Abstract
Energy harvesting devices can increase autonomy of submersible marine sensors. However, only the water movements can be used as energy source, since neither solar or temperature gradients are available bellow surface waters. A Linear Electromagnetic Generator (LEG), in a milliwatt energy harvester, is presented. Any moving parts are in contact with water, thus avoiding biofouling problems in the harvester. In this work, a 100mm length, 60mm diameter, cylindrical LEG was designed to maximize output power, and analyzed the effects of magnets size and geometry as well as coils position, at several working conditions. Two coils were used, with an internal resistance of 130 ? in 1500 turns, together with N38-N42 magnets. A mean electrical power of 25 mW (100 mW peak) was experimental measured in the optimized configuration, in realistic conditions, which is enough to power almost any electronic low-power sensor.

Abstract

Abstract
Consumer High Dynamic Range (HDR) displays are appearing on the market. Capable of generating a peak luminance of up to 2,000 nits, the improved dynamic range they provide can only be perceived when viewed in a dark environment. In this paper, we present a display architecture that is capable of generating a peak luminance of 10,000 nits. We demonstrate, with a subjective evaluation, that the increased peak luminance is required to perceive a high dynamic range in bright ambient environments. Furthermore, we show that by fitting a surface through the data, we can predict the dynamic range that can be perceived from the luminance and illuminance with low error. We can also invert the prediction to estimate the required peak luminance for a particular combination of dynamic range and ambient lighting.

Abstract
Managing large volumes of digital documents along with the information they contain, or are associated with, can be challenging. As systems become more intelligent, it increasingly makes sense to power retrieval through all available data, where every lead makes it easier to reach relevant documents or entities. Modern search is heavily powered by structured knowledge, but users still query using keywords or, at the very best, telegraphic natural language. As search becomes increasingly dependent on the integration of text and knowledge, novel approaches for a unified representation of combined data present the opportunity to unlock new ranking strategies. We tackle entity-oriented search using graph-based approaches for representation and retrieval. In particular, we propose the graph-of-entity, a novel approach for indexing combined data, where terms, entities and their relations are jointly represented. We compare the graph-of-entity with the graph-of-word, a text-only model, verifying that, overall, it does not yet achieve a better performance, despite obtaining a higher precision. Our assessment was based on a small subset of the INEX 2009 Wikipedia Collection, created from a sample of 10 topics and respectively judged documents. The offline evaluation we do here is complementary to its counterpart from TREC 2017 OpenSearch track, where, during our participation, we had assessed graph-of-entity in an online setting, through team-draft interleaving. © José Devezas, Carla Lopes, and Sérgio Nunes.

Abstract
Purpose: E-learning has been revealed as an a useful tool among continuing education within health professionals, namely for radiographers or radiologic technologists. Therefore like traditional learning, this teaching approach needs continuous evaluation in order to validate its effectiveness and impact. Kirkpatrick's model has been widely used for this purpose by health information management instructors. Our aim was to assess an E-learning Course on Breast Imaging for radiographers based on the first three levels of Kirkpatrick's framework: reaction, learning and behaviour. Methods and materials: An E-learning course was developed for radiographers in order to provide an easy-to-understand, succinct and current overview in breast imaging, namely mammography technique and image interpretation. The program structure were built based on the guidelines proposed by the European Society of Breast Cancer Specialists (EUSOMA). Learner's satisfaction was assessed through a questionnaire and Knowledge gain was assessed using pre- and post-testing. After 6 months of complying the course, the learners were contacted through a questionnaire in order to give feedback on whether their behaviour changed in workplace. Results: Two editions of the breast imaging course were performed by 64 learners. In general, 97% of the learners stated that the program content was very good and excellent, all learners considered the content was delivered in a very good or excellent way. High percentages of learners stated to be satisfied with the distribution of the content among each module (94%) and 86% of learners stated that your level of dedication was high or very high. Concerning improvement of knowledge, we found an evolution of 4 percentual points between pre and post-tests (p = 0,001). The learners have identified two main changes on their practice, the first one related with patient care, improving communications and positioning skills and the second one related with image interpretation, improving the image processing and analyses. Conclusion: These global results show that e-learning can provide statistically relevant knowledge gains in Radiographers. This course is an important contribution to the improvement of mammography education, impacting on the development of students' and radiographers' skills.

Abstract
Salinity measurement in water is typically performed with conductivity sensors. However, for long-term marine deployments, loss of precision is observed, mainly due to electrode drift (oxidation and degradation occurs in the presence of water, salts and bio-fouling), which results in inaccuracy of measurements. A cost-effective, low-power, four-probe salinity sensor is presented, to accurately measure long-term deployments in oceans, rivers and lakes. The four-probe methodology overcomes many of the drift problems, and the use of low-cost stainless-steel electrodes (avoiding platinum or titanium materials) can still achieve good long-term stability, in the practical salinity scale range from 2 to 42 PSU. Low-power electronics (200 µA in sleep-mode and 1 mA in active-mode) based on a ratiometric ADC conversion, and a low-power microcontroller with non-volatile memory, complements the proposed sensor, to achieve an autonomous salinity sensor for long-term marine deployments, with autonomy above 1 year with a 1 min-1 sample rate, using a common 2400 mA x 3.7 V lithium battery.