Near-field microwave sensing technology enhanced with machine learning for the non-destructive evaluation of packaged food and beverage products.

In the food industry, the increasing use of automatic processes in the production line is contributing to the higher probability of finding contaminants inside food packages. Detecting these contaminants before sending the products to market has become a critical necessity. This paper presents a pioneering real-time system for detecting contaminants within food and beverage products by integrating microwave (MW) sensing technology with machine learning (ML) tools.

In vitro prebiotic activity of rhLf and galactooligosaccharides on infant intestinal microbiota.

Objective: human lactoferrin (Lf) and human milk oligosaccharides possess a wide range of functions. So, the present study focusses on the role of Lf and/or galactooligosaccharides (GOS) in the modulation of gut microbiota composition. Methods: recombinant human lactoferrin (rhLf) was added to the first infant formula (0.

Graduate medical education-led continuous assessment of burnout and learning environments to improve residents' wellbeing.

Background: Promoting residents' wellbeing and decreasing burnout is a focus of Graduate Medical Education (GME). A supportive clinical learning environment is required to optimize residents' wellness and learning.

Objective: To determine if longitudinal assessments of burnout and learning environment as perceived by residents combined with applying continuous quality Model for Improvement and serial Plan, Do, Study, Act (PDSA) cycles to test interventions would improve residents' burnout.

A comparative study on predicting influenza outbreaks using different feature spaces: application of influenza-like illness data from Early Warning Alert and Response System in Syria.

Objective: An accurate forecasting of outbreaks of influenza-like illness (ILI) could support public health officials to suggest public health actions earlier. We investigated the performance of three different feature spaces in different models to forecast the weekly ILI rate in Syria using EWARS data from World Health Organization (WHO). Time series feature space was first used and we applied the seven models which are Naïve, Average, Seasonal naïve, drift, dynamic harmonic regression (Dhr), seasonal and trend decomposition using loess (STL) and TBATS.