Why did Canada nationalize liquor sales in the 1920s?: A political economy story

While the American episode of alcohol prohibition (1919-1933) is notorious and has been extensively studied, very little work has been done in a comparative international perspective. We contribute to this comparative international analysis by focusing here on the different path chosen by Canada in the 1920s. At the same time that its American neighbor went «bone dry», the Canadian provinces, one by one, starting with Quebec and British Columbia in 1921 and ending with Ontario in 1927, set up public liquor sale systems still with us today. This paper addresses the question of why and how did the Canadian provinces do this. The choice they faced between prohibition and nationalization can be analyzed with a political economy model by comparing the strength and stakes of the «drys» and the «wets» in the different provinces.

Defeasible Reasoning in SROEL: from Rational Entailment to Rational Closure

In this work we study a rational extension $SROEL^R T$ of the low complexity description logic SROEL, which underlies the OWL EL ontology language. The extension involves a typicality operator T, whose semantics is based on Lehmann and Magidor's ranked models and allows for the definition of defeasible inclusions. We consider both rational entailment and minimal entailment. We show that deciding instance checking under minimal entailment is in general $\Pi^P_2$-hard, while, under rational entailment, instance checking can be computed in polynomial time. We develop a Datalog calculus for instance checking under rational entailment and exploit it, with stratified negation, for computing the rational closure of simple KBs in polynomial time.Comment: Accepted for publication on Fundamenta Informatica

Effect of transmitter position on the torque generation of a magnetic resonance based motoring system

Strongly coupled magnetic resonance is most often used to transfer electrical power from a transmitter to a resonant receiver coil to supply devices over an air gap. In this work, the induced current in two receiver coils (stator and rotor) is used to generate torque on the rotor coil. The effect of the transmitter position relative to the stator and rotor receiver coils on the torque generation is studied in detail, both in simulation and experimentally. Results show a 36% to 37% gain in peak torque when properly varying the stator orientation for a given transmitter distance

Effect of stator slot openings in axial flux permanent magnet machines

The width of the stator slot openings near the air gap has a large influence on the power loss in the stator core and in the permanent magnets of axial flux permanent magnet synchronous machines. On the one hand, the increase in stator slot openings results in lower power loss in the stator iron. On the other hand, it also results in increased loss in the permanent magnets. Also the torque is reduced for large but also for very small slot openings. This paper deals with axial flux machines of the YASA type: yokeless and segmented armature. It is shown that the slot openings contribute to an unequal flux density level over the different laminations in the stator core. The effect on the power loss and the flux distribution is shown

Stochastic modeling error reduction using Bayesian approach coupled with an adaptive kriging based model

Magnetic material properties of an electromagnetic device can be recovered by solving an inverse problem where measurements are adequately interpreted by a mathematical forward model. The accuracy of the material properties recovered by the inverse problem is highly dependent on the accuracy of these forward models. In order to ensure the highest possible accuracy of the inverse problem solution, all physics of the electromagnetic device need to be perfectly modeled using for example a complex numerical model. However, the more accurate ‘fine’ models demand a high computational time and memory storage. Alternatively, less accurate ‘coarse’ models can be used with a demerit of the high expected recovery errors. Therefore, the Bayesian approximation error approach has been used for reducing the modeling error originating from using a coarse model instead of a fine model in the inverse problem procedure. However, the Bayesian approximation error approach may fail to compensate the modeling error completely when the used model in the inverse problem is too coarse. Therefore, there is a definitely need to use a quite accurate coarse model. In this paper, the electromagnetic device is simulated using an adaptive Kriging based model. The accuracy of this ‘coarse’ model is a priori assessed using the cross-validation technique. Moreover, the Bayesian approximation error approach is utilized for improving the inverse problem results by compensating the modeling errors. The proposed methodology is validated on both purely numerical and real experimental results. The results show a significant reduction in the recovery error within an acceptable computational time