沉管隧道对接端深基坑止水墙水下爆破对周围环境的影响研究

基于爆破动态效应的现场监测数据,研究了襄阳汉江鱼梁州沉管隧道对接端深基坑止水墙水下爆破对邻近建(构)筑物和周围水域的不利影响.结果 表明:爆源附近方圆50 rn范围内属于爆破振动近场区、方圆50～150 m范围内属于爆破振动过渡区、方圆150 m范围外属于爆破振动远场区;爆破振动近场区内薄壁支护结构爆破振动速度呈现高峰...     

发挥供销系统网络优势 延伸餐厨废弃物资源利用的思考

对处于国家中心城市地位的广州来说,随着城市人口、经济社会不断发展,尤其随着餐饮行业的蓬勃发展,日产餐厨废弃物近1万t,如不妥善合理处理,将对城市环境和城市管理造成极大压力。无论是焚烧、填埋、还是生化处理模式,餐厨废弃物资源化处理后产品的出路都没有得到有效解决。广州供销社系统在产业链后端延伸方面具有我国任何社会组织无法比拟的优势,是解决餐厨废弃物资源化产品深加工出路的重要力量。除了发挥供销社网络优势外,仍需要政府在政策、土地、资金等方面的大力支持,共同推进,才能实现餐厨废弃物"由餐桌中来,加工调制后再到农田中去,生产出来的有机食品再回到餐桌"的真正循环。     

Simulating wildfire patterns using a small-world network model

This paper presents the development and validation results of a weighted small-world network model designed to simulate fire patterns in real heterogeneous landscapes. Fire spread is simulated on a gridded landscape, a mosaic in which each cell represents an area of the land surface. In this model, the interaction between burning and non-burning cells (here, due to flame radiation) may extend well beyond nearest neighbors, and depends on local conditions of wind, topography, and vegetation. An approach based on the coupling of the solid flame model with the Monte Carlo method is used to predict the radiative heat flux from the flame generated by the burning of each combustible cell to its neighbors. The weighting procedure takes into account latency (a combustible cell will only ignite when it has accumulated enough energy along time) and flaming persistence of burning cells. The model is applied to very different fire scenarios: a historical Mediterranean fire that occurred in southeastern France in 2005 and experimental fires conducted in arid savanna fuels in South Africa in 1992. Model results are found to be in agreement with real fire patterns, in terms both of rate of spread, and of the area and shape of the burn. This work also shows that the fractal properties of fire patterns predicted by the model are similar to those observed from satellite images of three other Mediterranean fire scars.     

Social network properties within a fish assemblage invaded by non-native sunbleak Leucaspius delineatus

Network theory is commonly used to reveal social interactions and the organisation of interconnected nodes—but has not yet been applied to animal invasions. Non-native species invasions are now considered one of the foremost threats to natural ecosystems and biodiversity. This is the first attempt to assess social network properties within a freshwater fish assemblage invaded by a non-native fish species. We show that invasive sunbleak Leucaspius delineatus is socially more strongly interconnected with native species than the native species with each other. The social networks also reveal characteristics of a ‘small world’ such as low clustering coefficients C and short path lengths L. The findings may indicate potential traits of successful invaders and the implication for the spread of pathogens between individuals within a group of animals that contain a non-native invasive species. The success of establishment and subsequent invasion may be highlighted not only by the capacity of the new species to adapt to the new environment, but also in its capacity to penetrate the social circle of the native community.     

基于RBF神经网络的鄱阳湖表层水体总悬浮颗粒物浓度遥感反演

为进一步提高湖泊总悬浮颗粒物浓度遥感反演的准确性,引进适应复杂非线性映射的RBF神经网络模型,以鄱阳湖通江湖体为例进行了实证分析,根据实测水体悬浮颗粒物浓度和MODIS遥感数据,对遥感数据进行预处理,建立了RBF神经网络悬浮颗粒物浓度反演模型,神经元个数为8个,误差性能目标值为0.001,对悬浮颗粒物浓度进行反演。研究结果表明,验证样本相关系数R2=0.956 8,均方根误差RMSE=0.54。利用神经网络模型反演水悬浮颗粒物浓度是有效的,其反演结果优于非线性回归模型的结果。     

Prediction of high-density polyethylene pyrolysis using kinetic parameters based on thermogravimetric and artificial neural networks

