Scale-Invariant Two Component Dark Matter

We study a scale invariant extension of the standard model which can explain simultaneously dark matter and the hierarchy problem. In our set-up, we introduce a scalar and a spinor as two-component dark matter in addition to scalon field as a mediator. Interesting point about our model is that due to scale invariant conditions, compared to other two-component dark matter models, it has lower independent parameters. Possible astrophysical and laboratory signatures of two-component dark matter candidate are explored and it is shown that the most contribution of observed relic density of dark matter can be determined by spinor dark matter. Detectability of these dark matter particles is studied and the direct and invisible Higgs decay experiments are used to rule out part of the parameter space of the model. In addition, the dark matter self-interactions are considered and shown that their contribution saturate this constraint in the resonant regions.Comment: 22 pages, 14 figure

Application of Controlled Source Audio Magnetotelluric (Csamt) at Geothermal

CSAMT or Controlled Source Audio-Magnetotelluric is one of the Geophysics methods to determine the resistivity of rock under earth surface. CSAMT method utilizes artificial stream and injected into the ground, the frequency of artificial sources ranging from 0.1 Hz to 10 kHz, CSAMT data source effect correction is inverted. From the inversion results showed that there is a layer having resistivity values ranged between 2.5 Ω.m – 15 Ω.m, which is interpreted that the layer is clay

V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation

Convolutional Neural Networks (CNNs) have been recently employed to solve problems from both the computer vision and medical image analysis fields. Despite their popularity, most approaches are only able to process 2D images while most medical data used in clinical practice consists of 3D volumes. In this work we propose an approach to 3D image segmentation based on a volumetric, fully convolutional, neural network. Our CNN is trained end-to-end on MRI volumes depicting prostate, and learns to predict segmentation for the whole volume at once. We introduce a novel objective function, that we optimise during training, based on Dice coefficient. In this way we can deal with situations where there is a strong imbalance between the number of foreground and background voxels. To cope with the limited number of annotated volumes available for training, we augment the data applying random non-linear transformations and histogram matching. We show in our experimental evaluation that our approach achieves good performances on challenging test data while requiring only a fraction of the processing time needed by other previous methods

Effects of montmorillonite nano-clay fillers on PEI mixed matrix membrane for CO(2) removal

This paper focuses on the effect of montmorillonite nano-clay fillers on polyetherimide (PEI) mixed matrix membrane, specifically upon the removal of carbon dioxide. Five different types of montmorillonite (MMT) nano-clays, including unmodified and industrially modified clays, were used as filler to fabricate asymmetric flat sheet mixed matrix membrane (MMM) via a dry/wet phase inversion technique. The five types of clay used were: raw MMT, Cloisite 15A, general MMT, hydrophobic MMT and hydrophilic MMT. The MMTs were characterized by X-ray diffractometry (XRD), thermal gravimetric analysis (TGA), Fourier-transform infrared (FTIR). The fabricated MMMs were characterized by differential scanning calorimetry (DSC), field emission scanning electron microscopic (FESEM) and pure gas permeation testing. The gas permeation results revealed the following order in terms of the permselectivity for CO2/CH4 separation: Cloisite 15A > general MMT > hydrophilic MMT > hydrophobic MMT > raw MMT. The best results were obtained at 0.5 wt.% Cloisite 15A loading where the selectivity enhancement was about 28% as compared to that of neat PEI