Research on the structure function recognition of PLOS

PurposeThe present study explores and investigates the efficiency of deep learning models in identifying discourse structure and functional features and explores the potential application of natural language processing (NLP) techniques in text mining, information measurement, and scientific communication.MethodThe PLOS literature series has been utilized to obtain full-text data, and four deep learning models, including BERT, RoBERTa, SciBERT, and SsciBERT, have been employed for structure-function recognition.ResultThe experimental findings reveal that the SciBERT model performs outstandingly, surpassing the other models, with an F1 score. Additionally, the performance of different paragraph structures has been analyzed, and it has been found that the model performs well in paragraphs such as method and result.ConclusionThe study's outcomes suggest that deep learning models can recognize the structure and functional elements at the discourse level, particularly for scientific literature, where the SciBERT model performs remarkably. Moreover, the NLP techniques have extensive prospects in various fields, including text mining, information measurement, and scientific communication. By automatically parsing and identifying structural and functional information in text, the efficiency of literature management and retrieval can be improved, thereby expediting scientific research progress. Therefore, deep learning and NLP technologies hold significant value in scientific research

A Community Detection Algorithm Based on Topology Potential and Spectral Clustering

Community detection is of great value for complex networks in understanding their inherent law and predicting their behavior. Spectral clustering algorithms have been successfully applied in community detection. This kind of methods has two inadequacies: one is that the input matrixes they used cannot provide sufficient structural information for community detection and the other is that they cannot necessarily derive the proper community number from the ladder distribution of eigenvector elements. In order to solve these problems, this paper puts forward a novel community detection algorithm based on topology potential and spectral clustering. The new algorithm constructs the normalized Laplacian matrix with nodes’ topology potential, which contains rich structural information of the network. In addition, the new algorithm can automatically get the optimal community number from the local maximum potential nodes. Experiments results showed that the new algorithm gave excellent performance on artificial networks and real world networks and outperforms other community detection methods

GujiBERT and GujiGPT: Construction of Intelligent Information Processing Foundation Language Models for Ancient Texts

In the context of the rapid development of large language models, we have meticulously trained and introduced the GujiBERT and GujiGPT language models, which are foundational models specifically designed for intelligent information processing of ancient texts. These models have been trained on an extensive dataset that encompasses both simplified and traditional Chinese characters, allowing them to effectively handle various natural language processing tasks related to ancient books, including but not limited to automatic sentence segmentation, punctuation, word segmentation, part-of-speech tagging, entity recognition, and automatic translation. Notably, these models have exhibited exceptional performance across a range of validation tasks using publicly available datasets. Our research findings highlight the efficacy of employing self-supervised methods to further train the models using classical text corpora, thus enhancing their capability to tackle downstream tasks. Moreover, it is worth emphasizing that the choice of font, the scale of the corpus, and the initial model selection all exert significant influence over the ultimate experimental outcomes. To cater to the diverse text processing preferences of researchers in digital humanities and linguistics, we have developed three distinct categories comprising a total of nine model variations. We believe that by sharing these foundational language models specialized in the domain of ancient texts, we can facilitate the intelligent processing and scholarly exploration of ancient literary works and, consequently, contribute to the global dissemination of China's rich and esteemed traditional culture in this new era.Comment: 22pages,0 figur

Transcriptome Analysis of Arabidopsis thaliana in Response to Plasmodiophora brassicae during Early Infection

Clubroot disease is a serious threat to cruciferous plants worldwide, especially to oilseed rape. However, knowledge on pathogenic molecular mechanisms and host interaction is limited. We presume that the recognition between Arabidopsis thaliana and Plasmodiophora brassicae occurs at the early stage of infection and within a relatively short period. In this study, we demonstrated changes on gene expression and pathways in A. thaliana during early infection with P. brassicae using transcriptome analysis. We identified 1,903 and 1,359 DEGs at 24 and 48 h post-inoculation (hpi), respectively. Flavonoids and the lignin synthesis pathways were enhanced, glucosinolates, terpenoids, and proanthocyanidins accumulated and many hormonal- and receptor-kinase related genes were expressed, caused by P. brassicae infection during its early phase. Therefore, the early interaction between A. thaliana and P. brassicae plays an important role in the entire infection process. The results provide a new contribution to a better understanding of the interaction between host plants and P. brassicae, as well as the development of future measures for the prevention of clubroot

