Chilling stress is one of the major abiotic stresses affecting waxy maize plant growth. Melatonin (MT) is able to improve tolerance to abiotic stress in plants. To investigate the effects of seed priming with MT on tolerance to chilling stress in waxy maize, the seed germination characteristics and physiological parameters were tested with varied MT concentrations (0, 50, 100 µM) and treatment times (12, 24 h) at ambient (25 °C) and chilling (13 °C) temperature. MT primed seeds significantly enhanced the germination potential (by 20.29% and 50.71%, respectively), germination rate (by 20.88% and 33.72%), and increased the radicle length (by 90.73% and 217.14%), hypocotyl length (by 60.28% and 136.14%), root length (by 74.59% and 108.70%), and seed vigor index (46.13%, 63.81%), compared with the non-priming seeds under chilling stress. No significant difference was found in priming time between primed and non-primed seeds. In addition, lower H2O2 and malondialdehyde concentrations, increased antioxidant enzyme activities (superoxide dismutase, peroxidase, catalase and ascorbateperoxidase), and promoted starch metabolism were found in primed seeds compared to non-primed ones. It was suggested that seed priming with MT improved waxy maize seed germination under chilling stress through improving antioxidant system and starch metabolism, which protected from oxidative damage.

A growing number of electricity utilities use machine learning-based outage prediction models (OPMs) to predict the impact of storms on their networks for sustainable management. The accuracy of OPM predictions is sensitive to sample size and event severity representativeness in the training dataset, the extent of which has not yet been quantified. This study devised a randomized and out-of-sample validation experiment to quantify an OPM's prediction uncertainty to different training sample sizes and event severity representativeness. The study showed random error decreasing by more than 100% for sample sizes ranging from 10 to 80 extratropical events, and by 32% for sample sizes from 10 to 40 thunderstorms. This study quantified the minimum number of sample size for the OPM attaining an acceptable prediction performance. The results demonstrated that conditioning the training of the OPM to a subset of events representative of the predicted event's severity reduced the underestimation bias exhibited in high-impact events and the overestimation bias in low-impact ones. We used cross entropy (CE) to quantify the relatedness of weather variable distribution between the training dataset and the forecasted event.

No abstract

The Integrated Multisatellite Retrievals for Global Precipitation Measurement (GPM) (IMERG) Level 3 estimates rainfall from passive microwave sensors onboard satellites that are associated with several uncertainty sources such as sensor calibration, retrieval errors, and orographic effects. This study aims to provide a comprehensive investigation of multiple machine learning (ML) techniques (Random Forest, and Neural Networks), to stochastically generate an error-corrected improved IMERG precipitation product at a daily time scale and 0.1°-degree spatial resolution over the Brahmaputra river basin. In this study, we used the operational IMERG-Late Run version 06 product along with several meteorological and land surface parameters (elevation, soil type, land type, soil moisture, and daily maximum and minimum temperature) to produce an improved precipitation product in the Brahmaputra basin. We trained, tested, and optimized ML algorithms using 4 years (from 2015 through 2019) of reference rainfall data derived from the rain gauge. The ML generated precipitation product exhibited improved systematic and random error statistics for the study area, which is a strong indication for using the proposed algorithms in retrieving precipitation across the globe. We conclude that the proposed ML-based ensemble framework has the potential to quantify and correct the error sources for improving and promoting the use of satellite-based precipitation estimates for water resources applications.

No abstract