Enhanced desalination with polyamide thin-film membranes using ensemble ML chemometric methods and SHAP analysis.

Addressing global freshwater scarcity requires innovative technological solutions, among which desalination through thin-film composite polyamide membranes stands out. The performance of these membranes plays a vital role in desalination, necessitating advanced predictive modeling for optimization. This study harnesses machine learning (ML) algorithms, including support vector machine (SVM), neural networks (NN), linear regression (LR), and multivariate linear regression (MLR), alongside their ensemble techniques to predict and enhance average water flux (AWF) and average salt rejection (ASR) essential metrics of desalination efficiency.

Application of Deep Learning on Millimeter-Wave Radar Signals: A Review.

The progress brought by the deep learning technology over the last decade has inspired many research domains, such as radar signal processing, speech and audio recognition, etc., to apply it to their respective problems. Most of the prominent deep learning models exploit data representations acquired with either Lidar or camera sensors, leaving automotive radars rarely used.

Sepsis in the era of data-driven medicine: personalizing risks, diagnoses, treatments and prognoses.

Sepsis is a series of clinical syndromes caused by the immunological response to infection. The clinical evidence for sepsis could typically attribute to bacterial infection or bacterial endotoxins, but infections due to viruses, fungi or parasites could also lead to sepsis. Regardless of the etiology, rapid clinical deterioration, prolonged stay in intensive care units and high risk for mortality correlate with the incidence of sepsis.