The Catalytic Vapor Phase Butylation of Phenol

The purpose of this experiment is to study the reaction between secondary butyl alcohol and phenol in the vapor phase. The effect of temperature upon the formation of different butyl phenols and butyl phenyl ethers was observed

Automated machine learning: A biologically inspired approach

Machine learning is a robust process by which a computer can discover characteristics of underlying data that enable it to create a model for making future predictions or classifications from new data. Designing machine learning pipelines, unfortunately, is often as much an art as it is a science, requiring pairing of feature construction, feature selection, and learning methods, all with their own sets of parameters. No general machine learning pipeline solution exists; each dataset has unique characteristics that make a particular set of methods and parameters better suited to solving the problem than others. To respond to the challenge of machine learning pipeline design, the field of automated machine learning (autoML) has recently emerged. AutoML seeks to automate the often arduous work of a data scientist, so they can focus on the underlying meanings of the data and spend less time on the tedium of pipeline design and tuning. This dissertation adapts and applies genetic programming to the newly emergent field of automated machine learning. Genetic programming enables the artificial evolution of an algorithm through a nearly infinite search space that otherwise requires a randomized search. This dissertation shows that through the process of genetic programming, it is possible to produce machine learning pipelines, and the evolved pipelines can outperform those created by human researchers.Ph.D

Wearable sensor shown to specifically quantify pruritic behaviors in dogs

Abstract Background Wearable technology is an exciting new field in humans and animals. In dogs activity monitors have helped to provide objective measurement tools where pet owner observation had been the only source of information. Previous research has focused on measuring overall activity versus rest. This has been relatively useful in determining changes in activity in orthopedic disease or post-surgical cases [Malek et al., BMC Vet Res 8:185, 2012, Yashari et al., BMC Vet Res 11:146, 2015]. Assessment of pruritus via changes in activity, however, requires an assumption that increased activity is due to scratching or other pruritic behaviors. This is an inaccurate method with obvious flaws as other behaviors may also register as greater activity. The objective of this study was to validate the ability of a multidimensional high frequency sensor and advanced computer analysis system, (Vetrax®, AgLogica Holdings, Inc., Norcross, GA, USA) to specifically identify pruritic behaviors (scratching and head shaking). To establish differences between behaviors, sensor and time stamped video data were collected from 361 normal and pruritic dogs. Video annotations were made by two observers independently, while blinded to sensor data, and then evaluated for agreement. Annotations that agreed between the two were used for further analysis. The annotations specified behaviors at specific times in order to compare with sensor data. A computer algorithm was developed to interpret and differentiate between these behaviors. Test subject data was then utilized to test and score the system’s ability to accurately predict behaviors. Results Results for prediction of head shaking behavior included sensitivity and specificity of 72.16% and 99.78% respectively. Analysis of scratching produced sensitivity and specificity of 76.85% and 99.73% respectively. These results illustrate the ability of the system to accurately report both scratching and head shaking with an overall accuracy of 99.24% and 99.56% respectively. Conclusions This study validates the use of this system to accurately and objectively report scratching and head shaking in dogs. While a small portion of scratching or head shaking behaviors may be missed, as indicated by the sensitivity, when detected, the confidence that these behaviors occurred is extremely high. These factors make this system a very useful tool for objective assessment of pruritus in clinical and research settings