Determination of the rare earths, yttrium and scandium in silicate rocks and four new geological reference materials by electrothermal atomization from graphite and tantalum surfaces

An improved graphite furnace atomic-absorption method has been developed for the determination of Sc, Y and the rare-earth elements in silicate rocks and related materials. The method, which involves the separation of the lanthanides by ion-exchange followed by their determination by electrothermal atomization, with use of an automatic sampling device, is more rapid than a previous method based on separation by co-precipitation with calcium oxalate and hydrous ferric oxide followed by normal injection of the solution into the furnace. Greater sensitivity (~ 10-40-fold) for La, Ce, Pr, Gd, Tb and Lu is also achieved by using a tantalum foil-lined graphite furnace instead of a pyrolytically-coated furnace. Results obtained for five international reference rock samples, NIM-G, SCo-1, MAG-1, SDC-1 and BHVO-1, are compared with those obtained previously by the oxalate-hydrous oxide co-precipitation method and with other published values. Results are given for four new Canadian iron-formation reference materials, FeR-1 to FeR-4.     

Oscillatory behavior in the reaction of 3-alizarin-sulfonic acid sodium salt with acidic bromate

Uncatalyzed and catalyzed oscillatory behavior in the redox potential in the oxidation of 3-alizarin-sulfonic acid sodium salt with acidid (H₂SO₄) bromate is reported. Optimum and boundary conditions for each reactant exhibiting oscillatory behavior have been studied and a probable mechanism is suggested.

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- 3-- (H₂SO₄) . , .

     

On the Stable Radical of Some Non-Domestic String Algebras

Algebras and Representation Theory - We introduce the concept of a prime band in a string algebra Λ and use it to associate to Λ its finite bridge quiver. Then we introduce a new...     

A characterization of certain discrete exponential families

For independent random variables X and Y, define % MathType!MTEF!2!1!+-% feaafeart1ev1aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn% hiov2DGi1BTfMBaeXafv3ySLgzGmvETj2BSbqefm0B1jxALjhiov2D% aebbfv3ySLgzGueE0jxyaibaiGc9yrFr0xXdbba91rFfpec8Eeeu0x% Xdbba9frFj0-OqFfea0dXdd9vqaq-JfrVkFHe9pgea0dXdar-Jb9hs% 0dXdbPYxe9vr0-vr0-vqpWqaaeaabiGaciaacaqabeaadaqaaqGaaO% qaaiaabofaruWrL9MCNLwyaGqbciaa-bcacqGHHjIUcaWFGaGaa8hw% aiaa-TcacaWFzbaaaa!4551!\[{\rm{S}} \equiv X + Y\]. When the conditional expectations % MathType!MTEF!2!1!+-% feaafeart1ev1aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn% hiov2DGi1BTfMBaeXafv3ySLgzGmvETj2BSbqefm0B1jxALjhiov2D% aebbfv3ySLgzGueE0jxyaibaiGc9yrFr0xXdbba91rFfpec8Eeeu0x% Xdbba9frFj0-OqFfea0dXdd9vqaq-JfrVkFHe9pgea0dXdar-Jb9hs% 0dXdbPYxe9vr0-vr0-vqpWqaaeaabiGaciaacaqabeaadaqaaqGaaO% qaaiaadweacaGGBbqefCuzVj3zPfgaiuGajaaqcaWFNbGccaGGOaGa% amiwaiaacMcacaGG8bGaam4uaiaac2facqGHHjIUcaWGHbGaaiikai% aadofacaGGPaaaaa!4BC4!\[E[g(X)|S] \equiv a(S)\]and % MathType!MTEF!2!1!+-% feaafeart1ev1aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn% hiov2DGi1BTfMBaeXafv3ySLgzGmvETj2BSbqefm0B1jxALjhiov2D% aebbfv3ySLgzGueE0jxyaibaiGc9yrFr0xXdbba91rFfpec8Eeeu0x% Xdbba9frFj0-OqFfea0dXdd9vqaq-JfrVkFHe9pgea0dXdar-Jb9hs% 0dXdbPYxe9vr0-vr0-vqpWqaaeaabiGaciaacaqabeaadaqaaqGaaO% qaaiaadweacaGGBbGaamiAaiaacIcacaWGybGaaiykaiaacYhacaWG% tbGaaiyxaiabggMi6kaadkgacaGGOaGaam4uaiaacMcaaaa!4894!\[E[h(X)|S] \equiv b(S)\]are given, then under certain assumptions, the density function of X has the form of u(x)k()e^ax, where u(x) is uniquely determined by the functions a(·) and b(·).     

Chapter 11 Simulation studies of efficiency,returns to scale and misspecification with nonlinear functions in DEA

Using statistically designed experiments, 12,500 observations are generated from a 4-pieced Cobb-Douglas function exhibiting increasing and decreasing returns to scale in its different pieces. Performances of DEA and frontier regressions represented by COLS (Corrected Ordinary Least Squares) are compared at sample sizes ofn=50, 100, 150 and 200. Statistical consistency is exhibited, with performances improving as sample sizes increase. Both DEA and COLS generally give good results at all sample sizes. In evaluating efficiency, DEA generally shows superior performance, with BCC models being best (except at corner points), followed by the CCR model and then by COLS, with log-linear regressions performing better than their translog counterparts at almost all sample sizes. Because of the need to consider locally varying behavior, only the CCR and translog models are used for returns to scale, with CCR being the better performer. An additional set of 7,500 observations were generated under conditions that made it possible to compare efficiency evaluations in the presence of collinearity and with model misspecification in the form of added and omitted variables. Results were similar to the larger experiment: the BCC model is the best performer. However, COLS exhibited surprisingly good performances — which suggests that COLS may have previously unidentified robustness properties — while the CCR model is the poorest performer when one of the variables used to generate the observations is omitted.