Abstract
Phenology is one of the most reliable indicators of vegetation dynamics. Assessing and monitoring the dynamics of phenology is relevant to support several decisions in order to improve the efficiency of several farming practices. An open source application QPhenoMetrics - implemented in QGIS software that estimates vegetation phenology metrics is presented, using Earth Observation Systems (EOS) based time-series of Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) as proxies for phenology. QPhenoMetrics is characterized by freely-usable and updatable code, acceptance of satellite images or text formats, time-series analysis toolbox allowing the selection of region of interest with statistical quality assessment for Vegetation Indices (VI), and estimation of ensemble metrics. The application is structured in three components: (i) input data; (ii) pre-processing of the VI time-series and several fitting methods and (iii) computation of the phenological metrics. QPhenoMetrics produces a plot with the VI time-series and corresponding phenology metrics, and a spreadsheet is created with a list of NDVI or EVI values estimated using the selected fitting method. To evaluate the application, two main Portuguese crops, vineyards and maize, and MOD13 data from MODIS sensor during 2011-2012 were considered. QPhenoMetrics was validated with vineyard phenology observations (2007-2011). A comparative analysis with software products TimeSat and Spirits was also performed. It was concluded that QPhenoMetrics can be very useful for common users to extract phenology information for 16 daily MODIS data in HDF format, text files with NDVI/EVI data and ASCII files, through a simple and intuitive graphic interface. Furthermore, the user can evaluate the quality assessment of VI of the images used. QPhenoMetrics is an effective open source tool that in addition to being free, is readily modifiable by user according to the study requirements.

Abstract
An holistic approach to decision support systems for intelligent public lighting control, must address both energy efficiency and maintenance. Currently, it is possible to remotely control and adjust luminaries behaviour, which poses new challenges at the maintenance level. The luminary efficiency depends on several efficiency factors, either related to the luminaries or the surrounding conditions. Those factors are hard to measure without understanding the luminary operating boundaries in a real context. For this early stage on preventive maintenance design, we propose an approach based on the combination of two models of the network, wherein each is representing a different but complementary perspective on the classifying of the operating conditions of the luminary as normal or abnormal. The results show that, despite the expected and normal differences, both models have a high degree of concordance in their predictions.

Abstract
Similitude theory is a branch of engineering science concerned with establishing the necessary and sufficient conditions of similarity among phenomena, and has been applied to different fields such as structural engineering, vibration and impact problems. Testing of subscale models is still nowadays a valuable design tool, helping engineers to accurately predict the behavior of oversized prototypes through scaling laws applied to the obtained experimental results. However, several limitations and difficulties still persist when applying the similitude theory through the current methodologies to complex structures. This manuscript presents an original modular approach. The objective is to improve how the similitude theory is applied through the governing equations, overcoming part of the identified constraints. The major achievement is that, with the new approach, the similitude theory can be applied to complex structures through differential equations of its simple substructures, even if the governing equations for the whole structure are not available. A stiffened plate is selected to demonstrate the use of the modular approach and the accuracy of the scaled model testing. To that end, the modular approach has to be firstly applied to the generalized simple plate and beam. Even though the simplifying assumptions are minimized in the substructure governing equations, the modular, approach is able to avoid the expected increase in the mathematical effort and complexity of the process of applying the similitude theory. In a second step, a relation is established between the scaling relationships derived for each simple structural element of the complex prototype.

Abstract
With the rise in wearable technology and "Bhealth culture", we are seeing an increasing interest and affordances in studying how to not only prolong life expectancy but also in how to improve individuals' quality of life. On the one hand, this attempts to give meaning to the increasing life expectancy, as living above a certain threshold of pain and lack of autonomy or mobility is both degrading and unfair. On the other hand, it lowers the cost of continuous care, as individuals with high quality of life indexes tend to have lower hospital readmissions or secondary complications, not to mention higher physical and mental health. In this paper, we evaluate the current state of the art in physiological therapy (biofeedback) along with the existing medical grade and consumer grade hardware for physiological research. We provide a quick primer on the most commonly monitored physiologic metrics, as well as a brief discussion on the current state of the art in biofeedback-assisted medical applications. We then go on to present a comparative analysis between medical and consumer grade biofeedback devices and discuss the hardware specifications and potential practical applications of each consumer grade device in terms of functionality and adaptability for controlled (laboratory) and uncontrolled (field) studies. We end this article with some empirical observations based on our study so that readers might use take them into consideration when arranging a laboratory or real-world experience, thus avoiding costly time delays and material expenditures.

Abstract
Software has a longstanding association with a state of crisis considering its success rate. The explosion of Internet-connected devices - Internet-of-Things -adds to the complexity of software systems. The particular characteristics of these systems, such as its large-scale and heterogeneity, pose increasingly new challenges. Model-based approaches have been widely used as a mechanism to abstract low-level programming details and processes. By using such approaches, and leveraging concepts as reactive design, visual notations, and live programming, we believe to be able to reduce the complexity of creating, operate/monitor and evolve such systems. The main objective of this Ph.D. is to delve into the software engineering practices for developing IoT systems and systems of systems, exploiting models as a suitable abstraction, expecting to reduce the complexity of managing most of the software development lifecycle that targets IoT systems and to develop the prototype that will aid on the validation of such approach.

Abstract
Field spectra acquired from a handheld spectroradiometer and Sentinel-2 images spectra were used to investigate the applicability of hyperspectral and multispectral data in retrieving the maize leaf area index in low-input crop systems, with high spatial and intra-annual variability, and low yield, in southern Mozambique, during three years. Seventeen vegetation indices, comprising two and three band indices, and nine machine learning regression algorithms (MLRA) were tested for the statistical approach while five cost functions were tested in the look-up-table (LUT) inversion approach. The three band vegetation indices were selected, specifically the modified difference index (mDI(d: 725; 715; 565)) for the hyperspectral dataset and the modified simple ratio (mSR(c: 740; 705; 865)) for the multispectral dataset of field spectra and the three band spectral index (TBSIb: 665; 865; 783) for the Sentinel-2 dataset. The relevant vector machine was the selected MLRA for the two datasets of field spectra (multispectral and hyperspectral) while the support vector machine was selected for the Sentinel-2 data. When using the LUT inversion technique, the minimum contrast estimation and the Bhattacharyya divergence cost functions were the best performing. The vegetation indices outperformed the other two approaches, with the TBSIb as the most accurate index (RMSE = 0.35). At the field scale, spectral data from Sentinel-2 can accurately retrieve the maize leaf area index in the study area.