DISEASE RISK FORECASTING WITH BAYESIAN LEARNING NETWORKS: APPLICATION TO GRAPE POWDERY MILDEW (ERYSIPHE NECATOR) IN VINEYARDS

Disease Risk Forecasting with Bayesian Learning Networks: Application to Grape Powdery Mildew (Erysiphe necator) in Vineyards

Powdery mildew (Erysiphe necator) is a fungal disease causing significant loss of grape yield in commercial vineyards.The rate of development of this disease varies annually and is driven by complex interactions between the pathogen, its host, and environmental conditions.The long term impacts of weather and climate variability on disease developme

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A Real-Time Train Timetable Rescheduling Method Based on Deep Learning for Metro Systems Energy Optimization under Random Disturbances

Considering that iphone 14 price arizona uncertain dwell disturbances often occur at metro stations, researchers have proposed many methods for solving the train timetable rescheduling (TTR) problem.This paper proposes a Modified Genetic Algorithm-Gate Recurrent Unit (MGA-GRU) method, which is a real-time TTR method based on deep learning.The propo

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