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Usage control is concerned with how data is used after access to it has been granted. As such, it is particularly relevant to end users who own the data. System implementations of access and usage control enforcement mechanisms, however, do not always adequately reflect end user requirements. This is due to several reasons, one of which is the problem of mapping concepts in the end user's domain to technical events and artifacts. For instance, semantics of basic operators such as "copy" or "delete", which are fundamental for specifying privacy policies, tend to vary according to context. For this reason they can be mapped to different sets of system events. The behaviour users expect from the system, therefore, may differ from the actual behaviour. In this paper we present a translation of specification-level usage control policies into implementation-level policies which takes into account the precise semantics of domain-specific abstractions. A tool for automating the translation has also been implemented.

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The depletion of fossil fuels and rising environmental concerns have paved the way for the development of clean renewable energy sources. Photovoltaic (PV) cells are represented by electrical equivalent circuits. Finding the right circuit model parameters for PV cells is critical task. Estimating accurate parameters helps in better performance assessment, control, efficiency calculation and maximum power point tracking. This manuscript describes a new approach for obtaining PV system parameters using ensemble of constraint handling techniques (ECHT) with evolutionary algorithms (EA). Four distinguished technologies of solar PV cells are considered to estimate the parameters with best accuracy. Experiments reveal that ECHT outperforms each individual constraint handling approach by competing with state-of-the-art algorithms. The experimental data for these Kyocera cells is compared with estimated values obtained from the proposed algorithm using MATLAB 2021B for different irradiation. The performance plots show excellent match between the real and simulated values. The root mean square error (RMSE) values for research tax credit RTC France were found to be 7.325513*10-4 and Kyocera processing the normalize RMSE of 0.414%. On comparison with recent algorithms the proposed method achieves the lowest root mean square error (RMSE) meeting the main objective of proposed work.