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Introduction: Coronavirus disease 2019 (COVID-19) is a systemic disease characterized by a disproportionate inflammatory response in the acute phase. This study sought to identify clinical sequelae and their potential mechanism. Methods: We conducted a prospective single-center study (NCT04689490) of previously hospitalized COVID-19 patients with and without dyspnea during mid-term follow-up. An outpatient group was also evaluated. They underwent serial testing with a cardiopulmonary exercise test (CPET), transthoracic echocardiogram, pulmonary lung test, six-minute walking test, serum biomarker analysis, and quality of life questionaries. Results: Patients with dyspnea (n = 41, 58.6%), compared with asymptomatic patients (n = 29, 41.4%), had a higher proportion of females (73.2 vs. 51.7%; p = 0.065) with comparable age and prevalence of cardiovascular risk factors. There were no significant differences in the transthoracic echocardiogram and pulmonary function test. Patients who complained of persistent dyspnea had a significant decline in predicted peak VO2 consumption (77.8 (64–92.5) vs. 99 (88–105); p < 0.00; p < 0.001), total distance in the six-minute walking test (535 (467–600) vs. 611 (550–650) meters; p = 0.001), and quality of life (KCCQ-23 60.1 ± 18.6 vs. 82.8 ± 11.3; p < 0.001). Additionally, abnormalities in CPET were suggestive of an impaired ventilatory efficiency (VE/VCO2 slope 32 (28.1–37.4) vs. 29.4 (26.9–31.4); p = 0.022) and high PETCO2 (34.5 (32–39) vs. 38 (36–40); p = 0.025). Interpretation: In this study, >50% of COVID-19 survivors present a symptomatic functional impairment irrespective of age or prior hospitalization. Our findings suggest a potential ventilation/perfusion mismatch or hyperventilation syndrome.

Recent technological advances in the Power Generation and Information Technologies areas are helping to change the modern electricity supply system, in order to comply with higher energy efficiency and sustainability standards. Smart Grids are an emerging trend which introduces intelligence in the power grid to optimize resource usage. In order for this intelligence to be effective, it is necessary to retrieve enough information about the grid operation together with other context data such as environmental variables and intelligently modify the behaviour of the network elements accordingly. This paper presents a Multi-Agent System model for Virtual Power Plants, a new power plant concept in which generation no longer occurs in big installations, but is the result of the cooperation of smaller and more intelligent elements. The proposed model is not only focused on the management of the different elements, but includes a set of agents which are embedded with Artificial Neural Networks for collaborative forecasting of disaggregated energy demand of domestic end users, the results of which are also shown in this paper.

Received : 28 November 2012; in revised form: 18 February 2013 / Accepted: 20 February 2013 / Published: 5 March 2013 Abstract: Electricity is indispensable and of strategic importance to national economies. Consequently, electric utilities make an effort to balance power generation and demand in order to offer a good service at a competitive price. For this purpose, these utilities need electric load forecasts to be as accurate as possible. However, electric load depends on many factors (day of the week, month of the year, etc.), which makes load forecasting quite a complex process requiring something other than statistical methods. This study presents an electric load forecast architectural model based on an Artificial Neural Network (ANN) that performs Short-Term Load Forecasting (STLF). In this study, we present the excellent results obtained, and highlight the simplicity of the proposed model. Load forecasting was performed in a geographic location of the size of a potential microgrid, as microgrids appear to be the future of electric power supply.