Effect of post-deposition annealing on composition and electrical properties of dc reactive magnetron sputtered Al2O3 thin films

We have investigated the effect of post- deposition annealing on the composition and electrical properties of alumina (Al2O3) thin films. Al2O3 were deposited on n-type Si < 100 >. substrates by dc reactive magnetron sputtering. The films were subjected to post- deposition annealing at 623, 823 and 1023 K in vacuum. X-ray photoelectron spectroscopy results revealed that the composition improved with post- deposition annealing, and the film annealed at 1023 K became stoichiometric with an O/Al atomic ratio of 1.49. Al/Al2O3/Si metal-oxide-semiconductor (MOS) structures were then fabricated, and a correlation between the dielectric constant epsilon(r) and interface charge density Q(i) with annealing conditions were studied. The dielectric constant of the Al2O3 thin films increased to 9.8 with post- deposition annealing matching the bulk value, whereas the oxide charge density decreased to 3.11 x 10(11) cm(-2.) Studies on current-voltage IV characteristics indicated ohmic and Schottky type of conduction at lower electric fields (<0.16 MV cm(-1)) and space charge limited conduction at higher electric fields.

A Comparison of Machine Learning Techniques for Modeling River Flow Time Series: The Case of Upper Cauvery River Basin

Models of river flow time series are essential in efficient management of a river basin. It helps policy makers in developing efficient water utilization strategies to maximize the utility of scarce water resource. Time series analysis has been used extensively for modeling river flow data. The use of machine learning techniques such as support-vector regression and neural network models is gaining increasing popularity. In this paper we compare the performance of these techniques by applying it to a long-term time-series data of the inflows into the Krishnaraja Sagar reservoir (KRS) from three tributaries of the river Cauvery. In this study flow data over a period of 30 years from three different observation points established in upper Cauvery river sub-basin is analyzed to estimate their contribution to KRS. Specifically, ANN model uses a multi-layer feed forward network trained with a back-propagation algorithm and support vector regression with epsilon intensive-loss function is used. Auto-regressive moving average models are also applied to the same data. The performance of different techniques is compared using performance metrics such as root mean squared error (RMSE), correlation, normalized root mean squared error (NRMSE) and Nash-Sutcliffe Efficiency (NSE).

Adaptive executions of hyperbolic block-structured AMR applications on GPU systems

A block-structured adaptive mesh refinement (AMR) technique has been used to obtain numerical solutions for many scientific applications. Some block-structured AMR approaches have focused on forming patches of non-uniform sizes where the size of a patch can be tuned to the geometry of a region of interest. In this paper, we develop strategies for adaptive execution of block-structured AMR applications on GPUs, for hyperbolic directionally split solvers. While effective hybrid execution strategies exist for applications with uniform patches, our work considers efficient execution of non-uniform patches with different workloads. Our techniques include bin-packing work units to load balance GPU computations, adaptive asynchronism between CPU and GPU executions using a knapsack formulation, and scheduling communications for multi-GPU executions. Our experiments with synthetic and real data, for single-GPU and multi-GPU executions, on Tesla S1070 and Fermi C2070 clusters, show that our strategies result in up to a 3.23 speedup in performance over existing strategies.

An empirical understanding of use of internal analogies in conceptual design

Internal analogies are created if the knowledge of source domain is obtained only from the cognition of designers. In this paper, an understanding of the use of internal analogies in conceptual design is developed by studying: the types of internal analogies; the roles of internal analogies; the influence of design problems on the creation of internal analogies; the role of experience of designers on the use of internal analogies; the levels of abstraction at which internal analogies are searched in target domain, identified in source domain, and realized in the target domain; and the effect of internal analogies from the natural and artificial domains on the solution space created using these analogies. To facilitate this understanding, empirical studies of design sessions from earlier research, each involving a designer solving a design problem by identifying requirements and developing conceptual solutions, without using any support, are used. The following are the important findings: designers use analogies from the natural and artificial domains; analogies are used for generating requirements and solutions; the nature of the design problem influences the use of analogies; the role of experience of designers on the use of analogies is not clearly ascertained; analogical transfer is observed only at few levels of abstraction while many levels remain unexplored; and analogies from the natural domain seem to have more positive influence than the artificial domain on the number of ideas and variety of idea space.

Comparative evaluation of inversion approaches of the radiative transfer model for estimation of crop biophysical parameters

The inversion of canopy reflectance models is widely used for the retrieval of vegetation properties from remote sensing. This study evaluates the retrieval of soybean biophysical variables of leaf area index, leaf chlorophyll content, canopy chlorophyll content, and equivalent leaf water thickness from proximal reflectance data integrated broadbands corresponding to moderate resolution imaging spectroradiometer, thematic mapper, and linear imaging self scanning sensors through inversion of the canopy radiative transfer model, PROSAIL. Three different inversion approaches namely the look-up table, genetic algorithm, and artificial neural network were used and performances were evaluated. Application of the genetic algorithm for crop parameter retrieval is a new attempt among the variety of optimization problems in remote sensing which have been successfully demonstrated in the present study. Its performance was as good as that of the look-up table approach and the artificial neural network was a poor performer. The general order of estimation accuracy for para-meters irrespective of inversion approaches was leaf area index > canopy chlorophyll content > leaf chlorophyll content > equivalent leaf water thickness. Performance of inversion was comparable for broadband reflectances of all three sensors in the optical region with insignificant differences in estimation accuracy among them.

Tailored electrical conductivity, electromagnetic shielding and thermal transport in polymeric blends with graphene sheets decorated with nickel nanoparticles

Electromagnetic interference shielding (EMI) materials were designed using PC (polycarbonate)/SAN poly(styrene-co-acrylonitrile)] blends containing few-layered graphene nanosheets decorated with nickel nanoparticles (G-Ni). The graphene nanosheets were decorated with nickel nanoparticles via the uniform nucleation of the metal salt precursor on graphene sheets as the substrate. In order to localize the nanoparticles in the PC phase of the PC/SAN blends, a two-step mixing protocol was adopted. In the first step, graphene sheets were mixed with PC in solution and casted into a film, followed by dilution of these PC master batch films with SAN in the subsequent melt extrusion step. The dynamic mechanical properties, ac electrical conductivity, EMI shielding effectiveness and thermal conductivity of the composites were evaluated. The G-Ni nanoparticles significantly improved the electrical and thermal conductivity in the blends. In addition, a total shielding effectiveness (SET) of -29.4 dB at 18 GHz was achieved with G-Ni nanoparticles. Moreover, the blends with G-Ni exhibited an impressive 276% higher thermal conductivity and 29.2% higher elastic modulus with respect to the neat blends.