Abstract
As a recommendation technique based on historical user information, collaborative filtering typically predicts the classification of items using a single criterion for a given user. However, many application domains can benefit from the analysis of multiple criteria, e.g. tourists usually rate attractions (hotels, attractions, restaurants, etc.) using multiple criteria. In this paper, we argue that the personalised combination of multi-criteria data together with the creation and application of trust models should not only refine the tourist profile, but also improve the quality of the collaborative recommendations. The main contributions of this work are: (1) a novel profiling approach which takes advantage of the multi-criteria crowdsourced data and builds pairwise trust models and (2) the k-NN prediction of user ratings using trust-based neighbour selection. Significant experimental work has been performed using crowdsourced datasets from the Expedia and TripAdvisor platforms.

Abstract
The increasing demand for real-time analytics requires the fusion of Transactional (OLTP) and Analytical (OLAP) systems, eschewing ETL processes and introducing a plethora of proposals for the so-called Hybrid Analytical and Trans-actional Processing (HTAP) systems. Unfortunately, current benchmarking approaches are not able to comprehensively produce a unified metric from the assessment of an HTAP system. The evaluation of both engine types is done separately, leading to the use of disjoint sets of benchmarks such as TPC-C or TPC-H. In this paper we propose a new benchmark, HTAPBench, providing a unified metric for HTAP systems geared toward the execution of constantly increasing OLAP requests limited by an admissible impact on OLTP performance. To achieve this, a load balancer within HTAPBench regulates the coexistence of OLTP and OLAP workloads, proposing a method for the generation of both new data and requests, so that OLAP requests over freshly modified data are comparable across runs. We demonstrate the merit of our approach by validating it with different types of systems: OLTP, OLAP and HTAP; showing that the benchmark is able to highlight the differences between them, while producing queries with comparable complexity across experiments with negligible variability.

Abstract
Context. Z CMa is a complex pre-main sequence binary with a current separation of 110 mas, known to consist of an FU Orionis star (SE component) and an embedded Herbig Be star (NW component). Although it represents a well-studied and characterized system, the origin of photometric variabilities, the component properties, and the physical configuration of the system remain mostly unknown. Aims. Immediately when the late-2008 outburst of Z CMa was announced to the community, we initiated a high angular resolution imaging campaign aimed at characterizing the outburst state of both components of the system in the near-infrared. Methods. We used the VLT/NACO and the Keck/NIRC2 near-infrared adaptive optics instrument to monitor the astrometric position and the near-infrared photometry of the Z CMa components during the outburst phase and one year after. The VLT/SINFONI and Keck/OSIRIS integral field spectroscrographs were in addition used to characterize for the first time the resolved spectral properties of the FU Orionis and the Herbig Be component during and after the outburst. Results. We confirm that the NW star dominates the system flux in the 1.1-3.8 mu m range and is responsible for the photometric outburst. We extract the first medium-resolution (R similar to 2000-4000) near-infrared (1.1-2.4 mu m) spectra of the individual components. The SE component has a spectrum typical of FU Orionis objects. The NW component spectrum is characteristic of embedded outbursting protostars and EX Or objects. It displays numerous emission lines whose intensity correlates with the system activity. In particular, we find a correlation between the Br gamma equivalent width and the system brightness. The bluing of the continuum of the NW component along with the absolute flux and color-variation of the system during the outburst suggests that the outburst was caused by a complex interplay between a variation of the extinction in the line of sight of the NW component on one hand, and the emission of shocked regions close to the NW component on the other. We confirm the recently reported wiggling of the SE component jet from [Fe II] line emission. We find a point-like structure associated with a peak emission at 2.098 mu m coincidental with the clump or arm seen in broadband polarization di ff erential imaging as well as additional di ff use emission along a PA = 214 degrees. The origin of these two structures is unclear and deserves further investigation.

Abstract

Abstract
Modernization of electricity networks is currently being carried out using the concept of the smart grid; hence, the active participation of end-user consumers and distributed generators will be allowed in order to increase system efficiency and renewable power accommodation. In this context, this paper proposes a comprehensive methodology to optimally control lead-acid batteries operating under dynamic pricing schemes in both independent and aggregated ways, taking into account the effects of the charge controller operation, the variable efficiency of the power converter, and the maximum capacity of the electricity network. A genetic algorithm is used to solve the optimization problem in which the daily net cost is minimized. The effectiveness and computational efficiency of the proposed methodology is illustrated using real data from the Spanish electricity market during 2014 and 2015 in order to evaluate the effects of forecasting error of energy prices, observing an important reduction in the estimated benefit as a result of both factors: 1) forecasting error and 2) power system limitations.

Abstract
We demonstrate in this research that surface-enhanced resonance Raman scattering combined with Raman imaging can be effectively used for analysis of distinct forms of organic dyes in antimicrobial Ag-loaded textile fibers. The potential of this approach, as a non-destructive characterization method of fabrics, was evaluated with Raman studies performed on the molecular forms of methylene blue (MB), used here as the organic dye model. On the basis of the surface-enhanced Raman scattering spectra of MB monomers and dimers, the Raman imaging of Ag-loaded linen fibers previously treated with MB solution was performed and then used for identification of the adsorbate species in distinct regions of the substrates. A semi-quantitative analysis is then performed by considering the area of the Raman bands ascribed to the MB molecular forms and image analysis applied to Raman images. Copyright (c) 2017 John Wiley & Sons, Ltd.