The Casparian strip membrane domain proteins (CASPs) are pivotal for the formation of the Casparian strip (CS) in endodermal cells and play a crucial role in a plant’s response to environmental stresses. However, existing research on the CASP gene family in rice and Arabidopsis lacks a comprehensive bioinformatics analysis and necessitates further exploration. In this study, we identified 41 OsCASP and 39 AtCASP genes, which were grouped into six distinct subgroups. Collinearity analysis underscored the pivotal roles of WGD and TD events in driving the evolution of CASPs, with WGDs being the dominant force. On the one hand, the analysis of cis-elements indicated that most OsCASP and AtCASP genes contain MYB binding motifs. On the other hand, RNA-seq revealed that the majority of OsCASP and AtCASP genes are highly expressed in roots, particularly in endodermal cells, where OsCASP_like11/9 and AtCASP_like1/31 demonstrated the most pronounced expression. These results suggest that OsCASP_like11/9 and AtCASP_like1/31 might be candidate genes involved in the formation of the endodermis CS. RT-qPCR results demonstrated that OsCASP_like2/3/13/17/21/30 may be candidate genes for the ion defect process. Collectively, this study offers a theoretical foundation for unraveling the biological functions of CASP genes in rice and Arabidopsis.

This study pioneers the investigation of synthetic aperture radar (SAR) imaging algorithm during the ascending and descending periods of highly elliptical orbit (HEO). SAR operating in HEO has the potential to enhance observation capabilities of high-latitude maritime routes. However, current state-of-the-art methods struggle to balance full-scene focusing and high efficiency during the ascending and descending periods of HEO. In this article, an SAR imaging algorithm that leverages the unique echo characteristics during these periods is introduced. First, azimuth deramping is employed to address spectrum aliasing. We apply this method to the polynomial model and assess its impact on the model coefficients. Second, the reduction of aperture length is achieved through bulk processing and 2-D frequency-domain resampling. Third, the imaging results are refined by focusing in block unit. Due to the distinct echo characteristics observed during these periods of HEO, the proposed aperture length reduction processing can lead to a significant decrease in aperture length. This enables precise imaging through partitioning without the need for iteration. Thus, the proposed algorithm can achieve accurate focusing while maintaining efficiency. In addition, we propose the reason why frequency-domain resampling causes defocusing in the presence of severe spatial variations in light of the polar format algorithm. Furthermore, the calculation method for the frequency axes after twice resampling is presented. Simulation results validate that the proposed method is effective and efficient for the ascending and descending periods of an HEO with an eccentricity of 0.6.

This letter proposes a scheme for automatically and accurately extracting the parameters of weak radio frequency interference (RFI) present in the signal within the time-frequency domain. This scheme utilizes short-time Fourier transform (STFT) to transform each frame of radar echo containing interference into the time-frequency domain. In the time domain, a joint low-rank sparse robust principal component analysis based on truncated nuclear norms (TJLS-RPCA) is employed to divide the time-frequency representation into two joint low-rank sparse matrices, from which the interference parameters are extracted. Subsequently, K -means clustering is applied to determine the number of RFIs, and Gaussian fitting is applied to eliminate high-error items. This scheme can extract parameters such as the central frequency, bandwidth, and duration of the interference. The proposed scheme improves the accuracy of parameter extraction and processing speed, demonstrating notable effectiveness in handling weak interference. The Monte Carlo experimental results demonstrate that the scheme provides highly accurate parameter estimation when the signal-to-interference ratio (SIR) is less than 13 dB, with errors below 0.5%. Finally, the feasibility of the proposed scheme is validated using Sentinel-1A data, and the extracted results are consistent with the simulation results.

The coordinated development of urban e-commerce and green total factor productivity (GTFP) plays a crucial role in achieving high-quality development. This paper selects relevant data from the eight major urban agglomerations in China for research. A model that described the coupling coordination between the regions’ GTFP and urban e-commerce was presented. Then, unfold the spatiotemporal evolution characteristics of their coupling coordination from the “dynamic and static”, “overall and regional” dimensions. The results indicate: (1) The coupling coordination degree between two systems within China’s eight urban agglomerations exhibits an upward trend with fluctuations, transitioning from a state of near dysfunction to good coordination. (2) The spatial heterogeneity in the coupling coordination degree between the two systems in China’s eight major urban agglomerations is evident, with an overall downward trend and exhibiting a “V-shaped” pattern of “decline-rise”.(3) The coupling coordination degree between two systems across China’s eight urban agglomerations exhibits a trend toward centralization, with an amelioration of bipolar differentiation, and a progressive attenuation of spatial heterogeneity.

