Abstract-An Improved Differential Evolution (IDE) algorithm is proposed for optimization problems. With the novel mutant operation adopting sub-optimal individual, the convergence of Differential Evolution (DE) algorithm is accelerated without increasing the risk of premature. Five typical test functions are minimized using DE and IDE algorithms, and the results show the superior performance of IDE algorithm. Furthermore, the algorithm is applied to pattern synthesis of two antenna arrays. Broad nulls are formed in radiation pattern of a linear array to suppress broad-band interferences. In a microstrip patch array, the sidelobe level of array is decreased about 12.9 dB and the mainlobe can scan to the desired angle.

Genome editing, a revolutionary technology in molecular biology and represented by the CRISPR/Cas9 system, has become widely used in plants for characterizing gene function and crop improvement. Tomato, serving as an excellent model plant for fruit biology research and making a substantial nutritional contribution to the human diet, is one of the most important applied plants for genome editing. Using CRISPR/Cas9-mediated targeted mutagenesis, the re-evaluation of tomato genes essential for fruit ripening highlights that several aspects of fruit ripening should be reconsidered. Genome editing has also been applied in tomato breeding for improving fruit yield and quality, increasing stress resistance, accelerating the domestication of wild tomato, and recently customizing tomato cultivars for urban agriculture. In addition, genome editing is continuously innovating, and several new genome editing systems such as the recent prime editing, a breakthrough in precise genome editing, have recently been applied in plants. In this review, these advances in application of genome editing in tomato and recent development of genome editing technology are summarized, and their leaving important enlightenment to plant research and precision plant breeding is also discussed.

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Aspect-level sentiment classification, as a fine-grained task in sentiment classification, aiming to extract sentiment polarity from opinions towards a specific aspect word, has been made tremendous improvements in recent years. There are three key factors for aspect-level sentiment classification: contextual semantic information towards as

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