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Araneda, A., Sanhueza, V., & Bennun, L. (2016). Simplified Calibration for Total-Reflection X-ray Fluorescence. Anal. Lett., 49(11), 1711–1721.
Abstract: The usual method to determine the relative sensitivity curve for total-reflection X-ray fluorescence (TXRF) uses multielemental solutions, which may be purchased or prepared in the laboratory. In the former case, the accuracy and precision of the concentrations are certified by the provider, while in the latter, the experience of the technical staff determines the analytical quality. These procedures are costly and the quality of the solutions cannot be easily verified. The goal of this work was to use pure crystalline salts containing two elements that may be quantified by TXRF for the calibration of the spectrometer. The analysis of these samples along with a mathematical procedure assures good precision of the results. The reported method is economically efficient, simple, and eliminates the uncertainties of the element concentration in the samples produced by the standard methods, thereby improving the quality of TXRF results.
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Canessa, E., & Chaigneau, S. (2015). Calibrating Agent-Based Models Using a Genetic Algorithm. Stud. Inform. Control, 24(1), 79–90.
Abstract: We present a Genetic Algorithm (GA)-based tool that calibrates Agent-based Models (ABMs). The GA searches through a user-defined set of input parameters of an ABM, delivering values for those parameters so that the output time series of an ABM may match the real system's time series to certain precision. Once that set of possible values has been available, then a domain expert can select among them, the ones that better make sense from a practical point of view and match the explanation of the phenomenon under study. In developing the GA, we have had three main goals in mind. First, the GA should be easily used by non-expert computer users and allow the seamless integration of the GA with different ABMs. Secondly, the GA should achieve a relatively short convergence time, so that it may be practical to apply it to many situations, even if the corresponding ABMs exhibit complex dynamics. Thirdly, the GA should use a few data points of the real system's time series and even so, achieve a sufficiently good match with the ABM's time series to attaining relational equivalence between the real system under study and the ABM that models it. That feature is important since social science longitudinal studies commonly use few data points. The results show that all of those goals have been accomplished.
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