Abstract
Cardiovascular diseases are one of the leading causes of death in the world. In low income countries, heart auscultation is of capital importance since it is an efficient and low cost method to monitor the heart. In this paper, we propose a multi-spot system that aims to detect cardiac anomalies and to support a diagnosis in remote areas with limited heath care response. Our proposed solutions exploits data collected from the four main auscultation spots: Mitral, Pulmonary, Tricuspid and Aorta in a asynchronous way. From the several multi-spot systems implemented, the best results were obtained using a bi-modal system that only processes the Mitral and the Pulmonary spot simultaneously. Using these two spots we have achieved an accuracy between 85.7% (smallest value, using ANN) and the best value of 91.4% (obtained with a logistic regression algorithm). Taking into a account the pediatric population and the incident cardiac pathologies, it happens to be the spots where the observed murmurs were most audible. We have also find out that when using four auscultation spots, the choice of the algorithm is of secondary priority, which does not seem to be the case for a single auscultation spot system. With one single auscultation we have an average of 4% of difference between the results obtained with the algorithms and with four auscultation spots we have a smaller average of 2.1%.

Abstract
Background: The American Academy of Sleep Medicine guidelines suggest that clinical prediction algorithms can be used in patients with obstructive sleep apnea (OSA) without replacing polysomnography, which is the gold standard. Objective: This study aims to develop a clinical decision support system for OSA diagnosis according to its standard definition (apnea-hypopnea index plus symptoms), identifying individuals with high pretest probability based on risk and diagnostic factors. Methods: A total of 47 predictive variables were extracted from a cohort of patients who underwent polysomnography. A total of 14 variables that were univariately significant were then used to compute the distance between patients with OSA, defining a hierarchical clustering structure from which patient phenotypes were derived and described. Affinity from individuals at risk of OSA phenotypes was later computed, and cluster membership was used as an additional predictor in a Bayesian network classifier (model B). Results: A total of 318 patients at risk were included, of whom 207 (65.1%) individuals were diagnosed with OSA (111, 53.6% with mild; 50, 24.2% with moderate; and 46, 22.2% with severe). On the basis of predictive variables, 3 phenotypes were defined (74/207, 35.7% low; 104/207, 50.2% medium; and 29/207, 14.1% high), with an increasing prevalence of symptoms and comorbidities, the latter describing older and obese patients, and a substantial increase in some comorbidities, suggesting their beneficial use as combined predictors (median apnea-hypopnea indices of 10, 14, and 31, respectively). Cross-validation results demonstrated that the inclusion of OSA phenotypes as an adjusting predictor in a Bayesian classifier improved screening specificity (26%, 95% CI 24-29, to 38%, 95% CI 35-40) while maintaining a high sensitivity (93%, 95% CI 91-95), with model B doubling the diagnostic model effectiveness (diagnostic odds ratio of 8.14). Conclusions: Defined OSA phenotypes are a sensitive tool that enhances our understanding of the disease and allows the derivation of a predictive algorithm that can clearly outperform symptom-based guideline recommendations as a rule-out approach for screening.

Abstract
The number of electric vehicles (EVs) on the road is expected to continue to increase during the next decades due to various factors such as the rapid progress in EV technology and decreasing battery prices. The prolonged battery charging process, which is one of the main problems that affects the increased EV penetration, makes the fast charging units more attractive and efficient option for the charging stations. In this study, a control strategy for a DC microgrid including electric vehicle fast charging station (EVFCS) and distributed generation units is presented to examine the impacts of EVFCS on the grid as well as their potential contributions to the system operation in the case of considering the vehicle-to-grid (V2G) technology. It is especially aimed to mitigate the voltage sag and swell problems by using the EV battery as a DC source of a distribution static compensator (D-STATCOM) device. Simulation studies in MATLAB Simulink/SimPower systems show that considerable improvements can be achieved from the perspective of distribution system operation such as improved voltage quality and from the perspective of end users such as decreased charging durations.

Abstract
Electrification of society and economy is crucial to fight against climate changes assuming simultaneously a large-scale integration of electricity generation exploiting Renewable Energy Sources (RES). The increasing presence of RES and Electric Vehicles (EV) in Low Voltage (LV) networks, and the emergence of the Smart Grid paradigm will require relevant changes in the operational management of both LV and Medium Voltage (MV) networks. In this paper, two different strategies (separated and coordinated management) for the operational management of MV and LV networks are compared regarding their ability to integrate large amounts of RES and to accept increased electrification of consumption, including EV. Each management strategy is modeled through optimization problems, being then applied to an electrical distribution system consisting of MV and LV networks. Results show that a coordinated operational management outperforms the separated strategy, by allowing the integration of much higher volumes of RES and EV.

Abstract
In sectorization problems (SPs), a large area is divided into smaller regions for administrative purposes. SPs have applications in many fields. Since real-life problems are often dynamic, in this study, a new model for dynamic SP is proposed. In the problem, points are assigned to service centres and in this way sectors are formed. The sectors must be balanced in terms of distance and demand, which is defined in the objective function and constraints of the model. In the problem, in a certain time period, the coordinates and demands of some points change according to certain statistical distributions. A two-stage solution method is suggested for this problem. In the first stage, the expected values of coordinates and demands of the points are estimated by a Monte Carlo simulation, and in the second stage, the problem is solved like a deterministic optimization problem. The model is nonlinear, but after linearization, it is solved in Python’s Pulp library for benchmarks of different sizes and the results are discussed.

Abstract