Height prediction of water flowing fractured zones based on BP artificial neural network

Factures caused by deformation and destruction of bedrocks over coal seams can easily lead to water flooding(inrush)in mines,a threat to safety production.Fractures with high hydraulic conductivity are good watercourses as well as passages for inrush in mines and tunnels.An accurate height prediction of water flowing fractured zones is a key issue in today's mine water prevention and control.The theory of leveraging BP artificial neural network in height prediction of water flowing fractured zones is analysed and applied in Qianjiaying Mine as an example in this paper.Per the comparison with traditional calculation results,the BP artificial neural network better reflects the geological conditions of the research mine areas and produces more objective,accurate and reasonable results,which can be applied to predict the height of water flowing fractured zones.     

基于神经网络的印度洋长鳍金枪鱼(Thunnus alalunga)时空分布与海洋环境关系研究

长鳍金枪鱼(Thunnusalalunga)是主要的经济性金枪鱼鱼种之一,其空间分布与环境因子存在着密切联系。利用2012—2019年印度洋长鳍金枪鱼生产数据和海洋环境数据,包括海表面温度(sea surface temperature, SST)、叶绿素浓度(chlorophyll a, chl a)和海表面盐度(sea surface salinity, SSS)构建印度洋长鳍金枪鱼时空分布神经网络模型。以空间(经度,纬度)、环境因子(SST, chl a, SSS)为解释变量,局部渔获量为因变量,变化隐含层节点数,构建了18个BP空间分布模型,并采用10×10交叉验证模型稳定性,以均方误差(meansquareerror,MSE)、平均相对方差(averagerelativevariance,ARV)以及拟合优度(R~2)作为不同模型精度与稳定性的评判标准,最终选取5-18-1(隐含层节点18)模型为最佳模型,其平均MSE值为0.02232,平均ARV值为0.511。利用最优模型预测结果与同期实际捕捞产量进行叠加对比发现两者具有一致性。环境因子敏感性分析表明海表温度显著影响印度洋长鳍金枪鱼渔场分布,其贡献率达到0.2。印度洋长鳍金枪鱼高精度BP神经网络时空分布模型为其资源的可持续开发与动态管理提供了一种新思路。     

覆盆子-红花药对与男性不育症的治疗相关机制探讨

目的：基于网络药理学探讨覆盆子-红花药对治疗男性不育症的物质基础及作用机制。方法：用TCMSP数据库检索覆盆子、红花,收集其化学成分以及靶点信息;用GeneCards和OMIM数据库筛选男性不育症的疾病靶点;应用韦恩图筛选药物与疾病的共同靶点;应用R软件进行GO富集分析及KEGG分析;应用 Cytoscape 3.7.2 软件构建药物-活性成分-疾病-靶点互作网络。结果：从覆盆子、红花得到了22个活性成分及202个药物靶点。在药物-活性成分-疾病-靶点互作网络中,degree值较高的为槲皮素及木犀草素,表明覆盆子-红花药对治疗男性不育症的主要活性成分可能为槲皮素、木犀草素等。覆盆子-红花药对治疗男性不育症的可能靶点有131个,其中核心靶点为ALB、AKT1、IL-6、VEGFA、JUN等,这些靶点主要与氧化及抗氧化生物等生物学过程相关。覆盆子-红花药对的靶点主要通过P13K/Akt信号通道治疗男性不育症。结论：覆盆子-红花药对通过多靶点、多途径治疗男性不育症。本研究为开展覆盆子-红花药对治疗男性不育症的实验提供了理论基础,为男性不育症的治疗提供了更多的思路。     

