Populus euphratica is the only tall tree species that adapts to the desert environment. It has strong drought tolerance and is the subject of extreme concern at home and abroad. After 30 years of development, the scope of research on Populus euphratica is very extensive, but the research content has not yet been crystalized into a mature field, and research directions at home and abroad differ. In this study, we retrieved research references on ‘P. euphratica’ published from 1992 to 2022 in both the China National Knowledge Infrastructure core journals database and the Web of Science core collection database, and CiteSpace software was employed to conduct keyword-centered bibliometric analysis in both the spatial and temporal dimensions. The purpose of this study is to clarify the research areas, developmental changes, differences between domestic and international research priorities in the last 30 years, and future trends in the field of P. euphratica research. The results show that there were 1619 domestic papers published in China related to the field of P. euphratica research, while there were only 656 foreign papers in the same field. The development of domestic P. euphratica research went through three stages initiation (1992–2000), growth (2001–2008) and stability (2009–2021), whereas no significant international trend change was observed. The domestic disciplines focus on biology, while international research focuses on crop science. In terms of content, domestic research focuses on sustainable uses of P. euphratica forests and their response to drought, intending to improve P. euphratica ecosystems. International research, on the other hand, focuses on revealing mechanisms of environmental stresses, including genetic and physiological–morphological characteristics, to exploit the excellent characteristics of P. euphratica to serve agriculture and other fields. The development process of P. euphratica research in the past 30 years has generally evolved from an initial focus on its natural conditions towards the study of the relationship between environmental factors and P. euphratica physiological and morphological characteristics and, finally, the study of stress tolerance mechanisms and gene expression of P. euphratica. There is a trend towards ‘applications of P. euphratica tolerance genes’, which may represent a direction for future growth research.
In this article, a design method of asynchronous FIFO memory based on FPGA is put forward. With FPGA as the core controller, We adopt Verilog HDL and top-down design method to build a top-level module design and also analyze the mark logic of asynchronous FIFO and the elimination of semi-stable state under Quartus II development platform. Besides, with the application of Gray code conversion technology, not only the reliable transmission of data is guaranteed but also design efficiency is improved. Through contrast experiment analysis and simulation test, the validity and reliability of asynchronous FIFO memory are verified, meeting the basic requirement that FIFO can hold more enough data without spillovers despite the fullness of data.
Clustering is inherently a highly challenging research problem. The elastic net algorithm is designed to solve the traveling salesman problem initially, now is verified to be an efficient tool for data clustering in n-dimensional space. In this paper, by introducing a nearest neighbor learning method and a local search preferred strategy, we proposed a new Self-Organizing NN approach, called the Adaptive Clustering Elastic Net (ACEN) to solve the cluster analysis problems. ACEN consists of the adaptive clustering elastic net phase and a local search preferred phase. The first phase is used to find a cyclic permutation of the points as to minimize the total distances of the adjacent points, and adopts the Euclidean distance as the criteria to assign each point. The local search preferred phase aims to minimize the total dissimilarity within each clusters. Simulations were made on a large number of homogeneous and nonhomogeneous artificial clusters in n dimensions and a set of publicly standard problems available from UCI. Simulation results show that compared with classical partitional clustering methods, ACEN can provide better clustering solutions and do more efficiently.
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