ALBERT

Description: Current Continuous Glucose Monitors (CGM) exhibit increased estimation error during periods of aerobic physical activity. The use of readily-available exercise monitoring devices opens new possibilities for accuracy enhancement during these periods. The viability of an array of physical activity signals provided by three different wearable devices was considered. Linear regression models were used in this work to evaluate the correction capabilities of each of the wearable signals and propose a model for CGM correction during exercise. A simple two-input model can reduce CGM error during physical activity (17.46% vs. 13.8%, p 〈 0.005) to the magnitude of the baseline error level (13.61%). The CGM error is not worsened in periods without physical activity. The signals identified as optimal inputs for the model are “Mets” (Metabolic Equivalent of Tasks) from the Fitbit Charge HR device, which is a normalized measurement of energy expenditure, and the skin temperature reading provided by the Microsoft Band 2 device. A simpler one-input model using only “Mets” is also viable for a more immediate implementation of this correction into market devices.

Description: Microcellular sensory polymers prepared from solid sensory polymeric films were tested in an aqueous Hg(II) detection process to analyze their sensory behavior. First, solid acrylic-based polymeric films of 100 µm thickness were obtained via radical copolymerization process. Secondly, dithizone sensoring motifs were anchored in a simple five-step route, obtaining handleable colorimetric sensory films. To create the microporous structure, films were foamed in a ScCO2 batch process, carried out at 350 bar and 60 °C, resulting in homogeneous morphologies with cell sizes around 5 µm. The comparative behavior of the solid and foamed sensory films was tested in the detection of mercury in pure water media at 2.2 pH, resulting in a reduction of the response time (RT) around 25% and limits of detection and quantification (LOD and LOQ) four times lower when using foamed films, due to the increase of the specific surface associated to the microcellular structure.

Description: A preference defined on a set of alternatives can be extended to a preference on the subsets of alternatives (named opportunity sets) in different ways. We specifically consider the application of the indirect-utility (IU) criterion in various stages, when both the alternatives and the preferences can change over time. In other words, we maintain the symmetry over time as far as criteria are concerned, but neither in the preferences, nor in the alternatives. We characterize this criterion by three testable axioms. Our study bears comparison with Krause (Economic Theory, 2008) for the two-period model.

Description: Traditional potato growth models evidence certain limitations, such as the cost of obtaining the input data required to run the models, the lack of spatial information in some instances, or the actual quality of input data. In order to address these issues, we develop a model to predict potato yield using satellite remote sensing. In an effort to offer a good predictive model that improves the state of the art on potato precision agriculture, we use images from the twin Sentinel 2 satellites (European Space Agency—Copernicus Programme) over three growing seasons, applying different machine learning models. First, we fitted nine machine learning algorithms with various pre-processing scenarios using variables from July, August and September based on the red, red-edge and infra-red bands of the spectrum. Second, we selected the best performing models and evaluated them against independent test data. Finally, we repeated the previous two steps using only variables corresponding to July and August. Our results showed that the feature selection step proved vital during data pre-processing in order to reduce multicollinearity among predictors. The Regression Quantile Lasso model (11.67% Root Mean Square Error, RMSE; R2 = 0.88 and 9.18% Mean Absolute Error, MAE) and Leap Backwards model (10.94% RMSE, R2 = 0.89 and 8.95% MAE) performed better when predictors with a correlation coefficient 〉 0.5 were removed from the dataset. In contrast, the Support Vector Machine Radial (svmRadial) performed better with no feature selection method (11.7% RMSE, R2 = 0.93 and 8.64% MAE). In addition, we used a random forest model to predict potato yields in Castilla y León (Spain) 1–2 months prior to harvest, and obtained satisfactory results (11.16% RMSE, R2 = 0.89 and 8.71% MAE). These results demonstrate the suitability of our models to predict potato yields in the region studied.

Description: Image analysis of developmental processes in plants reveals both growth and organ movement. This study proposes a methodology to study growth and movement. It includes the standard acquisition of internal and external reference points and coordinates, coordinates transformation, curve fitting and the corresponding statistical analysis. Several species with different growth habits were used including Antirrhinum majus, A. linkianum, Petunia x hybrida and Fragaria x ananassa. Complex growth patterns, including gated growth, could be identified using a generalized additive model. Movement, and in some cases, growth, could not be adjusted to curves due to drastic changes in position. The area under the curve was useful in order to identify the initial stage of growth of an organ, and its growth rate. Organs displayed either continuous movements during the day with gated day/night periods of maxima, or sharp changes in position coinciding with day/night shifts. The movement was dependent on light in petunia and independent in F. ananassa. Petunia showed organ movement in both growing and fully-grown organs, while A. majus and F. ananassa showed both leaf and flower movement patterns linked to growth. The results indicate that different mathematical fits may help quantify growth rate, growth duration and gating. While organ movement may complicate image and data analysis, it may be a surrogate method to determine organ growth potential.