Grassland productivity in an alpine environment in response to climate change

Situated in a climatically stressful environment, alpine grassland is sensitive to subtle climate changes in its productivity. We remedy the current deficiency in studying grassland productivity by taking the integrated effect of all relevant factors into consideration. The relative importance of temperature, rainfall and evaporation to the alpine grassland productivity in western China was determined through analysis of their relationship with the normalized difference vegetation index (NDVI) between 1981 and 2000. Climate warming stimulated grassland productivity in the 1980s, but hampered it in the 1990s. Temperature is more important than rainfall to grassland productivity early in the growing season. However, their relative importance is reversed late in the growing season. Monthly summer month rainfall modified by maximum monthly temperature is a good predictor of alpine grassland productivity at 62.0 per cent. However, the best predictor is water deficiency, which is able to improve the estimation accuracy to 78.3 per cent. Hence, the impact of temperature on grassland productivity is better studied indirectly through evaporation.     

Traction image method for irregular free surface boundaries in finite difference seismic wave simulation

In this study, we propose a new numerical method, named as Traction Image method, to accurately and efficiently implement the traction-free boundary conditions in finite difference simulation in the presence of surface topography. In this algorithm, the computational domain is discretized by boundary-conforming grids, in which the irregular surface is transformed into a 'flat' surface in computational space. Thus, the artefact of staircase approximation to arbitrarily irregular surface can be avoided. Such boundary-conforming gridding is equivalent to a curvilinear coordinate system, in which the first-order partial differential velocity-stress equations are numerically updated by an optimized high-order non-staggered finite difference scheme, that is, DRP/opt MacCormack scheme. To satisfy the free surface boundary conditions, we extend the Stress Image method for planar surface to Traction Image method for arbitrarily irregular surface by antisymmetrically setting the values of normal traction on the grid points above the free surface. This Traction Image method can be efficiently implemented. To validate this new method, we perform numerical tests to several complex models by comparing our results with those computed by other independent accurate methods. Although some of the testing examples have extremely sloped topography, all tested results show an excellent agreement between our results and those from the reference solutions, confirming the validity of our method for modelling seismic waves in the heterogeneous media with arbitrary shape topography. Numerical tests also demonstrate the efficiency of this method. We find about 10 grid points per shortest wavelength is enough to maintain the global accuracy of the simulation. Although the current study is for 2-D P-SV problem, it can be easily extended to 3-D problem.     

区域产业布局优化及理论依据分析

目前关于产业布局的理论多强调其经济效益,在一定程度上忽视了环境效益和社会效益。从经济、社会与环境协调发展的角度对产业布局优化的内涵进行阐释,并论述实现区域产业布局优化的经济学、环境学及生态学理论依据。     

产业集群与浦东集成电路产业建设

产业集群的形成是现代经济发展中的一个非常重要的现象。浦东集成电路产业集群是“国家集成电路产业基地”的核心,产业集群的形成对浦东集成电路产业提高竞争力具有重要的意义。本文分析了浦东集成电路产业集群的发展现状。指出目前浦东集成电路产业集群的建设还处于一个初步阶段,无论是企业间,还是企业与高校、科研机构或者其他组织机构间的创新合作交流都十分有限,从而在一定程度上抑制了集成电路产业整体学习能力和竞争力的提高。文章针对这些问题,从产业链、区域性产业发展政策、环境配套设施建设等方面提出了加强浦东集成电路产业集群建设的对策建议。     

Characteristics of Permeability Changes in Bituminous Coal Under Conditions of Stress Variation Due to Repeated Mining Activities

The factors affecting permeability change under repeated mining of coal seams are important study aspects that need to be explored. This study combined various stress variation characteristics of protective seam mining and simplified the stress path of repeated mining in protective seam mines. Based on the results from the bespoke gas flow and displacement testing apparatus, seepage tests for simulated repetitive mining were carried out. The results simulated the actual behavior very well. With any drastic increase in the mining influence, the axial deviation stress in the stress path increased, and the greater the difference in coal permeability during the unloading and stress recovery stage, the more substantial the increase in permeability. The change in coal permeability was significantly influenced by the severity of simulated repeated mining cycles. When the mining stress exceeded a critical value, the permeability of the coal sample increased with the increase in the number of loading and unloading cycles, but the reverse was true when the mining stress was lower than the critical value. The effective sensitivity of seepage to the applied stress decreased with an increase in the number of stress cycles. With a decrease in the deviation stress, that is, with lower severity of mining influence, the effective sensitivity of coal seepage to stress gradually decreased.

     

3D Mineral Prospectivity Mapping with Random Forests: A Case Study of Tongling,Anhui, China

Natural Resources Research - In the past few decades, a variety of data-driven predictive modeling techniques has led to a dramatic advancement in mineral prospectivity mapping (MPM). The random...     

“四层一体”在地理研学资源开发中的运用——以哈尼梯田地理研学资源开发为例

自2016年教育部等十一部门印发《关于推进中小学生研学旅行的意见》以来,社会各界对于研学旅行的研究迅速增加。因地理学研究“人地关系地域系统”这一核心问题,无疑成为研学旅行研究和实践的重要阵地。但如何开发地理研学旅行资源,尤其是如何有机结合人文地理与自然地理研学要素成为困扰很多研学导师的难题。本文尝试使用“四层一体”分析方法,从自然地理要素、生计、制度、文化四个层面出发,探讨如何从地理学科视角挖掘和利用研学资源,发现各个地理研学要素之间的相互作用关系,增强其逻辑性。     

A hybrid integrated deep learning model for the prediction of citywide spatio-temporal flow volumes

ABSTRACT

The spatio-temporal residual network (ST-ResNet) leverages the power of deep learning (DL) for predicting the volume of citywide spatio-temporal flows. However, this model, neglects the dynamic dependency of the input flows in the temporal dimension, which affects what spatio-temporal features may be captured in the result. This study introduces a long short-term memory (LSTM) neural network into the ST-ResNet to form a hybrid integrated-DL model to predict the volumes of citywide spatio-temporal flows (called HIDLST). The new model can dynamically learn the temporal dependency among flows via the feedback connection in the LSTM to improve accurate captures of spatio-temporal features in the flows. We test the HIDLST model by predicting the volumes of citywide taxi flows in Beijing, China. We tune the hyperparameters of the HIDLST model to optimize the prediction accuracy. A comparative study shows that the proposed model consistently outperforms ST-ResNet and several other typical DL-based models on prediction accuracy. Furthermore, we discuss the distribution of prediction errors and the contributions of the different spatio-temporal patterns.     

Novel Hybrid Integration Approach of Bagging-Based Fisher’s Linear Discriminant Function for Groundwater Potential Analysis

Natural Resources Research - Groundwater is a vital water source in the rural and urban areas of developing and developed nations. In this study, a novel hybrid integration approach of...     

Effects of current climate,paleo-climate,and habitat heterogeneity in determining biogeographical patterns of evergreen broad-leaved woody plants in China

Journal of Geographical Sciences - Understanding biogeographic patterns and the mechanisms underlying them has been a main issue in macroecology and biogeography, and has implications for...