Assessing advertising content in a hospital advertising campaign: An application of Puto and Wells (1984) measure of informational and transformational advertising content

The following is a report of a study designed to measure advertising content based on the cognitive and affective elements of informational (i.e., information processing) and transformational (i.e., experiential) content using the measure of advertising informational and transformational content developed by Puto and Wells (1984). A university hospital advertising campaign designed to be high in transformational content did not appear to affect perceived quality of local university hospitals relative to private hospitals or increase the likelihood of choosing a university hospital in the future. Further, experiences with university hospitals that seemed to be in direct contrast to the content of the advertisements based on subject perceptions affected how university hospital advertisements were perceived in terms of content. Conclusions and implications for hospital advertising campaigns are discussed.     

Testing machine learning systems in real estate

Uncertainty about the inner workings of machine learning (ML) models holds back the application of ML-enabled systems in real estate markets. How do ML models arrive at their estimates? Given the lack of model transparency, how can practitioners guarantee that ML systems do not run afoul of the law? This article first advocates a dedicated software testing framework for applied ML systems, as commonly found in computer science. Second, it demonstrates how system testing can verify that applied ML models indeed perform as intended. Two system-testing procedures developed for ML image classifiers used in automated valuation models (AVMs) illustrate the approach.     

Initiating events and independent protection layers

Layer of protection analysis (LOPA) is a semiquantitative tool for analyzing and assessing process risk. The tool has grown greatly in popularity and usefulness since the publication of the first CCPS/AIChE guidebook on the subject, Layer of Protection Analysis, Simplified Process Risk Assessment (LOPA). This article is a summary of a new CCPS guideline book that includes the following:

• Initiating events (IEs).
• Independent protection layers (IPLs).
• Criteria for identifying the value of each prospective IE and prospective IPL.
• Example IE and IPL data.

This book is a necessary reference for those applying the LOPA methodology. © 2009 American Institute of Chemical Engineers Process Saf Prog, 2009