Analysis of the cores of these Improvements of Online Teaching System and Model－Based on the Evaluation and Feedback on the Online Teaching Model and Teaching Platform

In recent years, MOOC, SPOC and other online teaching modes have attracted widespread attention. Online teaching platforms such as Tencent Classroom and MOOC of Chinese universities have emerged in an endless stream. During the 2020 outbreak of COVID-19, schools at all levels actively engaged in online teaching. The problems and challenges faced in the course of this teaching process will push the research hotspots of modern educational technology to construct the online teaching model that meets the needs of colleges and universities in the context of Internet + Education . This study collected the evaluation and feedback of college students from different universities and different majors on online teaching mode and teaching platform, conducted a quantitative study of SPSS samples, and analyzed the influence of learners\u27 and teachers\u27 participation on online teaching effect. Results show that: there is a positive correlation between learners\u27 attitude towards online teaching and the effect of online teaching, and between learners\u27 participation and the effect of online teaching. There is also a positive correlation between teacher’s participation and effect of online teaching. There is no clear correlation between learners\u27 use of equipment and online teaching effectiveness. Through the interview, learners reported low self-evaluation in online learning, and there are some problems in the teaching process, such as lack of teaching experience, poor platform interaction ability, and low supervision ability of managers. This study argues that in the development of online teaching, learners\u27 and platform users\u27 sense of experience and teachers\u27 participation should be further improved and perfected

Analysis of the genetic relationships and diversity among 11 populations of Xanthoceras sorbifolia using phenotypic and microsatellite marker data

Background: Assessments of genetic diversity are essential for germplasm characterization and exploitation. Molecular markers are valuable tools for exploring genetic variation and identifying germplasm. They play key roles in a Xanthoceras sorbifolia breeding program. Results: We analyzed the genetic diversity of populations of this species using 23 simple sequence repeat (SSR) loci and data on kernel oil content. The 11 populations included in the study were distributed across a large geographic range in China. The kernel oil content differed significantly among populations. The SSR marker analysis detected high genetic diversity among the populations. All SSRs were polymorphic, and we identified 80 alleles across the populations. The number of alleles at each locus ranged from two to six, averaging 3.48 per primer pair. The polymorphism information content values ranged from 0.35 to 0.70, averaging 0.51. Expected heterozygosity, observed heterozygosity, and Shannon's information index calculations detected large genetic variations among populations of different provenance. The high average number of alleles per locus and the allelic diversity observed in the set of genotypes analyzed indicated that the genetic base of this species was relatively wide. The statistically significant positive correlation between genetic and geographic distances suggested adaptations to local conditions. Conclusions: Microsatellite markers can be used to efficiently distinguish X. sorbifolia populations and assess their genetic diversity. The information we have provided will contribute to the conservation and management of this important plant genetic resource

(αF,αF¯)-Information Fusion Generated by Information Segmentation and Its Intelligent Retrieval

Making use of the mathematical model with dynamic features and attribute disjunctive characteristics, the new concepts of αF-information segmentation, αF¯-information segmentation, (αF,αF¯)-information segmentation and their attribute characteristics are given, and the intelligent acquisition of matrix reasoning and information segmentation is given, as well as the information segmentation theorem. Moreover, the equivalence between information segmentation and information fusion is discussed, and the information fusion intelligent acquisition intelligent retrieval algorithm is given. Based on these theoretical results, the intelligent information fusion retrieval algorithm and its simple application in health big data are presented. In conclusion, the results presented in this paper are entirely based on new ideas

<inline-formula><math display="inline"><semantics><mrow><mo mathvariant="bold" stretchy="false">(</mo><msup><mi mathvariant="bold-italic">α</mi><mi mathvariant="bold-italic">F</mi></msup><mo mathvariant="bold">,</mo><msup><mi mathvariant="bold-italic">α</mi><mrow><mover accent="true"><mi mathvariant="bold-italic">F</mi><mo mathvariant="bold" stretchy="true">¯</mo></mover></mrow></msup><mo mathvariant="bold" stretchy="false">)</mo></mrow></semantics></math></inline-formula>-Information Fusion Generated by Information Segmentation and Its Intelligent Retrieval

Making use of the mathematical model with dynamic features and attribute disjunctive characteristics, the new concepts of αF-information segmentation, αF¯-information segmentation, (αF,αF¯)-information segmentation and their attribute characteristics are given, and the intelligent acquisition of matrix reasoning and information segmentation is given, as well as the information segmentation theorem. Moreover, the equivalence between information segmentation and information fusion is discussed, and the information fusion intelligent acquisition intelligent retrieval algorithm is given. Based on these theoretical results, the intelligent information fusion retrieval algorithm and its simple application in health big data are presented. In conclusion, the results presented in this paper are entirely based on new ideas

Sequence‐to‐sequence transfer transformer network for automatic flight plan generation

Abstract In this work, a machine translation framework is proposed to tackle the flight plan generation in the air transport field. Diverging from the traditional human expert‐based way, a novel sequence‐to‐sequence transfer transformer network to automatic flight plan generation with enhanced operational acceptability is presented. It allows the user to translate the departure and arrival airport pairs denoted as test sentences, into the flyable waypoint sequences denoted as the corresponding source sentences. The approach leverages deep neural networks to autonomously learn air transport specialized knowledge and human expert insights from industry legacy data. Moreover, a multi‐head attention mechanism is adopted to model the complex correlation between airport pairs. Besides, we introduce an innovative waypoint embedding layer to learn effective embeddings for waypoint sequences. Additionally, an extensive flight plan dataset is constructed utilizing real‐world data in China spanning from July to September 2019. Employing the proposed model, rigorous training and testing procedures are conducted on this dataset, yielding remarkably favourable outcomes based on automatic evaluation metrics that are BLEU and METEOR, which outperform other popular approaches. More importantly, the proposed approach achieves high performance in the operational validation and visualization, showing its application potential for real‐world air traffic operation

