Towards a super-resolution based approach for improved face recognition in low resolution environment

Multimedia Tools and Applications - The video surveillance activity generates a vast amount of data, which can be processed to detect miscreants. The task of identifying and recognizing an object...     

Modeling of diffusional limitations in styrene acrylonitrile random bulk copolymerization

Diffusional limitations (the gel, glass, and cage effects) are manifested in several bulk free radical homopolymerizations as well as in random copolymerizations. These are associated with decreases of several orders of magnitude of the rate constants of termination, propagation, and initiation (the initiator efficiency), respectively. These phenomena have been modeled earlier using the free volume theory for the diffusivities of primary radicals, macro‐radicals, and monomer molecules, and have been applied to homopolymerizations. In this study, a similar model is developed for random bulk copolymerizations. The parameters of the model are fitted using isothermal data on styrene acrylonitrile random copolymerization carried out in small ampoules. Thereafter, best‐fit global correlations have been developed for this system. This enables the model to be used for studying non‐isothermal copolymerizations, as well as for carrying out optimization of industrial reactors, where non‐isothermal conditions are a norm. POLYM. ENG. SCI., 55:2098–2110, 2015. © 2014 Society of Plastics Engineers     

Impact of Climate Change on Future Flood Susceptibility: an Evaluation Based on Deep Learning Algorithms and GCM Model

Floods are common and recurring natural hazards which damages is the destruction for society. Several regions of the world with different climatic conditions face the challenge of floods in different magnitudes. Here we estimate flood susceptibility based on Analytical neural network (ANN), Deep learning neural network (DLNN) and Deep boost (DB) algorithm approach. We also attempt to estimate the future rainfall scenario, using the General circulation model (GCM) with its ensemble. The Representative concentration pathway (RCP) scenario is employed for estimating the future rainfall in more an authentic way. The validation of all models was done with considering different indices and the results show that the DB model is most optimal as compared to the other models. According to the DB model, the spatial coverage of very low, low, moderate, high and very high flood prone region is 68.20%, 9.48%, 5.64%, 7.34% and 9.33% respectively. The approach and results in this research would be beneficial to take the decision in managing this natural hazard in a more efficient way.

     

Analytical Modeling of a Triple Material Double Gate TFET with Hetero-Dielectric Gate Stack

Silicon - In this paper, we propose and develop an analytical model of a Triple material double gate Tunnel Field Effect Transistor (TM-DG TFET) with hetero-dielectric gate oxide stack comprising...     

Impact of adhesive ingredients on adhesion between rubber and brass-plated steel wire in tire

Proficiency on underlying mechanism of rubber-metal adhesion has been increased significantly in the last few decades. Researchers have investigated the effect of various ingredients, such as hexamethoxymethyl melamine, resorcinol, cobalt stearate, and silica, on rubber-metal interface. The role of each ingredient on rubber-metal interfacial adhesion is still a subject of scrutiny. In this article, a typical belt skim compound of truck radial tire is selected and the effect of each adhesive ingredient on adhesion strength is explored. Out of these ingredients, the effect of cobalt stearate is found noteworthy. It has improved adhesion strength by 12% (without aging) and by 11% (humid-aged), respectively, over control compound. For detailed understanding of the effect of cobalt stearate on adhesion, scanning electron microscopy and energy dispersive spectroscopy are utilized to ascertain the rubber coverage and distribution of elements. X-ray photoelectron spectroscopy results helped us to understand the impact of Cu_XS layer depth on rubber-metal adhesion. The depth profile of the Cu_XS layer was found to be one of the dominant factors of rubber-metal adhesion retention. Thus, this study has made an attempt to find the impact of different adhesive ingredients on the formation of Cu_XS layer depth at rubber-metal interface and establish a correlation with adhesion strength simultaneously.     

Studies on the synthesis of SAPO-5

Silicoaluminophosphate (SAPO-5) molecular sieves have been synthesised from reaction mixtures having a molar composition of: 0.7–1.0 Al₂O₃:0.7–1.0 P₂O₅:0.01–2.0 SiO₂:xR:40 H₂O (where R = (C₂H₃)₃N or (C₂H₃)₄NOH and x = 1.5–2.5 for (C₂H₅)⁵N and 0.5 for (C₂H₅)₄NOH, at 473 K using various sources of alumina and silica. The effects of (i) varying the crystallinity of the alumina source (boehmite) and (ii) the use of different silica sources such as freshly prepared silica either from sodium silicate or paddy husk extract, silica gel from commercial water-glass, and tetraethyl orthosilicate have been studied. The crystallinity of boehmite has been found to have a strong effect on its reactivity towards the formation of SAPO-5. The activity of boehmite for SAPO-5 formation increased with a decrease in its crystallinity (or with increase in its moisture content). Any silica source devoid of sodium ions could be employed for the synthesis of SAPO-5. The process of crystallisation started as early as within 1.5 h of reaction and incorporation of silicon into the AlPO₄ framework has been noted at this stage. Formation of some tridymite phase as impurity has been observed under conditions such as (i) SiO₂ concentration > 1.7 mole and (ii) x > 2.0 when R = (C²H₅)₃N.