Abstract
The context in which service is delivered and experienced has, in many respects, fundamentally changed. For instance, advances in technology, especially information technology, are leading to a proliferation of revolutionary services and changing how customers serve themselves before, during, and after purchase. To understand this changing landscape, the authors engaged in an international and interdisciplinary research effort to identify research priorities that have the potential to advance the service field and benefit customers, organizations, and society. The priority-setting process was informed by roundtable discussions with researchers affiliated with service research centers and networks located around the world and resulted in the following 12 service research priorities: stimulating service innovation, facilitating servitization, service infusion, and solutions, understanding organization and employee issues relevant to successful service, developing service networks and systems, leveraging service design, using big data to advance service, understanding value creation, enhancing the service experience, improving well-being through transformative service, measuring and optimizing service performance and impact, understanding service in a global context, and leveraging technology to advance service. For each priority, the authors identified important specific service topics and related research questions. Then, through an online survey, service researchers assessed the subtopics' perceived importance and the service field's extant knowledge about them. Although all the priorities and related topics were deemed important, the results show that topics related to transformative service and measuring and optimizing service performance are particularly important for advancing the service field along with big data, which had the largest gap between importance and current knowledge of the field. The authors present key challenges that should be addressed to move the field forward and conclude with a discussion of the need for additional interdisciplinary research.

Abstract
Accurate measurements of global solar radiation and atmospheric temperature and relative humidity, as well as the availability of the predictions of their evolution over time, are important for different areas of applications, such as agriculture, renewable energy and energy management, or thermal comfort in buildings. For this reason, an intelligent, light-weight, self-powered and portable sensor was developed, using a nearest-neighbors algorithm and artificial neural network models as the time-series predictor mechanisms. The hardware and software design of the implemented prototype are described, as well as the forecasting performance related to three atmospheric variables, over a prediction horizon of 48-steps-ahead.

Abstract
We consider n individuals described by p variables, represented by points of the surface of unit hypersphere. We suppose that the individuals are fixed and the set of variables comes from a mixture of bipolar Watson distributions. For the mixture identification, we use EM and dynamic clusters algorithms, which enable us to obtain a partition of the set of variables into clusters of variables.Our aim is to evaluate the clusters obtained in these algorithms, using measures of within-groups variability and between-groups variability and compare these clusters with those obtained in other clustering approaches, by analyzing simulated and real data.

Abstract
Image mosaicking applications require both geometrical and photometrical registrations between the images that compose the mosaic. This paper proposes a probabilistic color correction algorithm for correcting the photometrical disparities. First, the image to be color corrected is segmented into several regions using mean shift. Then, connected regions are extracted using a region fusion algorithm. Local joint image histograms of each region are modeled as collections of truncated Gaussians using a maximum likelihood estimation procedure. Then, local color palette mapping functions are computed using these sets of Gaussians. The color correction is performed by applying those functions to all the regions of the image. An extensive comparison with ten other state of the art color correction algorithms is presented, using two different image pair data sets. Results show that the proposed approach obtains the best average scores in both data sets and evaluation metrics and is also the most robust to failures.

Abstract
This paper describes the application of the specialized genetic algorithm of Chu-Beasley to solve the Distribution System Reconfiguration, DSR, problem considering different demand scenarios. This algorithm is an approach inspired in the natural selection and evolution of species. The reconfiguration problem of distribution networks taking into account different demand scenarios aims at identifying the most adequate radial topology of a distribution system assuming that this topology is used for all demand scenarios under study. This search is driven by the minimization of the cost of energy losses in the network along a full operation year. The performance of the algorithm is evaluated considering test systems having 33, 70, 84 and 136 buses and a real system with 417 buses. The obtained results confirm the robustness and efficiency of the developed approach and its potential to be used in distribution control centers.

Abstract