Non profit organisations management: especial reference to small associations

Treball Final de Grau en Finances i Comptabilitat. Codi: FC1049. Curs: 2017/2018The growth of the associative movement is taking a lot of strength in recent decades and therefore the creation of small associations by people who are not prepared to face with the management of those, it is being a reality. In this project we want to carry out a study and analyse the management techniques that are developed in the non-profit entities so that they can operate correctly and have an economic control. Besides, we want to be a refernce for all those people who want to start a new project by creating an association, so they could find any answers related with the creation, management and organisation of this type of corporation, and so we make it easier for them to develop the suitable work that the association pursues

Basal Glucose Control in Type 1 Diabetes using Deep Reinforcement Learning: An In Silico Validation

People with Type 1 diabetes (T1D) require regular exogenous infusion of insulin to maintain their blood glucose concentration in a therapeutically adequate target range. Although the artificial pancreas and continuous glucose monitoring have been proven to be effective in achieving closed-loop control, significant challenges still remain due to the high complexity of glucose dynamics and limitations in the technology. In this work, we propose a novel deep reinforcement learning model for single-hormone (insulin) and dual-hormone (insulin and glucagon) delivery. In particular, the delivery strategies are developed by double Q-learning with dilated recurrent neural networks. For designing and testing purposes, the FDA-accepted UVA/Padova Type 1 simulator was employed. First, we performed long-term generalized training to obtain a population model. Then, this model was personalized with a small data-set of subject-specific data. In silico results show that the single and dual-hormone delivery strategies achieve good glucose control when compared to a standard basal-bolus therapy with low-glucose insulin suspension. Specifically, in the adult cohort (n=10), percentage time in target range [70, 180] mg/dL improved from 77.6% to 80.9% with single-hormone control, and to $85.6\%$ with dual-hormone control. In the adolescent cohort (n=10), percentage time in target range improved from 55.5% to 65.9% with single-hormone control, and to 78.8% with dual-hormone control. In all scenarios, a significant decrease in hypoglycemia was observed. These results show that the use of deep reinforcement learning is a viable approach for closed-loop glucose control in T1D

GluGAN: Generating Personalized Glucose Time Series Using Generative Adversarial Networks

Time series data generated by continuous glucose monitoring sensors offer unparalleled opportunities for developing data-driven approaches, especially deep learning-based models, in diabetes management. Although these approaches have achieved state-of-the-art performance in various fields such as glucose prediction in type 1 diabetes (T1D), challenges remain in the acquisition of large-scale individual data for personalized modeling due to the elevated cost of clinical trials and data privacy regulations. In this work, we introduce GluGAN, a framework specifically designed for generating personalized glucose time series based on generative adversarial networks (GANs). Employing recurrent neural network (RNN) modules, the proposed framework uses a combination of unsupervised and supervised training to learn temporal dynamics in latent spaces. Aiming to assess the quality of synthetic data, we apply clinical metrics, distance scores, and discriminative and predictive scores computed by post-hoc RNNs in evaluation. Across three clinical datasets with 47 T1D subjects (including one publicly available and two proprietary datasets), GluGAN achieved better performance for all the considered metrics when compared with four baseline GAN models. The performance of data augmentation is evaluated by three machine learning-based glucose predictors. Using the training sets augmented by GluGAN significantly reduced the root mean square error for the predictors over 30 and 60-minute horizons. The results suggest that GluGAN is an effective method in generating high-quality synthetic glucose time series and has the potential to be used for evaluating the effectiveness of automated insulin delivery algorithms and as a digital twin to substitute for pre-clinical trials

Continuous minimax optimization using modal intervals

AbstractMany real life problems can be stated as a continuous minimax optimization problem. Well-known applications to engineering, finance, optics and other fields demonstrate the importance of having reliable methods to tackle continuous minimax problems. In this paper a new approach to the solution of continuous minimax problems over reals is introduced, using tools based on modal intervals. Continuous minimax problems, and global optimization as a particular case, are stated as the computation of semantic extensions of continuous functions, one of the key concepts of modal intervals. Modal intervals techniques allow to compute, in a guaranteed way, such semantic extensions by means of an efficient algorithm. Several examples illustrate the behavior of the algorithms in unconstrained and constrained minimax problems

GluNet: A Deep Learning Framework For Accurate Glucose Forecasting

For people with Type 1 diabetes (T1D), forecasting of \red{blood glucose (BG)} can be used to effectively avoid hyperglycemia, hypoglycemia and associated complications. The latest continuous glucose monitoring (CGM) technology allows people to observe glucose in real-time. However, an accurate glucose forecast remains a challenge. In this work, we introduce GluNet, a framework that leverages on a personalized deep neural network to predict the probabilistic distribution of short-term (30-60 minutes) future CGM measurements for subjects with T1D based on their historical data including glucose measurements, meal information, insulin doses, and other factors. It adopts the latest deep learning techniques consisting of four components: data pre-processing, label transform/recover, multi-layers of dilated convolution neural network (CNN), and post-processing. The method is evaluated in−silico for both adult and adolescent subjects. The results show significant improvements over existing methods in the literature through a comprehensive comparison in terms of root mean square error (RMSE) (8.88 ± 0.77 mg/dL) with short time lag (0.83 ± 0.40 minutes) for prediction horizons (PH) = 30 mins (minutes), and RMSE (19.90 ± 3.17 mg/dL) with time lag (16.43 ± 4.07 mins) for PH = 60 mins for virtual adult subjects. In addition, GluNet is also tested on two clinical data sets. Results show that it achieves an RMSE (19.28 ± 2.76 mg/dL) with time lag (8.03 ± 4.07 mins) for PH = 30 mins and an RMSE (31.83 ± 3.49 mg/dL) with time lag (17.78 ± 8.00 mins) for PH = 60 mins. These are the best reported results for glucose forecasting when compared with other methods including the neural network for predicting glucose (NNPG), the support vector regression (SVR), the latent variable with exogenous input (LVX), and the auto regression with exogenous input (ARX) algorithm

