Abstract
User modelling has become a central subject for anybody interested in understanding how users interact with technology. Personalization is a key issue in an era when there is so much information and so many people interacting in so many ways. Modern users desire a customized experience that adapts itself to their requirements and understands what they need even before they notice it. In order to morph any system into an adapting one, every relevant interaction with its users has to be maintained. Then, a mathematical structure capable of discovering patterns amongst that information is necessary, being able to classify users according to the roles they play. With a correct user categorization, the system knows when, how and what to do to adapt its content, via a mixed-initiative approach. In this paper, an artificial neural network is selected as classifier and users are divided in three roles, from beginners to experts. ADSyS, the target system of this proposal, adapts its content based on who is operating it, providing a higher usability. This guide on how to adapt a system to its users is built as part of ADSyS, but is intended to be generalized as a foundation to other systems. © 2015 AISTI.

Abstract
In this present work we report fabrication of fiber optic liquid refractive index (RI) measurement sensor based on Michelson Interferometer method. This sensor was assembled by using graded index multimode (MM) fiber with core diameter 50 µm and the cladding of fiber was removed by simple chemical method. To perform this experiment a 2×2 3dB coupler is used. The fiber ends are then immersed in solvent and solution to provide reference and refractive index measurements, respectively. This method was successfully used to measure refractive index of Sodium Chloride (NaCl)-Water solution at different concentrations. The fringe contrast sensitivity of device is 92.90 dB/RIU measured in the RI range from 1.34 to 1.42 which is better than Mach-Zehnder Interferometer sensor [1] and Fabry perot based sensor [2]. The fabrication of sensor is simple, low cost and highly sensitive. © 2015 AIP Publishing LLC.

Abstract
Excessive dead time in complementary switches causes significant energy losses in DC-DC power conversion. The optimization of dead time prevents the degradation of overall efficiency by minimizing the body diode conduction of power switches and, as a consequence, also reduces reverse recovery losses. The present work aims at analyzing the influence of one of the most important characteristics of a digital controller, the timer resolution, in the context of dead-time optimization for synchronous buck converters. In specific, the analysis quantifies the efficiency dependency on the timer resolution, in a parameter set that comprises duty-cycle and dead-time, and also converter frequency and analog-to-digital converter accuracy. Based on a sensorless optimization strategy, the relationship between all these limiting factors is described, such as the number of bits of timer and analog-to-digital converter. To validate our approach experimental results are provided using a 12-to-1.8V DC-DC converter, controlled by low- and high-resolution pulse-width modulation signals generated with an XMC4200 microcontroller from Infineon Technologies. The measured results are consistent with our analysis, which predicts the power efficiency improvements not only with a fixed dead time approach, but also with the increment of timer resolution.

Abstract
Recommender systems have radically changed the way people find products, services and information. They are a precious tool in e-commerce and other online services and have slowly been clawing their way into the real-world stage. Location is one of the variables that can be useful in this new situation. While this particular area has been the subject of some research, it can go even further with the exploration of mobility. In this work, we analyze the integration of mobility in a recommender system with real mobility data from a public transportation network. We developed an algorithm that incorporates location and frequency in a conventional recommender system. Our results show successful recommendations of items adapted to users' mobility patterns.

Abstract
The Arteriolar-to-Venular Ratio (AVR) is an index used for the early diagnosis of diseases such as diabetes, hypertension or cardiovascular pathologies. This paper presents three automatic approaches for the estimation of the AVR in retinal images that result from the combination of different methodologies in some of the processing phases used for AVR estimation. Each one of these methods includes vessel segmentation, vessel caliber estimation, optic disc detection or segmentation, region of interest determination, vessel classification into arteries and veins and finally AVR calculation. The values produced by the proposed methods on 40 images of the INSPIRE-AVR dataset were compared with a ground-truth obtained by two medical experts using a semi-automated system. The results showed that the measured AVRs are not statistically different from the reference, with mean errors similar to those achieved by the two experts, thus demonstrating the reliability of the herein proposed approach for AVR estimation.

Abstract