Abstract
Climate change is projected to be a key influence on crop yields across the globe. Regarding viticulture, primary climate vectors with a significant impact include temperature, moisture stress, and radiation. Within this context, it is of foremost importance to monitor soils' moisture levels, as well as to detect pests, diseases, and possible problems with irrigation equipment. Regular monitoring activities will enable timely measures that may trigger field interventions that are used to preserve grapevines' phytosanitary state, saving both time and money, while assuring a more sustainable activity. This study employs unmanned aerial vehicles (UAVs) to acquire aerial imagery, using RGB, multispectral and thermal infrared sensors in a vineyard located in the Portuguese Douro wine region. Data acquired enabled the multi-temporal characterization of the vineyard development throughout a season through the computation of the normalized difference vegetation index, crop surface models, and the crop water stress index. Moreover, vigour maps were computed in three classes (high, medium, and low) with different approaches: (1) considering the whole vineyard, including inter-row vegetation and bare soil; (2) considering only automatically detected grapevine vegetation; and (3) also considering grapevine vegetation by only applying a normalization process before creating the vigour maps. Results showed that vigour maps considering only grapevine vegetation provided an accurate representation of the vineyard variability. Furthermore, significant spatial associations can be gathered through (i) a multi-temporal analysis of vigour maps, and (ii) by comparing vigour maps with both height and water stress estimation. This type of analysis can assist, in a significant way, the decision-making processes in viticulture.

Abstract
The feasibility of a compact, modular sensing system able to quantify the presence of nitrogen, phosphorus and potassium (NPK) in nutrient-containing fertilizer water was investigated. Direct UV-Vis spectroscopy combined with optical fibers were employed to design modular compact sensing systems able to record absorption spectra of nutrient solutions resulting from local producer samples. N, P, and K spectral interference was studied by mixtures of commercial fertilizer solutions to simulate real conditions in hydroponic productions. This study demonstrates that the use of bands for the quantification of nitrogen with linear or logarithmic regression models does not produce analytical grade calibrations. Furthermore, multivariate regression models, i.e., Partial Least Squares (PLS), which consider specimens interference, perform poorly for low absorbance nutrients. The high interference present in the spectra has proven to be solved by an innovative self-learning artificial intelligence algorithm that is able to find interference modes among a spectral database to produce consistent predictions. By correctly modeling the existing interferences, analytical grade quantification of N, P, and K has proven feasible. The results of this work open the possibility of real-time NPK monitoring in Micro-Irrigation Systems.

Abstract
This paper proposes a new methodology to automatically segment and measure the volume of pulmonary nodules in lung computed tomography (CT) scans. Estimating the malignancy likelihood of a pulmonary nodule based on lesion characteristics motivated the development of an unsupervised pulmonary nodule segmentation and volume measurement as a preliminary stage for pulmonary nodule characterization. The idea is to optimally cluster a set of feature vectors composed by intensity and shape-related features in a given feature data space extracted from a pre-detected nodule. For that purpose, a metaheuristic search based on evolutionary computation is used for clustering the corresponding feature vectors. The proposed method is simple, unsupervised and is able to segment different types of nodules in terms of location and texture without the need for any manual annotation. We validate the proposed segmentation and volume measurement on the Lung Image Database Consortium and Image Database Resource Initiative - LIDC-IDRI dataset. The first dataset is a group of 705 solid and sub-solid (assessed as part-solid and non-solid) nodules located in different regions of the lungs, and the second, more challenging, is a group of 59 sub-solid nodules. The average Dice scores of 82.35% and 71.05% for the two datasets show the good performance of the segmentation proposal. Comparisons with previous state-of-the-art techniques also show acceptable and comparable segmentation results. The volumes of the segmented nodules are measured via ellipsoid approximation. The correlation and statistical significance between the measured volumes of the segmented nodules and the ground-truth are obtained by Pearson correlation coefficient value, obtaining an R-value >= 92.16% with a significance level of 5%.

Abstract
The characterization of water and energy consumptions is essential in order to define strategies for their rational use. The way these resources are used in households is the path for efficient and rational management, interdependent from each other. It is believed that there are significant differences between the patterns of water and energy consumption in rural and urban areas, where influencing factors should also be identified. This article aims to provide some preliminary results of a research project named ENERWAT, with the main goal to characterize the relation between water and energy consumption at the end use level for urban and rural environments. One of the goals of the aforementioned project was the design, application, and results analysis of a survey, in order to find the main differences in the water and energy consumptions at the end use level and the factors that influence it in urban and rural households. A total of 245 households participated in the research during 2016 (110 urban dwellings and 135 rural), responding to questions on their family composition, dwellings characterization, water and energy consumption habits, and conservation behaviors of these resources. The project also includes the instrumentation and monitoring of dwellings in rural and urban environments to quantify the water consumption and related energy consumption. This stage is still in progress and includes in situ measurements of nine different households (four in rural and five in urban environments) during at least one year. In this article, some of the results obtained by the survey application and the in situ measurements are presented. Despite the large number of data and the associated complexity, it can be concluded that the joint analysis of the results allows identification of a connection between water and energy consumption, as well as a household’s consumption patterns.

Abstract
Lung cancer has an increasing preponderance in worldwide mortality, demanding for the development of efficient screening methods. With this in mind, a binary classification method using Lung-RADS (TM) guidelines to warn changes in the screening management is proposed. First, having into account the lack of public datasets for this task, the lung nodules in the LIDC-IDRI dataset were re-annotated to include a Lung-RADS (TM)-based referral label. Then, a wide residual network is used for automatically assessing lung nodules in 3D chest computed tomography exams. Unlike the standard malignancy prediction approaches, the proposed method avoids the need to segment and characterize lung nodules, and instead directly defines if a patient should be submitted for further lung cancer tests. The system achieves a nodule-wise accuracy of 0.87 +/- 0.02.

Abstract
We propose iW-Net, a deep learning model that allows for both automatic and interactive segmentation of lung nodules in computed tomography images. iW-Net is composed of two blocks: the first one provides an automatic segmentation and the second one allows to correct it by analyzing 2 points introduced by the user in the nodule's boundary. For this purpose, a physics inspired weight map that takes the user input into account is proposed, which is used both as a feature map and in the system's loss function. Our approach is extensively evaluated on the public LIDC-IDRI dataset, where we achieve a state-of-the-art performance of 0.55 intersection over union vs the 0.59 inter-observer agreement. Also, we show that iW-Net allows to correct the segmentation of small nodules, essential for proper patient referral decision, as well as improve the segmentation of the challenging non-solid nodules and thus may be an important tool for increasing the early diagnosis of lung cancer.