Nature of diamonds in Yakutian eclogites: views from eclogite tomography and mineral inclusions in diamonds

We have performed dissections of two diamondiferous eclogites (UX-1 and U33/1) from the Udachnaya kimberlite, Yakutia in order to understand the nature of diamond formation and the relationship between the diamonds, their mineral inclusions, and host eclogite minerals. Diamonds were carefully recovered from each xenolith, based upon high-resolution X-ray tomography images and three-dimensional models. The nature and physical properties of minerals, in direct contact with diamonds, were investigated at the time of diamond extraction. Polished sections of the eclogites were made, containing the mould areas of the diamonds, to further investigate the chemical compositions of the host minerals and the phases that were in contact with diamonds. Major- and minor-element compositions of silicate and sulfide mineral inclusions in diamonds show variations among each other, and from those in the host eclogites. Oxygen isotope compositions of one garnet and five clinopyroxene inclusions in diamonds from another Udachnaya eclogite (U51) span the entire range recorded for eclogite xenoliths from Udachnaya. In addition, the reported compositions of almost all clinopyroxene inclusions in U51 diamonds exhibit positive Eu anomaly. This feature, together with the oxygen isotopic characteristics, is consistent with the well-established hypothesis of subduction origin for Udachnaya eclogite xenoliths. It is intuitive to expect that all eclogite xenoliths in a particular kimberlite should have common heritage, at least with respect to their included diamonds. However, the variation in the composition of multiple inclusions within diamonds, and among diamonds, from the same eclogite indicates the involvement of complex processes in diamond genesis, at least in the eclogite xenoliths from Yakutia that we have studied.     

Experimental Studies and its Application using PLAXIS-2D for Lithomargic Clay Stabilized by GBFS and Lime

Geotechnical and Geological Engineering - Lithomargic clay is found along the Eastern and Western coasts of Southern India. Presence of weak soils like lithomargic clay below the foundations, cause...     

Estimation of Sea Ice Freeboard from SARAL/AltiKa Data

An attempt has been made to derive sea ice freeboard from Ka-band Altimeter (SARAL/AltiKa) over Arctic region for 15 March–15 April 2013 (spring) and 15 September–15 October 2013 (autumn). A waveform template matching technique is employed for classification of leads and floe pixels. The estimated sea ice freeboards were found in close agreement with “Operation IceBridge quick look” freeboards (RMSD = 0.30 m). The differences between the two freeboards were largely due to snow layer over sea ice (R = 0.8). The estimated freeboards were of the order of 0.08–0.15 m during the two seasons.     

Deterministic simulation of highly intermittent hydrologic time series

Application of a deterministic geometric approach for the simulation of highly intermittent hydrologic data is presented. Specifically, adaptations of the fractal-multifractal (FM) method and a Cantorian extension are advanced in order to simulate rainfall records measured at the daily scale and encompassing a water year. It is shown, using as case studies 2 years of rainfall sets gathered in Laikakota, Bolivia and Tinkham, Washington, USA, that the FM approach, relying on only at most 8 parameters, is capable of closely preserving either the whole record’s histogram (therefore including moments), the whole data’s Rényi entropy function and/or the maximum number of consecutive zero values present in the sets, resulting in suitable rainfall simulations, whose overall features and textures are similar to those of the observed sets. The study hence establishes the possibility of simulating highly intermittent sets in time in a deterministic and holistic way as a novel parsimonious methodology to supplement available stochastic frameworks.     