In recent years, skeleton detection based on side-output network has shown significant effectiveness. However, the existing methods are still unable to tackle the problem of image distortion in side-output structure caused by high-multiplier up/down-sampling, and the fixed receptive field limits the feature expression of the networks. To solve these problems, this paper proposes a local short-connection unidirectional fusion network based on side-output connection, which includes a feature extraction network and a side-output connection network. The feature extraction network is a deep convolutional neural network, which is used for multi-layer feature extraction. The side-output connection network consists of a local short-connection unit and a unidirectional fusion network. The local short-connection gradually constructs the continuous large receptive field features by integrating the adjacent features of receptive field, while the unidirectional fusion of multi-scale features from deep to shallow can achieve the characterization of the object from rough to fine. Experimental results on four commonly used skeleton detection datasets demonstrate the effectiveness of the proposed method.

Core Information Extraction (CIE) from web pages aims to extract valuable text to provide data for downstream Text Data Mining (TDM) tasks. Web page representations in existing CIE methods are either based on HTML structural features or visual features. Neither of these representations really understands the semantic associations inherent in the web page, leading to poor extraction quality. This paper proposes a new web page representation method based on semantic features from the perspective of readers’ reading and understanding of web pages. In this method, we introduce a new concept of web page skeleton to parse and represent the web page from a semantic point of view. To observe the relationship between the various parts of the skeleton, we project the skeleton onto the DOM tree and get the skeleton tree. Based on this new web page representation, we propose SCIEnt, a semantic-feature-based web page CIE framework. SCIEnt consists of four modules, i.e. Skeleton Tree Construction, Node Splitting, Node Classification, Semantic Aggregation and Correction. Algorithms in each module can be flexibly replaced according to the requirement of downstream TDM task. We evaluate SCIEnt in terms of three well-studied datasets. Results show that SCIEnt far outperforms baseline methods, and the semantic-feature-based web page representation have superiority in web page CIE.

The multiple elevation beam (MEB) mode is an effective technique for enhancing imaging width in spaceborne synthetic aperture radar (SAR) systems. This mode combines intrapulse beam-steering during transmitting and digital beamforming (DBF) during receiving. By sequentially illuminating the far sub-swath followed by the near sub-swath, echoes from different sub-swaths can reach the antenna at the same time and overlap each other in the receiving window. To separate the overlapping echoes, the linear constrained minimum variance (LCMV) beamformer has been used, which is a single-null constrained beamformer. In addition, a multinull constraint beamformer has also been proposed on this basis. However, these two methods are insufficient for effectively separating the overlapping echoes when there is a significant energy difference between different sub-beam signals. To solve this problem, an advanced multinull constrained beamformer with deepened nulls is proposed. Compared with other methods, the proposed method can flexibly adjust the width and depth of the nulls. The simulation results demonstrate that the proposed method can enhance echo separation quality. The experimental results verify the effectiveness of the proposed method. All the results indicate that the proposed method is helpful in improving the high-resolution and wide-swath imaging performance of future spaceborne SAR systems.

Manipulating microorganisms to increase soil organic carbon (SOC) in croplands remains a challenge. Soil microbes are important drivers of SOC sequestration, especially via their necromass accumulation. However, microbial parameters are rarely used to predict cropland SOC stocks, possibly due to uncertainties regarding the relationships between microbial carbon pools, community properties and SOC. Herein we evaluated the microbial community properties (diversity and network complexity), microbial carbon pools (biomass and necromass carbon) and SOC in 468 cropland soils across northeast China. We found that not only microbial necromass carbon but also microbial community properties (diversity and network complexity) and biomass carbon were correlated with SOC. Microbial biomass carbon and diversity played more important role in predicting SOC for maize, while microbial network complexity was more important for rice. Models to predict SOC performed better when the microbial community and microbial carbon pools were included simultaneously. Taken together our results suggest that microbial carbon pools and community properties influence SOC accumulation in croplands, and management practices that improve these microbial parameters may increase cropland SOC levels.

