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Solar flares originate from the release of the energy stored in the magnetic field of solar active regions, the triggering mechanism for these flares, however, remains unknown. For this reason, the conventional solar flare forecast is essentially based on the statistic relationship between solar flares and measures extracted from observational data. In the current work, the deep learning method is applied to set up the solar flare forecasting model, in which forecasting patterns can be learned from line-of-sight magnetograms of solar active regions. In order to obtain a large amount of observational data to train the forecasting model and test its performance, a data set is created from line-of-sight magnetogarms of active regions observed by SOHO/MDI and SDO/HMI from 1996 April to 2015 October and corresponding soft X-ray solar flares observed by GOES. The testing results of the forecasting model indicate that (1) the forecasting patterns can be automatically reached with the MDI data and they can also be applied to the HMI data; furthermore, these forecasting patterns are robust to the noise in the observational data; (2) the performance of the deep learning forecasting model is not sensitive to the given forecasting periods (6, 12, 24, or 48 hr); (3) the performance of the proposed forecasting model is comparable to that of the state-of-the-art flare forecasting models, even if the duration of the total magnetograms continuously spans 19.5 years. Case analyses demonstrate that the deep learning based solar flare forecasting model pays attention to areas with the magnetic polarity-inversion line or the strong magnetic field in magnetograms of active regions.

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Microbes regulate the composition and turnover of organic matter. Here we developed a framework called Energy-Diversity-Trait integrative Analysis to quantify how dissolved organic matter and microbes interact along global change drivers of temperature and nutrient enrichment. Negative and positive interactions suggest decomposition and production processes of organic matter, respectively. We applied this framework to manipulative field experiments on mountainsides in subarctic and subtropical climates. In both climates, negative interactions of bipartite networks were more specialized than positive interactions, showing fewer interactions between chemical molecules and bacterial taxa. Nutrient enrichment promoted specialization of positive interactions, but decreased specialization of negative interactions, indicating that organic matter was more vulnerable to decomposition by a greater range of bacteria, particularly at warmer temperatures in the subtropical climate. These two global change drivers influenced specialization of negative interactions most strongly via molecular traits, while molecular traits and bacterial diversity similarly affected specialization of positive interactions.

Short-term wind speed prediction plays an important role in large-scale wind power penetration. However, there is still a large gap between the requirement of prediction performance and current techniques. In this paper, we propose a pattern-based approach to short-term wind speed prediction. It is well accepted that wind varies in different patterns in different weather conditions. Thus, we should use different models to describe these patterns, whereas most current works conduct wind speed prediction with a single model. Based on this observation, we introduce generalized principal component analysis to automatically discover the patterns hidden in the historical data of wind speed. Then we train a predicting function for each pattern and combine their outputs for the final prediction. Experimental results show that the proposed approach performs better than the clustering-based approach, a single model, and persistence forecasting.

This paper aims to develop an applicable nonlinear control technique for aeroengines. An appro.ximate nonlinear model is presented and a rationai identification procedure is given. Exact input-output feedback linearization can be easily performed on this modei. The controller derived can approximateiy linearize the plant such that the clo.se-loop system exhibits iinear input-output dynamics locally. Modeling and controlling are exemplified and validated by a small turbofan engine. Simulation results illustrate that the modeling accuracy is good and linear close-loop system dynamics are achieved.