果最好,准确率为94.93%,召回率为90.34%,F₁值为92.58%,平均检测一张图像耗时0.10 s。为自然环境下马铃薯叶片病害识别和检测提供较为全面的深度学习算法和模型研究基础。     

Seismic behavior of reinforced-concrete and steel reinforced concrete ultra short columns

进行了4个钢筋混凝土(RC)超短柱、4个型钢混凝土(SRC)超短柱和6个RC分体柱的滞回性能试验研究.8个超短柱的剪跨比为1;6个分体柱的剪跨比为2.4.试验结果表明,RC超短柱的变形能力不满足现行抗震规范要求.不设剪力连接件的SRC超短柱的破坏模式为黏结破坏,与RC超短柱相比,其抗剪承载力和变形能力并无明显提高;设置剪力连接件的SRC超短柱的破坏模式为斜压破坏,其抗剪承载力和变形能力显著提高,但构件的变形能力仍然难以满足现行抗震规范要求.试验结果还表明,在RC超短柱中开缝,形成剪跨比大于2的RC分体柱,可有效提高柱子的延性和变形能力.对于RC分体柱,随轴压比的提高,构件的抗弯承载力提高,但延性和变形能力下降;当设计轴压比大于0.7时,RC分体柱的变形能力不满足现行抗震规范要求.根据试验结果,提出了SRC超短柱的剪力连接件设计方法和RC分体柱的设计建议.     

保温型刚性护面渠道设计

在刚性面层下设置一道性能良好的保温层来防止基土冻结,可以从根本上解决冻胀破坏的问题。这种带保温层的刚性护面渠道称为保温型刚性护面渠道,根据热工原理,提出了保温型刚性护面渠道横断面设计在构造方面的要求,进一步推导出计算保温层厚度的有关公式,并给出算例,还指出了需要进一步研究和探讨的问题。     

Object‐Based Mapping of Karst Rocky Desertification using a Support Vector Machine

Accurate and cost‐effective mapping of karst rocky desertification (KRD) is still a challenge at the regional and national scale. Visual interpretation has been utilised in the majority of studies, while an automated method based on pixel data has been investigated repeatedly. An object‐based method coupling with support vector machine (SVM) was developed and tested using Enhanced Thematic Mapper Plus (ETM+) images from three selected counties (Liujiang, Changshun and Zhenyuan) with different karst landscapes in SW China. The method supports a strategy of defining a mapping unit. It combined ETM+ images and ancillary data including elevation, slope and Normalized Difference Vegetation Index images. A sequence of scale parameters estimation, image segmentation, training data sampling, SVM parameters tuning and object classification was performed to achieve the mapping. A quantitative and semi‐automated approach was used to estimate scale parameters for segmenting an object at an optimal scale. We calculated the sum of area‐weighted standard deviation (WS), rate of change for WS, local variance (LV) and rate of change for LV at each scale level, and the threshold of the aforementioned index that indicated the optimal segment level and merge level. The KRD classification results had overall accuracies of 85·50, 84·00 and 84·86 per cent for Liujiang, Changshun and Zhenyuan, respectively, and kappa coefficients are up to 0·8062, 0·7917 and 0·8083, respectively. This approach mapped six classes of KRD and offered a visually appealing presentation. Moreover, it proposed a conceptual and size‐variable object from the classification standard of KRD. The results demonstrate that the application of our method provides an efficient approach for the mapping of KRD. Copyright © 2012 John Wiley & Sons, Ltd.     

基于高斯自适应拟合的苹果目标分割方法研究

苹果目标的准确识别是苹果机械化采摘需要解决的关键问题之一。为此,基于YUV颜色空间模型,提出了一种结合色差分量与高斯自适应拟合算法的苹果目标分割方法。该方法采用首先将苹果目标由RGB颜色空间转换至YUV颜色空间,并利用色差分量V建立果实与背景分割的高斯分布拟合模型,根据拟合结果自动获取分割阈值,以实现自然场景下苹果目标的准确分割。为了验证文中算法的有效性,利用多幅图像进行了试验并与Otsu自适应阈值分割算法进行了比较。试验结果表明,采用文中算法得到的苹果果实的平均检出率达87.08%,识别率领先Otsu算法9.91%。因此,对于着色度较为均匀的苹果目标,采用高斯自适应拟合方法可以有效提高其识别率。