Some anisotropic dark energy models in Bianchi type-V space-time

The paper deals with Bianchi type V Universe, which has dynamical energy density. We consider Bianchi type V space-time, introducing three different skewness parameters along spatial directions to quantify the deviation of pressure from isotropy. To study the anisotropic nature of the dynamical dark energy, we assume that the skewness parameters are time dependent. It is found that the Universe achieves flatness in quintessence model. The physical behavior of the Universe has been discussed in detail.     

A family of well behaved charge analogues of?a?well behaved neutral solution in?general relativity

A family of charge analogues of a neutral solution with g ₄₄=(1+Cr ²)⁶ has been obtained by using a specific electric intensity, which involves a parameter K. Both neutral and charged solutions are analysed physically subject to the surface density 2×10¹⁴ gm/cm³ (neutron star). The neutral solution is well behaved for 0.0<Ca ²≤0.10477 while its charge analogues are well behaved for a wide range of a parameter K (0≤K≤72) i.e. pressure, density, pressure-density ratio, velocity of sound is monotonically decreasing and the electric intensity is monotonically increasing in nature for the given range of the parameter K. The maximum mass and radius occupied by the neutral solution are 3.4126M _Θ and 18.9227 km for Ca ²=0.10447 respectively. While the red shift at centre Z ₀=0.9686 and red shift at the surface Z _a =0.4612. For the charged solution, the maximum mass and radius are 5.6111M _Θ and 17.2992 km respectively for K=3.0130 and Ca ²=0.2500, with the red shift Z ₀=3.0113 and Z _a =1.0538.     

Seismic source zonation for NE India on the basis of past EQs and spatial distribution of seismicity parameters

Journal of Seismology - The North East (NE) of India is an earthquake (EQ) prone zone and has experienced considerable damages in the past due to high intensity EQs. With rapidly growing...     

Dark energy models with anisotropic fluid in Bianchi Type-VI 0 space-time with time dependent deceleration parameter

We present two dark energy (DE) models with an anisotropic fluid in Bianchi type-VI ₀ space-time by considering time dependent deceleration parameter (DP). The equation of state (EoS) for dark energy ω is found to be time dependent and its existing range for derived models is in good agreement with the recent observations. Under the suitable condition, the anisotropic models approach to isotropic scenario. We also find that during the evolution of the universe, the EoS parameter for DE changes from ω>−1 to ω=−1 in first model whereas from ω>−1 to ω<−1 in second model which is consistent with recent observations. The cosmological constant Λ is found to be a positive decreasing function of time and it approaches a small positive value at late time (i.e. the present epoch) which is corroborated by results from recent type Ia supernovae observations. The cosmic jerk parameter in our derived models is also found to be in good agreement with the recent data of astrophysical observations. The physical and geometric aspects of both the models are also discussed in detail.     

Bulk viscous LRS Bianchi-I Universe with variable G and decaying Λ

The present study deals with spatially homogeneous and totally anisotropic locally rotationally symmetric (LRS) Bianchi type I cosmological model with variable G and Λ in presence of imperfect fluid. To get the deterministic model of Universe, we assume that the expansion (θ) in the model is proportional to shear (σ). This condition leads to A=ℓB ⁿ , where A, B are metric potential. The cosmological constant Λ is found to be decreasing function of time and it approaches a small positive value at late time which is supported by recent Supernovae Ia (SN Ia) observations. Also it is evident that the distance modulus curve of derived model matches with observations perfectly.     

Dark energy model with variable q and ω in LRS Bianchi-II space-time

The present study deals with spatial homogeneous and anisotropic locally rotationally symmetric (LRS) Bianchi-II dark energy model in general relativity. The Einstein’s field equations have been solved exactly by taking into account the proportionality relation between one of the components of shear scalar $(\sigma^{1}_{1})$ and expansion scalar (?), which, for some suitable choices of problem parameters, yields time dependent equation of state (EoS) and deceleration parameter (DP), representing a model which generates a transition of universe from early decelerating phase to present accelerating phase. The physical and geometrical behavior of universe have been discussed in detail.     

