Flexible strain sensors are an important component for future intelligent robotics. However, the majority of current strain sensors must be electrically connected to a corresponding monitoring system via conducting wires, which increases system complexity and restricts the working environment for monitoring strains. Here, stretchable graphene–polymer nanocomposites that act as strain sensors using a Joule heating effect are reported. When the resistance of the sensor changes in response to a strain, the resulting change in temperature is wirelessly detected in an intelligent robot. By engineering and optimizing the surface structure of graphene–polymer nanocomposites, the fabricated strain sensors exhibit excellent stability when subjected to periodic temperature signals over 400 cycles while being periodically strained and deliver a high strain sensitivity of 7.03 × 10−4 °C−1 %−1 for strain levels of 0% to 30%. As a wearable electronic device, the approach provides the capability to wirelessly monitor small strains for intelligent robots at a high strain resolution of ≈0.1%. Moreover, when the strain sensing system operates as a multichannel structure, it allows precise strain detection simultaneously, or in sequence, for each finger of an intelligent robot.

Ammonia is a major signal that regulates nitrogen fixation in most diazotrophs. Regulation of nitrogen fixation by ammonia in the Gram-negative diazotrophs is well-characterized. In these bacteria, this regulation occurs mainly at the level of nif (nitrogen fixation) gene transcription, which requires a nif-specific activator, NifA. Although Gram-positive and diazotrophic Paenibacilli have been extensively used as a bacterial fertilizer in agriculture, how nitrogen fixation is regulated in response to nitrogen availability in these bacteria remains unclear. An indigenous GlnR and GlnR/TnrA-binding sites in the promoter region of the nif cluster are conserved in these strains, indicating the role of GlnR as a regulator of nitrogen fixation. In this study, we for the first time reveal that GlnR of Paenibacillus polymyxa WLY78 is essentially required for nif gene transcription under nitrogen limitation, whereas both GlnR and glutamine synthetase (GS) encoded by glnA within glnRA operon are required for repressing nif expression under excess nitrogen. Dimerization of GlnR is necessary for binding of GlnR to DNA. GlnR in P. polymyxa WLY78 exists in a mixture of dimers and monomers. The C-terminal region of GlnR monomer is an autoinhibitory domain that prevents GlnR from binding DNA. Two GlnR-biding sites flank the -35/-10 regions of the nif promoter of the nif operon (nifBHDKENXhesAnifV). The GlnR-binding site Ⅰ (located upstream of -35/-10 regions of the nif promoter) is specially required for activating nif transcription, while GlnR-binding siteⅡ (located downstream of -35/-10 regions of the nif promoter) is for repressing nif expression. Under nitrogen limitation, GlnR dimer binds to GlnR-binding siteⅠ in a weak and transient association way and then activates nif transcription. During excess nitrogen, glutamine binds to and feedback inhibits GS by forming the complex FBI-GS. The FBI-GS interacts with the C-terminal domain of GlnR and stabilizes the binding affinity of GlnR to GlnR-binding site Ⅱ and thus represses nif transcription.

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Increasing the plasma density is one of the key methods in achieving an efficient fusion reaction. High-density operation is one of the hot topics in tokamak plasmas. Density limit disruptions remain an important issue for safe operation. An effective density limit disruption prediction and avoidance system is the key to avoid density limit disruptions for long pulse steady state operations. An artificial neural network has been developed for the prediction of density limit disruptions on the J-TEXT tokamak. The neural network has been improved from a simple multi-layer design to a hybrid two-stage structure. The first stage is a custom network which uses time series diagnostics as inputs to predict plasma density, and the second stage is a three-layer feedforward neural network to predict the probability of density limit disruptions. It is found that hybrid neural network structure, combined with radiation profile information as an input can significantly improve the prediction performance, especially the average warning time (T warn ). In particular, the T warn is eight times better than that in previous work (Wang et al 2016 Plasma Phys. Control. Fusion 58 055014) (from 5 ms to 40 ms). The success rate for density limit disruptive shots is above 90%, while, the false alarm rate for other shots is below 10%. Based on the density limit disruption prediction system and the real-time density feedback control system, the on-line density limit disruption avoidance system has been implemented on the J-TEXT tokamak.