Performance evaluation of the section of 3D reconstruction based on different PET/CT image fusion sequence.

In recent years, positron emission tomography imaging (PET) and Computed tomography (CT) fusion images can be observed metabolic information, they can get a more accurate spatial information. People have to construct 3D models in the first place when they try to examine images from different angles. Once a cross-section which we want to inspect has been revealed, it can be observed from any angles. However, a issue people encounter in above-mentioned procedures is that they have to either process images fusion at the beginning and then reconstruct the 3D models with these images to generate section or rebuilt the 3D models with these images and fuse section images as the second step. The main objective of this research is to discriminate the divergences and merits between two types of procedures.This research discovers that two different procedures will exactly bring about dissimilar types of images. Therefore, this research particularly aims at the analysis of two images and evaluates the extent of fringe and remaining information. We calculates entropy and standard deviation of the images. Nevertheless, finding section on 3D models first and fusing images secondly will generate the images which retain more information.

Predictive models of safety based on audit findings: Part 2: Measurement of model validity.

Part 1 of this study sequence developed a human factors/ergonomics (HF/E) based classification system (termed HFACS-MA) for safety audit findings and proved its measurement reliability. In Part 2, we used the human error categories of HFACS-MA as predictors of future safety performance. Audit records and monthly safety incident reports from two airlines submitted to their regulatory authority were available for analysis, covering over 6.5 years. Two participants derived consensus results of HF/E errors from the audit reports using HFACS-MA. We adopted Neural Network and Poisson regression methods to establish nonlinear and linear prediction models respectively. These models were tested for the validity of prediction of the safety data, and only Neural Network method resulted in substantially significant predictive ability for each airline. Alternative predictions from counting of audit findings and from time sequence of safety data produced some significant results, but of much smaller magnitude than HFACS-MA. The use of HF/E analysis of audit findings provided proactive predictors of future safety performance in the aviation maintenance field.

Predictive models of safety based on audit findings: Part 1: Model development and reliability.

This consecutive study was aimed at the quantitative validation of safety audit tools as predictors of safety performance, as we were unable to find prior studies that tested audit validity against safety outcomes. An aviation maintenance domain was chosen for this work as both audits and safety outcomes are currently prescribed and regulated. In Part 1, we developed a Human Factors/Ergonomics classification framework based on HFACS model (Shappell and Wiegmann, 2001a,b), for the human errors detected by audits, because merely counting audit findings did not predict future safety. The framework was tested for measurement reliability using four participants, two of whom classified errors on 1238 audit reports. Kappa values leveled out after about 200 audits at between 0.5 and 0.8 for different tiers of errors categories. This showed sufficient reliability to proceed with prediction validity testing in Part 2.