The Intentional Use of Service Recovery Strategies to Influence Consumer Emotion, Cognition and Behaviour

Service recovery strategies have been identified as a critical factor in the success of. service organizations. This study develops a conceptual frame work to investigate how specific service recovery strategies influence the emotional, cognitive and negative behavioural responses of . consumers., as well as how emotion and cognition influence negative behavior. Understanding the impact of specific service recovery strategies will allow service providers' to more deliberately and intentionally engage in strategies that result in positive organizational outcomes. This study was conducted using a 2 x 2 between-subjects quasi-experimental design. The results suggest that service recovery has a significant impact on emotion, cognition and negative behavior. Similarly, satisfaction, negative emotion and positive emotion all influence negative behavior but distributive justice has no effect

Estimating peak skin and eye lens dose from neuroperfusion examinations: Use of Monte Carlo based simulations and comparisons to CTDI_vol, AAPM report no. 111, and ImPACT dosimetry tool values.

Purpose: CT neuroperfusion examinations are capable of delivering high radiation dose to the skin or lens of the eyes of a patient and can possibly cause deterministic radiation injury. The purpose of this study is to: (a) estimate peak skin dose and eye lens dose from CT neuroperfusion examinations based on several voxelized adult patient models of different head size and (b) investigate how well those doses can be approximated by some commonly used CT dose metrics or tools, such as CTDIvol, American Association of Physicists in Medicine (AAPM) Report No. 111 style peak dose measurements, and the ImPACT organ dose calculator spreadsheet.Methods: Monte Carlo simulation methods were used to estimate peak skin and eye lens dose on voxelized patient models, including GSF's Irene, Frank, Donna, and Golem, on four scanners from the major manufacturers at the widest collimation under all available tube potentials. Doses were reported on a per 100 mAs basis. CTDIvol measurements for a 16 cm CTDI phantom, AAPM Report No. 111 style peak dose measurements, and ImPACT calculations were performed for available scanners at all tube potentials. These were then compared with results from Monte Carlo simulations.Results: The dose variations across the different voxelized patient models were small. Dependent on the tube potential and scanner and patient model, CTDIvol values overestimated peak skin dose by 26%-65%, and overestimated eye lens dose by 33%-106%, when compared to Monte Carlo simulations. AAPM Report No. 111 style measurements were much closer to peak skin estimates ranging from a 14% underestimate to a 33% overestimate, and with eye lens dose estimates ranging from a 9% underestimate to a 66% overestimate. The ImPACT spreadsheet overestimated eye lens dose by 2%-82% relative to voxelized model simulations.Conclusions: CTDIvol consistently overestimates dose to eye lens and skin. The ImPACT tool also overestimated dose to eye lenses. As such they are still useful as a conservative predictor of dose for CT neuroperfusion studies. AAPM Report No. 111 style measurements are a better predictor of both peak skin and eye lens dose than CTDIvol and ImPACT for the patient models used in this study. It should be remembered that both the AAPM Report No. 111 peak dose metric and CTDIvol dose metric are dose indices and were not intended to represent actual organ doses

Peak skin and eye lens radiation dose from brain perfusion CT based on Monte Carlo simulation.

OBJECTIVE: The purpose of our study was to accurately estimate the radiation dose to skin and the eye lens from clinical CT brain perfusion studies, investigate how well scanner output (expressed as volume CT dose index [CTDI(vol)]) matches these estimated doses, and investigate the efficacy of eye lens dose reduction techniques. MATERIALS AND METHODS: Peak skin dose and eye lens dose were estimated using Monte Carlo simulation methods on a voxelized patient model and 64-MDCT scanners from four major manufacturers. A range of clinical protocols was evaluated. CTDI(vol) for each scanner was obtained from the scanner console. Dose reduction to the eye lens was evaluated for various gantry tilt angles as well as scan locations. RESULTS: Peak skin dose and eye lens dose ranged from 81 mGy to 348 mGy, depending on the scanner and protocol used. Peak skin dose and eye lens dose were observed to be 66-79% and 59-63%, respectivelAmy, of the CTDI(vol) values reported by the scanners. The eye lens dose was significantly reduced when the eye lenses were not directly irradiated. CONCLUSION: CTDI(vol) should not be interpreted as patient dose; this study has shown it to overestimate dose to the skin or eye lens. These results may be used to provide more accurate estimates of actual dose to ensure that protocols are operated safely below thresholds. Tilting the gantry or moving the scanning region further away from the eyes are effective for reducing lens dose in clinical practice. These actions should be considered when they are consistent with the clinical task and patient anatomy

Model evaluation guidelines for geomagnetic index predictions

Geomagnetic indices are convenient quantities that distill the complicated physics of some region or aspect of near‐Earth space into a single parameter. Most of the best‐known indices are calculated from ground‐based magnetometer data sets, such as Dst, SYM‐H, Kp, AE, AL, and PC. Many models have been created that predict the values of these indices, often using solar wind measurements upstream from Earth as the input variables to the calculation. This document reviews the current state of models that predict geomagnetic indices and the methods used to assess their ability to reproduce the target index time series. These existing methods are synthesized into a baseline collection of metrics for benchmarking a new or updated geomagnetic index prediction model. These methods fall into two categories: (1) fit performance metrics such as root‐mean‐square error and mean absolute error that are applied to a time series comparison of model output and observations and (2) event detection performance metrics such as Heidke Skill Score and probability of detection that are derived from a contingency table that compares model and observation values exceeding (or not) a threshold value. A few examples of codes being used with this set of metrics are presented, and other aspects of metrics assessment best practices, limitations, and uncertainties are discussed, including several caveats to consider when using geomagnetic indices