Publications by authors named "Maximiliano Montenegro"

An inordinate amount of computation is required to evaluate predictions of simulation-based models. Following Myung et al (2007), we derived an analytic form expression of the REM model of recognition memory using a Fourier transform technique, which greatly reduces the time required to perform model simulations. The accuracy of the derivation is verified by showing a close correspondence between its predictions and those reported in Shiffrin and Steyvers (1997).

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In our novel technique of tumor vessels treatment, High Z (HZ) contrast atoms are injected into the blood vessel and the tumor region is irradiated with "narrowband" fluorescence photon (FP) beam tuned to the "resonance energies". Theoretically, this technique guarantees a dose 10(2) - 10(3) higher than that achieved in conventional radiation therapy (RT). Meanwhile, this high dose is confined to a region of tens of micrometers.

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It is shown that X-ray absorption can be considerably enhanced at resonant energies corresponding to K-shell excitation into higher shells with electron vacancies following Auger emissions in high-Z elements and compounds employed in biomedical applications. We calculate Auger resonant probabilities and cross sections to obtain total mass attenuation coefficients with resonant cross sections and detailed resonance structures corresponding to Kalpha, Kbeta, Kgamma, Kdelta, and Keta complexes lying between 6.4-7.

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We present numerical simulations of X-ray emission and absorption in a biological environment for which we have modified the general-purpose computer code Geant4. The underlying mechanism rests on the use of heavy nanoparticles delivered to specific sites, such as cancerous tumors, and treated with monoenergetic X-rays at resonant atomic and molecular transitions. X-ray irradiation of high-Z atoms results in Auger decays of photon emission and electron ejections creating multiple electron vacancies.

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A primary criterion on which models of cognition are evaluated is their ability to fit empirical data. To understand the reason why a model yields a good or poor fit, it is necessary to determine the data-fitting potential (i.e.

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We introduce a Fourier Transformation technique that enables one to derive closed-form expressions of performance measures (e.g., hit and false alarm rates) of simulation-based models of recognition memory.

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