金融公告信息披露了企业运营的关键数据，具有应用价值。无结构金融公告中涉及复杂的财务关系，即多元关系。该文设计了基于依存分析树和频繁子图挖掘的垂直域多元关系抽取方法TextMining,可大大降低对数据集的依赖。进一步，受图卷积神经网络启发，该文设计了垂直域优化的FTA-GCN算法。在构建的适用金融公告数据集上，算法较强地关注以金融公告中常见的名词实体为核心的多元关系抽取，实验结果表明，算法具有良好的抽取效果。

Worldwide degradation of the ecological environment could be the cause of poverty. The poverty-stricken areas may face the dilemma of a “vicious circle of poverty.” The complex ecological conditions have intertwined with poverty alleviation, which makes the demand for ecological poverty alleviation particularly prominent in these areas. However, the research on the relationship between agro-ecological efficiency and poverty are limited. It is far from clear what is the impact of the agro-ecological efficiency on poverty. To explore the impact of agro-ecological efficiency on poverty reduction, we adopt the panel data model based on cross-correlation and regression coefficient, using the data from 25 counties/districts in the Three Gorges Reservoir Region (TGRR) from 2006 to 2017. The results show that (1) there is significant heterogeneity in agro-ecological efficiency in the TGRR, and the agro-ecological efficiency in the middle area is significantly lower than that of the head and tail areas of the TGRR; (2) the improvement of regional agro-ecological efficiency could accelerate the alleviation of poverty; and (3) the widening of urban–rural income disparity is not conducive to poverty alleviation and eradication. This study would provide basis for further policy recommendations aimed at improving agro-ecological efficiency and alleviating poverty.

Excessive delivery of agricultural chemicals seriously threatens the ecology and environment of agricultural areas and restricts the sustainable development of agriculture. The analysis of agrochemical Environmental Kuznets Curve (EKC) adopting spatial econometric tools is limited. Therefore, this study adopted the spatial panel regression approach to analyze the agricultural chemicals EKC Three Gorges Reservoir Region (TGRR). The results show that (1) both EKC curves of chemical fertilizer and pesticide of the TGRR are inverted U-shaped, and there are 53.8% and 42.3% of the counties/districts did not meet the inflection point of the EKC as regards to chemical fertilizer and pesticide. (2) The EKC of agricultural chemicals of the TGRR are stable, and the variables such as cultivated area and the urban-rural income disparity have impact on the occurrence of the inflection point of EKC. (3) There is the spatial "imitation and convergence" of agricultural chemicals among the counties in the TGRR. The findings indicate that the ecological and environmental situations of agriculture in the TGRR need urgent attention. Countermeasures aiming to alleviate the contradiction between ecological and economic development should be put forward.

事件时序关系抽取是一项重要的自然语言理解任务,可以广泛应用于诸如知识图谱构建、问答系统等任务.已有事件时序关系抽取方法往往将该任务视为句子级事件对的分类问题,而基于有限的局部句子信息导致其抽取的事件时序关系的精度较低,且无法保证整体时序关系的全局一致性.针对此问题,提出一种融合上下文信息的篇章级事件时序关系抽取方法,使用基于双向长短期记忆(bidirectional long short-term memory,Bi-LSTM)的神经网络模型学习文章中事件对的时序关系表示,再利用自注意力机制融入上下文中其他事件对信息,从而得到更丰富的事件对时序关系表示用于时序关系分类通过 TB-Dense(timebank dense)和 M ATRES(multi-axis temporal relations for start-points)数据集的实验表明:此方法能够取得比当前主流的句子级方法更佳的抽取效果.

是把一篇文档压缩成一个更短描述的过程。随着互联网数据量的增长,文档压缩技术对文本分析、数据浏览等有着重大的应用价值。但在基于序列模型的单文档单句摘要生成即标题生成领域中仍然存在数据使用率不高的问题。该文提出基于关键信息指导的标题生成算法。算法中的关键信息除了主流方法中使用的新闻首段句子之外,还包括新闻后续内容中有实质信息的句子,以及新闻中的重点词语。该算法将这些关键信息作为序列模型的输入,指导其生成标题,使得生成的标题能够覆盖更多的新闻信息。实验表明,在基于序列模型生成标题时,使用关键信息能够提升新闻标题生成的效果。

随着深度学习的快速发展与应用,联合式抽取被广泛应用于实体抽取和实体间的关系预测。虽然端到端的联合抽取方法在该领域得到了较大关注,但这类方法目前未考虑multi-token实体;同时,抽取过程中忽略了关系预测与实体抽取之间的相互影响。针对以上问题,结合Encoder-Decoder框架的特点,引入标签校正机制,提出了一种基于标签校正的端到端实体关系联合抽取方法 CopyLC。实验结果证明:在更严格的评价方式下,所提出的方法与当前主流方法相比,在NYT和WebNLG数据集上均能获得更好的抽取效果。

随着信息技术的飞速发展,互联网成为了舆情传播的主要载体.各种舆情事件不断涌现,并在网民的参与下广泛传播,由此可能引发强烈的社会反响.因此,如何实现网络舆情事件快速发现与个性化监测需求的精准推送,成为了当前舆情的重点关注内容.对于舆情场景下用户交互信息稀疏导致的兴趣难以刻画的问题,提出了一种基于层次知识的话题推荐模型.模型通过引入层次知识来扩充语义增加话题之间的潜在信息关联,分别对层次知识、话题和用户建模得到对应的嵌入向量表示,再结合多层感知机匹配模型预测用户点击率.实验结果表明,该模型在与多个基线算法的对比中,在F1(the balanced F score)和AUC(the area under curve)指标的平均值上分别提升了6.7％和4.9％.

