黑龙江哈尔滨白渔泡国家湿地公园沼泽、林地和农田土壤物理、化学和生物性质差异

为了揭示黑龙江哈尔滨白渔泡国家湿地公园沼泽、林地和农田土壤物理、化学和生物性质的差异,于2018年7月25日~8月2日,在湿地公园内,在天然芦苇(Phragmites australis)沼泽、林地、旱田和水田中设置采样地,采集不同深度(0~10 cm、10~20 cm和20~30 cm)的土壤样品,测定土壤样品的物理、化学和生物指标。研究结果表明,白渔泡国家湿地公园不同采样地土壤指标存在差异;与天然芦苇沼泽土壤相比,其它采样地土壤的含水量明显偏低,土壤全氮、全磷、碱解氮和有机质含量都明显偏小,水田土壤速效磷含量偏大;天然芦苇沼泽土壤脲酶、硝酸还原酶、纤维素酶、蛋白酶和β-葡萄糖苷酶活性都高于林地和农田土壤,水田0~10 cm和10~20 cm深度土壤的硝酸还原酶活性显著高于旱田和林地;与天然芦苇沼泽土壤相比,旱田土壤小于0.25 mm的小团聚体含量偏大,而其它采样地土壤的各粒级团聚体的比例变化较小,水田土壤团聚体平均重量直径比天然芦苇沼泽和旱田土壤低。     

A machine learning approach for predicting computational intensity and domain decomposition in parallel geoprocessing

ABSTRACT

High performance computing is required for fast geoprocessing of geospatial big data. Using spatial domains to represent computational intensity (CIT) and domain decomposition for parallelism are prominent strategies when designing parallel geoprocessing applications. Traditional domain decomposition is limited in evaluating the computational intensity, which often results in load imbalance and poor parallel performance. From the data science perspective, machine learning from Artificial Intelligence (AI) shows promise for better CIT evaluation. This paper proposes a machine learning approach for predicting computational intensity, followed by an optimized domain decomposition, which divides the spatial domain into balanced subdivisions based on the predicted CIT to achieve better parallel performance. The approach provides a reference framework on how various machine learning methods including feature selection and model training can be used in predicting computational intensity and optimizing parallel geoprocessing against different cases. Some comparative experiments between the approach and traditional methods were performed using the two cases, DEM generation from point clouds and spatial intersection on vector data. The results not only demonstrate the advantage of the approach, but also provide hints on how traditional GIS computation can be improved by the AI machine learning.     

Mobile Agent系统结构及其关键技术

Mobile Agent技术是一门新兴的技术,具有移动性、智能性和异步计算的特点,已成为计算机网络和分布式系统最具活力的发展方向.现对Mobile Agent的概念、系统结构及其关键技术进行了分析和探讨.     

西秦岭温泉花岗岩体岩石学特征及岩浆混合标志

温泉花岗岩体由酸性端元的寄主岩石和暗色微细粒镁铁质包体群及基性岩墙群组成。无岩浆混合作用或岩浆混合作用较弱区段，寄主岩石以似斑状二长花岗岩为主．显示正常的花岗岩结构构造岩浆混合作用强烈区段。岩石的异常结构构造十分发育．矿物之间自形程度差异显著．常见包晶反应、包含结构、交代边、熔蚀边、交代蚕食的港湾状结构构造及交代缝合线、矿物镶边、斜长石异常环带和矿物残留等，多见指示岩浆混合的标志性矿物针状磷灰石。暗色微粒包体中多见寄主二长花岗岩中的捕掳晶。包体的形态、结构构造以及与寄主岩石强烈地成分交换等均是岩浆混合作用的标志。     

DISASTER ANALYSIS AND COUNTERMEASURES OF LAND SUBSIDENCE CAUSED BY COAL CUTTING IN CHINA

In recent years, many coal-producing countries have paid great attention to the land subsidence causedby coal cutting. In China, because of the dense population in coalfield areas, the land subsidence hazard is more seri-ous. After a brief analysis on the mechanism of land subsidence, this paper gives a comprehensive and systematical ac-count on all kinds of hazards caused by the land subsidence in China. The study shows that land subsidence has endan-gered land, buildings, traffic and communication lines, dykes and dams. It also causes damage to ecological and socialenvironment. In order to lessen the hazard of land subsidence, preventive measures should be taken to reduce the col-lapse amount, such as extraction with stowing, banded mining system, succession and coordination mining system, orhigh-pressure mudflow between rock strata. Measures of reinforcing or moving certain buildings should also be taken toreduce the destructive degree. In order to harness the subsidence land and bring them under control for fanning, mea-sures should be taken such as filling with spoil or fine breeze, excavating the deeper and covering the shallower land.     

无规取向、无吸收非球形粒子光散射的T-矩阵方法

较详细地研究了无规取向、无吸收椭球体粒子的T－矩阵收敛问题。首先，简要概括了nmax的3种收敛方案和它们的基本特性。然后，应用1993年提出的数学收敛方法（M－方法）和1998年提出和物理收敛方法（P－方法）研究收敛问题。结果表明椭球粒子收敛精度对粒子的尺度参数，纵横比以及椭球体的种类（例如，长／扁椭球）有很强的依赖性。当粒子的尺度参数不太大时，甚至在极端纵横比的条件下，P－收敛方案优于M－收敛方案。     

关于非球形粒子光散射的T-矩阵数值计算方法的研究

首先介绍了无规取向轴对称非球形粒子光散射的T－矩阵数值方法。然后，进一步研究了T－矩阵计算的收敛问题，提出了一种新的关于无规取向无吸收非线形散射物体的物理收敛方法，我们研究了椭球粒子的收敛问题，结果表明我们的数值方法和T－矩阵计算程序是有效的， 收敛精度与粒子的尺度和形状有很强的依赖性，在一定的条件下，我们的物理收敛速度优于NASA Mishchenko的数学收敛速序。     

新疆塔中南坡奥陶系的地层缺失和沉积相变化

按照奥陶系内部6个组沉积的时间片段拟定塔中南坡不同区块存在不同程度的缺失。部分关键层段的牙形石和几丁虫组合特征证明一间房组和恰尔巴克组在塔中部分井区是存在的,但恰尔巴克组的分布范围最狭窄。总体上,塔中南坡隆起区地层缺失较多,古城墟隆起基本完整。据缺失状况和岩相展布,显示塔中南坡的沉积单元具有由东往西迁移的特征,且各时段迁移的距离与速度存在较大差异。     

纺织品中偶氮染料还原液内芳香胺的电喷雾电离质谱分析

在优化的条件下,用电喷雾电离质谱(ESI-MS)检测了多种纺织品中由偶氮染料还原获得的芳香胺。当毛细管温度为100℃、鞘气流速为25单位、进样流速为10μL/min、电压为5kV时,信号的总离子流强度高且较稳定。实验分析了19种致癌芳香胺标准溶液的二级质谱,获得了相应物质的特征碎片离子;在此基础上,建立了基于ESI-MS对毛巾、棉布等样品中芳香胺进行快速定性分析的方法,具有灵敏度高、特异性好、检测速度快等优点。     

数字矿床模型研究与系统实现

数字矿床模型(DDM)是传统矿床模型的数字化.本文论述了数字矿床模型的概念,提出了其主要组成结构和建模关键技术.在吸收国内外地质领域专家系统建立的经验基础上,将人工智能技术与地理信息技术(WebGIS)相结合,通过构建矿床模型知识库,建立数字矿床模型推理网络结构,并以斑岩型铜矿为例论述了建立数字矿床模型的系统过程.本文是"十一五"期间国家高技术研究发展(863)计划的阶段成果.