Nowadays, how to non-destructively obtain the bending strength of the bronze-based matrix fabricated by low-temperature hot pressing sintering (HPS) is still difficult. The main contribution of our research is a proposed visual quantization model based on microstructure features of metallographic microscopy images and machine learning to predict the bending strength of bronze-based material. Exploring the interrelationship between microstructure features and mechanical properties will guide the modulation of grinding wheel composition and HPS parameters.

The research were incorporated an extended cohort monitoring campaign, validation of an existing exposure model and development of a predictive model for COPD exacerbations evaluated against historical electronic health records.

A miniature personal sensor unit were manufactured for the study from a prototype developed at the University of Cambridge. The units monitored GPS position, temperature, humidity, CO, NO, NO2, O3, PM10 and PM2.5.