Sea buckthorn berries from Hippophae rhamnoides, H. tibetana, and H. salicifolia were collected from the cold deserts of the Himalayas (Lahaul, Ladakh, and Spiti; India) and characterized in terms of the FA, carotenoid, tocopherol, and tocotrienol composition in their pulp oil. These varied from species to species. Total carotenoids ranged from 692 to 3420 mg/kg in pulp oils of fresh berries, and total tocols, from 666 to 1788 mg/kg. Hippophae salicifolia berries contained substantially lower amounts of pulp oil, with lower levels of carotenoids and tocopherols. There was little difference in the proportion of individual tocols in pulp among the three species. α-Tocopherol alone constituted 40-60% of total pulp tocols in berries. Pulp oils had palmitoleic acid (32-53%) as the most abundant FA followed by palmitic (25-35%), oleic (8-26%), linoleic (5-16%), and linolenic (0.6-2.6%) acids, with the highest deviation observed in the proportion of palmitoleic acid in these berries. Hippophae rhamnoides and H. tibetana contained the highest amount of the lipophilic carotenoids and tocols. Hippophae salicifolia berries had higher amounts of lipophobic constituents such as vitamin C and flavonols.

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The mechanisms of stabilization of water-in-crude oil emulsions have been investigated by changing the solvent-solute interactions in crude oil. Diluting the original crude oil with varying amounts of heptane, which is a poor solvent for asphaltenes, changes the solvent-solute interactions, leading to flocculation of asphaltenes and thus changing the emulsion stability. The interactions between the water droplets in an emulsion system have been quantified by measuring the radial distribution function and thereby the pair potential using the digitized optical imaging technique. It has been observed that the force of interaction between water droplets is oscillatory. This shows that non-DLVO forces, such as attractive depletion and repulsive structural forces, exist between the droplets. The interaction between the water droplets has been modeled by studying the properties of a thin liquid film sandwiched between the water droplets. Because of the film confinement effect, asphaltene-resin particles form a layered structure inside the thin liquid film. Also, the role of hydrodynamic interactions has been studied by using the film rheometer to measure the dynamic film tension and film elasticity. It has been found that, because of the adsorption of asphaltene at the film interfaces, the film elasticity plays a significant role in stabilizing these emulsions.

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Deep Learning is an effective technique and used in various fields of natural language processing, computer vision, image processing and machine vision. Deep fakes uses deep learning technique to synthesis and manipulate image of a person in which human beings cannot distinguish the fake one. By using generative adversarial neural networks (GAN) deep fakes are generated which may threaten the public. Detecting deep fake image content plays a vital role. Many research works have been done in detection of deep fakes in image manipulation. The main issues in the existing techniques are inaccurate, consumption time is high. In this work we implement detecting of deep fake face image analysis using deep learning technique of fisherface using Local Binary Pattern Histogram (FF-LBPH). Fisherface algorithm is used to recognize the face by reduction of the dimension in the face space using LBPH. Then apply DBN with RBM for deep fake detection classifier. The public data sets used in this work are FFHQ, 100K-Faces DFFD, CASIA-WebFace.

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