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Orbital angular momentum (OAM) detection underpins almost all aspects of vortex beams’ advances such as communication and quantum analogy. Conventional schemes are frustrated by low speed, complicated system, limited detection range. Here, we devise an intelligent processor composed of photonic and electronic neurons for OAM spectrum measurement in a fast, accurate and direct manner. Specifically, optical layers extract invisible topological charge information from incoming light and a shallow electronic layer predicts the exact spectrum. The integration of optical-computing promises us a compact single-shot system with high speed and energy efficiency (optical operations / electronic operations ~$${10}^{3}$$ 10 3 ), neither necessitating reference wave nor repetitive steps. Importantly, our processor is endowed with salient generalization ability and robustness against diverse structured light and adverse effects (mean squared error ~$$10^{(-5)}$$ 10 ( - 5 ) ). We further raise a universal model interpretation paradigm to reveal the underlying physical mechanisms in the hybrid processor, as distinct from conventional ‘black-box’ networks. Such interpretation algorithm can improve the detection efficiency up to 25-fold. We also complete the theory of optoelectronic network enabling its efficient training. This work not only contributes to the explorations on OAM physics and applications, and also broadly inspires the advanced links between intelligent computing and physical effects.

Structured light with customized topological patterns inspires diverse classical and quantum investigations underpinned by accurate detection techniques. However, the current detection schemes are limited to vortex beams with a simple phase singularity. The precise recognition of general structured light with multiple singularities remains elusive. Here, we report deep learning (DL) framework that can unveil multi-singularity phase structures in an end-to-end manner, after feeding only two intensity patterns upon beam propagation. By outputting the phase directly, rich and intuitive information of twisted photons is unleashed. The DL toolbox can also acquire phases of Laguerre–Gaussian (LG) modes with a single singularity and other general phase objects likewise. Enabled by this DL platform, a phase-based optical secret sharing (OSS) protocol is proposed, which is based on a more general class of multi-singularity modes than conventional LG beams. The OSS protocol features strong security, wealthy state space, and convenient intensity-based measurements. This study opens new avenues for large-capacity communications, laser mode analysis, microscopy, Bose–Einstein condensates characterization, etc.

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Laparoscopic partial nephrectomy and robot-assisted partial nephrectomy are attracting increased attention from urologists. They can achieve the same effect of oncology control as radical nephrectomy; moreover, they can offer better preservation of renal function, thus obtaining longterm living benefits. The indications are also expanding, making it possible for larger and more difficult tumors. Laparoscopic partial nephrectomy and robot-assisted partial nephrectomy can be performed by transperitoneal and retroperitoneal approaches, with their individual advantages and limitations. In addition, the renal tumor scoring systems have been widely used and studied in laparoscopic partial nephrectomy and robot-assisted partial nephrectomy. In order to better preserve renal function, the zero-ischemia technique is widely used. The application of intraoperative imaging technology provides convenience and greater benefits. Besides, whether minimal invasive partial nephrectomy can be performed without stop antiplatelet treatment is still disputed. Clinicians perform substantial exploration and practice to achieve the "trifecta" of surgery: complete resection of the tumor, maximum protection of renal function, and no complications.

BACKGROUND Gray mold caused by Botrytis cinerea Pers. is one of the most significant airborne diseases. It can infest a wide range of crops, causing significant losses in yield and quality worldwide. Pydiflumetofen, a new generation succinate dehydrogenase inhibitor (SDHI), is currently being registered in China to control gray mold in a variety of crops. The baseline sensitivity, resistance risk, and resistance mechanism of Botrytis cinerea to pydiflumetofen were assessed in this study. RESULTS A total of 138 strains of B. cinerea from 10 different regions were tested for their sensitivity to pydiflumetofen, and the mean EC50 value was 0.0056 μg mL−1. Eight mutants were obtained by fungicide adaption from five sensitive parental isolates, and the resistance factor (RF) ranged from 51 to 135. The mutants exhibited strong adaptive traits in conidial production, conidial germination, and pathogenicity. Positive cross‐resistance was only observed between other SDHIs (i.e. boscalid, fluopyram, and isopyrazam). Two different types of pydiflumetofen‐resistant mutants were identified: point mutation P225L in sdhB and double mutation G85A and I93V in sdhC. The in vivo control efficacy of pydiflumetofen on the resistant mutants carrying P225L in sdhB as well as G85A and I93V in sdhC was significantly decreased to 52.62% and 32.27%, respectively. CONCLUSION The fitness was significantly higher for all pydiflumetofen‐resistant mutants than the corresponding parental. Two types of point mutations, sdhB‐P225L and sdhC‐G85A and I93V, might confer resistance to pydiflumetofen in B. cinerea. A precautionary resistance management strategy should be implemented. © 2021 Society of Chemical Industry

Predicting the time series of 10.7-cm solar radio flux is a challenging task because of its daily variability. This paper proposed a non-linear method, a convolutional and recurrent neural network combined model to achieve end-to-end F10.7 forecasts. The network consists of a one-dimensional convolutional neural network and a long short-term memory network. The CNN network extracted features from F10.7 original data, then trained the feature signals in the long short-term memory network, and outputted the predicted values. The F10.7 daily data during 2003–2014 are used for the testing set. The mean absolute percentage error values of approximately 2.04%, 2.78%, and 4.66% for 1-day, 3-day, and 7-day forecasts, respectively. The statistical results of evaluating the root mean square error, spearman correlation coefficient shows a superior effect as a whole for the 1–27 days forecast, compared with the ordinary single neural network and combination models.