Domain-Specific Multi-Modeling of Security Concerns in Service-Oriented Architectures

As a common reference for many in-development standards and execution frameworks, special attention is being paid to Service-Oriented Architectures. SOAs modeling, however, is an area in which a consensus has not being achieved. Currently, standardization organizations are defining proposals to offer a solution to this problem. Nevertheless, until very recently, non-functional aspects of services have not been considered for standardization processes. In particular, there exists a lack of a design solution that permits an independent development of the functional and non-functional concerns of SOAs, allowing that each concern be addressed in a convenient manner in early stages of the development, in a way that could guarantee the quality of this type of systems. This paper, leveraging on previous work, presents an approach to integrate security-related non-functional aspects (such as confidentiality, integrity, and access control) in the development of services.

Self-reference Scrubber for TMR Systems Based on Xilinx Virtex FPGAs

SRAM-based FPGAs are sensitive to radiation effects. Soft errors can appear and accumulate, potentially defeating mitigation strategies deployed at the Application Layer. Therefore, Configuration Memory scrubbing is required to improve radiation tolerance of such FPGAs in space applications. Virtex FPGAs allow runtime scrubbing by means of dynamic partial reconfiguration. Even with scrubbing, intra-FPGA TMR systems are subjected to common-mode errors affecting more than one design domain. This is solved in inter-FPGA TMR systems at the expense of a higher cost, power and mass. In this context, a self-reference scrubber for device-level TMR system based on Xilinx Virtex FPGAs is presented. This scrubber allows for a fast SEU/MBU detection and correction by peer frame comparison without needing to access a golden configuration memory

Development of a wake model for wind farms based on an open source CFD solver. Strategies on parabolization and turbulence modeling

Wake effect represents one of the most important aspects to be analyzed at the engineering phase of every wind farm since it supposes an important power deficit and an increase of turbulence levels with the consequent decrease of the lifetime. It depends on the wind farm design, wind turbine type and the atmospheric conditions prevailing at the site. Traditionally industry has used analytical models, quick and robust, which allow carry out at the preliminary stages wind farm engineering in a flexible way. However, new models based on Computational Fluid Dynamics (CFD) are needed. These models must increase the accuracy of the output variables avoiding at the same time an increase in the computational time. Among them, the elliptic models based on the actuator disk technique have reached an extended use during the last years. These models present three important problems in case of being used by default for the solution of large wind farms: the estimation of the reference wind speed upstream of each rotor disk, turbulence modeling and computational time. In order to minimize the consequence of these problems, this PhD Thesis proposes solutions implemented under the open source CFD solver OpenFOAM and adapted for each type of site: a correction on the reference wind speed for the general elliptic models, the semi-parabollic model for large offshore wind farms and the hybrid model for wind farms in complex terrain. All the models are validated in terms of power ratios by means of experimental data derived from real operating wind farms.

Air quality modeling and mortality impact of fine particles reduction policies in Spain

Background: In recent years, Spain has implemented a number of air quality control measures that are expected to lead to a future reduction in fine particle concentrations and an ensuing positive impact on public health. Objectives: We aimed to assess the impact on mortality attributable to a reduction in fine particle levels in Spain in 2014 in relation to the estimated level for 2007. Methods: To estimate exposure, we constructed fine particle distribution models for Spain for 2007 (reference scenario) and 2014 (projected scenario) with a spatial resolution of 16x16 km2. In a second step, we used the concentration-response functions proposed by cohort studies carried out in Europe (European Study of Cohorts for Air Pollution Effects and Rome longitudinal cohort) and North America (American Cancer Society cohort, Harvard Six Cities study and Canadian national cohort) to calculate the number of attributable annual deaths corresponding to all causes, all non-accidental causes, ischemic heart disease and lung cancer among persons aged over 25 years (2005-2007 mortality rate data). We examined the effect of the Spanish demographic shift in our analysis using 2007 and 2012 population figures. Results: Our model suggested that there would be a mean overall reduction in fine particle levels of 1mg/m3 by 2014. Taking into account 2007 population data, between 8 and 15 all-cause deaths per 100,000 population could be postponed annually by the expected reduction in fine particle levels. For specific subgroups, estimates varied from 10 to 30 deaths for all non-accidental causes, from 1 to 5 for lung cancer, and from 2 to 6 for ischemic heart disease. The expected burden of preventable mortality would be even higher in the future due to the Spanish population growth. Taking into account the population older than 30 years in 2012, the absolute mortality impact estimate would increase approximately by 18%. Conclusions: Effective implementation of air quality measures in Spain, in a scenario with a short-term projection, would amount to an appreciable decline infine particle concentrations, and this, in turn, would lead to notable health-related benefits. Recent European cohort studies strengthen the evidence of an association between long-term exposure to fine particles and health effects, and could enhance the health impact quantification in Europe. Air quality models can contribute to improved assessment of air pollution health impact estimates, particularly in study areas without air pollution monitoring data.