Festividades sazonais e comunitárias no currículo em educação de infância

Relatório apresentado para a obtenção do grau de Mestre em Educação Pré-EscolarO presente relatório engloba o percurso formativo ao longo da prática de ensino supervisionada em contexto de Creche e Jardim de Infância, refletindo as aprendizagens realizadas e as dificuldades sentidas, assim como a emergência de questões decorrentes da prática. A problemática estudada, centrada nas valências de Creche e de Jardim de Infância, refere-se à Importância das festividades sazonais e comunitárias num currículo em Educação de Infância. A partir da abordagem do significado e importância das festividades feita por alguns autores, o presente estudo tem como objetivo geral compreender a importância atribuída pelas educadoras de infância à celebração das festividades, quais as datas que privilegiam, os motivos que justificam essas escolhas, assim como as implicações que identificam para as aprendizagens e para o desenvolvimento infantil. O estudo, qualitativo e de carácter exploratório, utiliza para a recolha de dados a técnica do inquérito por entrevista. Conclui-se que nas duas valências as profissionais integram no trabalho educativo a comemoração de determinados dias festivos, garantindo a sua importância e considerando que promovem aprendizagens em todas as áreas do desenvolvimento.The present report covers the formative path along the supervised teaching practice in the context of Nursery and Kindergarten, reflecting the learning achieved and difficulties experienced as well as the emergence of issues arising from practice. The studied problem, focusing on the valences of Nursery and Preschool, refers to the importance of seasonal and community festivities in a curriculum in Childhood Education. From the approach to the meaning and importance of the festivities by some authors, this study has the overall objective of understanding the importance given by the kindergarten teachers to the celebration of the festivities, which dates that privilege, the reasons for these choices, as well as identifying the implications for learning and child development. The study, of qualitative and exploratory nature, uses for data collection technique interview survey. It is concluded that on both valences, the kindergarten teachers integrate on the educational work the celebration of certain festive days, ensuring their importance as promoters of learning in all areas of development.info:eu-repo/semantics/publishedVersio

A Novel Local Maximum Potential Point Search Algorithm for Topology Potential Field

Topology potential field is a novel model to describe interaction and association of network nodes, which has attracted plenty of attention in community detection, node importance evaluation and network hot topics detection. The local maximum potential point search is a critical step for this research. Hill-climbing is a traditional algorithm for local maximum point search, which may leave out some local maximum potential points, and search performance is greatly influenced by initial node sequence. Based on the detailed analysis of local maximum potential points' characteristics, this paper presents a novel local maximum potential point search algorithm. The results of simulation experiments showed that the new algorithm has better performance than the traditional hill-climbing method. It can find all local maximum potential points with high search efficiency

An Improved Topology-Potential-Based Community Detection Algorithm for Complex Network

Topology potential theory is a new community detection theory on complex network, which divides a network into communities by spreading outward from each local maximum potential node. At present, almost all topology-potential-based community detection methods ignore node difference and assume that all nodes have the same mass. This hypothesis leads to inaccuracy of topology potential calculation and then decreases the precision of community detection. Inspired by the idea of PageRank algorithm, this paper puts forward a novel mass calculation method for complex network nodes. A node’s mass obtained by our method can effectively reflect its importance and influence in complex network. The more important the node is, the bigger its mass is. Simulation experiment results showed that, after taking node mass into consideration, the topology potential of node is more accurate, the distribution of topology potential is more reasonable, and the results of community detection are more precise