When analyzing various data in a given task,most of current researches only analyze single-source data and lack me-thods applied to multi-source data.But now data are becoming more abundant,therefore,this paper proposes a multi-source data fusion framework for fusing data from multiple network platforms.The data of the same platform contains text and various attri-butes,and there are also great differences in content and form among data of different platforms.Most existing network information mining methods only use part of the data in the same platform for analysis,and even ignore the interaction between the data of different platforms.Therefore,this paper proposes a data fusion framework,which can not only use more features of the same platform to improve the performance of a single platform,but also fuse the data features of different platforms to complement each other,thereby improving the performance of multiple platforms.This paper uses the task of event classification,and the abundant features effectively improve the F1 value,which verifies the effectiveness of the proposed multi-source data framework.

Metallic lithium is considered to be the potential anode for high energy density rechargeable Li batteries. Yet the growth of lithium dendrites impedes the industrial production of lithium metal batteries. Herein, we fabricate a dendrite-suppressed composite Li metal anode via introducing Ag particles as lithiophilic layer on the 3D copper foam (Cu-Ag). The Li ion prefers deposit on the surface of Cu-Ag foam, which is beneficial to the better adsorption of the Cu-Ag hierarchical heterojunction structure by the first-principles calculations. The heterojunction structure can further reduce the nucleation overpotential and surface current density of the composite Cu-Ag-Li anode to realize the homogeneous Li distribution for stable Li deposited/stripped. Thus, the Cu-Ag-Li composite electrode (CAL) presents better electrochemical performance in the symmetric battery and excellent rate performance in the full cell with a greatly enhanced capacity retention of 83% after 500 cycles. This strategy presents a general approach to suppress the growth of lithium dendrites and regulate the volume changes for long-life span lithium metal batteries.

利用事件报道描述内容高度相似的特点,提出了一种抽取式话题简短表示生成方法。把事件文档标题集中的标题作为处理对象,从不同的标题中抽取出保留原有语序的共性信息,并进一步融合这些共性信息,生成事件粒度的话题简短表示。在来自搜索引擎中的事件数据上,实验结果表明该方法能生成精练、准确、语义明确完整且可读性好的话题简短表示。

Ion doping strategy is a promising method to enhance the efficiency of Cu2ZnSn(S,Se)4 (CZTSSe) solar cells, however, most of the reported works focus on studying the single ion doping in CZTSSe absorber layers. Here, Li+&Ag+ and Li+&Cd2+ double-ion-doping strategy are applied to improve the microstructure and device performances. We found that the double-ion-doping strategy can obviously increase the grain size, reduce the thickness of fine-grain layers and enhance the device performances. Furthermore, the PCEs of Li+&Ag+ and Li+&Cd2+ doped CZTSSe solar cells are up to 8.87% and 8.39%, respectively. It shows an encouraging improvement of over 43.5% and 35.8% enhancement compared with the traditional method (6.18%), and significantly higher than single ion doping strategy (7.60%, 7.99% and 7.11% for Li+, Ag+, and Cd2+ doped CZTSSe solar cells). This work provides a new solution to improve the microstructure and device performances of CZTSSe solar cells.

Eradicating poverty is the primary goal for pursuing equitable and sustainable development of the world. Rural land consolidation (RLC) aims to achieve efficient and sustainable land use while promoting poverty alleviation. This study empirically examined the impact of RLC on multi-dimensional poverty, using the difference-indifferences (DID) method and survey data from impoverished households. Additionally, the role of human capital in moderating the impact of RLC on poverty alleviation is also emphasised. We found that (1) The RLC has had a positive and significant impact on the improvement of impoverished households' livelihoods in China. (2) Human capital (migrant worker, labour force, and education) has a positive moderating effect on the impact of RLC on poverty alleviation, and education exerts the most obvious moderating effect. We suggest that the Chinese government should not only continue to increase investment in and provide supporting policy for RLC, but also develop targeted RLC strategies. Education, labour capability, sustainable poverty reduction, sustainable land use, and sustainable rural development require sustained attention. It also could help to improve policy and decision-making for effective poverty reduction, sustainable rural development, and rural revitalisation.