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Objective To assess the prevalence of distress and burnout in otolaryngology trainees, including associations with relevant sociodemographic and professional factors, and to compare these results with those of attending otolaryngologists. Study Design A cross-sectional survey of trainees and attending physicians. Setting Twelve academic otolaryngology programs. Methods Distress and burnout were measured with the Expanded Physician Well-being Index and the 2-item Maslach Burnout Inventory. The Patient Health Questionnaire–2 and Generalized Anxiety Disorder–2 were used to screen for depressive disorders and anxiety disorders, respectively. Associations with sociodemographic and professional characteristics were assessed. Results Of the 613 surveys administered to trainees and attending physicians, 340 were completed (56%). Among 154 trainees, distress was present in 49%, professional burnout in 35%, positive depressive disorder screening in 5%, and positive anxiety disorder screening in 16%. In univariable analysis, female gender, hours worked in a typical week (HW), and nights on call in a typical week (NOC) were significantly associated with distress. In multivariable analysis, female gender (odds ratio, 3.91; P = .001) and HW (odds ratio for each 10 HW, 1.89; P = .003) remained significantly associated with distress. Female gender, HW, and NOC were significantly associated with burnout univariably, although only HW (odds ratio for each 10 HW, 1.92; P = .003) remained significantly associated with burnout in a multivariable setting. Attending physicians had less distress than trainees ( P = .02) and felt less callous and less emotionally hardened than trainees ( P < .001). Conclusion Otolaryngology trainees experience significant work-place distress (49%) and burnout (35%). Gender, HW, and NOC had the strongest associations with distress and burnout.

We describe and release a comprehensive solar irradiance, imaging, and forecasting dataset. Our goal with this release is to provide standardized solar and meteorological datasets to the research community for the accelerated development and benchmarking of forecasting methods. The data consist of three years (2014-2016) of quality-controlled, 1-min resolution global horizontal irradiance and direct normal irradiance ground measurements in California. In addition, we provide overlapping data from commonly used exogenous variables, including sky images, satellite imagery, and Numerical Weather Prediction forecasts. We also include sample codes of baseline models for benchmarking of more elaborated models.

A direct methodology for intra-day forecasts (1–6 h ahead) of power output (PO) from photovoltaic (PV) solar plants is proposed. The forecasting methodology uses publicly available images from geosynchronous satellites to predict PO directly without resorting to intermediate irradiance (resource) forecasting. Forecasts are evaluated using four years (January 2012–December 2015) of hourly PO data from 2 nontracking, 1 MWp PV plants in California. For both sites, the proposed methodology achieves forecasting skills ranging from 24% to 69% relative to reference persistence model results, with root-mean-square error (RMSE) values ranging from 90 to 136 kW across the studied horizons. Additionally, we consider the performance of the proposed methodology when applied to imagery from the next generation of geosynchronous satellites, e.g., Himawari-8 and geostationary operational environmental satellite (GOES-R).