GPS高程转换的神经元网络方法分析

GPS高程转换是GPS高程应用的关键问题之一，试图应用神经元网络方法来进行GPS高程的转换，并在网络的拓扑结构与网络结构的改进等方面作了分析，通过GPS高程转换的二次曲面法和神经元网络法进行了比较试验，结果表明了神经元网络转换GPS高程的可行性和可靠性，对于应用神经网络转换GPS高程具有实际的指导意义。     

基于图论的树状河系结构化绘制模型研究

结合树状河系自身的结构特点和图论的思想，提出了基于图论的河系结构化绘制模型的建立，利用图论的思想描述了河系的结构并建立其自动符号化模型，阐述了主流、流向自动确定和结构化绘制实现的详细算法，并对通用图形数据格式DXF文件记录的河系数据进行了测试。     

IGGOS as a potential partner in IGOS

This short note reviews our thinking on how IGGOS can best achieve a high status within the set of global monitoring programmes. If such a high status can be obtained, then the importance of geodetic networks and services will be recognized more widely, and their activities will consequently be better resourced in the long term. One particular aspect concerns how IGGOS can complement the roles of the various IGOS partners within global monitoring. The different ways in which IGGOS can contribute to IGOS are outlined.     

The International GPS Service tracking network: Enabling diverse studies and projects through international cooperation

The International GPS Service (IGS), formulated beginning in 1989 and formalized in 1994, was founded on the collaborative operation of approximately 30 permanent GPS stations to benefit global geodynamics. The same cooperative principles, today applied to a network of over 300 stations, still serve to maximize global benefit without unnecessary duplication of investment in global infrastructure. The scope of applications of the dataset has grown to include atmospheric, oceanographic, subdaily, and low-earth orbiter activities through working groups and pilot projects fostered within the IGS in the now traditional IGS spirit of collaboration. These activities and the IGS infrastructure are viewed as critical elements to the Global Geodetic Observing System. This presentation will review the present nature of the IGS tracking network and its ability to support new applications.     

Assessing the Comparability of Ammonium,Nitrate and Sulfate Concentrations Measured by Three Air Quality Monitoring Networks

Airborne fine particulate matter across the United States is monitored by different networks, the three prevalent ones presently being the Clean Air Status and Trend Network (CASTNet), the Interagency Monitoring of PROtected Visual Environment Network (IMPROVE) and the Speciation and Trend Network (STN). If combined, these three networks provide speciated fine particulate data at several hundred locations throughout the United States. Yet, differences in sampling protocols and samples handling may not allow their joint use. With these concerns in mind, the objective of this study is to assess the spatial and temporal comparability of the sulfate, nitrate and ammonium concentrations reported by each of these networks. One of the major differences between networks is the sampling frequency they adopted. While CASTNet measures pollution levels on seven-day integrated samples, STN and IMPROVE data pertain to 24-hour samples collected every three days. STN and IMPROVE data therefore exhibit considerably more short-term variability than their CASTNet counterpart. We show that, despite their apparent incongruity, averaging the data with a window size of four to six weeks is sufficient to remove the effects of differences in sampling frequency and duration and allow meaningful comparison of the signals reported by the three networks of concern. After averaging, all the sulfate and, to a lesser degree, ammonium concentrations reported are fairly similar. Nitrate concentrations, on the other hand, are still divergent. We speculate that this divergence originates from the different types of filters used to collect particulate nitrate. Finally, using a rotated principal component technique (RPCA), we determined the number and the geographical organization of the significant temporal modes of variation (clusters) detected by each network for the three pollutants of interest. For sulfate and ammonium, the clusters’ geographical boundaries established for each network and the modes of variations within each cluster seem to correspond. RPCA erformed on nitrate concentrations revealed that, for the CASTNet and IMPROVE networks, the modes of variation do not correspond to unified geographical regions but are found more sporadically. For STN, the clustered areas are unified and easily delineable. We conclude that the possibility of jointly using the data collected by CASTNet, IMPROVE and STN has to be weighed pollutant by pollutant. While sulfate and ammonium data show some potential for joint use, at this point, combining the nitrate data from these monitoring networks may not be a judicious choice.     

Interlocking directorships and trans-national linkages within the British Empire, 1900–1930

Recent research has highlighted the value of employing the idea of networks to describe the interrelatedness of today's globalizing economy. Networks facilitate flows of knowledge, ideas, managerial techniques and capital between firms both within and across political borders. This paper argues that the reconstruction of social connections through which information is created, given value and exchanged is fundamental to an understanding of not only contemporary but also historical patterns of economic globalization. We focus on the networks of the capitalist elite running 12 major multinational enterprises, active across British imperial territories, between c .1900 and c .1930. An examination is made of the economic and spatial interlocks between firms created by board members who were multiple directors. Social underpinnings of multiple directorates are examined by exploring the common, overlapping social spheres within which individuals engaged. A clearer grasp of the ways in which corporate activity operated in the early 1900s can provide a better understanding of the social context of global economic operations.     