Assessment of Aphid Infestation in Mustard by Hyperspectral Remote Sensing

Field experiment was conducted during 2009–10 and 2010–11 rabi season at research farm of IARI, New Delhi for assessing the aphid infestation in mustard. In aphid infested plant the LAI was 67 to 94% lower than healthy plant. Chlorophyll concentration decreased to 50% in infested plant as compared to healthy plant. Infestation was more severe in late sown crop and due to aphid infestation the percentage oil content and yield was reduced significantly. The spectral reflectance of aphid infested canopy and healthy canopy taken in the laboratory had significant difference in NIR region. In the visible region, the reflectance peak occurred in healthy canopy at around 550–560 nm while this peak was lower by 31% in the aphid infested canopy. The reflectance for healthy crop was found to be more in visible as well as NIR region as compared to aphid infested canopy. The most significant spectral bands for the aphid infestation in mustard are in visible (550–560 nm) and near infrared regions (700–1250 nm and 1950–2450 nm). The different level of aphid infestation can be identified in 1950–2450 nm spectral regions. Spectral indices viz NDVI, RVI, AI and SIPI had significant correlation with aphid infestation. Hence these indices could be used for identifying aphid infestation in mustard.     

Effect of Different Growing Environments on Population Dynamics of Sucking Pests in Relation to Various Spectral Indices in Cotton

Present study was designed to determine the effect of various growing environments on sucking pest population dynamics in cotton and to work out their relation with spectral indices. Crop spectral reflectance in four IRS bands was measured with ground truth radiometer during 1000–1200 h in all the treatment combinations. Incidence of sucking pest in cotton was found out to be highly influenced by growing environments. The leafhopper and whitefly population was highest in 15 May sown cotton crop and was lowest in 15 April sown crop. Cultivar HS-6 was highly affected by both the sucking pest than the other cultivar H-1226. The spectral indices (SR, NDVI and TVI) were highest in 15 April sown crop at all the phenophases and were lowest in 15 May sown crop. The cultivar H-1226 showed higher values of spectral indices as compared to HS-6. The relationship of pests’ population with various spectral indices was established. Multiple regression models based on spectral indices can be used for prediction of sucking pest population more than 69 and 74 % accuracy in leafhopper and whitefly, respectively in cotton crop.     

Ann Based Robust LULC Classification Technique Using Spectral, Texture and Elevation Data

Albeit the advent of fast computing facilities, digital image classification of remotely sensed data is still remain the topic of research. This might be due to the reason that the ancillary information such as texture and topography is absent in image classification. Since two decades, texture is widely applied in image classification but there is no explicit icon in most popularly used remote sensing software. Hence the aim of this study is to classify the Landsat ETM+ captured in 2000 using spectral information, topographic information and texture information. This study helps to throw light into statistical texture analysis i.e., the effect window size i.e., 3?×?3 to 9?×?9, on image classification. The ability of Grey Run Length Matrix (GRLM), which is computationally complex compared to industrially well-known Grey Level Co-occurrence Matrix (GLCM) but encompasses greater potential to discriminate between two classes, is explored. Eight spectral bands, 11 texture parameters extracted from Landsat ETM+ data and elevation, slope, aspect extracted from DEM data are classified individually using Artificial Neural Network (ANN) and the individually classified information is integrated using endorsement theory. Validations of classified results are performed using Google Maps and Landmap services updated in 2009. The results are compared with Maximum Likelihood classification (MLC) and hence all the evidence (spectral, texture and topography) with 5?×?5 texture window provided maximum classification accuracy of 70.44 %.     

Fuzzy Based Approach for Moist Deciduous Forest Identification Using MODIS Temporal Data

Fuzzy based soft classification have been used immensely for handling the mixed pixel and hence to extract the single class of interest. The present research attempts to extract the moist deciduous forest from MODIS temporal data using the Possibilistic c-Means (PCM) soft classification approach. Temporal MODIS (7 dates) data were used to identify moist deciduous forest and temporal AWiFS (7 dates) data were used as reference data for testing. The Simple Ratio (SR), Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI), and Transformed Normalized Difference Vegetation Index (TNDVI) were used to generate the temporal vegetation indices for both the MODIS and the AWiFS datasets. It was observed from the research that the MODIS temporal NDVI data set1, which contain the minimum number of images and avoids the temporal images corresponding to the highest frequency stages of onset of greenness (OG) and end of senescence (ES) activity of moist deciduous forest have been found most suitable data set for identification of moist deciduous forest with the maximum fuzzy overall accuracy of 96.731 %.