电气石和水之间的氢同位素分馏

作者对电气-水体系氢同位素平衡分馏和动力学分馏和动力学分馏开展了实验研究，丰富了羟基矿物氢同位素分馏资料。本文对该研究的实验技术、分析方法作了介绍，并对实验结果进行讨论与国外已有的该方面的资料作了对比。在800-650℃时电气石和水之间氢同位素平衡分馏系数与温度间线性关系为103lna电气石-水=-28.24（106/T2）+2.60；交换速率常数与温度间关系为lnk2=-0.19-6.70（103/T）     

基于网络药理学预测青皮治疗抑郁症的作用机制

目的：运用网络药理学研究青皮治疗抑郁症的作用机制。方法：采用中药系统药理学分析平台（TCMSP）检索青皮的有效成分及其作用靶点;采用OMIM、GeneCards数据库检索抑郁症的疾病靶点;应用R语言等计算软件获取青皮有效成分的作用靶点和抑郁症疾病靶点的交集基因;运用Perl软件与Cytoscape 3.7.0软件联合构建青皮-有效成分-靶点-抑郁症调控网络;使用STRING构建PPI网络;应用DAVID数据分析软件和Metascape数据库,对所筛选出的“核心基因”进行基因本体（GO）功能和京都基因与基因组百科全书（KEGG）通路富集分析,最终获得预测结果。结果：通过以上方法,获得青皮的5个有效成分,有效成分的基因靶点与抑郁症基因靶点的交集基因有30个。通过对青皮-有效成分-靶点-抑郁症调控网络的可视化分析,发现有效成分中的川陈皮素（nobiletin）所对应的交集基因最多。运用PPI网络预测到雌激素受体1（ESR1）、胱天蛋白酶3（CASP3）、过氧化物酶体增生激活受体γ（PPARG）等25个基因是青皮治疗抑郁症所干预的重要靶点。GO功能富集分析和KEGG富集分析结果显示,青皮有效成分在分子层面对抑郁症基因的干预有较好的作用。结论：青皮治疗抑郁症的机制主要是作用于ESR1、CASP3、PPARG等25个基因,并在分子层面起到改变抑郁症基因的转录翻译等作用。     

Influence Mechanism of Mine Pressure on Coal Seam Gas Emission During Mining

Geotechnical and Geological Engineering - Gas is one of the main factors causing coal mine accidents in China. Understanding the mechanism of the influence of mine pressure on coal seam gas...     

基于证据信度模型的致矿地质异常信息提取与集成

地质异常信息是找矿靶区圈定的重要依据,适当的异常提取方法和决策模型能从多元地学数据中获得准确可靠的致矿地质异常信息。以祁连山玉石沟-川刺沟铜铅锌矿区为研究区,应用证据信度模型评价岩性、断裂、遥感蚀变、化探元素的空间信息：首先由各致矿因子与矿点分布的关系计算证据层的可信度、怀疑度、不确定性3项指标,并采用分形、主成分分析等非线性提取方法剔除假异常;然后通过Dempster法则集成得到成矿综合信度图。结果显示,96%的矿点位于信度异常值区域（0.17~0.60）,且异常分布与实地勘查信息一致。模型增大证据因子输入参数的可靠性,减弱了证据因子间独立性假设的影响,有效地提取了单一因子的致矿地质异常及综合异常。     

河南鹤壁地区太原组有孔虫动物群及石炭-二叠系界限的探讨

通过对华北地台河南鹤壁地区太原组有孔虫动物群的系统研究,共鉴定出有孔虫化石18属33种,建立了一个区域性有孔虫动物组合带：Bradyina—Palaeotextularia组合带,根据有孔虫化石的属种类型、数量,应用数理统计方法得出组合带有孔虫化石在地层中的比例变化情况,并根据化石组合带与国内其它地区的有孔虫动物群进行对比研究,认为鹤壁剖面有孔虫动物群Bradyina—Palaeotextularia组合带归于晚石炭世晚期的早期（C2^2-）,进而对研究区石炭-二叠系界线的划分进行探讨,界限应该放在第16层（泥岩）和第15层（灰岩）之间。     

