Abstract

Abstract
Abstract Background Heart Rate Variability (HRV) is an increasing area of interest in patients with epilepsy. The effects of epilepsy on the autonomic control of the heart are not completely understood and that autonomic dysfunction has been implicated in some cases of Sudden Unexpected Death in Epilepsy (SUDEP). Objective To study the influence of generalized tonic-clonic seizures (GTCS) on HRV of patients with focal refractory epilepsy. Method We prospectively evaluated (January 2015 to July 2018) 121 patients admitted to our institution's Epilepsy Monitoring Unit. All patients performed a 48-hour Holter recording. Patients who had GTCS during the recording were included and we selected the first GTCS as the index seizure. HRV (AVNN, SDNN, RMSSD, pNN50, and LF/HF) was evaluated by analyzing 5-min-ECG epochs during inter-ictal and post-ictal periods: baseline, pre-ictal (5 min before the GTCS seizure), post-ictal (5 min after the seizure), and late post-ictal (>5 hours after the seizure). We compared HRV data from these patients with normative values for a healthy population (controlling age and gender). The study was approved by our Institution Ethics Committee and all patients gave informed consent. Results Twenty three patients were included (mean age: 38.61±11.58; 70% Female). Thirty percent presented cardiovascular risk factors without known cardiac disease. We found significant differences between the analyzed periods for all but one (LF/HF) HRV metrics (using Friedman test, p<0.05, two-tailed). Specifically during the post-ictal period, we found a significant reduction for AVNN, SDNN, RMSSD and pNN50 (Wilcoxon test, p<0.05; two-tailed). LF/HF was increased during this period, but changes were not statistically significant. There was also a tendency for a reduction of AVNN, SDNN, RMSSD and pNN50 and an increase of LF/HF in our patients during all the analyzed periods when compared to normative healthy population values. Conclusion Our work shows reduced HRV after a GTCS in patients with focal resistant epilepsy, both in inter-ictal and post-ictal periods, when compared to normative healthy population values. These results might reflect long term structural changes in autonomic centers. The HRV changes were significant particularly during the post-ictal period, and should prompt further investigation, giving this period is critical for SUDEP.

Abstract
The thermocline is a relatively narrow vertical region that separates the mixed layer at the surface from the deep-water layer. In this region, the gradient of temperature with respect to depth is higher than in the rest of the water column. The characteristics of the thermocline have strong impact in marine biology, since it may trap high-nutrient organisms, and it also affects sound propagation, with direct impact on underwater acoustic communications and military operations. Under adaptive sampling, Autonomous Underwater Vehicles are practical tools for efficient ocean observation. In this work, we describe an implementation of an Extremum Seeking Controller that performs identification and tracking of thermoclines at its point of highest temperature gradient in a completely autonomous way. The vehicle chosen to perform this tracking was an autonomous vertical profiler, and the algorithms were validated using both real and simulated data.

Abstract
The current algorithm to support platelets stock management assumes that there are always sufficient whole blood donations (WBD) to produce the required amount of pooled platelets. Unfortunately, blood donation rate is uncertain so there is the need to backup pooled platelets productions with single-donor (apheresis) collections to compensate periods of low WBD. The aim of this work was to predict the daily number of WBD to a tertiary care center to preemptively account for a decrease of platelets production. We have collected 62,248 blood donations during 3 years, the daily count of which was used to feed (standalone and ensemble versions of) six prediction models, which were evaluated using the Mean Absolute Error (MAE). Forecast models have shown better performances with a MAE of about 8.6 donations, 34% better than using means or medians alone. Trend lines of donations are better modeled by autoregressive integrated moving average (ARIMA) using a frequency of 365 days, the trade-off being the need for at least two years of data.

Abstract
This paper addresses the network expansion planning of an active microgrid that utilizes Distributed Energy Resources (DERs). The microgrid uses Combined Cooling, Heating and Power (CCHP) systems with their heating and cooling network. The proposed method uses a bi-level iterative optimization algorithm for optimal expansion and operational planning of the microgrid that consists of different zones, and each zone can transact electricity with the upward utility. The transaction of electricity with the upward utility can be performed based on demand response programs that consist of the time-of-use program and/or direct load control. DERs are CHPs, small wind turbines, photovoltaic systems, electric and cooling storage, gas fired boilers and absorption and compression chillers are used to supply different zones' electrical, heating, and cooling loads. The proposed model minimizes the system's investment, operation, interruption and environmental costs; meanwhile, it maximizes electricity export revenues and the reliability of the system. The proposed method is applied to a real building complex and five different scenarios are considered to evaluate the impact of different energy supply configurations and operational paradigm on the investment and operational costs. The effectiveness of the introduced algorithm has been assessed. The implementation of the proposed algorithm reduces the aggregated investment and operational costs of the test system in about 54.7% with respect to the custom expansion planning method.

Abstract
This paper provides an answer to the problem of State Estimation (SE) with multiple simultaneous gross errors, based on Generalized Error Correntropy instead of Least Squares and on an interior point method algorithm instead of the conventional Gauss-Newton algorithm. The paper describes the mathematical model behind the new SE cost function and the construction of a suitable solver and presents illustrative numerical cases. The performance of SE with the data set contaminated with up to five simultaneous gross errors is assessed with confusion matrices, identifying false and missed detections. The superiority of the new method over the classical Largest Normalized Residual Test is confirmed at a 99% confidence level in a battery of tests. Its ability to address cases where gross errors fall on critical measurements, critical sets or leverage points is also confirmed at the same level of confidence.