The current state of research and potential applications of insects for resource recovery and aquaculture feed.

Concerns about fishmeal use and its ecological footprints must be addressed for the aquaculture industry to move on as a sustainable food production sector. Through recent research outcomes, the insect-based meals in fish diets have promise and harnessed promises for commercial applications. In this midst, the efficiency of the selected insects in valorizing biological waste, as well as the nutritional profile of the harvested insects for use in fish diets, will be the driving forces behind such an approach.

Materials fatigue prediction using graph neural networks on microstructure representations.

The local prediction of fatigue damage within polycrystals in a high-cycle fatigue setting is a long-lasting and challenging task. It requires identifying grains tending to accumulate plastic deformation under cyclic loading. We address this task by transcribing ferritic steel microtexture and damage maps from experiments into a microstructure graph.

Acceptance of a computer vision facilitated protocol to measure adherence to face mask use: a single-site, observational cohort study among hospital staff.

Objectives: Mask adherence continues to be a critical public health measure to prevent transmission of aerosol pathogens, such as SARS-CoV-2. We aimed to develop and deploy a computer vision algorithm to provide real-time feedback of mask wearing among staff in a hospital.

Design: Single-site, observational cohort study.

A deep learning approach for complex microstructure inference.

Automated, reliable, and objective microstructure inference from micrographs is essential for a comprehensive understanding of process-microstructure-property relations and tailored materials development. However, such inference, with the increasing complexity of microstructures, requires advanced segmentation methodologies. While deep learning offers new opportunities, an intuition about the required data quality/quantity and a methodological guideline for microstructure quantification is still missing.

Automated Quantitative Analyses of Fatigue-Induced Surface Damage by Deep Learning.

The digitization of materials is the prerequisite for accelerating product development. However, technologically, this is only beneficial when reliability is maintained. This requires comprehension of the microstructure-driven fatigue damage mechanisms across scales.