Abstract
In this paper we propose a new methodology for evaluating prediction intervals (PIs). Typically, PIs are evaluated with reference to confidence values. However, other metrics should be considered, since high values are associated to too wide intervals that convey little information and are of no use for decision-making. We propose to compare the error distribution (predictions out of the interval) and the maximum mean absolute error (MAE) allowed by the confidence limits. Along this paper PIs based on neural networks for short-term load forecast are compared using two different strategies: (1) dual perturb and combine (DPC) algorithm and (2) conformal prediction. We demonstrated that depending on the real scenario (e.g., time of day) different algorithms perform better. The main contribution is the identification of high uncertainty levels in forecast that can guide the decision-makers to avoid the selection of risky actions under uncertain conditions. Small errors mean that decisions can be made more confidently with less chance of confronting a future unexpected condition.

Abstract
One of the major challenges for healthcare professionals in the XXI century is the increasing number of elderly in the world population. It is clearly important to find ways to stimulate cognitively this population, helping them to develop strategies and maintaining independency in their daily life activities. Conventional cognitive stimulation is time consuming task often causing discomfort in patients. Computer based tools may be used to perform cognitive stimulation and improve transferability in a setting that does not increase anxiety in individuals. This paper aims to present a pilot study of automatic platforms to enhance the cognitive process for older adults in order to promote an active aging.

Abstract
The formation of chemically etched optical fiber for use in refractive index sensor is addressed. This presented design of a refractive index (RI) sensor is based on recording the power loss exhibited by radiation propagating through an etched multimode fiber (MMF) immersed in the liquid under study. The decreasing diameters of fibers are found to be strongly dependent on the temperature and etchant composition. This experiment was performed for different unclad etched fibers for same sensing length and the RI changes from 1.33 RIU to 1.38 RIU. When the multimode fiber (MMF) is etched for 12 hours the sensitivity of the sensor is approximately 204.25dBm/RIU, which is larger than without etched fiber having sensitivity 127.2dBm/RIU. © 2016 Author(s).

Abstract
Hierarchical time series is a first order of importance topic. Effectively, there are several applications where time series can be naturally disaggregated in a hierarchical structure using attributes such as geographical location, product type, etc. Power networks face interesting problems related to its transition to computer-aided grids. Data can be naturally disaggregated in a hierarchical structure, and there is the possibility to look for both single and aggregated points along the grid. Along this work, we applied different hierarchical forecasting methods to them. Three different approaches are compared, two common approaches, bottom-up approach, top-down approach and another one based on the hierarchical structure of data, the optimal regression combination. The evaluation considers short-term forecasting (24-h ahead). Additionally,we discussed the importance associated to the correlation degree among series to improve forecasting accuracy. Our results demonstrated that the hierarchical approach outperforms bottom-up approach at intermediate/high levels. At lower levels, it presents a superior performance in less homogeneous substations, i. e. for the substations linked to different type of customers. Additionally, its performance is comparable to the top-down approach at top levels. This approach revealed to be an interesting tool for hierarchical data analysis. It allows to achieve a good performance at top levels as the top-down approach and at same time it allows to capture series dynamics at bottom levels as the bottom-up. © Springer Science+Business Media Singapore 2016.

Abstract
Photogrowth is a creativity support tool for the creation of nonphoto-realistic renderings of images. The authors discuss its evolution from a generative art application to an interactive evolutionary art tool and finally into a meta-level interactive art system in which users express their artistic intentions through the design of a fitness function. The authors explore the impact of these changes on the sense of authorship, highlighting the range of imagery that can be produced by the system.

Abstract
This paper develops a theoretical model to define tourism destination brand's online content. Message strategies and positioning attributes that brands want to communicate are the main inputs to develop communication content. However, the relationship between message strategies and attributes has not been studied in the literature. Our research examines this relationship, using 400 online brochures from around the world. Based on the positioning attributes and message strategies, a taxonomy is developed to analyse online brochures through content analysis. A comprehensive benchmark model was designed to assist tourism destination managers and advertisers while planning the concept and production of their online tourism brochures, and a new creative methodology based on several workable strategic alternatives are proposed.