红土镍矿化学成分的相关性探析

红土镍矿进口量大、成分复杂、差异性大。为全面了解红土镍矿中各化学成分的相关性,以日照港为主,连云港、天津港为辅,从2013年1月—12月,按进港量多少,每月抽取11~25个样本,共228个。通过元素分析,检出主要元素19个,逐对分析相关性。发现地质作用造成的多元素共生关系,也有外部环境风化淋滤后的弱相关、不相关,甚至有风化淋滤过程中形成的强相关。     

Retrieval of tea polyphenol at leaf level using spectral transformation and multi-variate statistical approach

In the present study, field based hyperspectral data was used to estimate the tea (Camellia sinensis L.) polyphenol at Deha Tea garden of Assam state, India. Leaf reflectance spectra were first filtered for noise and then transformed into normalized and first derivative reflectance for further analysis. Stepwise discriminant analysis was carried out to select sensitive bands for a range of polyphenol concentration by minimizing the effects of other factors such as age of the bushes and management practices. The wavelengths at 358, 369, 484, 845, 916, 1387, 1420, 1435, 1621 and 2294 nm were identified as sensitive to tea polyphenol, among which 2294 nm was found to be the most recurring band. The noise removed selected bands, their transformed derivatives and principal components were regressed with the tea polyphenol using univariate and multi-variate analysis. In univariate analysis the correlation was very poor with RMSE more than 3.0. A significant improvement in R² values were observed when multivariate analyses like stepwise multiple linear regression (SMLR) and partial least square regression (PLSR) was carried out. The PLSR of first derivative reflectance was most accurate (R² = 0.81 and RMSE = 1.39 mg g⁻¹) among all the uni- and multivariate analysis for predicting the polyphenol of fresh tea leaves.     

基于PCA的地震官方微博影响力评价研究

随着政务微博用户规模及影响力的不断提升,微博作为地震部门传播平台,在地震信息传播方面发挥着巨大的作用。本文在充分考虑地震部门行业特点的基础上,对30个地震官方微博数据进行收集,利用主成分分析法(PCA)归纳出3种地震微博的主要影响指标,即服务力主成分、交互力主成分和创作力主成分,并由此构建出地震官方微博影响力评估指标体系,在此基础上计算得出各地震官方微博的主成分指标得分和影响力综合得分,最后根据得分情况对地震官方微博影响力提出了具可行性的提升策略。     

Modelling of a medium-term dynamics in a shallow tidal sea, based on combined physical and neural network methods

The paper presents an approach towards a medium-term (decades) modelling of water levels and currents in a shallow tidal sea by means of combined hydrodynamic and neural network models. The two-dimensional version of the hydrodynamic model Delft3D, forced with realistic water level and wind fields, is used to produce a two-year-database of water levels and currents in the study area. The linear principal component analysis (PCA) of the results is performed to reveal dominating spatial patterns in the analyzed dataset and to significantly reduce the dimensionality of the data. It is shown that only a few principal components (PCs) are necessary to reconstruct the data with high accuracy (over 95% of the original variance). Feed-forward neural networks are set up and trained to effectively simulate the leading PCs based on water level and wind speed and direction time series in a single, arbitrarily chosen point in the study area. Assuming that the spatial modes resulting from the PCA are ‘universally’ applicable to the data from time periods not modelled with Delft3D, the trained neural networks can be used to very effectively and reliably simulate temporal and spatial variability of water levels and currents in the study area. The approach is shown to be able to accurately reproduce statistical distribution of water levels and currents in various locations inside the study area and thus can be viewed as a reliable complementary tool e.g., for computationally expensive hydrodynamic modelling. Finally, a detailed analysis of the leading PCs is performed to estimate the role of tidal forcing and wind (including its seasonal and annual variability) in shaping the water level and current climate in the study area.     

