Abstract
A novel technique for the creation of metal-organic framework (MOF) films based on soft-imprinting and their use as gas sensors was developed. The microporous MOF material [Zn-2(bpdc)(2)(bpee)] (bpdc = 4,4'-biphenyldicarboxylate; bpee = 1,2-bipyridylethene) was synthesized solvothermally and activated by removing the occluded solvent molecules from its inner channels. MOF particles were characterized by powder X-ray diffraction and fluorescence spectroscopy, showing high crystallinity and intense photoluminescence. Scanning electron microscope images revealed that MOF crystals were mainly in the form of microneedles with a high surface-to-volume ratio, which together with the high porosity of the material enhances its interaction with gas molecules. MOF crystals were soft-imprinted into cellulose acetate (CA) films on quartz at different pressures. Atomic force microscope images of soft-imprinted films showed that MOF crystals were partially embedded into the CA. With this procedure, mechanically stable films were created, with crystals protruding from the CA surface and therefore available for incoming gas molecules. The sensing properties of the films were assessed by exposing them to saturated atmospheres of 2,4-dinitrotoluene, which resulted in a substantial quenching of the fluorescence after few seconds. The soft-imprinted MOF films on CA/quartz exhibit good sensing capabilities for the detection of nitroaromatics, which was attributed to the MOF sensitivity and to the novel and more efficient film processing method based on soft-imprinting.

Abstract
The problem of Label Ranking is receiving increasing attention from several research communities. The algorithms that have been developed/adapted to treat rankings of a fixed set of labels as the target object, including several different types of decision trees (DT). One DT-based algorithm, which has been very successful in other tasks but which has not been adapted for label ranking is the Random Forests (RF) algorithm. RFs are an ensemble learning method that combines different trees obtained using different randomization techniques. In this work, we propose an ensemble of decision trees for Label Ranking, based on Random Forests, which we refer to as Label Ranking Forests (LRF). Two different algorithms that learn DT for label ranking are used to obtain the trees. We then compare and discuss the results of LRF with standalone decision tree approaches. The results indicate that the method is highly competitive.

Abstract
The usage of small-sized unmanned aerial systems (UAS) has increased in the last years, in many different areas, being agriculture and forestry those who benefit the most from this relatively new remote sensing platform. Leaf area index, canopy and plant volume are among the parameters that can be determined using the very high resolution aerial data obtained by sensors coupled in unmanned aerial vehicles (UAV). This remote sensing technology affords the possibility of monitoring the vegetative development, identifying different types of issues, enabling the application of the most appropriated treatments in the affected areas. In this paper, a methodology allowing to perform multi-temporal UAS-based data analysis obtained by different sensors is proposed. A case study in vineyards and chestnuts is used to prove the benefits of continuous crop monitoring in its management and productivity of agroforestry parcels/activities. (C) 2017 The Authors. Published by Elsevier B.V.

Abstract
The autonomy of robotic underwater vehicles is dependent on the ability to perform long-term and long-range missions without need of human intervention. While current state-of-the-art underwater navigation techniques are able to provide sufficient levels of precision in positioning, they require the use of support vessels or acoustic beacons. This can pose limitations on the size of the survey area, but also on the whole cost of the operations. Terrain Based Navigation is a sensor-based navigation technique that bounds the error growth of dead reckoning using a map with terrain information, provided that there is enough terrain variability. An obvious advantage of Terrain Based Navigation is the fact that no external aiding signals or devices are required. Because of this unique feature, terrain navigation has the potential to dramatically improve the autonomy of Autonomous Underwater Vehicles (AUVs). This paper consists on a comprehensive survey on the recent developments for Terrain Based Navigation methods proposed for AUVs. The survey includes a brief introduction to the original Terrain Based Navigation formulations, as well as a description of the algorithms, and a list of the different implementation alternatives found in the literature. Additionally, and due to the relevance, Bathymetric SLAM techniques will also be discussed.

Abstract
Raman and SERS spectra of benzaldehyde (Bz-CHO) and chromone-3-carboxaldehyde (Ch-CHO) on silver colloids have been analyzed, being subsequently compared to the spectra of the corresponding acids. In the SERS spectra of both aldehydes, the band corresponding to the.(C =O) stretching mode of the carboxaldehyde group at ca. 1700cm (-1) ismissing, while a newband at 13501400cm(-1), characteristic of the symmetric stretching mode of carboxylate group, is appearing in both cases. These results point out that aldehydes are oxidized to their corresponding acidswhen adsorbed on silver nanoparticles. This conclusion has been confirmed by means of HPLC-MS analysis and supported on the basis of DFT calculations. Copyright (c) 2016 John Wiley & Sons, Ltd.

Abstract
Ambient Intelligence has always been associated with the promise of exciting new applications, aware of the users' needs and state, and proactive towards their goals. However, the acquisition of the necessary information for supporting such high-level learning and decision-making processes is not always straightforward. In this chapter we describe a multi-faceted smart environment for the acquisition of relevant contextual information about its users. This information, acquired transparently through the technological devices in the environment, supports the building of high-level knowledge about the users, including a quantification of aspects such as performance, attention, mental fatigue and stress. The environment described is particularly suited for milieus such as workplaces and classrooms, in which this kind of information may be very important for the effective management of human resources, with advantages for organizations and individuals alike.