基于网络药理学探讨附子-细辛对高血压病的作用机制

目的：基于网络药理学研究附子-细辛对高血压病的作用机制。方法：运用TCMSP数据库挖掘出附子-细辛的有效成分及药物预测靶点,采用5个生物信息数据库检索出高血压病的疾病预测靶标,构建药物靶点-疾病靶标交集,使用Cytoscape生物信息分析软件绘制“成分-靶点-疾病”中药调控网络,基于STRING数据库构建“药物-疾病”相关靶点的蛋白互作网络（PPI）,并对其进行GO和KEGG的生物信息学分析。结果：通过生物利用度和类药性筛选得到附子-细辛的有效成分29个,对应药物靶点1807个,高血压病的疾病靶标8505个,构建的药物靶点-疾病靶标交集80个。与高血压病密切相关的GO富集条目91个,KEGG信号通路123条,主要涉及流体剪切应力与动脉粥样硬化、糖尿病并发症中的AGE-RAGE信号通路、白细胞介素（IL）-17信号通路、丝裂原活化蛋白激酶（MAPK）信号通路、PI3K-Akt信号通路等。结论：附子-细辛药对与高血压病密切相关,能够通过一条或多条通路协同发挥药效作用,考虑其可能是通过干预血液的流体剪切应力影响动脉粥样硬化与心血管重塑再生而实现的。     

基于网络药理学和分子对接探讨左金丸治疗肝癌的作用机制

目的：运用网络药理学和分子对接方法探讨左金丸治疗肝癌的作用机制。方法：从TCMSP和BATMAN-TCM数据库获取左金丸的化合物及其相应靶点,检索GeneCards、OMIM和TTD 3个数据库获得肝癌的相关靶基因,取两者靶基因交集得到左金丸治疗肝癌的预测靶基因;运用Cytoscape 3.7.1软件构建左金丸-化合物-靶点-肝癌和PPI网络图;运用R软件对预测靶基因进行GO和KEGG富集分析;最后运用分子对接技术对关键化合物和靶点进行验证。结果：共获取左金丸35个化合物及173个相应靶点,左金丸与肝癌的共同靶点有103个。PPI结果表明AKT1、MAPK1、TP53、JUN和RELA可能为左金丸治疗肝癌的关键靶点。富集分析示左金丸可能通过乙型肝炎、卡波西肉瘤相关疱疹病毒感染、人巨细胞病毒感染、丙型肝炎、MAPK信号通路、肝癌等信号通路抗肝癌。分子对接结果示槲皮素和黄连素均能与AKT1和MAPK1稳定结合。结论：本研究初步揭示了左金丸通过多成分、多靶点、多通路治疗肝癌的作用机制,为后续左金丸治疗肝癌提供理论参考。     

The information system of the French Peatland Observation Service: Service National d'Observation Tourbières – A valuable tool to assess the impact of global changes on the hydrology and biogeochemistry of temperate peatlands through long term monitoring

Mitigating and adapting to global changes requires a better understanding of the response of the Biosphere to these environmental variations. Human disturbances and their effects act in the long term (decades to centuries) and consequently, a similar time frame is needed to fully understand the hydrological and biogeochemical functioning of a natural system. To this end, the ‘Centre National de la Recherche Scientifique’ (CNRS) promotes and certifies long-term monitoring tools called national observation services or ‘Service National d'Observation’ (SNO) in a large range of hydrological and biogeochemical systems (e.g., cryosphere, catchments, aquifers). The SNO investigating peatlands, the SNO ‘Tourbières’, was certified in 2011 ( https://www.sno-tourbieres.cnrs.fr/ ). Peatlands are mostly found in the high latitudes of the northern hemisphere and French peatlands are located in the southern part of this area. Thus, they are located in environmental conditions that will occur in northern peatlands in coming decades or centuries and can be considered as sentinels. The SNO Tourbières is composed of four peatlands: La Guette (lowland central France), Landemarais (lowland oceanic western France), Frasne (upland continental eastern France) and Bernadouze (upland southern France). Thirty target variables are monitored to study the hydrological and biogeochemical functioning of the sites. They are grouped into four datasets: hydrology, fluvial export of organic matter, greenhouse gas fluxes and meteorology/soil physics. The data from all sites follow a common processing chain from the sensors to the public repository. The raw data are stored on an FTP server. After operator or automatic processing, data are stored in a database, from which a web application extracts the data to make them available ( https://data-snot.cnrs.fr/data-access/ ). Each year at least, an archive of each dataset is stored in Zenodo, with a digital object identifier (DOI) attribution ( https://zenodo.org/communities/sno_tourbieres_data/ ).     

