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In this work, we have considered a model of the flat Friedmann–Robertson–Walker (FRW) Universe filled with cold dark matter
and a chameleon field where the scale function is taken as (i) intermediate expansion and (ii) logamediate expansion. In both
cases we find the expressions of the chameleon field, chameleon potential, statefinder parameters, and slow-roll parameters.
Also, it has been shown that the potential always decreases with the chameleon field in both scenarios. The nature of the
slow-roll parameters has been shown diagrammatically. 相似文献
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In the Himalayan regions, precipitation-runoff relationships are amongst the most complex hydrological phenomena, due to
varying topography and basin characteristics. In this study, different artificial neural networks (ANNs) algorithms were used to
simulate daily runoff at three discharge measuring sites in the Himalayan Kosi River Basin, India, using various combinations
of precipitation-runoff data as input variables. The data used for this study was collected for the monsoon period (June to October)
during the years of 2005 to 2009. ANNs were trained using different training algorithms, learning rates, length of data
and number of hidden neurons. A comprehensive multi-criteria validation test for precipitation-runoff modeling has been undertaken
to evaluate model performance and test its validity for generating scenarios. Global statistics have demonstrated that
the multilayer perceptron with three hidden layers (MLP-3) is the best ANN for basin comparisons with other MLP networks
and Radial Basis Functions (RBF). Furthermore, non-parametric tests also illustrate that the MLP-3 network is the best network
to reproduce the mean and variance of observed runoff. The performance of ANNs was demonstrated for flows during
the monsoon season, having different soil moisture conditions during period from June to October. 相似文献
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Anirban Mukhopadhyay Arun Mondal Sandip Mukherjee Dipam Khatua Subhajit Ghosh Debasish Mitra Tuhin Ghosh 《Journal of Earth System Science》2014,123(6):1349-1360
In the Himalayan states of India, with increasing population and activities, large areas of forested land are being converted into other land-use features. There is a definite cause and effect relationship between changing practice for development and changes in land use. So, an estimation of land use dynamics and a futuristic trend pattern is essential. A combination of geospatial and statistical techniques were applied to assess the present and future land use/land cover scenario of Gangtok, the subHimalayan capital of Sikkim. Multi-temporal satellite imageries of the Landsat series were used to map the changes in land use of Gangtok from 1990 to 2010. Only three major land use classes (built-up area and bare land, step cultivated area, and forest) were considered as the most dynamic land use practices of Gangtok. The conventional supervised classification, and spectral indices-based thresholding using NDVI (Normalized Difference Vegetation Index) and SAVI (Soil Adjusted Vegetation Index) were applied along with the accuracy assessments. Markov modelling was applied for prediction of land use/land cover change and was validated. SAVI provides the most accurate estimate, i.e., the difference between predicted and actual data is minimal. Finally, a combination of Markov modelling and SAVI was used to predict the probable land-use scenario in Gangtok in 2020 AD, which indicted that more forest areas will be converted for step cultivation by the year 2020. 相似文献
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We present H-band (1.4–1.8 μm) images of Neptune with a spatial resolution of ∼0.06″, taken with the W.M. Keck II telescope
using the slit-viewing camera (SCAM) of the NIRSPEC instrument backed with Adaptive Optics. Images with 60-second integration
times span 4 hours each on UT 20 and 21 August, 2001 and ∼1 hour on UT 1 September, 2001. These images were used to characterize
the overall brightness distribution on Neptune, and to determine rotations periods (which translate into wind speeds) of individual
cloud features. 相似文献
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