Ecosystem-scale spatial heterogeneity of stable isotopes of soil nitrogen in African savannas

Soil ¹⁵N is a natural tracer of nitrogen (N) cycling. Its spatial distribution is a good indicator of processes that are critical to N cycling and of their controlling factors integrated both in time and space. The spatial distribution of soil δ¹⁵N and its underlying drivers at sub-kilometer scales are rarely investigated. This study utilizes two sites (dry vs. wet) from a megatransect in southern Africa encompassing locations with similar soil substrate but different rainfall and vegetation, to explore the effects of soil moisture and vegetation distribution on ecosystem-scale patterns of soil δ¹⁵N. A 300-m long transect was set up at each site and surface soil samples were randomly collected for analyses of δ¹⁵N, %N and nitrate content. At each soil sampling location the presence of grasses, woody plants, Acacia species (potential N fixer) as well as soil moisture levels were recorded. A spatial pattern of soil δ¹⁵N existed at the dry site, but not at the wet site. Woody cover distribution determined the soil δ¹⁵N spatial pattern at ecosystem-scale; however, the two Acacia species did not contribute to the spatial pattern of soil δ¹⁵N. Grass cover was negatively correlated with soil δ¹⁵N at both sites owing to the lower foliar δ¹⁵N values of grasses. Soil moisture did not play a role in the spatial pattern of soil δ¹⁵N at either site. These results suggest that vegetation distribution, directly, and water availability, indirectly, affect the spatial patterns of soil δ¹⁵N through their effects on woody plant and grass distributions.     

基于深层卷积神经网络的初生仔猪目标实时检测方法

针对初生仔猪目标较小、分娩栏内光线变化复杂、仔猪粘连和硬性遮挡现象较为严重等问题,提出一种基于深层卷积神经网络的初生仔猪目标识别方法。将分类和定位合并为一个任务,以整幅图像为兴趣域,利用特征金字塔网络(Feature pyramid network,FPN)算法定位识别仔猪目标;对比了不同通道数数据集以及不同迭代次数对模型效果的影响;该方法支持图像批量处理、视频与监控录像的实时检测和检测结果多样化储存。实验结果表明:在数据集总量相同时,同时包含夜间单通道和白天3通道的数据集,在迭代20 000次时接近模型最优值。模型在验证集和测试集上的精确率分别为95. 76%和93. 84%,召回率分别为95. 47%和94. 88%,对分辨率为500像素×375像素的图像检测速度为53. 19 f/s,对清晰度为720 P的视频检测速度为22 f/s,可满足实时检测的要求,对全天候多干扰场景表现出良好的泛化能力。     

Winter rain and summer ozone: a predictive relationship

Insights from dendrochronology have provided a new seasonal predictor for air pollution meteorology. In the San Francisco Bay Area summer ozone excesses over the federal ozone standard are correlated (correlation coefficient r = .87) with precipitation for the two preceding winters, a factor related to tree-ring width in a precipitation-stressed climate. The hypothesis that reactive hydrocarbon emissions from vegetative biomass affects these ozone excesses was supported by a similar correlation between summer hydrocarbon average maximums and the two-winter precipitation factor, reaching r = .88 at suburban stations. A weak tendency for hot summers to follow wet winters (in 16 years of California data) explains only a minor part of the ozone-rain relationship in multiple correlations.