AbstractKNOTTED-like homeodomain (KNOX) genes are transcriptional regulators that play an important role in morphogenesis. In the present study, a comparative analysis was performed to investigate the molecular evolution of the characteristics of the KNOX gene family in 10 different plant species. We identified 129 KNOX gene family members, which were categorized into two subfamilies based on multiple sequence alignment and phylogenetic tree reconstruction. Several segmental duplication pairs were found, indicating that different species share a common expansion model. Functional divergence analysis identified the 15 and 52 amino acid sites with significant changes in evolutionary rates and amino acid physicochemical properties as functional divergence sites. Additional selection analysis showed that 14 amino acid sites underwent positive selection during evolution, and two groups of co-evolutionary amino acid sites were identified by Coevolution Analysis using Protein Sequences software. These sites could play critical roles in the molecular evolution of the KNOX gene family in these species. In addition, the expression profiles of KNOX duplicated genes demonstrated functional divergence. Taken together, these results provide novel insights into the structural and functional evolution of the KNOX gene family.

Unique to plants, growth regulatory factors (GRFs) play important roles in plant growth and reproduction. This study investigated the evolutionary and functional characteristics associated with plant growth. Using genome-wide analysis of 15 plant species, 173 members of the GRF family were identified and phylogenetically categorized into six groups. All members contained WRC and QLQ conserved domains, and the family’s expansion largely depended on segmental duplication. The promoter region of the GRF gene family mainly contained four types of cis-acting elements (light-responsive elements, development-related elements, hormone-responsive elements, and environmental stress-related elements) that are mainly related to gene expression levels. Functional divergence analysis revealed that changes in amino acid site evolution rate played a major role in the differentiation of the GRF gene family, with ten significant sites identified. Six significant sites were identified for positive selection. Moreover, the four groups of coevolutionary sites identified may play a key role in regulating the transcriptional activation of the GRF protein. Expression profiles revealed that GRF genes were generally highly expressed in young plant tissues and had tissue or organ expression specificity, demonstrating their functional conservation with distinct divergence. The results of these sequence and expression analyses are expected to provide molecular evolutionary and functional references for the plant GRF gene family.

Wireless sensor network (WSN) can play an important role during precision agriculture production to promote the growth of the agricultural economy. The application of WSN in agricultural production can achieve precision agriculture. WSN has the biggest challenge of energy efficiency. This paper proposes a model to efficiently utilize the energy of sensor nodes in precision agriculture production. The proposed model provides a comprehensive analysis of the precision agriculture. The model focuses on the characteristics of WSN and expands its application in precision agriculture. In addition, this paper also puts forward some technical prospects to provide a good reference for comprehensively and effectively improving the overall development level of precision agriculture. The paper analyzes the applicability and limitations of the existing sensor networks used for agricultural production technology. The ZigBee and Lora wireless protocols are utilized to have the best power consumption and communication in short distance and long distance. Our proposed model also suggests improvement measures for the shortcomings of existing WSN in the context of energy efficiency to provide an information platform for WSN to play a better role in agricultural production.

In the past five years, the scientific and technological innovation ability of Liaoning has declined year by year. Using literature research, field research, comparative analysis and expert interviews, this work analyzed the current situation of Liaoning's scientific and technological innovation ability from three aspects, i.e., scientific and technological innovation investment, innovation output and innovation potential, and screened important factors restricting Liaoning's scientific and technological innovation ability. This work proposed several path selections to enhance the comprehensive strength of Liaoning's scientific and technological innovation, to promote transformation and upgrading of Liaoning's industrial structure and to provide theoretical reference as well as reality for revitalization of Liaoning's old industrial bases through increasing innovation investment. The actions are conducive to strengthening dominant position of enterprises, integrating resources with innovation and expanding excavation of innovation potential.

In this paper, we conduct an in-depth research on the corresponding enterprises, combined with some problems existing in the process of data processing and use. We establish a deep learning model on the extensive collection and comprehensive investigation of the research results of domestic and foreign enterprises in all aspects of the process of data processing and use, and determine the research directions. Firstly, in view of the increasing complexity and dimension of enterprise data, and the difficulties of enterprise data application, this paper studies the related data preprocessing methods. Secondly, aiming at the problems of enterprise cost control and customer relationship management, this paper studies the prediction based on enterprise data through the analysis of practical problems and the processing of corresponding data. Finally, in order to progress and advance the efficiency and scientific usefulness of enterprise management, we in this paper study the evaluation based on enterprise data. The model is verified through simulations and compared with several models i.e. cross hybrid and sequential hybrid models. Using certain assumptions, the attained outcomes confirm that the accuracy of the deep learning structure of the single model is sophisticated and greater than that of the cross hybrid model, but lower than that of the sequential hybrid model.