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Abstract This paper assesses Brazil's real convergence (1822–2019) through unit root tests and Markov Regime-Switching (MS) models in three different scenarios: towards (i) other six Latin American countries (LA6); (ii) Portugal; and (iii) the technological frontier country, the US. The extended unit root test results favour Brazil's very long-run real convergence towards LA6 and Portugal, but not the US. The estimated MS models, involving two different regimes, real convergence and real non-convergence/divergence, capture institutional quality's positive effect in promoting Brazil's real convergence.

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In Portugal, almonds are a very important crop, due to their nutritional properties. In the northeastern part of the country, the almond sector has endured over time, with strong cultural traditions and key economic significance. In these areas, several cultivars are used. In effect, the presence of various almond cultivars implies differentiated management in irrigation, disease control, pruning system, and harvest planning. Therefore, cultivar classification is essential over large agricultural areas. Over the last decades, remote-sensing data have led to important breakthroughs in the classification of different cultivars for several crops. Nonetheless, for almonds, studies are incipient. Thus, this study aims to fill this knowledge gap and explore the classification of almond cultivars in an almond orchard. High-resolution multispectral data were acquired by an unmanned aerial vehicle (UAV). Vegetation indices (VIs) and tree structural parameters were, subsequently, estimated. To obtain an accurate cultivar identification, four machine learning classifiers, such as K-nearest neighbour (kNN), support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost), were applied and optimized through the fine-tuning process. The accuracy of machine learning classifiers was analysed. SVM and RF performed best with OAs of 76% and 74% using VIs and spectral bands (GREEN, GRVI, GN, REN, ClRE). Adding the canopy height model (CHM) improved performance, with RF and XGBoost having OAs of 88% and 84%. kNN performed worst with an OA of 73% using only VIs and spectral bands, 80% with VIs, spectral bands and CHM, and 93% with VIs, CHM, and tree crown area (TCA). The best performance was achieved by RF and XGBoost with OAs of 99% using VIs, CHM, and TCA. These results demonstrate the importance of the feature selection process. Moreover, this study reveals the feasibility of remote-sensing data and machine learning classifiers in the classification of almond cultivars.

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The independence and autonomy of both elderly and disabled people have been a growing concern of today's society. Consequently, the increase in life expectancy combined with the ageing of the population has created the ideal conditions for the introduction of Intelligent Wheelchairs (IWs). For this purpose, several adapted sensors should be used to optimize the control of a wheelchair. During this work, the Leap Motion sensor was analyzed to convert the user's will into one of four fundamental driving commands, move forward, turn right, left, or stop. Leap Motion aims to determine the direction to follow according to the hand gesture identified. For this task, data was collected from volunteers while they were performing certain gestures. Thereby it was possible to produce a data set that after being processed and extracted some features enabled the classification of the data with an F1-Score higher than 0.97. Additionally, when tested in a real-time application, this sensor reinforced its high performance.

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Process Analytical Technology (PAT) has been increasingly used in the pharmaceutical industry to monitor essential parameters in real-time during pharmaceutical processes. The concentration of Active Pharmaceutical Ingredients (APIs), such as paracetamol, is one of these parameters, and controlling its variations allows for optimization of the production process. In this study, a refractometric sensor, implemented by an interrogation system based on an Erbium-Doped Fiber Ring Cavity (EDFRC), was presented and experimentally demonstrated. The Cavity Ring proposed included a 1 x 3 coupler. One port of the coupler was used to increase the optical power of the system through a Fiber Bragg Grating (FBG), and the other two ports were used as sensing head and reference. The sensor detected variations of paracetamol concentration with a sensitivity of [(-1.00 +/- 0.05) x 10(-3)] nW/(g/kg) and a resolution of 5.53 g/kg. The results demonstrate the potential of this technology as a possible non-invasive PAT tool.

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