Extreme Learning Machine Based Modeling of Resilient Modulus of Subgrade Soils

This paper investigates the potential of two variants of extreme learning machine based regression approaches in predicting the resilient modulus of cohesive soils. Support vector regression was used to compare the performance of the proposed extreme learning machine based regression approaches. The dataset used in this study was derived from literature and consists of 9 input parameters with a total of 891 cases. For testing, two methods i.e. train/test and tenfold cross validation was used. In case of train and test methods, a total of 594 randomly selected cases were used to train different algorithms and the remaining 297 data were used to test the created models. Correlation coefficient value of 0.991 (root mean square error = 3.47 MPa) was achieved by polynomial kernel based extreme learning machine in comparison to 0.990 and 0.990 (root mean square error = 4.790 and 4.290 MPa) by simple extreme learning machine and radial basis kernel function based support vector regression respectively with test dataset. Comparisons of results with tenfold cross validation also suggest that polynomial kernel based extreme learning machine works well in terms of root mean square error and computational cost with the used dataset. Sensitivity analysis suggests the importance of confining stress and deviator stress in predicting the resilient modulus when using with polynomial kernel based extreme learning machine modeling approach.     

Ground water quality classification by decision tree method in Ardebil region,Iran

A decision tree-based approach is proposed to predict ground water quality based on the United States Salinity Laboratory (USSL) diagram using the data from aquifers in agricultural lands of Ardebil province, northwest of Iran. Several combinations of hydro chemical parameters of groundwater and monthly precipitation with different lag time were considered to find an accurate and economical alternative for groundwater quality classification. The performance evaluation was based on the number of correctly classified instances (CCI) and kappa statistics. The results suggested the suitability of decision tree-based classification approach for the used data sets. The overall average of CCI and kappa statistic for the prediction of groundwater quality classes based on the USSL diagram was 0.88 and 0.83 %, respectively. Principal component analysis (PCA) was also used to determine the important parameters for groundwater quality classification. The results showed that groundwater quality classification by decision tree is more precise and efficient in comparison with PCA. The best alternative could evaluate groundwater quality class with only two parameters: electrical conductivity and cumulative precipitation of 11 months earlier. The developed model is able to predict water quality class by only two variables and this lead to a reduction in the number of variables analyzed on a routine basis, resulting in a significant reduction in laboratory costs and latency times between the sampling moment and the outcome of the laboratory analyses.     

Instance‐based learning compared to other data‐driven methods in hydrological forecasting

Data‐driven techniques based on machine learning algorithms are becoming popular in hydrological modelling, in particular for forecasting. Artificial neural networks (ANNs) are often the first choice. The so‐called instance‐based learning (IBL) has received relatively little attention, and the present paper explores the applicability of these methods in the field of hydrological forecasting. Their performance is compared with that of ANNs, M5 model trees and conceptual hydrological models. Four short‐term flow forecasting problems were solved for two catchments. Results showed that the IBL methods often produce better results than ANNs and M5 model trees, especially if used with the Gaussian kernel function. The study showed that IBL is an effective data‐driven method that can be successfully used in hydrological forecasting. Copyright © 2007 John Wiley & Sons, Ltd.     

Some magnetized bulk viscous string cosmological models in?General Relativity

Some Bianchi type-I viscous fluid string cosmological models with magnetic field are investigated. The viscosity coefficient of bulk viscous fluid is assumed to be a power function of mass density ξ(t)=ξ ₀ ρ ^m , where ξ ₀ and m are constants. To get a determinate model, we assume conditions ρ=(1+ω)λ, where ρ is rest energy density, ω a positive constant and λ the string tension density and expansion θ is proportional to eigen value σ ₁¹ of the shear tensor σ _j ⁱ . The behaviour of the models from physical and geometrical aspects in presence and absence of magnetic field is discussed.      

Modelling pile capacity using Gaussian process regression

This paper investigates the potential of a Gaussian process (GP) regression approach to predict the load-bearing capacity of piles. Support vector machines (SVM) and empirical relations were used to compare the performance of the GP regression approach. The first dataset used in this study was derived from actual pile-driving records in cohesion-less soil. Out of a total of 94 pieces of data, 59 were used to train and the remaining 35 data were used to test the created models. A radial basis function and Pearson VII function kernels were used with both GP and SVM. The results from this dataset indicate improved performance by GP regression in comparison to SVM and empirical relations. To validate the performance of the GP regression approach, another dataset consisting of 38 pieces of data was considered. The results from this dataset also suggest improved performance by the Pearson VII function kernel-based GP regression modelling approach in comparison to SVM.