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We articulate the design imperatives for machine learning based digital twins for nonlinear dynamical systems, which can be used to monitor the "health" of the system and anticipate future collapse. The fundamental requirement for digital twins of nonlinear dynamical systems is dynamical evolution: the digital twin must be able to evolve its dynamical state at the present time to the next time step without further state input-a requirement that reservoir computing naturally meets. We conduct extensive tests using prototypical systems from optics, ecology, and climate, where the respective specific examples are a chaotic CO 2 laser system, a model of phytoplankton subject to seasonality, and the Lorenz-96 climate network. We demonstrate that, with a single or parallel reservoir computer, the digital twins are capable of a variety of challenging forecasting and monitoring tasks. Our digital twin has the following capabilities: (1) extrapolating the dynamics of the target system to predict how it may respond to a changing dynamical environment, e.g., a driving signal that it has never experienced before, (2) making continual forecasting and monitoring with sparse real-time updates under non-stationary external driving, (3) inferring hidden variables in the target system and accurately reproducing/predicting their dynamical evolution, (4) adapting to external driving of different waveform, and (5) extrapolating the global bifurcation behaviors to network systems of different sizes. These features make our digital twins appealing in applications, such as monitoring the health of critical systems and forecasting their potential collapse induced by environmental changes or perturbations. Such systems can be an infrastructure, an ecosystem, or a regional climate system.

Seasonal variation of heavy metal contents in leaves and their relationships with soil heavy metal pollution levels were studied through measuring and analyzing the leaves of the common tree species in Beijing and soil heavy metal contents, to detect heavy metal accumulation ability of plant leaves. The results showed that: (1) the contents of Cu, Pb, Zn in plant leaves first decreased and then increased, again declined with changing the seasons (from spring to winter). Cr concentration showed the trend of first increase and then decrease from spring to winter, and the highest in the autumn; the accumulation capacities of Cu for Babylonica and Japonica were higher in the spring, summer and autumn, while Tabuliformis was in winter; the higher accumulation capacities for Cr, Pb were Japonica and Platycladus, and in winter were Platycladus and Bungeana; the higher accumulation capacities for Zn were Babylonica and Bungeana, while Platycladus in winter; (2) the pollution degree of four kinds of heavy metals (Cu, Cr, Pb, Zn) from downtown to suburbs showed that: Jingshan (C =2.48, C is contamination factor) > Olympic (C = 1.27) > Songshan (C = 1.20) > Shuiguan (C = 1. 18); (3) the heavy metals concentration of same plant leaves in the water of the Great Wall changed larger, but those in the other three areas showed that: Jingshan > Olympic > Songshan; the ability of same species leaf to absorb different sorts of heavy metals showed that: Zn >Cu >Pb >Cr; the difference between Zn content and Cr content was significant (P <0.01); (4) the relationship between heavy metal content in plant leaves and soil heavy metal pollution levels presented a quadratic polynomial relation; the significant correlation was found between other three heavy metal contents of plant samples and soil samples, but they were not the case for the Cu, and the correlation coefficients were above 0. 9.

Organic light-emitting devices (OLEDs) with emission peak at 520 nm were designed. The electroluminescence (EL) spectra including the integrated intensity, the peak width at half height, and the intensity and the position of the peak of the EL spectra of the OLEDs and microcavity OLEDs (MOLEDs), the total thickness of organic layers which is changeable, were calculated and theoretically analyzed with the thickness of the layer of NPB and light-emitting layer of Alq3 ranging from 10 to 100 nm, respectively. According to these studies, it was found that the optimized OLEDs should be constructed with 70 nm NPB and 62 nm Alq3, and this structure should be more suitable to configurate the MOLEDs. These results suggest that the suitable structure of OLEDs/MOLEDs could be designed with help of theoretical calculation, which is also helpful to the light-emitting properties of OLEDs and MOLEDs.

Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), UV-Vis spectra, laser nano size detector (LNSD) and scanning electron microscope (SEM) were employed to analyze the characters and structure of enzyme and octenyl succinic anhydride modified starch. The results indicated that the enzymatic starch reacted with octenyl succinic anhydride, bringing only octenyl succinic anhydride groups but not any other groups. The esterification of enzymatic starch only took place in amorphous region, but had no effect on the crystal form of starch granule. The clarity of EOSS increased with the increase in substitution degree. The particle size of oil emulsion made by EOSS was fine and well-distributed, meaning that the emulsion has excellent emulsibility and emulsifying stability. The embedding of oil encapsulated with EOSS is fine. It can be concluded that the properties of EOSS is excellent, and can be used as emulsifier and wall material of microcapsule.