Abstract
The interest in expanding the production of hops outside the traditional cultivation regions, mainly motivated by the growth of the craft brewery business, justifies the intensification of studies into its adaptation to local growing conditions. In this study, four field trials were undertaken on a twenty-year-old hop garden, over periods of up to three years to assess the effect of important agro-environmental variation factors on hop phenol and phenolic composition and to establish its relationship with the elemental composition of hop cones. All the field trials were arranged as factorial designs exploring the combined effect of: (1) plots of different vigour plants x year; (2) plots of different plant vigor x algae- and nutrient-rich foliar sprays x year; (3) plot x liming x year; and (4) cultivars (Nugget, Cascade, Columbus) x year. Total phenols in hops, were significantly influenced by most of the experimental factors. Foliar spraying and liming were the factors that least influenced the measured variables. The year had the greatest effect on the accumulation of total phenols in hop cones in the different trials and may have contributed to interactions that often occurred between the factors under study. The year average for total phenol concentrations in hop cones ranged from 11.9 mg g(-1) to 21.2 mg g(-1). Significant differences in quantity and composition of phenolic compounds in hop cones were also found between cultivars. The phenolic compounds identified were mainly flavonols (quercetin and kaempferol glycosides) and phenolic carboxylic acids (p-coumaric and caffeic acids).

Abstract
Wildfires can profoundly impact many aspects of matter flows and energy budgets in ecosystems. Exacerbated by projected shifts in climate, land use, and forest management, changes in fire regimes can lead to decreased ecosystem resilience, regime shifts, and ecosystem collapse. Thorough assessments of ecosystem resilience to wildfires are thus critical to bridge gaps between science, policy, and management. To that end, approaches based on ecosystem functioning offer an integrative view of ecosystem responses to wildfire-induced changes and provide quicker, quantifiable responses to disturbances that are more directly connected to ecosystem services. In that regard, satellite remote sensing can be employed to easily and frequently monitor multiple dimensions of ecosystem functioning over large areas and across time, and to evaluate ecosystem functioning resilience to wildfires. This study describes an approach for identifying potential regime shifts based on satellite-based surrogates of four key dimensions of ecosystem functioning: primary production, water content, albedo, and sensible heat. To that end, we classified the trajectories after wildfires in 2005, in NW Iberian Peninsula, for the 2000–2018 period, into five main types, using two metrics of medium-to-long term post-fire recovery. Then, we derived a synthetic indicator to analyse the overall “strength-of-evidence� of potential regime shifts across dimensions. Potential regime shifts were identified for each dimension of ecosystem functioning considered, with the main effects associated with the sudden removal of vegetation. For primary production, regime shifts may be linked to changes in land cover and use, as well as management. Changes in the concentrations of impervious and radiation-absorbing materials following wildfires may be responsible for regime shifts in water content and albedo, with loss of canopy moisture due to fire-related damage leading to vegetation mortality during post-fire recovery. On the other hand, regime shifts in sensible heat were less frequent, since wildfires tend to have transient effects on this dimension of ecosystem functioning. Overall, our results show that our approach successfully captured different patterns of post-fire recovery and resilience across multiple dimensions of ecosystem functioning. We argue that our approach can provide an enhanced characterization of ecosystem resilience to wildfires, and support the identification of potential regime shifts after such disturbances, ultimately upholding promising implications for post-fire ecosystem management.

Abstract
Modern software engineering practices to enable reproducible and easy to deploy robotics solutions have been embraced in recent years, leading to an increasing adoption of container technologies within the Robot Operating System (ROS) community. However, there is still no common procedure or tools for creating, testing, and deploying containerized ROS packages. A common way to work with containerized ROS applications would prove beneficial by increasing even more the level of collaboration among development teams, help in reusing existing solutions, and automate the development of new ones. This paper presents a software framework to support the development of ROS applications using Docker containers, across all its stages. Besides containerizing ROS packages, the presented tool also assists in the deployment of containerized solutions as well as the creation of complex simulation environments for testing. The tool also provides a way for these simulations to be assessed at run-time using a property-specific language targeting ROS applications. An industrial and a scientific scenario are presented to portray the usage of the proposed tool.

Abstract
Cascade cropping systems in soilless horticulture (where drainage collected from the main crop is used in fertigation of secondary crops) are potentially interesting for Mediterranean countries as they enhance water and nutrient use efficiency. However, their agronomic and long-term environmental impact has been poorly addressed. In this case study, lettuce grown hydroponically or in soil (previously exposed to drainage for five years) was fertigated, throughout the cultivation period, with a nutrient solution composed of 0, 25, 50 or 100 % of drainage (0D, 25D, 50D and 100D) mixed with a fresh nutrient solution. Plant performance analysis included growth parameters and leaf mineral composition. Drainage was analyzed for nutrients and Plant Protection Products (PPP) residues, and bioassays were performed exposing aquatic organisms (Raphidocelis subcapitata, Aliivibrio fischeri and Daphnia magna) to drainage and soil elutriate. When analyzing plant performance in both cultivation systems, a significant effect was only found at 100D in hydroponics, resulting in 41 % less leaf area, 20 % smaller head diameter and 43 % lower yield. Drainage analysis showed high nutrient content, presence of PPP residues (up to 6 substances, reaching 3.29 mu g.L-1 in total) and revealed toxicity to D. magna (EC50 = 66.6 %). Moreover, soil elutriate presented toxicity to R. subcapitata (EC50 = 20.6 %) and to A. fischeri (EC50 = 14.9 %). This study demonstrates the potential of using relatively high drainage percentages (up to 50 %) from soilless cultivation systems if applied to hydroponically-grown secondary crops. However, attention should be paid to the use of cascade cropping systems when drainages are applied to fertigate soil-grown crops, as it may contribute to soil degradation and environmental pollution on a long run.

Abstract

Abstract
This work presents a novel methodology for the quality assessment of material extrusion parts through AI-based Computer Vision. To this end, different techniques are integrated using inspection methods that are applied to other areas in additive manufacturing field. The system is divided into four main points: (1) pre-processing, (2) color analysis, (3) shape analysis, and (4) defect location. The color analysis is performed in CIELAB color space, and the color distance between the part under analysis and the reference surface is calculated using the color difference formula CIE2000. The shape analysis consists of the binarization of the image using the Canny edge detector. Then, the Hu moments are calculated for images from the part under analysis and the results are compared with those from the reference part. To locate defects, the image of the part to be analyzed is first processed with a median filter, and both the original and filtered image are subtracted. Then, the resulting image is binarized, and the defects are located through a blob detector. In the training phase, a subset of parts was used to evaluate the performance of different methods and to set the values of parameters. Later, in a testing and validation phase, the performance of the system was evaluated using a different set of parts. The results show that the proposed system is able to classify parts produced by additive manufacturing, with an overall accuracy of 86.5%, and to locate defects on their surfaces in a more effective manner.