In order to fully explore the functional genes and utilize the biological resources of Taxus chinensis var. mairei, PacBio Sequel third-generation sequencing technology was used to conduct high-throughput transcriptome sequencing, and systematic bioinformatics analysis was employed to study the Taxus chinensis var. mairei obtained from Mount Fanjing, Guizhou Province. The study obtained 68.03 Gb of raw data, which included 1 001 302 raw sequence reading fragments. After assembling, splicing and de-redundancy, 65 152 unigenes with an average length of 2 591 bp were obtained. Database comparison showed that the gene expression of cellular and metabolic activities of the Taxus chinensis var. mairei were higher, indicating strong metabolic activity and genetic information processing capacity. Meanwhile, transcription factors in the transcriptome related to growth and development, stress resistance, secondary metabolism and synthesis provided new insight into the study of the comprehensive utilization and genetic breeding of Taxus chinensis var. mairei.

The treatment of low temperature and short sunshine before strawberry transplanting can promote the early flowering of strawberry and prolong the fruit cycle. At present, the main techniques of promoting flower and fruit production in advance in strawberry production are cold storage low temperature seedling raising and plateau seedling raising. However, there is a unified standard for low-temperature and short-day treatment of strawberries, which leads to inconsistent flowering periods of strawberries and frequent occurrence of fruit stubble. In this paper, the factors affecting the flower bud differentiation of strawberry, such as seedling, temperature, light and hormone, were reviewed. The relationship between low temperature and flower bud differentiation of strawberry was emphatically expounded,such as the advantages and disadvantages of different low temperature treatment methods, the effect of low temperature on the flowering period of strawberry, the effect of low temperature on the endogenous hormones of strawberry, and the physiological mechanism of low temperature promoting flower of strawberry, which provided some theoretical help for the early flower promotion technology of strawberry.

In order to improve the control performance of distributed driving electric vehicles, and for the situation that some vehicle state parameters cannot be directly measured by sensors, this paper used unscented Kalman filters to design a nonlinear observer for vehicle state and parameter coupling, and estimated the vehicle state and actuator failure coefficients. The nonlinear vehicle dynamic model was established, so that the motor fault diagnosis problem was transformed into a real-time parameter estimation problem. The yaw speed and vehicle speed were estimated in real time by UKF (Unscented Kalman Filter). Finally, the Carsim/Simulink co-simulation was used to verify the problem. The simulation results show that the observer can accurately estimate the above related vehicle states and parameters, which verifies that the estimation algorithm has high real-time performance and accuracy.

This article proposes a diesel engine fault diagnosis method that combines Variational Mode Decomposition (VMD) and Extreme Learning Machine (ELM) to diagnose and classify diesel engine faults. Aiming at the nonlinear and non-stationary characteristics of diesel engine vibration signals, an optimized VMD decomposition method based on Sparrow Search Algorithm (SSA) is proposed to achieve good decomposition performance. A classification model based on Grey Wolf Optimization (GWO) algorithm for optimizing ELM is proposed to address diverse types of fault signals in diesel engines, making the classification performance more stable. Finally, the proposed method is applied to the fault detection and recognition of Isuzu 6BB1 diesel engine, with a fault recognition accuracy of 98.04%. The diagnostic results verify that GWO-ELM has high accuracy, and this method is feasible and effective.

This paper analyzes and summarizes the issues in various Transformer-based multi-object tracking architectures, considering Transformer-based multi-object tracking as "query-based" methods. It focuses on the structure and functionality of these systems, with the "query" mechanism as their core. The different Transformer-based multi-object tracking systems are categorized into four main types: DETR-based trackers, memory-based trackers, temporal-spatial motion trackers, and other miscellaneous trackers. The performance of each algorithm is assessed using the MOTA metric on the MOT17 benchmark dataset. Based on these evaluations, the paper also suggests future trends in Transformer-based multi-object tracking.

