Abstract
Emergent and established paradigms, such as the Internet of Things (IoT), cloud and fog/edge computing, together with increasingly cheaper computing technologies – with very low power requirements, available to exchange data with increased efficiency – and intelligent systems, have evolved to a level where it is virtually possible to create and deploy monitoring solutions, even in Precision Agriculture (PA) practices. In this work, LoRa®(Long Range) technology and LoRaWAN™protocol, are tested in a Precision Viticulture (PV) scenario, using low-power data acquisition devices deployed in a vineyard in the UTAD University Campus, distanced 400 m away from the nearest gateway. The main goal of this work is to evaluate sensor data integration in the mySense environment, a framework aimed to systematize data acquisition procedures to address common PA/PV issues, using LoRa®technology. mySense builds over a 4-layer technological structure: sensor and sensor nodes, crop field and sensor networks, cloud services and front-end applications. It makes available a set of free tools based on the Do-It-Yourself (DIY) concept and enables the use of low-cost platforms to quickly prototype a complete PA/PV monitoring application. © Springer Nature Switzerland AG 2019.

Abstract
Nature inspired metaheuristics algorithms have been the target of several studies in the most varied scientific areas due to their high efficiency in solving real world problems. This is also the case of agriculture. Among the most well-established nature inspired metaheuristics the ones selected to be addressed in this work are the following: genetic algorithms, differential evolution, simulated annealing, harmony search, particle swarm optimization, ant colony optimization, firefly algorithm and bat algorithm. For each of them, the mechanism that inspired it and a brief description of its operation is presented, followed by a review of their most relevant agricultural applications. © Springer Nature Switzerland AG 2019.

Abstract
Tawny Port wine is a category of the famous Portuguese fortified wine commercialized worldwide and produced in the Douro Demarcated Region. Tawny Port wine oxidative aging is a multifactorial process critical for reaching the wanted quality. Real time monitoring of important intrinsic and extrinsic factors that are known to affect both time and quality of the aging process are important to optimize and to manage the natural variability between wines aged in different long-used wood barrels. This study presents the design, development and implementation of a remote distributed system to monitor parameters that are known to be critical for Tawny Port wine aging process. Results indicate that the distributed monitoring system was capable to detect differences between oak wood barrels and between the different storage conditions. Indeed, oxygen and redox potential were the wine's parameters where the differences found between different barrels were greater under the same storage conditions. Considering that Tawny Port wine aging process is oxidative, a variation in the wine's aging process between different wood barrels is to be expected. Actually, significant differences were detected in the oxygen consumption rate amongst the different barrels. Differences in the phenolic composition was also observed in the aged wine (controlled temperature and room temperature).

Abstract
Traditionally farmers have used their perceptual sensorial systems to diagnose and monitor their crops health and needs. However, humans possess five basic perceptual systems with accuracy levels that can change from human to human which are largely dependent on the stress, experience, health and age. To overcome this problem, in the last decade, with the help of the emergence of smartphone technology, new agronomic applications were developed to reach better, cost-effective, more accurate and portable diagnosis systems. Conventional smartphones are equipped with several sensors that could be useful to support near real-time usual and advanced farming activities at a very low cost. Therefore, the development of agricultural applications based on smartphone devices has increased exponentially in the last years. However, the great potential offered by smartphone applications is still yet to be fully realized. Thus, this paper presents a literature review and an analysis of the characteristics of several mobile applications for use in smart/precision agriculture available on the market or developed at research level. This will contribute to provide to farmers an overview of the applications type that exist, what features they provide and a comparison between them. Also, this paper is an important resource to help researchers and applications developers to understand the limitations of existing tools and where new contributions can be performed.

Abstract
Recent studies highlight the ability of inductive architectures to deliver therapeutic magnetic stimuli to target tissues and to be embedded into small-scale intracorporeal medical devices. However, to date, current micro-scale biomagnetic devices require very high electric current excitations (usually exceeding 1 A) to ensure the delivery of efficient magnetic flux densities. This is a critical problem as advanced implantable devices demand self-powering, stand-alone and long-term operation. This work provides, for the first time, a novel small-scale magnetic stimulation system that requires up to 50-fold lower electric current excitations than required by relevant biomagnetic technology recently proposed. Computational models were developed to analyse the magnetic stimuli distributions and densities delivered to cellular tissues during in vitro experiments, such that the feasibility of this novel stimulator can be firstly evaluated on cell culture tests. The results demonstrate that this new stimulative technology is able to deliver osteogenic stimuli (0.1-7 mT range) by current excitations in the 0.06-4.3 mA range. Moreover, it allows coil designs with heights lower than 1 mm without significant loss of magnetic stimuli capability. Finally, suitable core diameters and stimulator-stimulator distances allow to define heterogeneity or quasi-homogeneity stimuli distributions. These results support the design of high-sophisticated biomagnetic devices for a wide range of therapeutic applications.

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.