Arsenite removal from water by electro-coagulation on zinc–zinc and copper–copper electrodes

Removal of arsenite from aqueous solution was carried out using electro-coagulation method. The experiments were conducted using copper–copper and zinc–zinc electrodes. The optimized experimental parameters were 2.0 mg/L initial concentration, 16.0-min processing time, 6.0 pH, 3.0-V applied voltage and 30 °C temperature for zinc–zinc electrodes while these values for copper–copper electrodes were 2.0 mg/L initial concentration, 20.0-min processing time, 7.0 pH, 5.0-V applied voltage and 30 °C temperature. The results demonstrated that zinc–zinc and copper–copper electrodes removed arsenite up to 99.89 and 99.56 %, respectively. The treated water was clear, colorless and odorless without any secondary contamination. There was no change in water quality after the removal of arsenite. The reported method is capable to remove arsenite from water at 6–7 pH range, which is a pH range of natural water. Therefore, this method may be the choice of arsenite removal from natural ground water.     

Drift solitary structures in inhomogeneous degenerate quantum plasmas with trapped electrons

In the present work, we have considered the nonlinear effects due to trapped electrons in an inhomogeneous degenerate quantum plasma. The formation of drift solitary structures has been investigated for both fully and partially degenerate plasmas. The Sagdeev potential approach has been employed to obtain arbitrary amplitude solitary structures. Interestingly, for a fixed value of density, not only compressive but rarefactive solitary structures have been obtained for a certain temperature range. Furthermore, it has been observed that the drift solitary structures exist only for the case when the drift velocity is smaller than the velocity of the nonlinear structure. The theoretical results obtained have been analyzed numerically for the parameters typically found in white dwarfs and the relevance of the results with regard to white dwarf asteroseismology is also pointed out.     

Application of Continuous Wavelet Transform in?Examining Soil Spatial Variation: A?Review

An adequate understanding of soil spatial variation as a function of space and scale is necessary in ecological modeling, environmental prediction, precision agriculture, soil quality assessment and natural resources management. Soil spatial variation can be partitioned into frequencies (scale) and positions (location) by the wavelet transform. This review focuses mainly on different applications of the continuous wavelet transform (CWT) for the identification of the scale and location dependence of soil attributes. We discussed both wavelet spectra and wavelet coherence in our analysis of soil spatial variation. Global wavelet spectra, being the sum of wavelet spectra over all spatial locations at a scale, can be used to examine the dominant scale of variation. Furthermore, some variations at a particular scale persist over all locations (termed global features), whereas others are present at only a few locations (localized features). Wavelet spectra can be used to identify both localized and global features. The combination of localized and global features provides a complete picture of the scale-location information of different processes in the field and may provide better guidance in designing efficient management practices. Wavelet coherency partitions the total correlation between two variables into correlations at different scales and locations, while also revealing the scale- and location-specific relationship between those two variables. This relationship may be helpful in developing predictive links between one property and another.     

Continuous wavelet transforms of spectral decomposition analyses for fluvial reservoir characterization of Miano Gas Field,Indus Platform,Pakistan

Fluvial depositional systems constitute diverse host for the hydrocarbons all over the globe. Nevertheless, predicting the hydrocarbon sands within these assorted sand systems is a challenge for the prospect generation. We apply the continuous wavelet transform of spectral decomposition technique to a 3D seismic data of the Miano Gas Field in the Indus Platform, Pakistan. Full spectrum seismic attributes like the amplitude and root mean square (RMS) detect the reservoir sands down to a restricted reservoir compartment. Nonetheless, the continuous wavelet transforms can be advantageous in imaging and enactment of these clastic reservoirs within the whole reservoir zone. Rock physics reveal high-quality reservoir with an average porosity ～25% together with the discrimination of lithology/fluids, signifying the bright chances for ascertaining the potential stratigraphic reservoirs.     

Porosity prediction of lower cretaceous unconventional resource play,south Indus Basin,Pakistan, using the seismic spectral decomposition technique

Incised-valley shale systems are renowned as the fruitful exploration domains. However, the stratigraphic heterogeneity is significant, and thus, identifying the porous compartments within the thin-bedded and fractured shale system is one of the unconventional stratigraphic challenges. We present an innovative scheme for the exploration of the porous unconventional resources within the south Indus basin, Pakistan. The continuous wavelet transforms (CWT) exclusively detects the porous shales within the complete fractured system as compared to the full spectrum seismic data interpretation tool. The seismic amplitude, instantaneous frequency, and reflection strength tools show some tuning effects due to lithology and fluids. However, the tuning block of 28 Hz envelope sub-bands in conjunction with instantaneous phase tool accurately mark out the shale-plugged pinch out reservoirs. The synthetic wedge model based on the conventional seismic wavelet resolves the shale bed (incised-valley shale-filled lens) of about 23 m thickness, but shows limited imaging capability due to frequency band limitation for the porous shale locale. The acoustic impedance based on instantaneous spectral porosity modeling using the tuning volume of 28 Hz CWT has spectacularly resolved the thin-beds (gas shows) within the porosity range of 20 to 30%, which may have implications for future exploration of unconventional stratigraphic plays.     

