Abstract
ENDURE and BLUECOM+ are two EEA Grants projects that aim at enabling the monitoring of large and remote ocean areas by providing wireless communications and energy to humans and systems, namely unmanned vehicles, which are crucial for making large scale ocean monitoring cost-effective. ENDURE targets enabling autonomous underwater vehicles (AUVs) to remain in operation for longer periods of time than what is practical today, thus increasing the possibility of covering larger areas at lower costs. BLUECOM+ aims at connecting systems and humans in remote ocean areas by providing cost-effective, broadband, and reliable communications in alternative to satellite communications. Together these projects will bring up the enablers for data collection under the environmental monitoring programme, as well as to the achievement and sustainability of a GES in marine waters. © 2016 Taylor & Francis Group, London.

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.

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Abstract
Locally advanced breast cancer (LABC) constitutes a heterogeneous entity that includes advanced-stage primary tumours, cancers with extensive nodal involvement and inflammatory breast carcinomas. Although the definition of LABC can be broadened to include some large operable breast tumours, we use this term to strictly refer to inoperable cancers that are included in the above-mentioned categories. The prognosis of such tumours is often unfavourable; despite aggressive treatment, many patients eventually develop distant metastases and die from the disease. Advances in systemic therapy, including radiation treatment, surgical techniques and the development of new targeted agents have significantly improved clinical outcomes for patients with this disease. Notwithstanding these advances, LABC remains an important clinical problem, particularly in developing countries and those without widely adapted breast cancer awareness programmes. The optimal management of LABC requires a multidisciplinary approach, a well-coordinated treatment schedule and close cooperation between medical, surgical and radiation oncologists. In this Review, we discuss the current state of the art and possible future treatment strategies for patients with LABC.

Abstract
Common breast cancer treatments, as the removal of axillary lymph nodes, cause severe impairments in women's upper-body function. As a result, several daily activities are affected which contributes to a decreased QOL. Thus, the assessment of functional restrictions after treatment is essential to avoid further complications. This paper presents a pioneer work, which aims to develop an upper-body function evaluation method, traduced by the identification of lymphedema. Using the Kinect, features of the upper-limbs motion are extracted and supervised learning algorithms are used to construct a predictive classification model. Very promising results are obtained, with high classification accuracy.