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Forecasting models for photovoltaic energy production are important tools for managing energy flows. The aim of this study was to accurately predict the energy production of a PV plant in Italy, using a methodology based on support vector machines . The model uses historical data of solar irradiance, environmental temperature and past energy production to predict the PV energy production for the next day with an interval of 15 min. The technique used is based on $$\nu $$ -SVR, a support vector regression model where you can choose the number of support vectors. The forecasts of energy production obtained with the proposed methodology are very accurate, with the $$R^{2}$$ coefficient exceeding 90 % . The quality of the predicted values strongly depends on the goodness of the weather forecast, and the $$R^{2}$$ value decreases if the predictions of irradiance and temperature are not very accurate. [ABSTRACT FROM AUTHOR]

Introduction: Readmissions after an acute care hospitalization are relatively common, costly to the health care system, and are associated with significant burden for patients. As one way to reduce costs and simultaneously improve quality of care, hospital readmissions receive increasing interest from policy makers. It is only relatively recently that strategies were developed with the specific aim of reducing unplanned readmissions using prediction models to identify patients at risk. EPIC's Risk of Unplanned Readmission model promises superior performance. However, it has only been validated for the US setting. Therefore, the main objective of this study is to externally validate the EPIC's Risk of Unplanned Readmission model and to compare it to the internationally, widely used LACE+ index, and the SQLAPE® tool, a Swiss national quality of care indicator., Methods: A monocentric, retrospective, diagnostic cohort study was conducted. The study included inpatients, who were discharged between the 1st of January 2018 and the 31st of December 2019 from the Lucerne Cantonal Hospital, a tertiary-care provider in Central Switzerland. The study endpoint was an unplanned 30-day readmission. Models were replicated using the original intercept and beta coefficients as reported. Otherwise, score generator provided by the developers were used. For external validation, discrimination of the scores under investigation were assessed by calculating the area under the receiver operating characteristics curves (AUC). Calibration was assessed with the Hosmer-Lemeshow X2 goodness-of-fit test This report adheres to the TRIPOD statement for reporting of prediction models., Results: At least 23,116 records were included. For discrimination, the EPIC´s prediction model, the LACE+ index and the SQLape® had AUCs of 0.692 (95% CI 0.676-0.708), 0.703 (95% CI 0.687-0.719) and 0.705 (95% CI 0.690-0.720). The Hosmer-Lemeshow X2 tests had values of p<0.001., Conclusion: In summary, the EPIC´s model showed less favorable performance than its comparators. It may be assumed with caution that the EPIC´s model complexity has hampered its wide generalizability-model updating is warranted., Competing Interests: The authors have declared that no competing interests exist.

Introduction: Nivolumab (NIV) was recently approved in several countries for patients with pretreated advanced NSCLC. NIV is not cost-effective compared with docetaxel (DOC) for the treatment of squamous NSCLC. However, its cost-effectiveness for nonsquamous NSCLC and the consequences of programmed death ligand 1 (PD-L1) testing are unknown., Methods: This literature-based health economic study used CheckMate-057 trial data to model the incremental cost-effectiveness ratio (ICER) of NIV versus DOC in the Swiss health care setting. The effect of PD-L1 positivity for patient selection was assessed., Results: In the base case model, NIV (mean cost CHF66,208; mean effect 0.69 quality-adjusted life-years [QALYs]) compared with DOC (mean cost CHF37,618; mean effect 0.53 QALYs) resulted in an ICER of CHF177,478/QALY gained. Treating only patients with PD-L1-positive tumors (threshold ≥10%) with NIV compared with treating all patients with DOC produced a base case ICER of CHF124,891/QALY gained. Reduced drug price, dose, or treatment duration decreased the ICER partly below a willingness-to-pay threshold of CHF100,000/QALY. Health state utilities strongly influenced cost-effectiveness., Conclusions: Compared with DOC, NIV is not cost-effective for the treatment of nonsquamous NSCLC at current prices in the Swiss health care setting. Price reduction or PD-L1 testing and selection of patients for NIV on the basis of test positivity improves cost-effectiveness compared with DOC., (Copyright © 2016 International Association for the Study of Lung Cancer. Published by Elsevier Inc. All rights reserved.)

A cradle-to-grave environmental life cycle assessment (LCA) of a few poly(3-hydroxybutyrate) (PHB) based composites has been performed and was compared to commodity petrochemical polymers. The end products studied are a cathode ray tube (CRT) monitor housing (conventionally produced from high-impact polystyrene, HIPS) and the internal panels of an average car (conventionally produced from glass-fibers-filled polypropylene, PP-GF). The environmental impact is evaluated on the basis of nonrenewable energy use (NREU) and global warming potential over a 100 years time horizon (GWP100). Sugar cane bagasse (SCB) and nanoscaled organophilic montmorillonite (OMMT) are used as PHB fillers. The results obtained show that, despite the unsatisfying mechanical properties of PHB composites, depending on the type of filler and on the product, it is possible to reach lower environmental impacts than by use of conventional petrochemical polymers. These savings are mainly related to the PHB production process, while there are no improvements related to composites preparation. SCB-based composites seem to be environmentally superior to clay-based ones.