Design of monitoring applications and prediction of key industrial metrics: IIoT + AI

The global industry has suffered deep changes in the last years because of the successful development and integration of new technologies. Industry 4.0 has emerged as a new standard for achieving efficiency and improving processes. Among the technologies used in Industry 4.0, Internet of Things applied to industry (IIoT) enable real-time, intelligent, and autonomous access, collection, analysis, communications, and exchange of process, product and/or service information, within the industrial environment, so as to optimize overall production value. Because of its importance, in this project, a methodology for extracting, analyzing and using the data gathered by IIoT devices is proposed in order to extract meaningful information and to predict industrial key metrics with Artificial Intelligence. In addition, for the complete validation of the proposed methodology, a practical implementation of all the mentioned aspects is carried out by developing a study of the industrial process in the wastewater treatment field using the data collected by an Industrial Internet of Things infrastructure and modelling key time series metrics, such as total organic carbon (TOC) and carbon removal performance (CRP) by using Machine Learning models XGBOOST Regressor, Multi-Layer Perceptron (MLP) Regressor and Support Vector Regressor (SVR) to implement a dashboard with an operational panel and a decision-making panel that helps anticipate possible deviations in the performance of the industrial process

Persistent Respiratory Failure and Re-Admission in Patients with Chronic Obstructive Pulmonary Disease Following Hospitalization for COVID-19

Background: Chronic obstructive pulmonary disease (COPD) has been associated with worse clinical evolution/survival during a hospitalization for SARS-CoV2 (COVID-19). The objective of this study was to learn the situation of these patients at discharge as well as the risk of re-admission/mortality in the following 12 months.Methods: We carried out a subanalysis of the RECOVID registry. A multicenter, observational study that retrospectively collected data on severe acute COVID-19 episodes and follow-up visits for up to a year in survivors. The data collection protocol includes general demographic data, smoking, comorbidities, pharmacological treatment, infection severity, complications during hospitalization and required treatment. At discharge, resting oxygen saturation (SpO2), dyspnea according to the mMRC (modified Medical Research Council) scale and long-term oxygen therapy prescription were recorded. The follow-up database included the clinical management visits at 6 and 12 months, where re-admission and mortality were recorded.Results: A total of 2047 patients were included (5.6% had a COPD diagnosis). At discharge, patients with COPD had greater dyspnea and a greater need for prescription home oxygen. After adjusting for age, sex and Charlson comorbidity index, patients with COPD had a greater risk of hospital re-admission due to respiratory causes (HR 2.57 [1.35-4.89], p = 0.004), with no significant differences in survival.Conclusion: Patients with COPD who overcome a serious SARS-CoV2 infection show a worse clinical situation at discharge and a greater risk of re-admission for respiratory causes