This is a review of journals in the area
of using Artificial Intelligence for Intrusion Detection
and Prevention Systems. The journals have discussed the
milestones that have been achieved in the area of network
security particularly intrusion detection. They has also
highlighted several Machine Learning algorithms that
can be applied in this area to improve the IDS systems.
This paper documents a numerical model, developed for the McGill Rocket Team based on classical chemical thermodynamics coupled with the Trebble-Bishnoi equation of state, to solve for the oxidizer tank conditions (pressure, temperature, mole flowrate and liquid/vapour equilibrium) during the operation of a hybrid rocket. This model is modular and can be coupled to fluid mechanics and combustion chamber models for a more detailed analysis of a hybrid rocket engine.
We compare major factor models and find that the Stambaugh and Yuan (2016) four-factor model is the overall winner in the time-series domain. The Hou, Xue, and Zhang (2015) q-factor model takes second place and the Fama and French (2015) five-factor model and the Barillas and Shanken (2018) six-factor model jointly take third place. But the pairwise cross-sectional R2 and the multiple model comparison tests show that the Hou, Xue, and Zhang (2015) q-factor model, the Fama and French (2015) five-factor and four-factor models, and the Barillas and Shanken (2018) six-factor model take equal first place in the horse race.