Abstract
The 2014 Intergovernmental Panel on Climate Change (IPCC) report asserts that investment in low-carbon electricity production will need to rise by several hundred billion dollars annually, before 2030, in order to stabilize greenhouse gas concentrations in the atmosphere by 2100. In recognition of this urgent need to mitigate climate change, many governments have already established policies to spur renewable energy investment in the electricity sector. One such policy measure is a renewable energy target (RET), which sets a target percentage of electricity production to be generated from renewable sources by a specified date. Variations on this policy have been implemented around the world, from the EU 20-20-20 to diverse renewable portfolio standards in U.S. states and municipalities. This work analyzes economic, environmental and social aspects of a geographic attribution (i.e. Isolated, Regional or Country) of an RET to gain insights on the associated tradeoffs. In the case study of the Azores Islands, Portugal, the regional geographic attribution of an RET captures the best of all three tradeoffs.

Abstract
Decision making in companies requires an assessment of the efficiency and productivity of individual inputs to provide insights into the scope for improvement of inputs' use. This paper estimates an input-specific Luenberger productivity growth indicator that can be decomposed to identify the contributions of input-specific technological change, technical efficiency change and scale efficiency change. These components for a specific input sum up to the aggregated indicators which are then compared with the traditional Luenberger indicator. The application focuses on panel data of Spanish and Portuguese construction firms over the period 2002-2011, accounting for three inputs: materials, labor and capital. The results show that aggregated productivity change and its components computed from the input-specific productivity indicator are different from those obtained using a traditional approach. The results also indicate that productivity change is negative for labor and capital for construction firms in both Spain and Portugal, while productivity change of materials is positive for Portugal and negative for Spain. Productivity decline is worse for capital in the Spanish construction firms, and for labor in Portugal.

Abstract
Monitoring the quality of high refractive index edible oils is of great importance for the human health. Uncooked edible oils in general are healthy foodstuff, olive oil in particular, however, they are frequently used for baking and cooking. High quality edible oils are made from seeds, nuts or fruits by mechanical processes. Nevertheless, once the mechanical extraction is complete, up to 15% of the oil remains in oil pomace and in the mill wastewater, which can be extracted using organic solvents, often hexane. Optical fiber sensors based on long period fiber gratings (LPFG) have very low wavelength sensitivity when the surround refractive index is higher than the refractive index of the cladding. Titanium dioxide (TiO2) coated LPFG could lead to the realization of high sensitivity chemical sensor for the food industry. In this work LPFG coated with a TiO2 thin film were successfully used for to detect small levels of hexane diluted in edible oils and for real time monitoring the thermal deterioration of edible oils. For a TiO2 coating of 30 nm a wavelength sensitivity of 1361.7 nm/RIU (or 0.97 nm /% V/V) in the 1.4610-1.4670 refractive index range was achieved, corresponding to 0 to 12 % V/V of hexane in olive oil. A sensitivity higher than 638 nm/RIU at 225 degrees C was calculated, in the 1.4670-1.4735 refractive index range with a detection limit of thermal deterioration of about 1 minute.

Abstract
Recognizing a place with a visual glance is the first capacity used by humans to understand where they are. Making this capacity available to robots will make it possible to increase the redundancy of the localization systems available in the robots, and improve semantic localization systems. However, to achieve this capacity it is necessary to build a robust visual signature that could be used by a classifier. This paper presents a new approach to extract a global descriptor from an image that can be used as the visual signature for indoor scenarios. This global descriptor was tested using videos acquired from three robots in three different indoor scenarios. This descriptor has shown good accuracy and computational performance when compared to other local and global descriptors.

Abstract
Robustness, real-time properties and resource efficiency are key properties to embedded devices of the CPS/IoT era. In this paper we propose a language approach RTFM-core, and show its potential to facilitate the development process and provide highly efficient and statically verifiable implementations. Our programming model is reactive, based on the familiar notions of concurrent tasks and (single-unit) resources. The language is kept minimalistic, capturing the static task, communication and resource structure of the system. Whereas C-source can be arbitrarily embedded in the model, and/or externally referenced, the instep to mainstream development is minimal, and a smooth transition of legacy code is possible. A prototype compiler implementation for RTFM-core is presented. The compiler generates C-code output that compiled together with the RTFM-kernel primitives runs on bare metal. The RTFM-kernel guarantees deadlock-lock free execution and efficiently exploits the underlying interrupt hardware for static priority scheduling and resource management under the Stack Resource Policy. This allows a plethora of well-known methods to static verification (response time analysis, stack memory analysis, etc.) to be readily applied. The proposed language and supporting tool-chain is demonstrated by showing the complete process from RTFM-core source code into bare metal executables for a lightweight ARM-Cortex M3 target.

Abstract
Fingerprint segmentation is a crucial step of an automatic fingerprint identification system, since an accurate segmentation promote both the elimination of spurious minutiae close to the foreground boundaries and the reduction of the computation time of the following steps. In this paper, a new, and more robust fingerprint segmentation algorithm is proposed. The main novelty is the introduction of a more robust binarization process in the framework, mainly based on the fuzzy C-means clustering algorithm. Experimental results demonstrate significant benchmark progress on three existing FVC datasets.