Abstract
The demand for cost-effective broadband wireless underwater communications has increased in the past few years, motivated by the video collection performed by Autonomous Underwater Vehicles (AUVs) in areas such as environmental monitoring and oil and gas industries. However, the current technological limitations make it hard to implement a viable broadband wireless communications system for transferring the large amounts of data collected. Existing underwater communications solutions, using wireless optical or Radio Frequency (RF), limit high definition wireless video transfer to distances up to tens of meters. In case of underwater acoustic communications, long ranges can be achieved, but the low bandwidth makes them unsuitable for video streaming, even for standard definition video.In this paper we propose a solution, named Underwater Adaptive and Reliable Video Streaming (UARVS), that offers a video streaming service built upon the GROW data muling approach. UARVS exploits the use of data mules - small and agile AUVs - that travel between two physical nodes, bringing the data from an underwater survey unit to a central station at the surface. To validate the solution, an experimental testbed was built using airtight PVC cylinders, on a freshwater tank. The experimental results obtained show that UARVS enables an adaptive and continuous flow of video, avoids butter underruns, and reacts to data mule losses and delays.

Abstract
Background: Trauma is a public health issue with a significant social and economic impact. However, national data on its characterization and the role of nursing in its management is still scarce. Objective: To assess the effectiveness of prehospital nursing interventions in stabilizing trauma victims provided by nurses of Immediate Life Support Ambulances in Portugal. Methodology: Observational, prospective, and descriptive-correlational study. Data were collected by nurses of the Immediate Life Support Ambulances in mainland Portugal, from 01/03/2019 to 30/04/2020, and the Azores, from 01/10/2019 to 30/04/2020. Trauma severity indices were assessed before and after the nursing interventions. Results: This study included 606 cases (79.4% blunt trauma; 40.8% road accidents) reported by 171 nurses. Nurses performed mostly interventions for hemodynamic support (88.9%) and non-pharma-cological pain control (90.6%) of trauma victims. The nursing interventions improved the Revised Trauma Score and the Shock Index (p<0.001). Conclusion: Prehospital nursing interventions improve trauma victims’ clinical status.

Abstract
Sap flow measurements of trees are today the most common method to determine evapotranspiration at the tree and the forest/crop canopy level. They provide independent measurements for flux comparisons and model validation. The most common approach to measure the sap flow is based on intrusive solutions with heaters and thermal sensors. This sap flow sensor technology is not very reliable for more than one season crop; it is intrusive and not adequate for low diameter trunk trees. The non-invasive methods comprise mostly Radio-frequency (RF) technologies, typically using satellite or air-born sources. This system can monitor large fields but cannot measure sap levels of a single plant (precision agriculture). This article studies the hypothesis to use of RF signals attenuation principle to detect variations in the quantity of water present in a single plant. This article presents a well-defined experience to measure water content in leaves, by means of high gains RF antennas, spectrometer, and a robotic arm. Moreover, a similar concept is studied with an off-the-shelf radar solution-for the automotive industry-to detect changes in the water presence in a single plant and leaf. The conclusions indicate a novel potential application of this technology to precision agriculture as the experiments data is directly related to the sap flow variations in plant.

Abstract
Ocean related activities are often supported by offshore equipment with particular power demands. These are usually deployed at remote locations and have limited space, thus small energy harvesting technologies, such as photovoltaic panels or wind turbines, are used to power their instruments. However, the inherent energy sources are intermittent and have lower density and predictability than an alternative source: wave energy. Here, we propose and critically assess triboelectric nanogenerators (TENGs) as a promising technology for integration into wave buoys. Three TENGs based on rolling-spheres were developed and their performance compared in both a "dry" bench testing system under rotating motions, and in a large-scale wave basin under realistic sea-states installed within a scaled navigation buoy. Both experiments show that the electrical outputs of these TENGs increase with decreasing wave periods and increasing wave amplitudes. However, the wave basin tests clearly demonstrated a significant dependency of the electrical outputs on the pitch degree of freedom and the need to take into account the full dynamics of the buoy, and not only that of TENGs, when subjected to the excitations of waves. This work opens new horizons and strategies to apply TENGs in marine applications, considering realistic hydrodynamic behaviors of floating bodies.

Abstract
The recent Bluetooth 5.1 specification introduced the use of Angle-of-Arrival (AoA) information which enables the design of novel low-cost indoor positioning systems. Existing approaches rely on multiple fixed gateways equipped with antenna arrays, in order to determine the location of an arbitrary number of simple mobile omni-directional emitters. In this paper, we instead present an approach where mobile receivers are equipped with antenna arrays, and the fixed infrastructure is composed of battery-powered beacons. We implement a simulator to evaluate the solution using a real-world data set of AoA measurements. We evaluated the solution as a function of the number of beacons, their transmission period, and algorithmic parameters of the position estimation. Sub-meter accuracy is achievable using 1 beacon per 15 m(2) and a beacon transmission period of 500 ms.

Abstract
Lung cancer is still the leading cause of cancer death in the world. For this reason, novel approaches for early and more accurate diagnosis are needed. Computer-aided decision (CAD) can be an interesting option for a noninvasive tumour characterisation based on thoracic computed tomography (CT) image analysis. Until now, radiomics have been focused on tumour features analysis, and have not considered the information on other lung structures that can have relevant features for tumour genotype classification, especially for epidermal growth factor receptor (EGFR), which is the mutation with the most successful targeted therapies. With this perspective paper, we aim to explore a comprehensive analysis of the need to combine the information from tumours with other lung structures for the next generation of CADs, which could create a high impact on targeted therapies and personalised medicine. The forthcoming artificial intelligence (AI)-based approaches for lung cancer assessment should be able to make a holistic analysis, capturing information from pathological processes involved in cancer development. The powerful and interpretable AI models allow us to identify novel biomarkers of cancer development, contributing to new insights about the pathological processes, and making a more accurate diagnosis to help in the treatment plan selection.