Publications by authors named "E D Sharman"
Accurately and rapidly acquiring the microscopic properties of a material is crucial for catalysis and electrochemistry. Characterization tools, such as spectroscopy, can be a valuable tool to infer these properties, and when combined with machine learning tools, they can theoretically achieve fast and accurate prediction results. However, on the path to practical applications, training a reliable machine learning model is faced with the challenge of uneven data distribution in a vast array of non-negligible solvent types.
View Article and Find Full Text PDFLabel-free data mining can efficiently feed large amounts of data from the vast scientific literature into artificial intelligence (AI) processing systems. Here, we demonstrate an unsupervised syntactic distance analysis (SDA) approach that is capable of mining chemical substances, functions, properties, and operations without annotation. This SDA approach was evaluated in several areas of research from the physical sciences and achieved performance in information mining comparable to that of supervised learning, as shown by its satisfactory scores of 0.
View Article and Find Full Text PDFArticle Synopsis
- Traditional trial-and-error methods for optimizing catalysts are slow and often ineffective, but machine learning (ML) shows promise in speeding up this research by utilizing its predictive capabilities.
- The selection of the right input features, or descriptors, is crucial for enhancing the accuracy of ML models and understanding what drives catalytic activity and selectivity.
- The review discusses methods for utilizing catalytic descriptors, examines the pros and cons of different descriptors, and outlines new techniques and research approaches that merge computational and experimental ML models, while also addressing current challenges and future directions in this field.
The physiological consequences of overstocking require more investigation, and no research has explored whether dietary supplements could mitigate the anticipated negative physiological effects. OmniGen AF (OG, Phibro Animal Health Corporation, Teaneck, NJ, USA) is a nutritional supplement that has been shown to support the immune system of cattle following internal and environmental stressors. This study aimed to determine if a 45-day period of OG feed supplementation would influence whole blood leukocyte messenger RNA abundance, energy metabolism and glucocorticoid concentration, during a two-week period of overstocking.
View Article and Find Full Text PDFBackground And Objectives: In 2019, a 'Heart Health Check' Medicare Benefit Schedule (MBS) item (699) was introduced to support cardiovascular risk assessment. This study sought to determine the uptake of Item 699 and changes to existing health assessment item claims, before and after the COVID‑19 outbreak.
Method: National MBS data for health assessment items were analysed for adults aged ≥35 years.