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Problem Definition Moral distress (MoD) is a vital clinical indicator linked to clinician burnout and provider concerns about declining patient care quality. Yet it is not routinely assessed. Earlier, real-time recognition may better target interventions aimed at alleviating MoD and thereby increase provider well-being and improve patient care quality. Initial Approach and Testing Combining two validated MoD instruments (the Moral Distress Thermometer [MDT] and the Measure of Moral Distress for Healthcare Professionals [MMD-HP]), the authors developed a novel mobile and Web-based application environment to measure and report levels MoD and their associated causes. This app was tested for basic feasibility and acceptability in two groups: graduate nursing students and practicing critical care nurses. Results The MDT app appears feasible and acceptable for future use. All participants (n = 34) indicated the MDT app was satisfying to use, and 91.2% (n = 31) indicated the app was “very appropriate” for measuring MoD. In addition, 84.2% (n =16) of practicing nurses indicated the app fit either “somewhat well” (47.4%, n = 9) or “very well” (36.8%, n = 7) into their typical workday, and 68.4% (n = 13) said they were either “extremely likely” or “somewhat likely” to use the app daily in clinical practice. Key Insights and Next Steps Education about moral distress and its associated causes proved important to the MDT app's success. It is ready for future validity and reliability testing, as well as examining usability beyond nursing, longitudinal data monitoring, and possible leveraging to pre- and postintervention evaluation studies. 

Testing mobile robots is difficult and expensive, and many faults go undetected. In this work we explore whether fuzzing, an automated test input generation technique, can more quickly find failure inducing inputs in mobile robots. We developed a simple fuzzing adaptation, BASE-FUZZ, and one specialized for fuzzing mobile robots, PHYS-FUZZ. PHYS-FUZZ is unique in that it accounts for physical attributes such as the robot dimensions, estimated trajectories, and time to impact measures to guide the test input generation process. The results of evaluating PHYS-FUZZ suggest that it has the potential to speed up the discovery of input scenarios that reveal failures, finding 56.5% more than uniform random input selection and 7.0% more than BASE-FUZZ during 7 days of testing 

Configuration space complexity makes the big-data software systems hard to configure well. Consider Hadoop, with over nine hundred parameters, developers often just use the default configurations provided with Hadoop distributions. The opportunity costs in lost performance are significant. Popular learning-based approaches to auto-tune software does not scale well for big-data systems because of the high cost of collecting training data. We present a new method based on a combination of Evolutionary Markov Chain Monte Carlo (EMCMC)} sampling and cost reduction techniques tofind better-performing configurations for big data systems. For cost reduction, we developed and experimentally tested and validated two approaches: using scaled-up big data jobs as proxies for the objective function for larger jobs and using a dynamic job similarity measure to infer that results obtained for one kind of big data problem will work well for similar problems. Our experimental results suggest that our approach promises to improve the performance of big data systems significantly and that it outperforms competing approaches based on random sampling, basic genetic algorithms (GA), and predictive model learning. Our experimental results support the conclusion that our approach strongly demonstrates the potential toimprove the performance of big data systems significantly and frugally.