Yunqi Zhang^#, Ying Li^#, Yashi Deng, Yilin Ou, Daocheng Ma, Dongdong Li, Weidong Xie^*, Zailiu Li^*

Phyton-International Journal of Experimental Botany, Vol.93, No.12, pp. 3293-3312, 2024, DOI:10.32604/phyton.2024.058083 - 31 December 2024

Abstract To enhance the productivity of Carya illinoinensis cv. ‘Shaoxing’ and mitigate the loss of flowers and fruits, the effects of different concentrations of Piperonyl Butoxide (PBO) wettable powder (2, 5, and 10 g·L^–1) and Paclobutrazol (PP₃₃₃) (150, 300, and 450 mg·L^–1, based on active ingredients) on 6-year-old ‘Shaoxing’ plants were investigated with water sprayed as the control. The results showed that: (1) Treatment with 10 g·L^–1 PBO and 450 mg·L^–1 PP₃₃₃ significantly inhibited the excessive growth of ‘Shaoxing’ branches. Also, 10 g·L^–1 PBO exhibited the best diameter increment effect on fruiting branches, and 150 mg·L^–1 PP₃₃₃ exhibited the best diameter… More >

Xiabin Zhang^1,2, Zhongyi Hu^1,2,*, Lei Xiao^1,2, Hui Huang^1,2

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2879-2905, 2024, DOI:10.32604/cmc.2024.048725 - 15 May 2024

Abstract Various deep learning models have been proposed for the accurate assisted diagnosis of early-stage Alzheimer’s disease (AD). Most studies predominantly employ Convolutional Neural Networks (CNNs), which focus solely on local features, thus encountering difficulties in handling global features. In contrast to natural images, Structural Magnetic Resonance Imaging (sMRI) images exhibit a higher number of channel dimensions. However, during the Position Embedding stage of Multi Head Self Attention (MHSA), the coded information related to the channel dimension is disregarded. To tackle these issues, we propose the RepBoTNet-CESA network, an advanced AD-aided diagnostic model that is capable… More >

Wei Zhang¹, Weifeng Shi², Bing Sun³

Intelligent Automation & Soft Computing, Vol.25, No.3, pp. 423-431, 2019, DOI:10.31209/2018.100000045

Abstract An intelligent optimization algorithm for a marine generator excitation controller is proposed to improve dynamic performance of shipboard power systems. This algorithm combines a belief–desire–intention agent with a quantum-behaved particle swarm optimization (QPSO) algorithm to optimize a marine generator excitation controller. The shipboard zonal power system is simulated under disturbance due to load change or severe fault. The results show that the proposed optimization algorithm can improve marine generator stability compared with conventional excitation controllers under various operating conditions. Moreover, the proposed intelligent algorithm is highly robust because its performance is insensitive to the accuracy More >