Collection and analysis of training simulator data

The purposes of this paper are: (1) to review the objectives, approach, and results of a series of research experiments performed on nuclear power plant training simulators in support of regulatory and research programs of the US Nuclear Regulatory Commission (NRC), and (2) to identify general research issues that may lead to an improved research methodology using the training simulator as a field setting. Research products consist of a refined field research methodology, a data store on operator performance, and specific results pertinent to NRC regulatory positions. Issues and potential advances in operator performance measurement are discussed

Human factors review for nuclear power plant severe accident sequence analysis

The paper discusses work conducted to: (1) support the severe accident sequence analysis of a nuclear power plant transient based on an assessment of operator actions, and (2) develop a descriptive model of operator severe accident management. Operator actions during the transient are assessed using qualitative and quantitative methods. A function-oriented accident management model provides a structure for developing technical operator guidance on mitigating core damage preventing radiological release

Evaluating Modeling and Validation Strategies for Tooth Loss

Prediction models learn patterns from available data (training) and are then validated on new data (testing). Prediction modeling is increasingly common in dental research. We aimed to evaluate how different model development and validation steps affect the predictive performance of tooth loss prediction models of patients with periodontitis. Two independent cohorts (627 patients, 11,651 teeth) were followed over a mean ± SD 18.2 ± 5.6 y (Kiel cohort) and 6.6 ± 2.9 y (Greifswald cohort). Tooth loss and 10 patient- and tooth-level predictors were recorded. The impact of different model development and validation steps was evaluated: 1) model complexity (logistic regression, recursive partitioning, random forest, extreme gradient boosting), 2) sample size (full data set or 10%, 25%, or 75% of cases dropped at random), 3) prediction periods (maximum 10, 15, or 20 y or uncensored), and 4) validation schemes (internal or external by centers/time). Tooth loss was generally a rare event (880 teeth were lost). All models showed limited sensitivity but high specificity. Patients' age and tooth loss at baseline as well as probing pocket depths showed high variable importance. More complex models (random forest, extreme gradient boosting) had no consistent advantages over simpler ones (logistic regression, recursive partitioning). Internal validation (in sample) overestimated the predictive power (area under the curve up to 0.90), while external validation (out of sample) found lower areas under the curve (range 0.62 to 0.82). Reducing the sample size decreased the predictive power, particularly for more complex models. Censoring the prediction period had only limited impact. When the model was trained in one period and tested in another, model outcomes were similar to the base case, indicating temporal validation as a valid option. No model showed higher accuracy than the no-information rate. In conclusion, none of the developed models would be useful in a clinical setting, despite high accuracy. During modeling, rigorous development and external validation should be applied and reported accordingly

Feasibility of quantitative performance measures for evaluating nuclear power plant operators

A more valid measure of team performance in nuclear power plants is needed. A study is described which was oriented towards evaluating the feasibility of synthesizing performance measures by deriving measures for crews responding to an off-normal event in a full-scope simulator. The thesis was that performance assessment is based on the subjective judgment of training instructors. The procedure used to synthesize the performance measure consisted of: identification of the factors believed to be important to performance assessment, development of example crew performances and ratings on each by instructors, and derivation of the measure by capturing the instructors' assessment rules. A performance measure was derived which explains nearly all of the variance of the instructors' team performance assessments. There is reason to believe that this method of synthesizing measures can be applied to other events. 7 tabs

Examination of Fluconazole-Induced Alopecia in an Animal Model and Human Cohort.

Fluconazole-induced alopecia is a significant problem for patients receiving long-term therapy. We evaluated the hair cycle changes of fluconazole in a rat model and investigated potential molecular mechanisms. Plasma and tissue levels of retinoic acid were not found to be causal. Human patients with alopecia attributed to fluconazole also underwent detailed assessment and in both our murine model and human cohort fluconazole induced telogen effluvium. Future work further examining the mechanism of fluconazole-induced alopecia should be undertaken

Maintenance personnel performance simulation (MAPPS): a model for predicting maintenance performance reliability in nuclear power plants

The NRC has developed a structured, quantitative, predictive methodology in the form of a computerized simulation model for assessing maintainer task performance. Objective of the overall program is to develop, validate, and disseminate a practical, useful, and acceptable methodology for the quantitative assessment of NPP maintenance personnel reliability. The program was organized into four phases: (1) scoping study, (2) model development, (3) model evaluation, and (4) model dissemination. The program is currently nearing completion of Phase 2 - Model Development

Living on thin abstractions: more power/economic knowledge

Debates over the role of knowledge and know-how as key economic assets in the contemporary economy, although far from new, are now increasingly couched in terms of a new-found economic immateriality which allows for their costless reproduction and widespread geographical dissemination. In the rush to tie down and reproduce economic know-how in abstract codifiable form, it has become almost baffling to argue that our stock of economic knowledge may rest upon affects as much as analysis, expressive symbolism as much as abstract symbolism. This paper is an attempt to think through how such 'elusive' economic knowledges may be grasped, yet neither formalized nor codified in abstract terms. It is also a plea to consider the geography of economic knowledge outside of the tacit - explicit distinction

Machine Learning for Health: Algorithm Auditing & Quality Control

Developers proposing new machine learning for health (ML4H) tools often pledge to match or even surpass the performance of existing tools, yet the reality is usually more complicated. Reliable deployment of ML4H to the real world is challenging as examples from diabetic retinopathy or Covid-19 screening show. We envision an integrated framework of algorithm auditing and quality control that provides a path towards the effective and reliable application of ML systems in healthcare. In this editorial, we give a summary of ongoing work towards that vision and announce a call for participation to the special issue Machine Learning for Health: Algorithm Auditing & Quality Control in this journal to advance the practice of ML4H auditing