To improve the gas-sensing performance of WO₃ for NO₂, Ce-doped WO₃ nanofibers were prepared by electrostatic spinning technique, and WO₃ and Ce-WO₃ gas sensors with different doping ratios were prepared for gas-sensing experiments. The experimental results showed that the doping of Ce elements significantly improved the sensing performance of WO₃ for NO₂. When the operating temperature was reduced to 100℃, the response value of the 2 mol% Ce-WO₃ sensor was 22.16 with the concentration of NO₂ of 5 ppm. The structure, morphology, and surface chemical states were characterized by XRD, SEM, and XPS, and the bandgap was characterized and analyzed using UV-vis. The analysis showed that the enhancement of the NO₂ performance of the 2 mol% Ce-WO₃ sensor was mainly due to the increase of the adsorbed oxygen content on the surface and the reduction of the bandgap after Ce doping.

Four curved steel-concrete-steel composite plates (CSCSCPs) with different curvatures were designed to investigate the explosion resistance, and the damage, displacement and energy dissipation of the CSCSCPs under blast loading were studied by using the finite element software ABAQUS. Moreover, taking the CSCSCP with curvature of 0.75 as an example, parametric study was carried out to investigate the effect of blasting distance, charge mass, steel plate thickness, concrete thickness, stud spacing and its diameter on the dynamic response of the CSCSCP. The results show that under the same blast loading, the maximum kinetic energy and maximum mid-span displacement of the CSCSCP decrease as the curvature increases. When the total thickness of the inner and outer steel plates remains unchanged, moderately increasing the thickness of the inner steel plate is more conducive to reduce the maximum mid-span displacement of the CSCSCP. Appropriately increasing the curvature of the CSCSCP, reducing the stud spacing and increasing the stud diameter are helpful to improve the explosion resistance of the CSCSCP. Two empirical formulas of mid-span maximum displacement, steel plate’s thickness (concrete’s thickness) and explosion charges of the composite plate are obtained according to nonlinear regression.

Aiming at the problems of rough stroke details and fuzzy style features caused by insufficient attention to detail features in current font generation algorithms, a Chinese character font generation algorithm based on multi-receptive field features was proposed. In view of the lack of stroke details, a Multi-Receptive Field Feature Pyramid (MRFP) module was introduced to capture features of different scales and reconstruct stroke details. In view of the impact of redundant features on the quality of the generated results, Spatial and Channel Reconstruction Convolution (SCConv) was used instead of ordinary convolution, and the fused methods of separation reconstruction and separation transformation were adopted to suppress the redundancy of space and channels. Therefore, the attention to the overall font features was improved and the generalization ability of the model was strengthened. In the experiment, 10 types of Chinese character libraries were used for training, the experimental results showed that the generation result of this method was superior to other font generation algorithms in terms of overall style and stroke details. The evaluation indexes of PSNR, SSIM and LPIPS were all improved.

Aiming at the problems of low extraction accuracy and high computational complexity in the current volume maximization endmember extraction technology, the Extraction MethodBased on Genetic Algorithm and Volume Maximization (EE-GAVM) is proposed to improve the existing endmember extraction methods. The EE-GAVM method locates the endmember set by searching for the pixel vector set with the largest volume of the simplex, and expresses the endmember extraction problem as a single target, which saves computing resources and improves accuracy. The experimental results show that the proposed method outperforms other comparative algorithms in terms of performance and accuracy.

To enhance the effectiveness of font family generation, this paper proposes a method that integrates SCConv and attention mechanisms, specifically targeting the unique characteristics of font families by adjusting the network to focus on local font features and stroke intersections. This method replaces standard convolution with spatial-channel reconstruction convolution (SCConv) to improve the efficiency of network generation. Additionally, the model incorporates shuffle attention (SA), which combines and groups font features to increase feature interaction, and uses a multi-head attention mechanism to fuse the font features. Experimental results show that the proposed model outperforms the DG-Font algorithm in both metrics and visual outcomes for generating font family characters.

In response to the limitations in accuracy and resolution of existing key node identification methods, an improved K-shell ranking method is proposed. This method builds on traditional K-shell decomposition, integrating node degree, neighboring node influence, edge weights, and information entropy theory to refine the relative importance of nodes within the same K-shell level. Experimental results show that this method significantly improves the accuracy and monotonicity of rankings, effectively distinguishing the importance of nodes within the same K-shell level and accurately identifying key nodes with substantial network impact. This algorithm considers from multiple dimensions that affect the importance of key node identification, significantly improving accuracy and resolution. It holds great significance for the mining of key nodes in network anonymity and privacy protection.

