In June 4, 2020, Corona Virus Disease 2019(COVID-19) cases in Wuhan were cleared, and the epidemic situation was basically controlled. Such public safety infectious disease includes influences great pressure on the national economy. At present, some countries and regions in the world are still in epidemic situation, and there is an urgent need to judge the infection situation and travel risk in the region. In a relatively fine scale down to perceive the surrounding situation, and then rational zoning decisions to promote the resumption of production and work. In this study, indicators for the evaluation of COVID-19 epidemic were constructed using multi-sourced data. A computational evaluation of 736 fine-grained grids was performed using the GeoDetector model and the decision tree model. The study found that the risk level in older neighborhoods was much higher than in newer neighborhoods; the population density was the most important determinant of infection; the number of urban people slumped to 37% of that in usual times according to Tencent data after the “city closure”; The model this paper used portrays the major factor in defining low-risk areas and high-risk areas, and offers suggestions and assessment from a geographical perspective to fight COVID-19, thus presenting great practical value.

The online public opinion is the sum of public views, attitudes and emotions spread on major public health emergencies through the Internet, which maps out the scope of influence and the disaster situation of public health events in real space. Based on the multi-source data of COVID-19 in the context of a global pandemic, this paper analyzes the propagation rules of disasters in the coupling of the spatial dimension of geographic reality and the dimension of network public opinion, and constructs a new gravity model-complex network-based geographic propagation model of the evolution chain of typical public health events. The strength of the model is that it quantifies the extent of the impact of the epidemic area on the surrounding area and the spread of the epidemic, constructing an interaction between the geographical reality dimension and online public opinion dimension. The results show that: The heterogeneity in the direction of social media discussions before and after the “closure” of Wuhan is evident, with the center of gravity clearly shifting across the Yangtze River and the cyclical changing in public sentiment; the network model based on the evolutionary chain has a significant community structure in geographic space, divided into seven regions with a modularity of 0.793; there are multiple key infection trigger nodes in the network, with a spatially polycentric infection distribution.

The urban structure is the spatial reflection of various economic and cultural factors acting on the urban territory. Different from the physical structure, urban structure is closely related to the population mobility. Taxi trajectories are widely distributed, completely spontaneous, closely related to travel needs, and massive in data volume. Mining it not only can help us better understand the flow pattern of a city, but also provides a new perspective for interpreting the urban structure. On the basis of massive taxi trajectory data in Chengdu, we introduce a network science approach to analysis, propose a new framework for interaction analysis, and model the intrinsic connections within cities. The spatial grid of fine particles and the trajectory connections between them are used to resolve the urban structure. The results show that: (1) Based on 200,000 taxi trajectories, we constructed a spatial network of traffic flow using the interaction analysis framework and extracted the cold hot spots among them. (2) We divide the 400 traffic flow network nodes into 6 communities. Community 2 has high centrality and density, and belongs to the core built-up area of the city. (3) A traffic direction field is proposed to describe the direction of the traffic flow network, and the direction of traffic flow roughly presents an inflow from northeast to southwest and an outflow from southeast to northwest of the study area. The interaction analysis framework proposed in this study can be applied to other cities or other research areas (e.g., population migration), and it could extract the directional nature of the network as well as the hierarchical structure of the city.

Purpose: This study aims to extract knowledge of ancient Chinese scientific and technological documents bibliographic summaries (STDBS) and provide the knowledge graph (KG) comprehensively and systematically. By presenting the relationship among content, discipline, and author, this study focuses on providing services for knowledge discovery of ancient Chinese scientific and technological documents.Design/methodology/approach: This study compiles ancient Chinese STDBS and designs a knowledge mining and graph visualization framework. The authors define the summaries' entities, attributes, and relationships for knowledge representation, use deep learning techniques such as BERT-BiLSTM-CRF models and rules for knowledge extraction, unify the representation of entities for knowledge fusion, and use Neo4j and other visualization techniques for KG construction and application. This study presents the generation, distribution, and evolution of ancient Chinese agricultural scientific and technological knowledge in visualization graphs.Findings: The knowledge mining and graph visualization framework is feasible and effective. The BERT-BiLSTM-CRF model has domain adaptability and accuracy. The knowledge generation of ancient Chinese agricultural scientific and technological documents has distinctive time features. The knowledge distribution is uneven and concentrated, mainly concentrated on C1-Planting and cultivation, C2-Silkworm, and C3-Mulberry and water conservancy. The knowledge evolution is apparent, and differentiation and integration coexist.Originality/value: This study is the first to visually present the knowledge connotation and association of ancient Chinese STDBS. It solves the problems of the lack of in-depth knowledge mining and connotation visualization of ancient Chinese STDBS.

Dual-Path RNN(DPRNN) has achieved great progress in single-channel speech separation. However, RNN-based model needs to pass information through intermediate states and it does not allow parallel computing. Meanwhile, inter-chunk modeling of DPRNN only modeled between multiples of the chunk length, which means the underutilization of contextual information. To address these problems, we propose a multi-scale dual-path temporal convolutional network, DPTCN-ATPP. DPTCN-ATPP utilizes a stacked of one-dimensional dilated convolutions (1-D CNNs) instead of RNN to learn local and global information. Besides, DPTCN-ATPP applies parallel encoders to extract multi-scale input features. Finally, DPTCN-ATPP introduces the ATPP block to summarize multi-scale deep features. The experiment results show that DPTCN-ATPP achieves 19.6dB and 19.8dB on the metrics of scale-invariant source-to-noise ratio(SI-SNR) and source-to-distortion (SDR) respectively, outperforming baseline DPRNN and single-scale TCN (Conv-TasNet).

Purpose: This research aims to extract structured knowledge from bibliographic summaries of ancient Chinese agricultural books while addressing data imbalance issues. Through the recognition of structural functions, this approach facilitates accurate comprehension of summary content. Design/methodology/approach: The study develops a framework for categorizing structural functions of summaries into six components: title information, responsible person information, book formation process, edition information, document content and evaluative information. To address the few-shot learning challenge in Chinese agricultural bibliographic summaries, the study implements data augmentation for underrepresented categories. Four methods are employed to resolve class imbalance: synonym replacement, random swapping, random deletion and back translation. Large language models, such as ChatGPT, are utilized to augment samples in minority classes, with t-SNE visualization employed to validate the augmentation’s effectiveness. Then BERT-BiLSTM model is implemented for structural function recognition in agricultural bibliographic summaries. Findings: Data enhancement based on large language models can effectively extend the original samples and retain the semantic relationship of the original samples. On this basis, the structural function recognition experiment using BERT-BiLSTM model can identify the structural function of the Chinese agricultural bibliographic summaries more accurately. Originality/value: This research presents an innovative method for structuring Chinese agricultural bibliographic summaries and pioneers the application of large language models in this domain. It addresses the challenge of making historical Chinese agricultural bibliographic summaries more accessible and transmittable.