Classification of rocks radionuclide data using machine learning techniques

The aim of this study is to assess the performance of linear discriminate analysis, support vector machines (SVMs) with linear and radial basis, classification and regression trees and random forest (RF) in the classification of radionuclide data obtained from three different types of rocks. Radionuclide data were obtained for metamorphic, sedimentary and igneous rocks using gamma spectroscopic method. A P-type high-purity germanium detector was used for the radiometric study. For analysis purpose, we have determined activity concentrations of ²³²Th, ²²⁶Ra and ⁴⁰K radionuclides, published elsewhere (Rafique et al. in Russ Geol Geophys 55:1073–1082, 2014), in different rock samples and built the classification model after pre-processing the data using three times tenfold cross-validation. Using this model, we have classified the new samples into known categories of sedimentary, igneous and metamorphic. The statistics depicts that RF and SVM with radial kernel outperform as compared to other classification methods in terms of error rate, area under the curve and with respect to other performance measures.     

Seismic activity prediction using computational intelligence techniques in northern Pakistan

Earthquake prediction study is carried out for the region of northern Pakistan. The prediction methodology includes interdisciplinary interaction of seismology and computational intelligence. Eight seismic parameters are computed based upon the past earthquakes. Predictive ability of these eight seismic parameters is evaluated in terms of information gain, which leads to the selection of six parameters to be used in prediction. Multiple computationally intelligent models have been developed for earthquake prediction using selected seismic parameters. These models include feed-forward neural network, recurrent neural network, random forest, multi layer perceptron, radial basis neural network, and support vector machine. The performance of every prediction model is evaluated and McNemar’s statistical test is applied to observe the statistical significance of computational methodologies. Feed-forward neural network shows statistically significant predictions along with accuracy of 75% and positive predictive value of 78% in context of northern Pakistan.     

柴达木盆地昆特依盐湖沉积特征及其盐类资源评价

昆特依盐湖位于柴达木盆地西北部,是一个相对独立的沉积盆地,可反映盆地西北部的演化历史,对研究盆地内盐湖演化有重要意义。基于研究区已有的研究成果,总结和评价了昆特依盐湖沉积与盐类矿产资源,并得到以下结论:1)昆特依湖自1 925～774 ka期间以暖湿型气候为主,其后呈干冷与暖湿气候交替出现,最早成盐期为1150 ka,最终于30.6 ka成为干盐湖。昆特依盆地早期沉积的细碎屑沉积物沉积速率(0.22 mm/a)慢于晚期沉积的石盐层(0.33 mm/a);2)昆特依盐湖液体KCl储量为1.32×10~8 t,MgCl储量为2.79×10~8 t,NaCl储量为24.15×10~8 t;3)估算出赋存于昆特依盐湖石盐矿中的锂资源量达39×10~4 t,同时估算出液体中的锂资源量约有74×10~4 t,二者总量达百万吨;4)昆特依矿田的成因属典型的内陆湖相沉积,成矿控制因素以构造作用为主,其次为气候及物质来源。关于昆特依盐湖沉积特征与盐类矿产资源特征的研究,有利于对本地区地质演化更全面的认识,对合理开发利用盐类资源有重要意义。     

Origin of Boron in the Gas Hure Salt Lake of NorthwesternQaidam Basin,China: Evidence from Hydrochemistry andBoron Isotopes

The Gas Hure Salt Lake(GHSL) in the northwestern Qaidam Basin, western China, is rich in boron(B)resources, but its B-resource origin is hardly known. Hydrochemical compositions and B isotope characteristics of different waters were collected around the GHSL, including the river water, stream water, spring water, salt-lake brine,intercrystalline brine, well water, drilling brine, and solar pond brine. The hydrochemical signatures suggest that silicates,carbonates and evaporates are the main B-bearing rocks during the water dynamic. The reservoir estimation of B resources shows that the Kulamulekesay River(KLMR) and the Atekan River(ATKR) contribute annually 18.3 tons and 22.84 tons of B, respectively, with a total amount of 11.72 × 10~4 tons of B during the past 5.7 ka. In comparison with the known B reservoir(32.96 × 10~4 tons) in the GHSL, a significant amount of B in the GHSL was probably recharged from deep fluids and sediments around the GHSL. The B concentration and B-enrichment degree are shaped by the evaporation process,which are highly elevated at the carnallite and bischofite stages.     

A simple model for the evolution of supermassive black holes and the quasar population

An empirically motivated model is presented for accretion-dominated growth of supermassive black holes (SMBH) in galaxies, and the implications are studied for the evolution of the quasar population in the Universe. We investigate the core aspects of the quasar population, including space density evolution, evolution of the characteristic luminosity, plausible minimum masses of quasars, the mass function of SMBH and their formation epoch distribution. Our model suggests that the characteristic luminosity in the quasar luminosity function arises primarily as a consequence of a characteristic mass scale above which there is a systematic separation between the black hole and the halo merging rates. At lower mass scales, black hole merging closely tracks the merging of dark haloes. When combined with a declining efficiency of black hole formation with redshift, the model can reproduce the quasar luminosity function over a wide range of redshifts. The observed space density evolution of quasars is well described by formation rates of SMBH above  ∼10⁸  M_⊙  . The inferred mass density of SMBH agrees with that found independently from estimates of the SMBH mass function derived empirically from the quasar luminosity function.