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...     

ResearchGate and Google Scholar: how much do they differ in publications,citations and different metrics and why?

ResearchGate has emerged as a popular professional network for scientists and researchers in a very short span. Similar to Google Scholar, the ResearchGate indexing uses an automatic crawling algorithm that extracts bibliographic data, citations, and other information about scholarly articles from various sources. However, it has been observed that the two platforms often show different publication and citation data for the same institutions, journals, and authors. While several previous studies analysed different aspects of ResearchGate and Google Scholar, the quantum of differences in publications, citations, and metrics between the two and the probable reasons for the same are not explored much. This article, therefore, attempts to bridge this research gap by analysing and measuring the differences in publications, citations, and different metrics of the two platforms for a large data set of highly cited authors. The results indicate that there are significantly high differences in publications and citations for the same authors captured by the two platforms, with Google Scholar having higher counts for a vast majority of the cases. The different metrics computed by the two platforms also differ in their values, showing different degrees of correlation. The coverage policy, indexing errors, author attribution mechanism, and strategy to deal with predatory publishing are found to be the main probable reasons for the differences in the two platforms.

     

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...     

Structural Studies of TeO2–SeO2–Na2O Glass System

Glass Physics and Chemistry - The growth of nanocrystalization in TeO2–SeO2–Na2O glasses is achieved by the conventional heat treatment method. The influence of Na2O concentration on...     

Bulk free radical polymerizations of methyl methacrylateunder non-isothermal conditions and with intermediate additionof initiator: Experiments and modeling

Experimental data on the monomer conversion, x_m, and the weight average molecular weight, M_w, have been generated under several isothermal and non-isothermal conditions for the polymerization of methyl methacrylate in a rheometer-reactor assembly. The non-isothermal results, in particular, can be used to provide more stringent tests of kinetic models than isothermal data alone. A simple empirical model has been used to describe this system that accounts for the gel (Trommsdorff) and glass effects. The model involves only x_m and the temperature, and is quite general. The model parameters are tuned using only three sets of isothermal data. Good agreement is found between the experimental results and model predictions for a whole variety of experimental conditions, including non-isothermal operation and with intermediate addition of initiator. Because of its generality, this model is quite suitable for use for on-line optimizing control as well as for describing industrial reactors.