Abstract
Intelligent agriculture in general, but especially when agricultural fields are very heterogeneous, requires a large number of sensors in order to obtain an effective control and thus increase productivity. This need becomes more evident in vineyards on the farms of the demarcated Douro region due to the specificities of the territory and the vineyards themselves. Thus, it is necessary to have low cost sensors which are, essentially, easy to install and maintain. In the present work, a node with these characteristics was developed, which, in addition, is low consumption and communicates wirelessly through a Long Rang Wide Area Network (LoRaWAN) network. To obtain an easy installation, a library of clusters was created for the LoRaWAN network and dedicated to sensors used in agriculture, especially those using an asynchronous serial protocol for intelligent sensors. Three nodes were developed and tested with sensors used in agriculture to measure several environmental parameters (soil and air temperature; wind speed, gust and direction; soil water content, water tension and electrical conductivity; solar radiation; precipitation; atmospheric and vapor pressure; relative humidity; and lightning strikes count). The three nodes send data to a server through an existing gateway on the farm. The data are decoded and sent to an Internet-of-Things analytics platform where it is aggregated, viewed and analyzed. Samples of the data collected are presented. The developed nodes are of small dimensions (85x65x35mm), thus making them easy to handle and install. Energy consumption depends on the distance to the gateway, and the number and type of sensors connected to each node. In the implemented cases, the maximum consumption was approximate to 400 mu A. The development of a cluster based library makes the node plug-and-play. The developed nodes will be a great step forward for the use of wireless sensors in smart agriculture in Douro vineyards.

Abstract
This project is being developed to access elder dependent people, inspired by a geriatric care management@home. According to the project’s main assessment, three stages have been completed and now, in the fourth stage, we are implementing a bio-network with several sensors that assists the caregiver in applying the therapeutic plan. At this point, we have collected data regarding the level of dependency of the elderly, and the risk of development of pressure ulcers, so that we can adapt the sensors according to their need. For example, sensors installed in a bed can warn the caregiver that it is time to change the position of the elderly patient. As such, after testing a prototype in laboratory, we installed the system in two homes where we are currently collecting data. © 2020, Springer Nature Switzerland AG.

Abstract
The physiotherapeutic process is widely discussed and of fundamental importance for the recovery of patients who suffer from any injury or adverse muscle condition. For best results, it is of primary importance that the patient maintains a pace of treatment and remains engaged in the activities required by the Physiotherapist. In this context, an approach that improves such engagement with concern for usability and acceptance by patients is explored in this article. A gamified platform was created, capable of capturing the time of exposure to the movement of squeezing a handgrip and expressing the patient's results in a swordsman-themed versus game, through the reading of neuromuscular signals captured by a MYO armband. © 2020 IEEE.

Abstract

Abstract
The bioelectrical impedance analysis (BIA) is a non-destructive technique that has been successfully used to assess the body and carcass composition of farm species. This study aimed to predict intramuscular fat (IMF) and physicochemical traits in the longissimus thoracis et lumborum muscle (LM) of beef, using BIA. These traits were evaluated in LM samples of 52 crossbred heifer carcasses. The BIA was performed in LM, using a 50 Hz frequency high precision impedance converter system. A correlation analysis of the studied variables was performed. Then a stepwise with a k-folds cross validation procedure was used to modelling the prediction of IMF and physicochemical traits from BIA parameters (24.5% <= CV <= 47.3%). Wide variation was found for IMF and BIA parameters. In general, correlations of BIA parameters with IMF and physicochemical traits were moderate to high and were similar for all BIA parameters (-0.50 <= r <= 0.50 only for total pigments, a* and pH48). It was possible to predict IMF and physicochemical traits from BIA. The best fit explained 79.3% of the variation in IMF, while for physicochemical traits the best fits were for sarcomere length and shear force (64.4% and 60.5%, respectively). The results confirmed the potential of BIA for objective measurement of meat quality.

Abstract
Developed focusing agriculture sustainability, mySense is a comprehensive close-range sensor-based data management environment to improve precision farming practices. It integrates discussion platforms for quick problem solving through experts support and a computational intelligence layer for multipurpose application (e.g. vine variety discrimination, plant disease detection and identification). Attending the need for keeping track of agricultural crops not only based on close-range sensing but also at a macro perspective, mySense was complemented with proper functionalities to unlock macro-monitoring features, through the implementation of a Web-based Geographical Information System (WebGIS) planned as a sidekick application that provides agriculture professionals with visual decision support tools over remote sensed data. This paper presents and discusses its specification and implementation.