To address the challenges of overall blurriness and structural inaccuracies in few-shot Chinese font generation, a deformable generative network that integrates channel-prior convolutional attention and depthwise separable convolutions is proposed. The network architecture which builds upon the foundation of deformable generative networks is enhanced with channel-prior convolutional attention and depthwise separable convolutions to capture global features better and ensure accurate structural generation of Chinese font characters. Comparative evaluations against four classic models, DG-Font, DG-Font++, MX-Font, and CF-Font, demonstrate that the proposed method excels in metrics such as SSIM (Structural Similarity Index Measure) and FID (Fréchet Inception Distance). Furthermore, ablation experiments validate the effectiveness of the proposed network model. The experimental results demonstrate that the proposed network model effectively mitigates the issues of overall blurriness and structural errors in few-shot Chinese font generation.

A path-enhanced multi-hop question answering method based on knowledge graph embedding is proposed. A "comparation-aggregation" framework is constructed, which can enhance the question-to-answer path to solve the problem of poor question answering effect caused by not making full use of the path and natural language question answering order in previous models. Different comparison functions are designed in this paper, and SUBMULT+NN is chosen as the comparison function. Experiments were carried out on MateQA and WebQuestionsSP, two benchmark question-and-answer datasets in open fields. Compared with previous multi-jump question-and-answer methods, the results are improved on the complete knowledge graph and excellent on the incomplete knowledge graph.

This article takes the core course "Structural Mechanics" in engineering as an example to explore how to rely on the green classroom ecology to carry out course construction under the background of top-quality course construction. By transforming educational and teaching concepts, emphasizing student-centered approaches, we aim to achieve a transformation between teaching and learning. Integrate teaching content and resources and design diverse learning activities to cultivate students' advanced thinking and innovative abilities. To build a systematic teaching mode, the 3F model of pre-class guidance, in-class discussion and post-class expansion is used to promote the improvement of teaching effectiveness. Reform the teaching evaluation system, achieving formative evaluation throughout the entire process and comprehensively assessing the learning outcomes of students. Through these measures, we explore the closed-loop design of the construction of the "Structural Mechanics" course, which promotes the improvement of course teaching quality and assists in the cultivation of applied talents in China's engineering field.

Education, as a key factor in promoting human capital accumulation and social development, has a significant impact on entrepreneurs’ well-being. Based on human capital theory, status attainment theory, and social capital theory, this study utilizes cross-sectional data from the 2021 Chinese General Social Survey (CGSS) to deeply explore the mechanism of how educational attainment affects entrepreneurs’ well-being. The results show that educational level can significantly and directly enhance entrepreneurs’ well-being. Further analysis reveals that educational level influences entrepreneurs’ well-being through three mediating variables: income, perceived social status, and social interaction. Among these, income has a negative impact on entrepreneurs’ well-being, while perceived social status and social interaction have a positive impact. Additionally, the study finds significant differences in the impact of educational attainment on entrepreneurs’ well-being across different age groups, household registration status, and entrepreneurial motivations. These conclusions provide a strong basis for universities to carry out targeted entrepreneurship education and for the government to formulate refined entrepreneurship policies.

Career education is an important link between job seeking and recruitment, also an important link between the education system and the labor system. It involves the problem of graduates' adaptation to the labor system and it relates to their adaptation to the entire social environment. Therefore, the career system should play a multidimensional and comprehensive role in shaping people. From the perspective of structural functionalism, through comprehensive examination of various systems, it can be found that these systems not only play their respective functions in career education, but also work together in the process and results of career education through integration between systems. By comprehensively examining the development process of career education in China since modern times, it is helpful to gain corresponding insights and propose relevant countermeasures from different levels.

This study explores the role of multi-ball training in table tennis training on the development of motor skills. By outlining the definition and principles of multi-ball training, as well as the classification and implementation methods, the impact of multi-ball training on the technical level and psychological quality is further analyzed. The research results show that the multi-ball training method can improve athletes' serving, receiving, and attacking skills, as well as cultivate their ability to concentrate, react quickly, make decisions, and resist pressure. When implementing multi-ball training, it is necessary to develop a reasonable training plan, select appropriate environments and equipments, and consider the personalized needs of athletes. Future research can combine technological means for training, introduce customized methods, and further explore the improvement and innovation of multi-ball training methods.