Prediction of creep characteristic of rock under varying environment

The strain developed due to creep is mainly proportional to the logarithm of the time under load, and is mostly proportional to the stress and temperature. At higher temperature the creep rate falls slowly with respect to time, and the creep strain is proportional to a fractional power of time, with the exponent increasing as the temperature increases and reaching a value approximately one-third at temperatures of about 0.5°C. At these temperatures, the creep increases with stress according to a power greater than unity and possibly exponentially. It increases with temperature as (−U/kT), where U is an activation energy and k is Boltzman’s constant. There are different methods to determine the creep strain and the energy of Jog (B) including experimental methods, multivariate regression analysis, and by numerical simulation. These methods are less cumbersome and time consuming. In the present investigation, artificial neural network technique has been used for prediction of the creep strain and energy of Jog (B). Two different networks have been tested and validated. Both the networks have four input neurons and one hidden layer with five neurons, and one output neuron. The data for different rocks at temperatures up to 750°C under conditions of compressive or tortional stress are taken from the literatures. The training and testing data sets used were 163 and 14, respectively. To deal with the problem of overfitting of data, Bayesian regulation has been used and network is trained with suitable training epochs. The coefficients of correlation among the predicted and observed values are found high and they improve the confidence of the users. The mean absolute percentage error obtained are also very low.     

Understanding governance and networks: EU-US interactions and the regulation of genetically modified organisms

EU-US (European Union-United States) interactions in relation to the regulation of genetically modified organisms (GMOs) have been examined in detail in recent years. To do this scholars have tended to focus on a small number of high profile processes, such as the formal complaint of the US to the World Trade Organisation regarding the regulation of GMOs in the Europe. It is important to analyse developments of this kind but overemphasis on them has also lead to a distorted view of the case and a failure to appreciate the significance of other, less visible, types of interaction. In this paper we focus instead on trying to understand the roles played by various EU-US transnational networks—The Transatlantic Business Dialogue, The Transatlantic Economic Partnership, The Transatlantic Consumer Dialogue, The Transatlantic Environmental Dialogue and the EU-US Consultative Forum on Biotechnology. These networks have been trying to shape the regulation of GMOs in both jurisdictions since the mid 1990s. By analysing them through a ‘governance lens’ we find that we can better understand EU-US interactions and the dynamics and influences around the regulation of GMOs. This gives us valuable insights into processes of contemporary governance.     

Efficient estimation of flood forecast prediction intervals via single‐ and multi‐objective versions of the LUBE method

Prediction intervals (PIs) are commonly used to quantify the accuracy and precision of a forecast. However, traditional ways to construct PIs typically require strong assumptions about data distribution and involve a large computational burden. Here, we improve upon the recent proposed Lower Upper Bound Estimation method and extend it to a multi‐objective framework. The proposed methods are demonstrated using a real‐world flood forecasting case study for the upper Yangtze River Watershed. Results indicate that the proposed methods are able to efficiently construct appropriate PIs, while outperforming other methods including the widely used Generalized Likelihood Uncertainty Estimation approach. Copyright © 2016 John Wiley & Sons, Ltd.     

A network theory approach for a better understanding of overland flow connectivity

Hydrological connectivity describes the physical coupling (linkages) of different elements within a landscape regarding (sub‐) surface flows. A firm understanding of hydrological connectivity is important for catchment management applications, for example, habitat and species protection, and for flood resistance and resilience improvement. Thinking about (geomorphological) systems as networks can lead to new insights, which has also been recognized within the scientific community, seeing the recent increase in the use of network (graph) theory within the geosciences. Network theory supports the analysis and understanding of complex systems by providing data structures for modelling objects and their linkages, and a versatile toolbox to quantitatively appraise network structure and properties. The objective of this study was to characterize and quantify overland flow connectivity dynamics on hillslopes in a humid sub‐Mediterranean environment by using a combination of high‐resolution digital‐terrain models, overland flow sensors and a network approach. Results showed that there are significant differences between overland flow connectivity on agricultural areas and semi‐natural shrubs areas. Significant positive correlations between connectivity and precipitation characteristics were found. Significant negative correlations between connectivity and soil moisture were found, most likely because of soil water repellency and/or soil surface crusting. The combination of structural networks and dynamic networks for determining potential connectivity and actual connectivity proved a powerful tool for analysing overland flow connectivity. Copyright © 2016 John Wiley & Sons, Ltd.