Use of Composition-Graded Bi-Electrolyte Cells for Thermodynamic Studies on Lanthanum Aluminates

Innovative bi-electrolyte solid-state cells incorporating single crystal CaF2 and composition-graded solid electrolyte (LaF3) y (CaF2) 1-y (y = 0 to 0.32) were used for measurement of the standard Gibbs energy of formation of hexagonal La0.885Al11.782O19 and cubic LaAlO3 from component binary oxides La2O3 and alpha-Al2O3 in the temperature range from 875 to 1175 K. The cells were designed based on experimentally verified relevant phase relations in the systems La2O3-Al2O3LaF3 and CaF2-LaF3. The results can be summarized as: 5.891 alpha-Al2O3 + 0.4425 La2O3 (A-rare earth)-> La0.885Al11.782O19 (hex), Delta G(f(ox))(degrees)(+/- 2005)/Jmol(-1) = -80982 + 7.313(T/K); 1/2 La2O3 (A-rare earth) + 1/2 a-Al2O3 -> LaAlO3 (cubic), Delta G(f(ox))(degrees)(+/- 2100)/Jmol(-1) = -59810 + 4.51(T/K). Electron probe microanalysis was used to ascertain the non-stoichiometric range of the hexaaluminate phase. The results are critically analyzed in the light of earlier electrochemical measurements. Several imperfections in the electrochemical cells used by former investigators are identified. Data obtained in the study for LaAlO3 are consistent with calorimetric enthalpy of formation and entropy derived from heat capacity data. Estimated are the standard entropy and the standard enthalpy of formation from elements of hexagonal La0.885Al11.782O19 and rhombohedral LaAlO3 at 298.15 K. c 2014 The Electrochemical Society. All rights reserved.

Development and Evaluation of Statistical Downscaling Models for Monthly Precipitation

Several statistical downscaling models have been developed in the past couple of decades to assess the hydrologic impacts of climate change by projecting the station-scale hydrological variables from large-scale atmospheric variables simulated by general circulation models (GCMs). This paper presents and compares different statistical downscaling models that use multiple linear regression (MLR), positive coefficient regression (PCR), stepwise regression (SR), and support vector machine (SVM) techniques for estimating monthly rainfall amounts in the state of Florida. Mean sea level pressure, air temperature, geopotential height, specific humidity, U wind, and V wind are used as the explanatory variables/predictors in the downscaling models. Data for these variables are obtained from the National Centers for Environmental Prediction-National Center for Atmospheric Research (NCEP-NCAR) reanalysis dataset and the Canadian Centre for Climate Modelling and Analysis (CCCma) Coupled Global Climate Model, version 3 (CGCM3) GCM simulations. The principal component analysis (PCA) and fuzzy c-means clustering method (FCM) are used as part of downscaling model to reduce the dimensionality of the dataset and identify the clusters in the data, respectively. Evaluation of the performances of the models using different error and statistical measures indicates that the SVM-based model performed better than all the other models in reproducing most monthly rainfall statistics at 18 sites. Output from the third-generation CGCM3 GCM for the A1B scenario was used for future projections. For the projection period 2001-10, MLR was used to relate variables at the GCM and NCEP grid scales. Use of MLR in linking the predictor variables at the GCM and NCEP grid scales yielded better reproduction of monthly rainfall statistics at most of the stations (12 out of 18) compared to those by spatial interpolation technique used in earlier studies.

ANRC Hybrid Test Bed Implementation and an End-to-End Performance Characterization of Dynamic Spectrum Access

An abundance of spectrum access and sensing algorithms are available in the dynamic spectrum access (DSA) and cognitive radio (CR) literature. Often, however, the functionality and performance of such algorithms are validated against theoretical calculations using only simulations. Both the theoretical calculations and simulations come with their attendant sets of assumptions. For instance, designers of dynamic spectrum access algorithms often take spectrum sensing and rendezvous mechanisms between transmitter-receiver pairs for granted. Test bed designers, on the other hand, either customize so much of their design that it becomes difficult to replicate using commercial off the shelf (COTS) components or restrict themselves to simulation, emulation /hardware-in-Ioop (HIL), or pure hardware but not all three. Implementation studies on test beds sophisticated enough to combine the three aforementioned aspects, but at the same time can also be put together using COTS hardware and software packages are rare. In this paper we describe i) the implementation of a hybrid test bed using a previously proposed hardware agnostic system architecture ii) the implementation of DSA on this test bed, and iii) the realistic hardware and software-constrained performance of DSA. Snapshot energy detector (ED) and Cumulative Summation (CUSUM), a sequential change detection algorithm, are available for spectrum sensing and a two-way handshake mechanism in a dedicated control channel facilitates transmitter-receiver rendezvous.