Abstract
The advent of wearable technology is fundamental to the dissemination of wearable personal health monitoring devices. Recent developments of biomedical sensors have decreased the form factor and power consumption that can be worn on a permanent basis. This paper discusses a low cost reflective photoplethysmography (PPG) system using a dedicated integrated circuit (IC) solution as the core of a wearable health monitoring device. The measurement of two physiological indicators is performed, namely the pulse rate (HR) and the blood oxygen saturation (SpO(2)). The paper analyses in depth the PPG signals sensing architecture, guaranteeing high resolution measurements due to a delta-sigma analog to digital conversion unit. Post-processing digital filter operations are implemented to enhance low noise PPGs acquisition for physiological signals extraction. A complete system design is presented and a detailed evaluation is made in a real-time processing scenario. The test platform is completed with a PC based graphics application for on-line and off-line data analysis. Minimizing power dissipation is the main challenge in,a wearable design. However, it restrains PPG signal measurement sensitivity by lowering signal quality. Using the developed prototype power consumption, studies concerning the characterization of power consumption and signal quality over various working conditions are performed. Next, a performance merit figure is proposed as the main research contribution, which addresses the power consumption and signal quality trade-off subject. It aims to be used as an analysis for trade-offs between these two conflicting design criteria.

Abstract
Here we explore the possibility of controlling the inhomogeneities in quasi-1D Bose-Einstein condensates using a spatial variation of the transverse confinement potential and explore different optical strategies to realize these pinched traps. Furthermore, we also present some early stage results on the dynamics of matter-wave solitons in such systems using computational simulations of the full 3D Gross-Pitaevskii equation.

Abstract
This paper proposes a novel Case Based Reasoning (CBR) application for intelligent management of energy resources in residential buildings. The proposed CBR approach enables analyzing the history of previous cases of energy reduction in buildings, and using them to provide a suggestion on the ideal level of energy reduction that should be applied in the consumption of houses. The innovations of the proposed CBR model are the application of the k-Nearest Neighbors algorithm (k-NN) clustering algorithm to identify similar past cases, the adaptation of Particle Swarm Optimization (PSO) meta-heuristic optimization method to optimize the choice of the variables that characterize each case, and the development of expert systems to adapt and refine the final solution. A case study is presented, which considers a knowledge base containing a set of scenarios obtained from the consumption of a residential building. In order to provide a response for a new case, the proposed CBR application selects the most similar cases and elaborates a response, which is provided to the SCADA House Intelligent Management (SHIM) system as input data. SHIM uses this specification to determine the loads that should be reduced in order to fulfill the reduction suggested by the CBR approach. Results show that the proposed approach is capable of suggesting the most adequate levels of reduction for the considered house, without compromising the comfort of the users.

Abstract
Search engines are evolving towards richer and stronger semantic approaches, focusing on entity-oriented tasks where knowledge bases have become fundamental. In order to support semantic search, search engines are increasingly reliant on robust information extraction systems. In fact, most modern search engines are already highly dependent on a well-curated knowledge base. Nevertheless, they still lack the ability to e ectively and automatically take advantage of multiple heterogeneous data sources. Central tasks include harnessing the information locked within textual content by linking mentioned entities to a knowledge base, or the integration of multiple knowledge bases to answer natural language questions. Combining text and knowledge bases is frequently used to improve search results, but it can also be used for the query-independent ranking of entities like events. In this work, we present a complete information extraction pipeline for the Portuguese language, covering all stages from data acquisition to knowledge base population. We also describe a practical application of the automatically extracted information, to support the ranking of upcoming events displayed in the landing page of an institutional search engine, where space is limited to only three relevant events. We manually annotate a dataset of news, covering event announcements from multiple faculties and organic units of the institution. We then use it to train and evaluate the named entity recognition module of the pipeline. We rank events by taking advantage of identified entities, as well as partOf relations, in order to compute an entity popularity score, as well as an entity click score based on implicit feedback from clicks from the institutional search engine. We then combine these two scores with the number of days to the event, obtaining a final ranking for the three most relevant upcoming events. © José Devezas and Sérgio Nunes

Abstract
Bayesian networks are one of the most intuitive statistical models for both estimation, classification and prediction of patients' outcomes. However, the availability of inference software in clinical settings is still limited. This work presents preliminary steps towards the creation of simple web-based forms that can access a powerful Bayesian network inference engine, making the derived models usable at bedside by both the clinicians and the patients themselves.

Abstract
The heating energy demand stated in energy performance certificates (EPC) and in other instruments used in the of evaluation of building's energy performance is usually determined assuming very specific (reference) indoor behavioral/heating patterns. Particularly, they tend to assume that households heat (nearly) the entire house to a "comfort" temperature during (nearly) all the heating season. However, several field studies have shown that there are major niches of the housing stock which do not follow this pattern (even the majority, in some geographical areas). Considering this matter, it would be interesting to build models able to estimate heating energy use values resultant from occupation and heating patterns different from those considered as "reference". This work aimed at producing tools to assess the relationship between heating energy use and indoor temperatures at different levels of occupant behavior (in terms of where, when and at what temperature households heat their dwellings). This relationship was expressed through models while still takes advantage of the information from the certificates. The work developed artificial neural networks (ANN) that characterize the relationship between heating energy use, indoor temperatures and the heating energy demand under reference conditions (typically available from energy rating/certificates) in the residential buildings, for different occupant behaviors heating patterns. Theoretically, these models can be applicable to any national geographical context. The data for building the ANNs was obtained from dynamic thermal building simulations using ESP-r, considering a large number of housing types and hypothetical occupation and heating patterns (i.e., which parts of the house are heated, when and at what temperature). From the analysis performed, it was possible to conclude that the developed ANN models proved to perform well (R-2 > 0.93) in estimating either heating energy use or indoor temperature, both at an individual and at the building stock level. This work may have important contributions in the energy planning practices regarding the residential building stock.