Two-part tariff licensing mechanisms

Most of the patent licensing agreements that are observed include royalties, in particular per-unit or ad valorem royalties. This paper shows that in a differ entiated duopoly that competes á la Cournot the optimal contract for an internal patentee always includes a positive royalty. Moreover, we show that the patentee would prefer to use ad valorem royalties rather than per-unit royalties when goods are complements or when they are substitutes and the degree of differentiation is suffciently low. The reason is that by including an ad valorem royalty in the licensing contract the patentee can commit strategically to be more (less) aggressive when goods are complements (substitutes) since his licensing revenues become increasing with the price of output of his rival. As a result, licensing may hurt consumers although it always increases social welfare.

The part-time phenomenon:evidence from the UK and the EU

When, in the early 1990s, the European Commission began to promote part-time working as an important strategy for job creation and enhancing labour market flexibility, this mode of working already accounted for a large and growing share of total employment in the EU.

Role of G protein coupled inwardly rectifier K+ channels (GIRK) on the neurobiology depression

353 págs.

Virtual Sensors for On-line Wheel Wear and Part Roughness Measurement in the Grinding Process

Grinding is an advanced machining process for the manufacturing of valuable complex and accurate parts for high added value sectors such as aerospace, wind generation, etc. Due to the extremely severe conditions inside grinding machines, critical process variables such as part surface finish or grinding wheel wear cannot be easily and cheaply measured on-line. In this paper a virtual sensor for on-line monitoring of those variables is presented. The sensor is based on the modelling ability of Artificial Neural Networks (ANNs) for stochastic and non-linear processes such as grinding; the selected architecture is the Layer-Recurrent neural network. The sensor makes use of the relation between the variables to be measured and power consumption in the wheel spindle, which can be easily measured. A sensor calibration methodology is presented, and the levels of error that can be expected are discussed. Validation of the new sensor is carried out by comparing the sensor's results with actual measurements carried out in an industrial grinding machine. Results show excellent estimation performance for both wheel wear and surface roughness. In the case of wheel wear, the absolute error is within the range of microns (average value 32 mu m). In the case of surface finish, the absolute error is well below R-a 1 mu m (average value 0.32 mu m). The present approach can be easily generalized to other grinding operations.

Energy retrofits in social housing. Analysis of its thermal behaviour.

468 p.