The effects of isotopic separation on closed nuclear fuel cycles

This paper investigates the potential benefits to the fuel cycle outcomes of removing a single isotope during separation processes. Two strategies for managing the removed isotope are considered. The first strategy looks at removal of a short to intermediate lived isotope from a mass stream to be recycled and subsequently recycling its decay daughter in a transmuting reactor. The second investigates the effect of removing a long lived fission product from high level waste and recycling it into the transmuting reactor. This analysis shows that the removal of Cm-244 using the first strategy provides a marked benefit to several fuel cycle metrics. The second strategy benefits the long term radioactivity measured from the high level waste from isotopes including Zr-93 and Cs-137.Mechanical Engineerin

An analysis of schema change intervention

Successful organizational transformation relies on being able to achieve paradigm or collective schema change, and more particularly, the ability to manage the interplay between pre-existing schemas and alternative schemas required for new environments. This conceptual paper presents an analysis and critique of collective schema change dynamics. Two schema change pathways are reflected in the literature: frame-juxtapose-transition and frame-disengage-learning. Research findings in each pathway are limited and/or contradictory. Moreover, research on schema change focuses primarily on social dynamics and less on the relationship between social schema change dynamics and individual schema change dynamics. One implication of this lack of focus on individual schema change dynamics is the masking of the high level of cognitive processing and cognitive effort required by individuals to effect schema change. The capacity to achieve organizational transformation requires that more attention is given to managing these dynamics, which, in turn, requires significant investment in developing the change leadership capabilities of managers and the organizations they manage

A bias correction for the minimum error rate in cross-validation

Tuning parameters in supervised learning problems are often estimated by cross-validation. The minimum value of the cross-validation error can be biased downward as an estimate of the test error at that same value of the tuning parameter. We propose a simple method for the estimation of this bias that uses information from the cross-validation process. As a result, it requires essentially no additional computation. We apply our bias estimate to a number of popular classifiers in various settings, and examine its performance.Comment: Published in at http://dx.doi.org/10.1214/08-AOAS224 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Stochastic Satbility and Performance Robustness of Linear Multivariable Systems

Stochastic robustness, a simple technique used to estimate the robustness of linear, time invariant systems, is applied to a single-link robot arm control system. Concepts behind stochastic stability robustness are extended to systems with estimators and to stochastic performance robustness. Stochastic performance robustness measures based on classical design specifications are introduced, and the relationship between stochastic robustness measures and control system design parameters are discussed. The application of stochastic performance robustness, and the relationship between performance objectives and design parameters are demonstrated by means of example. The results prove stochastic robustness to be a good overall robustness analysis method that can relate robustness characteristics to control system design parameters

Spectroelectrochemical Elucidation of the Kinetics of Two Closely Spaced Electron Transfers

The use of spectroelectrochemistry to facilitate the analysis of an EE mechanism was reported in this work. Using a set of spectra as a function of potential, the spectra of all three oxidation states were determined using evolving window factor analysis. From these spectra, the concentration of each species in solution was determined for each potential. Using these data, the current was calculated. Unlike the direct measurement of current, the current due to each redox process was determined, allowing one to analyze each redox process separate from the other. With the use of the Butler–Volmer equation, the redox potential and the heterogeneous electron transfer parameters were measured. The spectrally determined current has the advantage of determining the current due to each redox process which is not generally possible with voltammetric data when the redox potentials are close together. This method was applied to the spectroelectrochemical reduction of Escherichia coli sulfite reductase hemoprotein (SiR-HP) in a phosphate buffer and in the presence of cyanide. The electrochemical parameters (E°’s, k°’s and α’s) for each electron transfer were calculated for both the uncoordinated and cyanide coordinated species. The rates of electron transfer for the siroheme and iron–sulfur cluster were slower than the rates observed for other heme proteins. This is probably due to the fact that this protein is significantly larger than most of the heme protein previously studied. This approach is a powerful tool for two-electron transfers when the E° values are close together

Use of Evolutionary Factor Analysis in the Spectroelectrochemistry of Escherichia coli Sulfite Reductase Hemoprotein and a Mo/Fe/S Cluster

The deconvolution of spectroelectrochemical data is often quite difficult if the spectra of intermediates are not known. Factor analysis, however, has been shown to be a powerful technique which can make it possible to deconvolute overlapping spectra. In this work, evolving factor analysis will be used to determine the number of intermediates and the spectra of those species for two typical spectroelectrochemical experiments:  linear scan voltammetry and chronoabsorptometry in a thin-layer cell. The first system was the reduction of E. coli sulfite reductase hemoprotein (SiR-HP). Principal factor analysis indicated that three species were present. By using evolving factor analysis, the potential regions where each of the species were present were identified, and their concentrations and spectra were determined by the use of the mass balance equation. The spectra of the one-electron (SiR-HP1-) and two-electron (SiR-HP2-) reduced product were compared with previous work. The second experiment was the chronoabsorptometry of Cl2FeS2MoS2FeCl22- in methylene chloride. This experiment indicated that five species were present during the experiment. The entire set of 61 spectra were fit by assuming that there were 4 species present during the electrolysis. The rate constant for the appearance of subsequent species fit quite well with the rate constant for the disappearance of previous species. The spectra of the intermediates and final product were obtained using evolving factor analysis and a mass balance equation. Identification of the fifth species, which was probably the initial reduction product, Cl2FeS2MoS2FeCl23-, was difficult due to its low concentration and the fact that it was present in the same time region as the starting material

Use of Factor Analysis in Multi‐Electron Spectroelectrochemistry

Spectroelectrochemistry and voltammetry contain both unique and complementary information. For multielectron transfers, information on each electron exchange is only directly accessible in the voltammetric data if the potentials are well separated so that two distinct waves can be observed. If the E°’s are close together, the voltammetric data will contain the sum of the two exchanges which can only be deconvoluted by modeling the system and solving the appropriate equations. On the other hand, the spectroscopic data contains direct information on each electron exchange even when the E°’s are close together. Unfortunately, this information cannot be readily extracted if the intermediate oxidation state does not have a potential region where it is the dominant species. Chemometric methods such as factor analysis though can be used to deduce the spectra of each species even if they don’t dominate in any potential region. Initial work on the application of factor analysis to spectroelectrochemistry has been reported. Traditional methods of electroanalytical analysis are based on models that relate the concentration of electroactive materials to electrode potentials and solution concentrations. The model and parameters are adjusted to obtain the best fit to a model. Chemometric methods such as factor analysis allow the experimenter to determine solution concentrations without knowledge of the precise electrochemical mechanism. The utility of this approach will be demonstrated by the study of a protein, E. coli sulfite reductase hemoprotein, which is capable of transferring two-electrons and the ΔE° values are less than 100 mV, causing the waves to overlap. With these methods more detailed information on the electron transfer rate and associated kinetics processes can be more clearly identified