Maps of epicentre distribution in 1993

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Geochemistry of magnetite in beach sands, stream sediments,and in situ magnetites in surrounding rocks at north Taiwan island

The geochemistry of magnetite provides constraints on the source(s) of the magnetite and other ore minerals. In this study, we constructed a magnetite distribution map, compared the chemical composition of magnetite present in hard rocks with that present in sand beach and stream deposits to determine magnetite’s provenance,investigated relationships among different trace elements,examined variations in the concentrations of trace elements present in magnetite, and identified the type of studied magnetite. Laser ablation inductively coupled plasma mass spectrometry(LA-ICP-MS) and inductively coupled plasma mass spectrometry/inductively coupled plasma atomic emission spectroscopy was used to analyze samples available in small and large amounts, respectively. The average magnetite compositions of beach and river samples were substantially similar to those of andesite rocks,revealing that the magnetite samples may have originated from surrounding andesite rocks;this finding is consistent with the chemical composition of the samples analyzed using LA-ICP-MS. Some of the andesite rocks found in the study area were the primary source of magnetite aggregations in the beach sands and stream sands near or slightly away from these rocks. High Ti–V magnetites lie within the fields of Fe–Ti–V bearing magnetite and titanomagnetite(ulv?spinel). These titanomagnetites may be undergoing transformation to spinel minerals such as galaxite,gahnite, magnesiochromite, and chromite, which are characterized by the high concentrations of Al, Ti, Mg, Cr,Ni, Mn, and Zn. The positive correlation between Fe and Ti, Cr and Ni, and also the negative correlation between Fe and Mg, Mn and Zn can be attributed to various magnetite phases present in the study area. V, Co, Mo, Nb, Ga, Sn,and Ta exhibited a positive correlation with Fe and a negative correlation with Al. The positive correlation between Fe and these elements indicates the enrichment of these elements in the magnetite series. We observed a negative correlation between Fe and Li, Rb, Ca, Ba, Sr, Cu,Cd, Sb, As, Sc, Y, REE, U, and Th. A positive correlation between Al and these elements indicates the enrichment of these elements in the spinel series.

Guidance method to use mixed coal in blending for coking

Based on the principle that the adaptation can be reflected by the overlap of reflectance distribution peaks,the effect of various types mixed coal for coking is analyzed.Based on the action of the vitrinite of different reflectance range and the adaptation,a new method for guidance blending coal is established.Through simulation,blending coal using the software of HD automatic microscope photometer,makes the synthetic blending coal reflectance distribution map to nothing notch wide single peak flat-shaped symmetrical distribution,blending coal random reflectance meets 1.1 - 1.2,the standard deviation meets 0.35 - 0.4.It is based on the conditions that active component in the blending is excessive.Using this method,the mixed coal can be used as much as possible and play a positive role.The problem about implementation process is discussed.

可数度量空间上的完全分布混沌映射

设X={x_0,x_1,…}是一个自密的可数度量空间.则对任意0<δ<diamX,存在X上的同胚φ满足:(a)X={φ~ix_0:i∈Z}且{φ~ix_0:i≥0},{φ^(-i)x_0:i≥0}均为X的稠密子集;(b)φ,φ^(-1)均以X为其一个分布式δ-混沌集.

Development of 3-actoxymethoxycarbonyl-2,2,5,5-tetramethyl-1-pyrrolidinyloxyl as an electron paramagnetic resonance imaging reagent for in vivo mapping brain oxygen distribution and infarction in ischemic brain

Measurement of oxygen concentration and distribution in brain is essential to understanding the pathophysiology of stroke. Although brain oxygen level is critical for brain tissue survival,

Rapid detection of chlorophyll content and distribution in citrus orchards based on low-altitude remote sensing and bio-sensors

The accuracy of detecting the chlorophyll content in the canopy and leaves of citrus plants based on sensors with different scales and prediction models was investigated for the establishment of an easy and highly-efficient real-time nutrition diagnosis technology in citrus orchards.The fluorescent values of leaves and canopy based on the Multiplex 3.6 sensor,canopy hyperspectral reflectance data based on the FieldSpec4 radiometer and spectral reflectance based on low-altitude multispectral remote sensing were collected from leaves of Shatang mandarin and then analyzed.Additionally,the associations of the leaf SPAD(soil and plant analyzer development)value with the ratio vegetation index(RVI)and normalized differential vegetation index(NDVI)were analyzed.The leaf SPAD value predictive model was established by means of univariate and multiple linear regressions and the partial least squares method.Variable distribution maps of the relative canopy chlorophyll content based on spectral reflectance in the orchard were automatically created.The results showed that the correlations of the SPAD values obtained from the Multiplex 3.6 sensor,FieldSpec4 radiometer and low-altitude multispectral remote sensing were highly significant.The measures of goodness of fit of the predictive models were R^(2)=0.7063,RMSECV=3.7892,RE=5.96%,and RMSEP=3.7760 based on RVI_((570/800)) and R^(2)=0.7343,RMSECV=3.6535,RE=5.49%,and RMSEP=3.3578 based on NDVI[(570,800)(570,950)(700,840)].The technique to create spatial distribution maps of the relative canopy chlorophyll content in the orchard was established based on sensor information that directly reflected the chlorophyll content of the plants in different parts of the orchard,which in turn provides evidence for implementation of orchard productivity evaluation and precision in fertilization management.

On-site identification of Ophiocordyceps sinensis using multispectral imaging and chemometrics

For the reasonable and effective collection of Ophiocordyceps sinensis,a new method of on-site identification was attempted using a portable multispectral imaging(MSI)technique.Three dimensional(3D)data-cubes of representative Ophiocordyceps sinensis and weeds samples were acquired and pre-processed with standard normal variate transformation(SNV).Principal component analysis(PCA)and simulated annealing particle swarm optimisation(SAPSO)algorithms were used to extract characteristic images and develop the support vector classification(SVC)models.Results show that the fused feature model of SAPSO-SVC has the best performance,resulting in a recognition accuracy of the prediction set of 96.30%.Moreover,on-site distribution map of Ophiocordyceps sinensis and weeds was created using the spectral feature model of SAPSO-SVC,and the target could be easily identified from the distribution map.This work demonstrates the potential for on-site identification of Ophiocordyceps sinensis in the Qinghai-Tibet Plateau using a portable MSI technique combined with the SAPSO-SVC algorithm.