Describing alpine lake influence on stream network temperatures: A statistical modelling approach

Systematic variations in atmospheric heat exchange, surface residence time, and groundwater influx across montane stream networks commonly produce an increasing stream temperature trend with decreasing elevation. However, complex stream temperature profiles that differ from this common longitudinal trend also exist, suggesting that stream temperatures may be influenced by complex interactions among hydrologic and atmospheric processes. Lakes within stream networks form one potential source of temperature profile complexity due to the spatially variable contribution of lake-sourced water to stream flow. We investigated temperature profile complexity in a multi-season stream temperature dataset collected across a montane stream network containing many alpine lakes. This investigation was performed by making comparisons between multiple statistical models that used different combinations of stream and lake characteristics to represent specific hypotheses for the controls on stream temperature. The compared models included a set of models which used a topographically derived estimate of the hydrologic influence of lakes to separate and quantify the effects of stream elevation and lake source-water contributions to longitudinal stream temperature patterns. This source-water mixing model provided a parsimonious explanation for complex stream-network temperature patterns in the summer and autumn, and this approach may be further applicable to other systems where stream temperatures are influenced by multiple water sources. Simpler models that discounted lake effects were more optimal during the winter and spring, suggesting that complex patterns in stream temperature profiles may emerge and subside temporally, across seasons, in response to diversity of water temperatures from different sources.     

MARC: a robust method for multiple-aspect trajectory classification via space,time, and semantic embeddings

ABSTRACT

The increasing popularity of Location-Based Social Networks (LBSNs) and the semantic enrichment of mobility data in several contexts in the last years has led to the generation of large volumes of trajectory data. In contrast to GPS-based trajectories, LBSN and context-aware trajectories are more complex data, having several semantic textual dimensions besides space and time, which may reveal interesting mobility patterns. For instance, people may visit different places or perform different activities depending on the weather conditions. These new semantically rich data, known as multiple-aspect trajectories, pose new challenges in trajectory classification, which is the problem that we address in this paper. Existing methods for trajectory classification cannot deal with the complexity of heterogeneous data dimensions or the sequential aspect that characterizes movement. In this paper we propose MARC, an approach based on attribute embedding and Recurrent Neural Networks (RNNs) for classifying multiple-aspect trajectories, that tackles all trajectory properties: space, time, semantics, and sequence. We highlight that MARC exhibits good performance especially when trajectories are described by several textual/categorical attributes. Experiments performed over four publicly available datasets considering the Trajectory-User Linking (TUL) problem show that MARC outperformed all competitors, with respect to accuracy, precision, recall, and F1-score.     

Pedestrian network generation based on crowdsourced tracking data

ABSTRACT

Pedestrian networks play an important role in various applications, such as pedestrian navigation services and mobility modeling. This paper presents a novel method to extract pedestrian networks from crowdsourced tracking data based on a two-layer framework. This framework includes a walking pattern classification layer and a pedestrian network generation layer. In the first layer, we propose a multi-scale fractal dimension (MFD) algorithm in order to recognize the two different types of walking patterns: walking with a clear destination (WCD) or walking without a clear destination (WOCD). In the second layer, we generate the pedestrian network by combining the pedestrian regions and pedestrian paths. The pedestrian regions are extracted based on a modified connected component analysis (CCA) algorithm from the WOCD traces. We generate the pedestrian paths using a kernel density estimation (KDE)-based point clustering algorithm from the WCD traces. The pedestrian network generation results using two actual crowdsourced datasets show that the proposed method has good performance in both geometrical correctness and topological correctness.