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A new series of linear−comb and 4-arm star− comb side chain liquid crystalline polymers (Lc-/Sc-SCLCPs) have been synthesized and characterized. The treatment of hydride siloxane-containing terminated liquid crystalline and high 1,2-/1,4-(high vinyl, hv/low vinyl, lv) linear or 4-arm star polybutadienes (L-/S-PBs) was conducted via the methods in combination of living anionic polymerization and "reverse" hydrosilylation to obtain SCLCPs with wide mesomorphic temperature range (ΔT) and narrow polydispersity index (PDI). The possible molecular arrangement model of two analogous hv-/lv-architectures was constructed, that was used to systematically investigate the effects of Lc-and Sctopological morphology on liquid crystalline (LC) properties and molecular microstructures. SCLCPs exhibited the same smectic A phase around room temperature, but thermal properties were significantly different due to differences of interaction force resulting from different macromolecular side chains packing. Surprisingly, the trend of lv-SCLCP displaying the effects of topology on phase transitions and microstructures was just contrary to that of hv-topology. hv-Sc-SCLCPs containing high density mesogenic composition were desired to generate wider ΔT and higher molecular layer order in comparison with Lc analogues, which provided a unexpected analyzed model that are of interest to be explored. In particular, the uniaue differences of macromolecular aggregation state arrangement in liquid crystal state dependent on free cooling between hv-Lc-and Sc-SCLCPs were observed from POM.

Zero-index metamaterials (ZIMs) offer unprecedented ways to manipulate the flow of light, and are of interest for wide range of applications including optical cloaking, super-coupling, and unconventional phase-matching properties in nonlinear optics. Impedance-matched ZIMs can be obtained through a photonic Dirac-cone (PDC) dispersion induced by an accidental degeneracy of two linear bandstypically an electric monopole mode and a transverse magnetic dipole mode -at the center of the Brillouin zone. Consequently, PDC can only be achieved for a particular combination of geometric parameters of the metamaterial, and hence is sensitive to fabrication imperfections. These fabrication imperfections may limit the usefulness in practical applications. In this work we overcome this obstacle and demonstrate robust all-dielectric (AD) ZIM that supports PDC dispersion over wide parameter space. Our structure, consisting of an array of Si pillars on silica substrate, is fabricated in silicon-oninsulator (SOI) platform and operates at telecom wavelengths.

In the area of plant protection and precision farming, timely detection and classification of plant diseases and crop pests play crucial roles in the management and decision-making. Recently, there have been many artificial neural network (ANN) methods used in agricultural classification tasks, which are task specific and require big datasets. These two characteristics are quite different from how humans learn intelligently. Undoubtedly, it would be exciting if the models can accumulate knowledge to handle continual tasks. Towards this goal, we propose an ANN-based continual classification method via memory storage and retrieval, with two clear advantages: Few data and high flexibility. This proposed ANN-based model combines a convolutional neural network (CNN) and generative adversarial network (GAN). Through learning of the similarity between input paired data, the CNN part only requires few raw data to achieve a good performance, suitable for a classification task. The GAN part is used to extract important information from old tasks and generate abstracted images as memory for the future task. Experimental results show that the regular CNN model performs poorly on the continual tasks (pest and plant classification), due to the forgetting problem. However, our proposed method can distinguish all the categories from new and old tasks with good performance, owing to its ability of accumulating knowledge and alleviating forgetting. There are so many possible applications of this proposed approach in the agricultural field, for instance, the intelligent fruit picking robots, which can recognize and pick different kinds of fruits; the plant protection is achieved by automatic identification of diseases and pests, which can continuously improve the detection range. Thus, this work also provides a reference for other studies towards more intelligent and flexible applications in agriculture.

With the rise of artificial intelligence, deep learning is gradually applied to the field of agriculture and plant science. However, the excellent performance of deep learning needs to be established on massive numbers of samples. In the field of plant science and biology, it is not easy to obtain a large amount of labeled data. The emergence of few-shot learning solves this problem. It imitates the ability of humans’ rapid learning and can learn a new task with only a small number of labeled samples, which greatly reduces the time cost and financial resources. At present, the advanced few-shot learning methods are mainly divided into four categories based on: data augmentation, metric learning, external memory, and parameter optimization, solving the over-fitting problem from different viewpoints. This review comprehensively expounds on few-shot learning in smart agriculture, introduces the definition of few-shot learning, four kinds of learning methods, the publicly available datasets for few-shot learning, various applications in smart agriculture, and the challenges in smart agriculture in future development.

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