The statistics of weak lensing at small angular scales: probability distribution function

Weak gravitational lensing surveys have the potential to probe mass density fluctuation in the Universe directly. Recent studies have shown that it is possible to model the statistics of the convergence field at small angular scales by modelling the statistics of the underlying density field in the highly non-linear regime. We propose a new method to model the complete probability distribution function of the convergence field as a function of smoothing angle and source redshift. The model relies on a hierarchical ansatz for the behaviour of higher order correlations of the density field. We compare our results with ray-tracing simulations and find very good agreement over a range of smoothing angles. Whereas the density probability distribution function is not sensitive to the cosmological model, the probability distribution function for the convergence can be used to constrain both the power spectrum and cosmological parameters.     

Can the big-bang singularity be avoided in the scale-covariant theory?

The Raychaudhuri equation is written in terms of atomic units in the scale-covariant theory of Canutoet al. (1977) and it is pointed out that even dust-filled nonrotating cosmological models may not have an observable singularity of infinite density in the beginning. Simple explicit solutions of Einstein's equations are also constructed in terms of atomic units and its is shown that the Einstein-de Sitter solution in gravitational units may appear to be a static or an oscillating universe in atomic units.     

Porous Structure Reconstruction Using Convolutional Neural Networks

The three-dimensional high-resolution imaging of rock samples is the basis for pore-scale characterization of reservoirs. Micro X-ray computed tomography (µ-CT) is considered the most direct means of obtaining the three-dimensional inner structure of porous media without deconstruction. The micrometer resolution of µ-CT, however, limits its application in the detection of small structures such as nanochannels, which are critical for fluid transportation. An effective strategy for solving this problem is applying numerical reconstruction methods to improve the resolution of the µ-CT images. In this paper, a convolutional neural network reconstruction method is introduced to reconstruct high-resolution porous structures based on low-resolution µ-CT images and high-resolution scanning electron microscope (SEM) images. The proposed method involves four steps. First, a three-dimensional low-resolution tomographic image of a rock sample is obtained by µ-CT scanning. Next, one or more sections in the rock sample are selected for scanning by SEM to obtain high-resolution two-dimensional images. The high-resolution segmented SEM images and their corresponding low-resolution µ-CT slices are then applied to train a convolutional neural network (CNN) model. Finally, the trained CNN model is used to reconstruct the entire low-resolution three-dimensional µ-CT image. Because the SEM images are segmented and have a higher resolution than the µ-CT image, this algorithm integrates the super-resolution and segmentation processes. The input data are low-resolution µ-CT images, and the output data are high-resolution segmented porous structures. The experimental results show that the proposed method can achieve state-of-the-art performance.     

Levels of organochlorine pesticide residues in some organs of the Ganges perch, Lates calcarifer, from the Ganges-Brahmaputra-Meghna Estuary, Bangladesh

Levels of organochlorine pesticide residues (p,p′ DDT, DDD, DDE, Aldrin, Dieldrin, Lindane, Heptachlor and BHC) were analysed in the dry and wet seasons in four organs (muscle, liver, gut and egg samples) of Ganges Perch, Lates calcarifer, collected during October–November–December, 1996 and May–June–July, 1997 from the Ganges–Brahmaputtra–Meghna estuary. The residues were analysed by using gas-chromatography (GC) in electron capture detector (ECD) mode and were verified by thin layer chromatography (TLC). Among the four organs analysed, the residues were found in the order egg > gut > muscle > liver. The pesticide residues were found in the order ∑DDT > Heptachlor >Dieldrin > Aldrin. Higher levels of residues were found during the dry season due to high lipid content in fishes. A positive correlation was observed between the pesticide residues (∑DDT and ∑OCs) and lipid contents of fish, and the correlation was found to be linear. The concentrations of pesticide residues in muscle, liver and gut were below the FAO/WHO (1993) recommended permissible limit except in eggs.