An environment for protecting the privacy of e-shoppers

Privacy, an everyday topic with weekly media coverage of loss of personal records, faces its bigger risk during the uncontrolled, involuntary or inadvertent disclosure and collection of personal and sensitive information. Preserving one's privacy while e-shopping, especially when personalisation is involved, is a big challenge. Current initiatives only offer customers opt-out options. This research proposes a `privacy-preserved' shopping environment (PPSE) which empowers customers to disclose information safely by facilitating a personalised e- shopping experience that protects their privacy. Evaluation delivered positive results which suggest that such a product would indeed have a market in a world where customers are increasingly concerned about their privacy

Simulation of Levelized Costs of Electricity Considering Externalities

This research presents long-term projects. Applies stochastic models using Monte Carlo simulation to identify the impact that changes have in the input parameters on the output variable (LCOEE). Uses tornado analysis to identify the impact of the input variables. The results show that; in thermoelectric coal plant, the LCOEE is more sensitive to changes in the price of CO2 emissions than discount rate. In the combined cycle, the LCOEE is most sensitive to the plant factor than CO2 emissions price, discount rate. In the nuclear power plant, the discount rate has greater impact on the LCOE than overnight cost.  In contrast to previous work, this research uses Mexico’s country-risk in the discount rate. This research’s limitation is that the costs related to transmission, distribution, and backup fee are not included. Concluding that stochastic models provide useful information for decision-making by incorporating historical data and projections of the main variables that could affect the output variable (LCOEE).Simulación de costos nivelados de electricidad considerando externalidadesEsta investigación presenta proyectos de largo plazo. Aplica modelos estocásticos mediante simulación de Monte Carlo para identificar el impacto que los cambios en los parámetros de entrada tienen en la variable de salida (LCOEE). Utiliza análisis de tornados para identificar el impacto de las variables de entrada. Los resultados muestran; en termoeléctrica de carbón, que el LCOEE es más sensible a los cambios en el precio de las emisiones de CO2 que a la tasa de descuento. En el ciclo combinado, el LCOEE es más sensible al factor de planta que al precio de las emisiones de CO2. En la central nuclear, la tasa de descuento tiene mayor impacto en el LCOE que el costo unitario de inversión. A diferencia de trabajos anteriores, se usa el riesgo país de México en la tasa de descuento. Limitaciones: los costos relacionados con la transmisión, distribución y tarifa de respaldo no están incluidos. Concluyendo que los modelos estocásticos brindan información útil para la toma de decisiones al incorporar datos históricos y proyecciones de las principales variables que podrían afectar al LCOEE