Long-term responses of floating structures pose a great concern in their design phase. Existing approaches for addressing long-term extreme responses are extremely cumbersome for adoption. This work aims to develop an...Long-term responses of floating structures pose a great concern in their design phase. Existing approaches for addressing long-term extreme responses are extremely cumbersome for adoption. This work aims to develop an approach for the long-term extreme-response analysis of floating structures. A modified gradient-based retrieval algorithm in conjunction with the inverse first-order reliability method(IFORM) is proposed to enable the use of convolution models in long-term extreme analysis of structures with an analytical formula of response amplitude operator(RAO). The proposed algorithm ensures convergence stability and iteration accuracy and exhibits a higher computational efficiency than the traditional backtracking method. However, when the RAO of general offshore structures cannot be analytically expressed, the convolutional integration method fails to function properly. A numerical discretization approach is further proposed for offshore structures in the case when the analytical expression of the RAO is not feasible. Through iterative discretization of environmental contours(ECs) and RAOs, a detailed procedure is proposed to calculate the long-term response extremes of offshore structures. The validity and accuracy of the proposed approach are tested using a floating offshore wind turbine as a numerical example. The long-term extreme heave responses of various return periods are calculated via the IFORM in conjunction with a numerical discretization approach. The environmental data corresponding to N-year structural responses are located inside the ECs, which indicates that the selection of design points directly along the ECs yields conservative design results.展开更多
With the forms of pistil stigma of " Guiti 2" Chinese water chestnut cultivar during the pollination as the object of observation,we reveal the pollination process of Chinese water chestnut from the microsco...With the forms of pistil stigma of " Guiti 2" Chinese water chestnut cultivar during the pollination as the object of observation,we reveal the pollination process of Chinese water chestnut from the microscopic point of view,to provide reference for Chinese water chestnut crossbreeding. The results show that the Chinese water chestnut pistil has 2- 4 stigmas which present white filament and vascular bundle forms,and the vessels on epidermis are thick with long translucent branched hairs; after pollination,pollen grains are tightly bound on branched hairs,and after identification,the pollen tube can penetrate branched hairs,continue to grow,and transfer the genetic material in pollen to vascular bundle.展开更多
In this paper,the research on the teaching method of children’s image cognition based on AR technology is carried out.By analyzing the principle ofARtechnology to recognize images,we understand thatARtechnology can p...In this paper,the research on the teaching method of children’s image cognition based on AR technology is carried out.By analyzing the principle ofARtechnology to recognize images,we understand thatARtechnology can promote children’s image cognition,and this teaching method is in line with the Tower of Experience theory.It further analyzes the current situation of children’s image cognition teaching with simple teaching methods,backward AR teaching tools,and poor perception of teaching objects.Teachers’traditional image teaching methods cannot effectively and efficiently improve children’s image cognition.Therefore,based on AR technology,two commonly used image cognition teaching methods are proposed:AR interactive picture book education andARinteractive game education.Both of these educational methods can improve children’s ability to recognize images.展开更多
基金Supported by the National Natural Science Foundation of China (Grant Nos.52088102 and 51879287)National Key Research and Development Program of China (Grant No.2022YFB2602301)。
文摘Long-term responses of floating structures pose a great concern in their design phase. Existing approaches for addressing long-term extreme responses are extremely cumbersome for adoption. This work aims to develop an approach for the long-term extreme-response analysis of floating structures. A modified gradient-based retrieval algorithm in conjunction with the inverse first-order reliability method(IFORM) is proposed to enable the use of convolution models in long-term extreme analysis of structures with an analytical formula of response amplitude operator(RAO). The proposed algorithm ensures convergence stability and iteration accuracy and exhibits a higher computational efficiency than the traditional backtracking method. However, when the RAO of general offshore structures cannot be analytically expressed, the convolutional integration method fails to function properly. A numerical discretization approach is further proposed for offshore structures in the case when the analytical expression of the RAO is not feasible. Through iterative discretization of environmental contours(ECs) and RAOs, a detailed procedure is proposed to calculate the long-term response extremes of offshore structures. The validity and accuracy of the proposed approach are tested using a floating offshore wind turbine as a numerical example. The long-term extreme heave responses of various return periods are calculated via the IFORM in conjunction with a numerical discretization approach. The environmental data corresponding to N-year structural responses are located inside the ECs, which indicates that the selection of design points directly along the ECs yields conservative design results.
文摘With the forms of pistil stigma of " Guiti 2" Chinese water chestnut cultivar during the pollination as the object of observation,we reveal the pollination process of Chinese water chestnut from the microscopic point of view,to provide reference for Chinese water chestnut crossbreeding. The results show that the Chinese water chestnut pistil has 2- 4 stigmas which present white filament and vascular bundle forms,and the vessels on epidermis are thick with long translucent branched hairs; after pollination,pollen grains are tightly bound on branched hairs,and after identification,the pollen tube can penetrate branched hairs,continue to grow,and transfer the genetic material in pollen to vascular bundle.
文摘In this paper,the research on the teaching method of children’s image cognition based on AR technology is carried out.By analyzing the principle ofARtechnology to recognize images,we understand thatARtechnology can promote children’s image cognition,and this teaching method is in line with the Tower of Experience theory.It further analyzes the current situation of children’s image cognition teaching with simple teaching methods,backward AR teaching tools,and poor perception of teaching objects.Teachers’traditional image teaching methods cannot effectively and efficiently improve children’s image cognition.Therefore,based on AR technology,two commonly used image cognition teaching methods are proposed:AR interactive picture book education andARinteractive game education.Both of these educational methods can improve children’s ability to recognize images.
