Abstract
Motion sensing plays an important role in the study of human movements, motivated by a wide range of applications in different fields, such as sports, health care, daily activity, action recognition for surveillance, assisted living and the entertainment industry. In this paper, we describe how to classify a set of human movements comprising daily activities using a wearable motion capture suit, denoted as FatoXtract. A probabilistic integration of different classifiers recently proposed is employed herein, considering several spatiotemporal features, in order to classify daily activities. The classification model relies on the computed confidence belief from base classifiers, combining multiple likelihoods from three different classifiers, namely Na < ve Bayes, artificial neural networks and support vector machines, into a single form, by assigning weights from an uncertainty measure to counterbalance the posterior probability. In order to attain an improved performance on the overall classification accuracy, multiple features in time domain (e.g., velocity) and frequency domain (e.g., fast Fourier transform), combined with geometrical features (joint rotations), were considered. A dataset from five daily activities performed by six participants was acquired using FatoXtract. The dataset provided in this work was designed to be extremely challenging since there are high intra-class variations, the duration of the action clips varies dramatically, and some of the actions are quite similar (e.g., brushing teeth and waving, or walking and step). Reported results, in terms of both precision and recall, remained around 85 %, showing that the proposed framework is able to successfully classify different human activities.

Abstract
Optimising the design of water distribution networks (WDNs) is a well-known problem that has been studied by numerous researchers. This work proposes a heuristic based on simulated annealing and improved by using concepts from the cross-entropy method. The proposed optimization approach is presented and used in two case studies of different complexity. The results show not only a fall in the computational effort of the new approach relative to simulated annealing but also include a comparison with other heuristic results from the literature, used to solve the same problems.

Abstract
Knowledge regarding Ecosystem Services (ES) delivery and the socio-ecological factors that influence their proficiency is essential to allow cities to adopt policies that lead to resource-efficient planning and greater resilience. As one of the matrix elements of urban ecological structure, vegetation may play a major role in promoting ES proficiency through planting design. This research addresses the heterogeneity of ES delivered by the urban vegetation of Porto, a Portuguese city. A methodology is proposed to investigate associations between socioeconomic indicators and structural variables of the urban forest, and also which structural variables of the urban forest, if any, differ along a socioeconomic gradient. Our results reveal that before setting planning and management goals, it is crucial to understand local patterns of ES and their relationships with socioeconomic patterns, which can be affected by variables such as building age. This should be followed by the identification of structural variables of the urban forest that better explain the differences, in order to target these through planning and management goals. The conceptual framework adopted in this research can guide adaptation of our methodology to other cities, providing insights for planning and management suitable to site-specific conditions and directly usable by stakeholders.

Abstract
In this study, hyperspectral reflectance (HySR) data derived from a handheld spectroradiometer were used to assess the water status of three grapevine cultivars in two sub-regions of Douro wine region during two consecutive years. A large set of potential predictors derived from the HySR data were considered for modelling/predicting the predawn leaf water potential (Psi(pd)) through different statistical and machine learning techniques. Three HySR vegetation indices were selected as final predictors for the computation of the models and the in-season time trend was removed from data by using a time predictor. The vegetation indices selected were the Normalized Reflectance Index for the wavelengths 554 nm and 561 nm (NRI554;561), the water index (WI) for the wavelengths 900 nm and 970 nm, and the D1 index which is associated with the rate of reflectance increase in the wavelengths of 706 nm and 730 nm. These vegetation indices covered the green, red edge and the near infrared domains of the electromagnetic spectrum. A large set of state-of-the-art analysis and statistical and machine-learning modelling techniques were tested. Predictive Modelling techniques based on generalized boosted model (GBM), bagged multivariate adaptive regression splines (B-MARS), generalized additive model (GAM), and Bayesian regularized neural networks (BRNN) showed the best performance for predicting Psi(pd), with an average determination coefficient (R-2) ranging between 0.78 and 0.80 and RMSE varying between 0.11 and 0.12 MPa. When cultivar Touriga Nacional was used for training the models and the cultivars Touriga Franca and Tinta Barroca for testing (independent validation), the models performance was good, particularly for GBM (R-2 = 0.85; RMSE = 0.09 MPa). Additionally, the comparison of Psi(pd) observed and predicted showed an equitable dispersion of data from the various cultivars. The results achieved show a good potential of these predictive models based on vegetation indices to support irrigation scheduling in vineyard.

Abstract
While an understanding of evolutionary processes in shifting environments is vital in the context of rapid ecological change, one of the most potent selective forces, sexual selection, remains curiously unexplored. Variation in sexual selection across a species range, especially across a gradient of temperature regimes, has the potential to provide a window into the possible impacts of climate change on the evolution of mating patterns. Here, we investigated some of the links between temperature and indicators of sexual selection, using a cold-water pipefish as model. We found that populations differed with respect to body size, length of the breeding season, fecundity, and sexual dimorphism across a wide latitudinal gradient. We encountered two types of latitudinal patterns, either linear, when related to body size, or parabolic in shape when considering variables related to sexual selection intensity, such as sexual dimorphism and reproductive investment. Our results suggest that sexual selection intensity increases toward both edges of the distribution and that the large differences in temperature likely play a significant role. Shorter breeding seasons in the north and reduced periods for gamete production in the south certainly have the potential to alter mating systems, breeding synchrony, and mate monopolization rates. As latitude and water temperature are tightly coupled across the European coasts, the observed patterns in traits related to sexual selection can lead to predictions regarding how sexual selection should change in response to climate change. Based on data from extant populations, we can predict that as the worm pipefish moves northward, a wave of decreasing selection intensity will likely replace the strong sexual selection at the northern range margin. In contrast, the southern populations will be followed by heightened sexual selection, which may exacerbate the problem of local extinction at this retreating boundary.

Abstract
Olive trees are one of the most economically important perennial crops in Portugal. During the last decade, the Alentejo olive-growing region has suffered a significantly change in the crop production system, with the regional pollen index (RPI) and olive fruit production registering a significant growth. The aim of this study was to ascertain the utility of this highly variable production and pollen data in crop forecasting modeling. Airborne pollen was sampled using a Cour-type trap from 1999 to 2015. A linear regression model fitted with the regional pollen index as the independent variable showed an accuracy of 87% in estimating olives fruit production in Alentejo. However, the average deviation between observed and modeled production was 32% with half of the tested years presenting deviations between 36 and 66%. The low accuracy of this model is a consequence of the great overall variation and significant upward trend observed in both the production and the RPI dataset that conceal the true association between these variables. In order to overcome this problem, a detrend procedure was applied to both time series to remove the trend observed. The regression model fitted with the fruit production and the RPI detrended data showed a lowest forecasting accuracy of 63% but the average deviation between observed and modeled production decrease to 14% with a maximum deviation value of 33%. This procedure allows focusing the analysis on the production fluctuations related to the biological response of the trees rather than with the changes in the production system.