Texture-based identification of urban slums in Hyderabad, India using remote sensing data

This paper outlines a methodology to identify informal settlements out of high resolution satellite imagery using the concept of lacunarity. Principal component analysis and line detection algorithms were applied alternatively to obtain a high resolution binary representation of the city of Hyderabad, India and used to calculate lacunarity values over a 60 × 60 m grid. A number of ground truthing areas were used to classify the resulting datasets and to identify lacunarity ranges which are typical for settlement types that combine high density housing and small dwelling size - features characteristic for urban slums in India. It was discovered that the line detection algorithm is advantageous over principal component analysis in providing suitable binary datasets for lacunarity analysis as it is less sensitive to spectral variability within mosaicked imagery. The resulting slum location map constitutes an efficient tool in identifying particularly overcrowded areas of the city and can be used as a reliable source in vulnerability and resilience assessments at a later stage. The proposed methodology allows for rapid analysis and comparison of multi-temporal data and can be applied on many developing urban agglomerations around the world.     

Chemical characteristics of wet precipitation at an urban site of Guangzhou, South China

The pH variation and chemical characteristics of rainwater were investigated from January 2006 to December 2006 at an urban site of Guangzhou, South China. The rainwater was typically acidic with a volume-weighted mean pH value of 4.49, which ranged from 3.52 to 6.28. The volume-weighted mean equivalent concentration of components followed the order: SO₄²⁻ > Ca²⁺ > Cl⁻ > NH₄⁺ > Na⁺ > NO₃⁻ > K⁺ > Mg²⁺ > F⁻, indicating that SO₄²⁻, Cl⁻ and NO₃⁻ were the main anions, while Ca²⁺ and NH₄⁺, were the main cations. Ca²⁺ and NH₄⁺ were major neutralization constituents of the precipitation. Furthermore, correlation analysis and principal component analysis method were performed to identify possible common sources of major ions. Sources of the major ions were assessed based on enrichment factor method.     

Classification of natural mineral and spring bottled waters of Portugal using Principal Component Analysis

Considering its area, Portugal is one of the world's richest countries in mineral and spring waters. There are 33 different types of bottled water, 18 of which are classified as natural mineral water and the remaining as spring water. The majority of these waters are of low mineralisation in comparison to most European bottled waters.     

基于主成分回归的阿克苏市城市需水量预测

科学地预测城市需水量对城市的发展具有十分重要的意义。城市需水量受到多重因素的影响,各因素之间的相关性较大,从而导致一些预测计算结果失真,使一些公式的适用性不是很强。为此,采用主成分回归建模,借助主成分分析与典型相关分析的思路,有效地解决了各因子之间的相关问题,建立了城市需水量预报模型。结果表明．模型的拟合和预报精度均较好。     

Improved Wildcat Modelling of Mineral Prospectivity

Wildcat modelling of mineral prospectivity has been proposed for greenfields geologically-permissive terranes where mineral targets have not yet been discovered but a geological map is available as a source of spatial data of predictors of mineral prospectivity. This paper (i) revisits the initial way of assigning wildcat scores (S_c) to predictors of mineral prospectivity and (ii) proposes an improvement by transforming S_c into improved wildcat scores (IS_c) by using a logistic function. This was shown in wildcat modelling of prospectivity for low-sulphidation epithermal-Au (LSEG) deposits in Aroroy district (Philippines). Based on knowledge of characteristics of and controls on LSEG mineralization in the Philippines, the spatial predictors of LSEG prospectivity used in the study are proximity to porphyry plutonic stocks, faults/fractures and fault/fracture intersections. The S_c and IS_c of spatial predictors are input separately to principal components analysis to extract a favourability function that can be interpreted as a wildcat model of LSEG prospectivity. The predictive capacity of the wildcat model of LSEG prospectivity based on the IS_c of geological predictors is roughly 70% higher than that of the wildcat model of LSEG prospectivity based on the S_c of geological predictors. A slight increase of predictive capacity of wildcat modelling of LSEG prospectivity is also achieved when the IS_c of geological predictors are integrated with the IS_c of geochemical anomalies, but not with the S_c of geochemical anomalies. The proposed improvement is significant because if the study district were a greenfields exploration area, then a wildcat model of LSEG prospectivity based on the old wildcat methodology would have caused several LSEG targets to be missed.     

山东省飞机增雨降水区分级预报方法研究

利用1997～2000年春秋季山东主要降水天气系统中尺度客观分析和降水模拟资料，统计分析了与降水密切相关的15个因子，并对因子的稳定性进行检验。用全部入选因子的前5个主成分作为新的预报因子，建立全省25个区域0～6h和0～12h平均降水量的3类判别方程，判别效果较好。以此方法为基础建立的决策系统为增雨作业指挥提供了实用性技术工具。