Determining dense velocity fields for fluid images based on affine motion

In this article, we address the problem of estimating fluid flows between two adjacent images containing fluid and non-fluid objects. Typically, traditional optical flow estimation methods lack accuracy, because of the highly deformable nature of fluid, the lack of definitive features, and the motion differences between fluid and non-fluid objects. Our approach captures fluid motions using an affine motion model for each small patch of an image. To obtain robust patch matches, we propose a best-buddies similarity-based method to address the lack of definitive features but many similar features in fluid phenomena. A dense set of affine motion models was then obtained by performing nearest-neighbor interpolation. Finally, dense fluid flow was recovered by applying the affine transformation to each patch and was improved by minimizing a variational energy function. Our method was validated using different types of fluid images. Experimental results show that the proposed method achieves the best performance

Amplification of optical Schr\"{o}dinger cat states with implementation protocol based on frequency comb

We proposed and analyzed a scheme to generate large-size Schr\"{o}dinger cat states based on linear operations of Fock states and squeezed vacuum states and conditional measurements. By conducting conditional measurements via photon number detectors, two unbalanced Schr\"{o}dinger kitten states combined by a beam splitter can be amplified to a large-size cat state with the same parity. According to simulation results, two Schr\"{o}dinger odd kitten states of $\beta=1.06$ and $\beta=1.11$ generated from one-photon-subtracted squeezed vacuum states of $-$3 dB, are amplified to an odd cat state of $\beta=1.73$ with a fidelity of $F=99\%$. A large-size Schr\"{o}dinger odd cat state with $\beta=2.51$ and $F=97.30\%$ is predicted when the input squeezed vacuum states are increased to $-$5.91 dB. According to the analysis on the impacts of experimental imperfections in practice, Schr\"{o}dinger odd cat states of $\beta>2$ are available. A feasible configuration based on a quantum frequency comb is developed to realize the large-size cat state generation scheme we proposed

Analysis of the genetic relationships and diversity among 11 populations of Xanthoceras sorbifolia using phenotypic and microsatellite marker data

Background: Assessments of genetic diversity are essential for germplasm characterization and exploitation. Molecular markers are valuable tools for exploring genetic variation and identifying germplasm. They play key roles in a Xanthoceras sorbifolia breeding program. Results: We analyzed the genetic diversity of populations of this species using 23 simple sequence repeat (SSR) loci and data on kernel oil content. The 11 populations included in the study were distributed across a large geographic range in China. The kernel oil content differed significantly among populations. The SSR marker analysis detected high genetic diversity among the populations. All SSRs were polymorphic, and we identified 80 alleles across the populations. The number of alleles at each locus ranged from two to six, averaging 3.48 per primer pair. The polymorphism information content values ranged from 0.35 to 0.70, averaging 0.51. Expected heterozygosity, observed heterozygosity, and Shannon's information index calculations detected large genetic variations among populations of different provenance. The high average number of alleles per locus and the allelic diversity observed in the set of genotypes analyzed indicated that the genetic base of this species was relatively wide. The statistically significant positive correlation between genetic and geographic distances suggested adaptations to local conditions. Conclusions: Microsatellite markers can be used to efficiently distinguish X. sorbifolia populations and assess their genetic diversity. The information we have provided will contribute to the conservation and management of this important plant genetic resource

Spatial Patterns in Different Stages of Regeneration after Clear-Cutting of a Black Locust Forest in Central China

Estimating underlying mechanisms and dynamics from observed tree patterns can provide guidance for plantation management. Robinia pseudoacacia can reproduce via clonally produced ramets, leading to a complex distribution of stems. Three second generation plots and three third generation plots (each plot 50 m &times; 50 m) were established across a wide age range after clear-cutting in a Robinia pseudoacacia plantation in central China. We measured spatial coordinates, diameter at breast height (DBH) or diameter at basal stem, and heights of all recruits, as well as the coordinates and base diameter of all stumps, in six plots. The spatial pattern in different plots and the spatial relation between stumps and regenerations after clear-cutting were analyzed. To estimate the underlying processes of the observed patterns, we fitted Mat&eacute;rn and Variance-Gamma cluster processes to the observed dataset. The results revealed that the percentage of ramets from stumps decreasing with age in the two types of stands (from 40.4% to 30.1%, from 57.6% to 35.7%), and trees exhibited an aggregated distribution in all plots, but the degree of aggregation exhibited a decreasing trend with age, and aggregation occurred at different scale. Furthermore, a large proportion of ramets had their nearest neighbor at a short distance (&lt;1 m) based on analysis of the nearest neighbour function. The bivariate analysis revealed that the spatial relation between stumps and ramets changed with age, and a repulsion trend was found between them in all the six plots. The Variance-Gamma process with covariate of Cartesian coordinates fitted the observed patterns better than others. The observed pattern was likely driven by root dispersal limitation, seed dispersal limitation, human disturbance, and intraspecific competition. Spatial patterns are important characteristics in forest stand structure, and understanding the pattern change and its underlying mechanisms could allow for better timing of artificial disturbances to optimize stand structure and promote stand growth