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Network analytics has the potential to examine new behaviour patterns that are often hidden by the complexity of online interactions. One of the varied network analytics approaches and methods, the model of collective attention, takes an ecological system perspective to exploring the dynamic process of participation patterns in online and flexible learning environments. This study selected "Fundamentals of C++ programming (Spring 2019)" on XuetangX as an example through which to observe the allocation patterns of attention within MOOC videos, as well as how video features and engagement correlate with the accumulation, circulation, and dissipation pattern of collective attention. The results showed that the types of instructions in videos predicted attention allocation patterns, but they did not predict the engagement of video watching. Instead, the length and whether the full screen was used in the videos had a strong impact on engagement. Learners were more likely to reach a high level of engagement in video watching when their attention had been circulated around the videos. The results imply that understanding the patterns and dynamics of attention flow and how learners engage with videos will allow us to design cost-effective learning resources to prevent learners from becoming overloaded.

As conflicts between emissions reductions and economic growth intensify, an increasing number of countries and regions are focusing on green total factor productivity (GTFP). Financial agglomeration (QWS), resource endowment (SE), and innovation-driven (ID) are the main factors that cause regional differences in GTFP. However, few scholars have comprehensively considered the impact of the interaction between SE, ID, and QWS on GTFP. This article uses the Global Malmquist-Luenberger (GML) index to calculate the GTFP of 30 Chinese provinces from 2003 to 2020 and constructs a dynamic panel model to present the first comprehensive study of the interactions between SE, ID, and QWS and their relationship with GTFP. The conclusions are as follows. (1) SE is positively correlated with GTFP, which is different from the "resource curse" theory proposed by existing research. (2) There is a crowding-out effect between SE and ID, and their interaction will restrain the growth of GTFP. (3) QWS promotes ID, and the interaction between the two promotes the growth of GTFP. (4) There is a restraining effect between QWS and SE, and their interaction will hinder the growth of GTFP. (5) The key policy directions to improve China's GTFP are to undertake strategic planning from a comprehensive perspective, including encouraging innovation, guiding financial development reasonably, and avoiding excessive consumption of resources. [ABSTRACT FROM AUTHOR]

SUMMARY: Bermudagrass (Cynodon dactylon) is a globally distributed, extensively used warm‐season turf and forage grass with high tolerance to salinity and drought stress in alkaline environments. However, the origin of the species and genetic mechanisms for salinity tolerance in the species are basically unknown. Accordingly, we set out to study evolution divergence events in the Cynodon genome and to identify genes for salinity tolerance. We developed a 604.0 Mb chromosome‐level polyploid genome sequence for bermudagrass 'A12359' (n = 18). The C. dactylon genome comprises 2 complete sets of homoeologous chromosomes, each with approximately 30 000 genes, and most genes are conserved as syntenic pairs. Phylogenetic study showed that the initial Cynodon species diverged from Oropetium thomaeum approximately 19.7–25.4 million years ago (Mya), the A and B subgenomes of C. dactylon diverged approximately 6.3–9.1 Mya, and the bermudagrass polyploidization event occurred 1.5 Mya on the African continent. Moreover, we identified 82 candidate genes associated with seven agronomic traits using a genome‐wide association study, and three single‐nucleotide polymorphisms were strongly associated with three salt resistance genes: RAP2‐2, CNG channels, and F14D7.1. These genes may be associated with enhanced bermudagrass salt tolerance. These bermudagrass genomic resources, when integrated, may provide fundamental insights into evolution of diploid and tetraploid genomes and enhance the efficacy of comparative genomics in studying salt tolerance in Cynodon. Significance Statement: We have successfully sequenced the first heterozygous, tetraploid genome of Cynodon dactylon, a key warm‐season turfgrass and forage crop. Our GWAS analysis has identified genes linked to vital agronomic traits, enhancing our understanding of bermudagrass cultivation. [ABSTRACT FROM AUTHOR]

Genome assembly remains to be a major task in genomic research. Despite the development over the past decades of different assembly software programs and algorithms, it is still a great challenge to assemble a complete genome without any gaps. With the latest DNA circular consensus sequencing (CCS) technology, several assembly programs can now build a genome from raw sequencing data to contigs; however, some complex sequence regions remain as unresolved gaps. Here, we present a novel gap-filling software, DEGAP (D ynamic E longation of a G enome A ssembly P ath), that resolves gap regions by utilizing the dual advantages of accuracy and length of high-fidelity (HiFi) reads. DEGAP identifies differences between reads and provides 'GapFiller' or 'CtgLinker' modes to eliminate or shorten gaps in genomes. DEGAP adopts an iterative elongation strategy that automatically and dynamically adjusts parameters according to three complexity factors affecting the genome to determine the optimal extension path. DEGAP has already been successfully applied to decipher complex genomic regions in several projects and may be widely employed to generate more gap-free genomes. [ABSTRACT FROM AUTHOR]

In digital holography, reconstructed image quality can be primarily limited due to the inability of a single small aperture sensor to cover the entire field of a hologram. The use of multi-sensor arrays in synthetic aperture digital holographic imaging technology contributes to overcoming the limitations of sensor coverage by expanding the area for detection. However, imaging accuracy is affected by the gap size between sensors and the resolution of sensors, especially when dealing with a limited number of sensors. An image reconstruction method is proposed that combines physical constraint characteristics of the imaging object with a score-based diffusion model, aiming to enhance the imaging accuracy of digital holography technology with extremely sparse sensor arrays. Prior information of the sample is learned by the neural network in the diffusion model to obtain a score function, which alternately constrains the iterative reconstruction process with the underlying physical model. The results demonstrate that the structural similarity and peak signal-to-noise ratio of the reconstructed images using this method are higher than the traditional method, along with a strong generalization ability. [ABSTRACT FROM AUTHOR]