An intelligent decision support system for groundwater supply management and electromechanical infrastructure controls.

This study presents an intelligent Decision Support System (DSS) aimed at bridging the theoretical-practical gap in groundwater management. The ongoing demand for sophisticated systems capable of interpreting extensive data to inform sustainable groundwater decision-making underscores the critical nature of this research. To meet this challenge, telemetry data from six randomly selected wells were used to establish a comprehensive database of groundwater pumping parameters, including flow rate, pressure, and current intensity. Statistical analysis of these parameters led to the determination of threshold values for critical factors such as water pressure and electrical current. Additionally, a soft sensor was developed using a Random Forest (RF) machine learning algorithm, enabling real-time forecasting of key variables. This was achieved by continuously comparing live telemetry data to pump design specifications and results from regular field testing. The proposed machine learning model ensures robust empirical monitoring of well and pump health. Furthermore, expert operational knowledge from water management professionals, gathered through a Classical Delphi (CD) technique, was seamlessly integrated. This collective expertise culminated in a data-driven framework for sustainable groundwater facilities monitoring. In conclusion, this innovative DSS not only addresses the theory-application gap but also leverages the power of data analytics and expert knowledge to provide high-precision online insights, thereby optimizing groundwater management practices.

Efficacy and safety of convalescent plasma therapy in severe COVID-19 patients with acute respiratory distress syndrome.

Since SARS-CoV-2 infection is rapidly spreading all around the world, affecting many people and exhausting health care resources, therapeutic options must be quickly investigated in order to develop a safe and effective treatment. The present study was designed to evaluate the safety and efficacy of convalescent plasma (CP) for treating severe cases of COVID-19 who developed acute respiratory distress syndrome (ARDS). Among 64 confirmed cases of severe COVID-19 with ARDS in this study, 32 patients received CP besides first line treatment. Their clinical response and outcome in regard to disease severity and mortality rate were evaluated and compared with the other 32 patients in the control group who were historically matched while randomly chosen from previous patients with the same conditions except for receiving CP therapy. Analysis of the data was performed using SPSS software. Patients with plasma therapy showed improvements in their clinical outcomes including a reduction in disease severity in terms of SOFA and APACHE II scores, the length of ICU stay, need for noninvasive ventilation and intubation and also showed an increase in oxygenation. They also showed reduction in mortality which was statistically significant in less severe cases with mild or moderate ARDS. Early administration of the convalescent plasma could successfully contribute to the treatment of severe COVID-19 patients with mild or moderate ARDS at risk of progressing to critical state.