● Reducting the sampling frequency can enhance the modelling process. ● The pyrolysis of HDPE was investigated at three different heating rates. ● The average E_a and k₀ were calculated by Friedman, KAS, FWO, and CR methods. ● ANN was employed to predict the HDPE weight loss with the optimal MSE and R². Pyrolysis is considered an attractive option and a promising way to dispose waste plastics. The thermogravimetric experiments of high-density polyethylene (HDPE) were conducted from 105 °C to 900 °C at different heating rates (10 °C/min, 20 °C/min, and 30 °C/min) to investigate their thermal pyrolysis behavior. We investigated four methods including three model-free methods and one model-fitting method to estimate dynamic parameters. Additionally, an artificial neural network model was developed by providing the heating rates and temperatures to predict the weight loss (wt.%) of HDPE, and optimized via assessing mean squared error and determination coefficient on the test set. The optimal MSE (2.6297 × 10⁻²) and R² value (R² > 0.999) were obtained. Activation energy and pre-exponential factor obtained from four different models achieves the acceptable value between experimental and predicted results. The relative error of the model increased from 2.4 % to 6.8 % when the sampling frequency changed from 50 s to 60 s, but showed no significant difference when the sampling frequency was below 50 s. This result provides a promising approach to simplify the further modelling work and to reduce the required data storage space. This study revealed the possibility of simulating the HDPE pyrolysis process via machine learning with no significant accuracy loss of the kinetic parameters. It is hoped that this work could potentially benefit to the development of pyrolysis process modelling of HDPE and the other plastics.     

《昆明-蒙特利尔全球生物多样性框架》下的生物多样性监测

生物多样性监测是执行和评估《昆明-蒙特利尔全球生物多样性保护框架》（简称《昆-蒙框架》）长期目标和行动指标所需要的关键数据的主要来源。简要介绍了《昆-蒙框架》下的全球生物多样性观测网络（The global biodiversity observation network,GEO BON）概况、中国已有的生物多样性监测/观测网络,构建全球生物多样性观测系统（A global biodiversity observation system,GBiOS）的目的和核心监测指标,分析了生物多样性监测网络建设的一般步骤和GEO BON建议的4种生物多样性监测新方法。提出,地理空间单元与生物类群的全覆盖监测是建设生物多样性监测网络的主要目标,传统监测方法和新监测技术的互补是当前生物多样性监测的重点工作内容。     

海南省省控土壤环境监测基础点位布设思路与方法

为满足新时期土壤污染防治管理要求,掌握海南省土壤环境质量状况及变化趋势,海南省亟需加强省控土壤环境监测基础点位布设,完善土壤环境监测网。通过梳理海南省土壤环境监测工作基础和监测网络构建情况,形成了省控基础点位布设思路、原则和方法,并应用GIS技术完成点位布设。海南省省控土壤环境监测基础点位基本覆盖了海南省农用地主要土壤类型和成土母岩,监测结果可以反映海南省农用地土壤环境质量整体状况。     

深度学习在裸地扬尘源监测中的应用研究

裸地是扬尘的重要来源,施工建设过程中形成的裸地极易在大风天气作用下造成扬尘污染。因此,快速、有效地定位裸地位置,并确认其管控措施落实情况,对于开展裸地扬尘源监管具有重要意义。基于高分辨率遥感监测数据,结合人工解译裸地扬尘源数据集,以北京市大兴区为例,利用深度学习方法对裸地和防尘网覆盖裸地进行分类识别。同时,利用颜色匹配法对大兴区防尘网覆盖裸地进行识别,横向评估深度学习方法的识别精度。结果显示：深度学习方法对防尘网覆盖裸地的识别精度达97%,对裸地的识别精度达61%;颜色匹配法对防尘网覆盖裸地的识别精度达85%。防尘网覆盖裸地的颜色特征鲜明,深度学习方法和颜色匹配法对防尘网覆盖裸地的识别精度都在85%以上。深度学习方法对于面积大于2 000 m²的图斑有着较好的识别精度。深度学习方法可以提高裸地遥感解译的效率,实现规范化图像识别,可以作为人工判读的辅助手段。在实际应用中,可通过进一步积累样本来增强模型性能。深度学习方法适用于裸地扬尘源线索快速发现、工地防尘网措施落实情况快速检测等场景。     

提高人工神经网络洪水峰值预报精度的研究

以动量项与学习率自适应调整相结合的改进BP算法作为人工神经网络计算方法，阐述了洪水预报网络模型的构造和数据处理，以及转移函数对洪水预报精度的影响，提出了合理的洪水预报网络模型的设计方案。