Abstract
Medical data is particularly interesting as a subject for relational data mining due to the complex interactions which exist between different entities. Furthermore, the ambiguity of medical imaging causes interpretation to be complex and error-prone, and thus particularly amenable to improvement through automated decision support. Probabilistic Inductive Logic Programming (PILP) is a particularly well-suited tool for this task, since it makes it possible to combine the relational nature of this field with the ambiguity inherent in human interpretation of medical imaging. This work presents a PILP setting for breast cancer data, where several clinical and demographic variables were collected retrospectively, and new probabilistic variables and rules reflecting domain knowledge were introduced. A PILP predictive model was built automatically from this data and experiments show that it can not only match the predictions of a team of experts in the area, but also consistently reduce the error rate of malignancy prediction, when compared to other non-relational techniques.

Abstract
This paper describes the calibration of an underwater navigation system in enclosed scenarios. The work was performed in the context of the VAMOS project addressing the development of robotic solutions for flooded open pit mine exploration. An algorithm for calibration of extrinsic parameters for DVL and USBL systems is presented. Field experiments were performed with the ROAZ autonomous surface vehicle equipped with the underwater sensors and using precision IMU/GNSS fused data as groundtruth. The tests were performed in Douro River and in the Bejanca open pit mine, one of the VAMOS test sites, both in northern Portugal. The procedure was validated in the operational scenarios and results are presented showing the error correction and navigation quality improvement.

Abstract
Studies relating ageing and countries' economic performance address mostly developed economies. However, extant studies demonstrate that less developed countries (LDC) and emerging economies (EE) are reaching the transition process faster than those from developed regions, which renders the speed of ageing, besides ageing, a critical variable to explore in this context. Comparing system dynamic panel data estimations for 40 LDC, 19 EE and 28 developed countries (DC), between 1990 and 2013, we uncovered that ageing is detrimental to countries' economic growth, with noticeable nuances depending on countries' development level. The current level of ageing significantly and negatively impacts on DC's growth but not on that of LDC or EE. For these latter groups, the most relevant issue is the speed of ageing. The current annual growth of old age dependency ratio significantly diminishes EE's growth prospects whereas the lagged annual growth of the ageing index and the old age dependency ratio significantly curtails LDC's growth. Such results emphasize the need for urgent public policies that might mitigate the imbalance in LDCs' age structure before the speed of ageing leads LDCs to become even much poorer.

Abstract

Abstract
Spreadsheets are used in professional business contexts to make decisions based on collected data. Usually, these spreadsheets are developed by end users in an ad-hoc way. Thus, the business logic of a concrete spreadsheet is not explicit to end users, making its correctness hard to assess and users have to trust. We present an approach where structure and computational behavior of a spreadsheet are specified by a model with a process-like notation based on pre-defined functional spreadsheet services with typed interfaces. This enables a consistent construction process of a spreadsheet that comprises defining its structure and computational behavior as well as filling it with data and executing the defined computational behavior. Thus, concrete spreadsheets are equipped with a specification of their construction process. This supports their understanding and correct usage, even in case of legacy spreadsheets. The approach has been developed in cooperation with an industrial partner.

Abstract
A Fabry-Perot air bubble microcavity fabricated between a section of single mode fiber and a multimode fiber that requires only the use of a commercial fusion splicer is proposed. The study of the microcavities growth with the number of applied arcs is performed and several sensors are tested. The sensors are tested for lateral load measurements, and it is observed that there is dependence between the sensor dimensions and its sensitivity. The maximum sensitivity of 2.11 nm/N was obtained for the 161-mu m-long cavity. Moreover, given the low temperature sensitivity (<1 pm/degrees C), the proposed cavity should be adequate to perform temperature-independent measurements. The accurate technique control leads to the fabrication of reproducible cavities with the sensitivity required for the application. The way of manufacturing using a standard fusion splicer, given that no oils or etching solutions are involved, emerges as an alternative to the previously developed air bubble-based sensors.