Analysis of Spatio-Temporal Evolution Characteristics of Drought and Its Driving Factors in Yangtze River Basin Based on SPEI

Using a dataset of 114 meteorological stations in the Yangtze River Basin from 1980–2019, the standardized precipitation evapotranspiration index (SPEI) was calculated based on the Penman-Monteith evapotranspiration model for multiple time scales, and the spatial and temporal evolution characteristics and driving factors of drought in the Yangtze River Basin were analyzed by combining spatial and temporal analysis methods as well as geodetector. The main results obtained are as follows: (1) The climate of the Yangtze River Basin is an overall wet trend, and the trend of summer drought is more similar to the annual scale trend. (2) Most areas in the Yangtze River Basin showed mild drought or no drought, and there is little difference in drought condition among the Yangtze River Basin regions. The areas with drought conditions are mainly distributed in the southwest and east of the Yangtze River Basin. (3) There are significant seasonal differences in drought conditions in all regions, and the drought condition is more different in autumn compared to spring, summer and winter. (4) The average annual precipitation and elevation factors are the dominant driving factors of drought in the Yangtze River Basin, and the double-factor interaction has a greater influence on the drought variation in the Yangtze River Basin than the single-factor effect, indicating that the difference of drought condition in the Yangtze River Basin is the result of the combination of multiple factors

Safety-Risk Assessment for TBM Construction of Hydraulic Tunnel Based on Fuzzy Evidence Reasoning

Due to multiple factors influencing the construction safety of TBM hydraulic tunnels, risk assessment is a critical point of a construction management plan to avoid possible risks. In this paper, a safety-risk evaluation index system of TBM construction for hydraulic tunnels is built based on the identification and analysis of possible sources of risk in techniques, geologic, equipment, management, and accidents. Considering the influence of factors such as the experience level and the expertise of decision makers, a combination assignment method of index weights is proposed based on binary semantics. On the basis of a fuzzy normal distribution used as the subordinate function distribution of fuzzy evaluation levels, the subordinate function distribution of fuzzy evaluation levels under multi-level intersection situations is introduced, and a comprehensive evaluation model of safety risks for TBM tunnel construction is built. The validity and practicality of the evaluation model is examined with the combination of a long-distance water conveyance tunnel project. Results show that the construction safety-risk of the TBM hydraulic tunnel project belongs to the middle-high level, and the safety accident risk belongs to the low level. The study provides guidance of evaluation and control of risks for this tunneling construction being successfully completed

An Autonomous Attack Guidance Method with High Aiming Precision for UCAV Based on Adaptive Fuzzy Control under Model Predictive Control Framework

With its superior performance, the unmanned combat air vehicle (UCAV) will gradually become an important combat force in the future beyond-visual-range (BVR) air combat. For the problem of UCAV using the BVR air-to-air missile (AAM) to intercept the highly maneuvering aerial target, an autonomous attack guidance method with high aiming precision is proposed. In BVR air combat, the best launching conditions can be formed through the attack guidance and aiming of fighters, which can give full play to the combat effectiveness of BVR AAMs to the greatest extent. The mode of manned fighters aiming by manual control of pilots is inefficient and obviously not suitable for the autonomous UCAV. Existing attack guidance control methods have some defects such as low precision, poor timeliness, and too much reliance on manual experience when intercepting highly maneuvering targets. To address this problem, aiming error angle is calculated based on the motion model of UCAV and the aiming model of BVR attack fire control in this study, then target motion prediction information is introduced based on the designed model predictive control (MPC) framework, and the adaptive fuzzy guidance controller is designed to generate control variable. To reduce the predicted aiming error angle, the algorithm iteratively optimizes and updates the actual guidance control variable online. The simulation results show that the proposed method is very effective for solving the autonomous attack guidance problem, which has the characteristics of adaptivity, high timeliness, and high aiming precision