Simulating urban land use change by integrating a convolutional neural network with vector-based cellular automata

ABSTRACT

Vector-based cellular automata (VCA) models have been applied in land use change simulations at fine scales. However, the neighborhood effects of the driving factors are rarely considered in the exploration of the transition suitability of cells, leading to lower simulation accuracy. This study proposes a convolutional neural network (CNN)-VCA model that adopts the CNN to extract the high-level features of the driving factors within a neighborhood of an irregularly shaped cell and discover the relationships between multiple land use changes and driving factors at the neighborhood level. The proposed model was applied to simulate urban land use changes in Shenzhen, China. Compared with several VCA models using other machine learning methods, the proposed CNN-VCA model obtained the highest simulation accuracy (figure-of-merit = 0.361). The results indicated that the CNN-VCA model can effectively uncover the neighborhood effects of multiple driving factors on the developmental potential of land parcels and obtain more details on the morphological characteristics of land parcels. Moreover, the land use patterns of 2020 and 2025 under an ecological control strategy were simulated to provide decision support for urban planning.     

A building label placement method for 3D visualizations based on candidate label evaluation and selection

Adding building labels greatly improves the recognizability of buildings and the readability of three-dimensional (3D) city scenes. However, building label placement is much more complex in 3D scenes than in two-dimensional (2D) maps. The annotation effect is influenced by the attributes of the 3D label, building visibility, and the spatial relationship between the building and viewpoint. In this context, automatically generating building labels for 3D scenes during interactions requires highly complex computations. By contrast, evaluating candidate labels and then selecting the suitable label for each building can be effectively implemented. This paper introduces an approach for labeling buildings in 3D scenes based on evaluations of label candidates. The proposed method predefines a candidate label set for each building. These candidates are then evaluated in terms of their attributes and the relationship between the labels and viewpoint at runtime. The best candidate label, or a situational alternative for each building, is then placed in order of comprehensive label priority to avoid annotation conflicts. A series of experiments demonstrate that this method effectively enhances the correlation of labels and buildings, improves interactive efficiency, and realizes a viable global label layout.     

上海邻近海域风暴潮数据同化与特征分析

风暴潮是一种复杂的对众多因素敏感又备受关注的海洋现象.本文基于协方差局地化的集合卡尔曼滤波方法(EnKF),选择201810号台风"安比"登陆上海的风暴潮过程,首次将海洋站和FVCOM数值模拟的不同来源、不同误差信息、不同时空分辨率的风暴潮进行数据同化融合,获得了逐72 h的上海海域风暴潮的最优解,进行了同化结果评估验...     

高代价的中国草地生态政策需要根本性的策略转变（英文）

过度放牧加剧了中国草地的退化程度,破坏了草地生态系统重要的生态和经济价值。“草畜平衡”政策在中国全面实施已超过十年,旨在平衡饲草产量与牲畜消耗之间的关系。本文梳理了政策评价的统计资料和相关文献,指出以草畜平衡为主导的生态补偿政策不仅对草地的恢复效果存在很大的不确定性,而且阻碍了部分超载地区的减畜,甚至还对牧民生计产生负面影响。为解决当前困境,本文基于生态学理论提出了从控制牲畜数量到维持和改善草地健康的根本性策略性转变的理论框架和实现途径,以期推动我国草地的可持续发展。研究结果表明,“草畜平衡”政策失灵的根本原因在于其干预了牧民自主使用承包草场的行为,而且基础理论和方法也存在缺陷。相比而言,以草地生态系统健康为衡量标准实施奖惩政策,不仅能够激发牧民自主经营草原的积极性,而且比“草畜平衡”政策更具科学性和可行性。