Coastally trapped disturbances caused by the tramontane wind on the northwestern Mediterranean: numerical study and sensitivity to short-wave radiation

The Tramontane-Cierzo wind system is a recurrent feature of the north-western Mediterranean basin in front of Catalan coast (NE Spain). Associated with this feature, northeast wind surges affect occasionally the coast and become a weather hazard for low-level aircraft operations, affecting for example the Barcelona international airport. This paper first reports these surges characterizing them as Coastal-Trapped Disturbances (CTDs). Climatological features are described, showing that CTDs occur frequently during the warm season and between the afternoon and evening. We classified CTDs related to two synoptic patterns related to the location of a mid-level tropospheric geopotential trough and the Iberian Peninsula: pattern A, with the trough crossing eastwards north Spain; and pattern B, with the trough over the Mediterranean, after crossing the Iberian Peninsula. To study the CTDs in detail, numerical simulations were conducted using the non-hydrostatic and convection-permitting NWP model HARMONIE-AROME. Two cases, one for each synoptic pattern, were studied showing that CTDs generate in the discontinuity between cool outflows and warmer air progressing southward as a density current, trapped by the mountain ranges parallel to the coastline. Cool outflows may have two different sources: in Pattern A the origin of the cold air is the Tramontane itself, while in Pattern B convective outflows associated with storm downdrafts play this role. Both cases show similarities with CTDs studied on the California coast, showing an antitriptic and ageostrophic flow behind the CTD. An additional numerical sensitivity experiment was conducted by varying the short-wave radiation to explore the effects of diabatic warming on CTDs. It is demonstrated that a large warming influences on CTDs by enhancing the potential temperature gradient between the density current and the environment modulating its intensity and speed.This work was performed under the framework of the Hydrological Mediterranean Experiment (HyMeX) programme and was partially supported by the Spanish projects CGL2015-65627-C3-2-R (MINECO/FEDER), CGL2016-81828-REDT (MINECO) and the Water Research Institute (IdRA) of the University of Barcelona

The AEMET-γSREPS convection-permitting LAM-EPS in Antarctica

Póster presentado en: EMS Annual Meeting: European Conference for Applied Meteorology and Climatology celebrado del 9 al 13 de septiembre de 2017 en Copenhague, Dinamarca.During the last austral summer Spanish Antarctic Campaign (1st December – 31st March), coinciding with the Southern Hemisphere Special Observation Period of the Year of Polar Prediction (YOPP), the 2.5 km AEMET-γSREPS convection-permitting LAM-EPS was integrated daily at 00 UTC up to 48 around Livingston Island (South Shetland Islands, Antarctica) with the aim to improve the forecasts and consequently making Juan Carlos I Spanish Antarctic station activities safer

Unannounced Meals in the Artificial Pancreas: Detection Using Continuous Glucose Monitoring

[EN] The artificial pancreas (AP) system is designed to regulate blood glucose in subjects with type 1 diabetes using a continuous glucose monitor informed controller that adjusts insulin infusion via an insulin pump. However, current AP developments are mainly hybrid closed-loop systems that include feed-forward actions triggered by the announcement of meals or exercise. The first step to fully closing the loop in the AP requires removing meal announcement, which is currently the most effective way to alleviate postprandial hyperglycemia due to the delay in insulin action. Here, a novel approach to meal detection in the AP is presented using a sliding window and computing the normalized cross-covariance between measured glucose and the forward difference of a disturbance term, estimated from an augmented minimal model using an Unscented Kalman Filter. Three different tunings were applied to the samemeal detection algorithm: (1) a high sensitivity tuning, (2) a trade-off tuning that has a high amount of meals detected and a low amount of false positives (FP), and (3) a low FP tuning. For the three tunings sensitivities 99 +/- 2%, 93 +/- 5%, and 47 +/- 12% were achieved, respectively. A sensitivity analysis was also performed and found that higher carbohydrate quantities and faster rates of glucose appearance result in favorable meal detection outcomes.This work was funded by the Spanish Government through grants DPI2016-78831-C2-1-R and DPI2016-78831-C2-2-R, the University of Girona through grant BR2014/51, and the European Union through Fondo Europeo de Desarrollo Regional (FEDER) Funds.Ramkissoon, C.; Herrero, P.; Bondía Company, J.; Vehí, J. (2018). Unannounced Meals in the Artificial Pancreas: Detection Using Continuous Glucose Monitoring. Sensors. 18(3):1-18. https://doi.org/10.3390/s18030884S11818