Short-term solar eruptive activity prediction models based on machine learning approaches:A review

Solar eruptive activities,mainly including solar flares,coronal mass ejections(CME),and solar proton events(SPE),have an important impact on space weather and our technosphere.The short-term solar eruptive activity prediction is an active field of research in the space weather prediction.Numerical,statistical,and machine learning methods are proposed to build prediction models of the solar eruptive activities.With the development of space-based and ground-based facilities,a large amount of observational data of the Sun is accumulated,and data-driven prediction models of solar eruptive activities have made a significant progress.In this review,we briefly introduce the machine learning algorithms applied in solar eruptive activity prediction,summarize the prediction modeling process,overview the progress made in the field of solar eruptive activity prediction model,and look forward to the possible directions in the future.

Processing of molybdenum industrial waste into sustainable and efficient nanocatalysts for water electrolysis reactions

The increasing need for sustainable energy and the transition from a linear to a circular economy pose great challenges to the materials science community.In this view,the chance of producing efficient nanocatalysts for water splitting using industrial waste as starting material is attractive.Here,we report low-cost processes to convert Mo-based industrial waste powder into efficient catalysts for oxygen evolution reaction(OER)and hydrogen evolution reaction(HER).pH controlled hydrothermal processing of Mo-based industrial waste powder leads to pure orthorhombic MoO_(3) nanobelts(50–200 nm wide,10µm long)with promising OER performances at 10 mA·cm^(−2) with an overpotential of 324 mV and Tafel slope of 45 mV·dec^(−1) in alkaline electrolyte.Indeed,MoS_(2)/MoO_(3) nanostructures were obtained after sulfurization during hydrothermal processes of the MoO_(3) nanobelts.HER tests in acidic environment show a promising overpotential of 208 mV at 10 mA·cm^(−2) and a Tafel slope of 94 mV·dec^(−1).OER and HER performances of nanocatalysts obtained from Mo industrial waste powder are comparable or better than Mo-based nanocatalysts obtained from pure commercial Mo reagent.This work shows the great potential of reusing industrial waste for energy applications,opening a promising road to join waste management and efficient and sustainable nanocatalysts for water splitting.

基于机器学习方法的短期太阳爆发活动预报模型综述

太阳爆发活动主要包括太阳耀斑、日冕物质抛射(coronal mass ejections,CME)和太阳质子事件(solar proton events,SPE),太阳爆发活动对空间天气和高技术领域有重要影响.太阳爆发活动短期预报是空间天气预报中一个活跃的研究领域.目前,数值的、统计的和机器学习的方法被用来建立太阳爆发活动预报模型.随着天基和地基观测设备的发展,积累了大量的太阳观测数据,数据驱动的太阳爆发活动预报模型取得了重大进展.本文介绍了机器学习算法在太阳爆发活动预报中的应用,总结了预报建模过程,概述了太阳爆发活动预报模型的进展,并展望了未来可能的研究方向.