The inherent symmetry in traffic flow patterns plays a fundamental role in urban transportation systems. This study proposes a Dynamic Graph Convolutional Recurrent Adaptive Network (DGCRAN) for traffic flow prediction, leveraging symmetry principles in spatial–temporal dependencies. Unlike conventional models relying on static graph structures that often break real-world symmetry relationships, our approach introduces two key innovations respecting the dynamic symmetry of traffic networks: First, a Dynamic Graph Convolutional Recurrent Network (DGCRN) is proposed that preserves and adapts to the time-varying symmetry in node associations, and an Adaptive Graph Convolutional Network (AGCN) that captures the symmetric and asymmetric patterns between nodes. The experimental results on PEMS03, PEMS04, and PEMS08 datasets demonstrate that DGCRAN maintains superior performance symmetry across metrics: reducing MAE, RMSE, and MAPE by average margins of 12.7%, 10.3%, and 14.2%, respectively, compared to 15 benchmarks. Notably, the model achieves maximum MAE reduction of 21.33% on PEMS08, verifying its ability to model the symmetric and asymmetric characteristics in traffic flow dependencies while significantly improving prediction accuracy and generalization capability.

Spatiotemporal information and application have received considerable attention. Spatiotemporal information is often imprecise or uncertain; therefore, the establishment of a reasonable and effective fuzzy spatiotemporal data model is vital. The traditional tool-based modeling method cannot meet the needs of fuzzy spatiotemporal data modeling. XML has become the standard of Web data representation and exchange. Based on the fuzzy set and probability theory, this paper analyzes the characteristics of fuzzy spatiotemporal data and establishes a fuzzy spatiotemporal XML data model. We present a fuzzy spatiotemporal UML data model and provide the rules for converting the fuzzy spatiotemporal UML data model to the fuzzy spatiotemporal XML model. We demonstrate our model and the conversion by applying them to a meteorological event.

With the rapid development of the Web, a large number of electronic resources have been generated. Currently, XML has been an important tool for data representation and exchange over the Web. The incompleteness of information in the real-world is inherent. To deal with imprecise and uncertain data, fuzzy XML and fuzzy ontology modeling recently receive more attention. In order to represent the fuzzy information, we concentrate on fuzzy information modeling in a fuzzy XML model and fuzzy OWL 2 ontology in this paper. Furthermore, we propose an approach of transforming fuzzy OWL 2 ontologies (including structures and instances) into fuzzy XML models. Then we prove that the sementics of this transformation approach are preserved and propose a transforming example to explain the transforming process. This paper provides a new approach for the fuzzy XML modeling and fuzzy XML mapping based on the fuzzy OWL 2 ontologies.

In order to solve the problems in fuzzy computation tree logic model checking with cost operator, we propose a fuzzy decision process computation tree logic model checking method with cost. Firstly, we introduce a fuzzy decision process model with cost, which can not only describe the uncertain choice and transition possibility of systems, but also quantitatively describe the cost of the systems. Secondly, under the model of the fuzzy decision process with cost, we give the syntax and semantics of the fuzzy computation tree logic with cost operators. Thirdly, we study the problem of computation tree logic model checking for fuzzy decision process with cost, and give its matrix calculation method and algorithm. We use the example of medical expert systems to illustrate the method and model checking algorithm.

The task of named entity recognition (NER) is to identify entities in the text and predict their categories. In real-life scenarios, the context of the text is often complex, and there may exist nested entities within an entity. This kind of entity is called a nested entity, and the task of recognizing entities with nested structures is referred to as nested named entity recognition. Most existing NER models can only handle flat entities, and there has been limited research progress in Chinese nested named entity recognition, resulting in relatively few models in this direction. General NER models have limited semantic extraction capabilities and cannot capture deep semantic information between nested entities in the text. To address these issues, this paper proposes a model that uses the GlobalPointer module to identify nested entities in the text and constructs the IDCNNLR semantic extraction module to extract deep semantic information. Furthermore, multiple-head self-attention mechanisms are incorporated into the model at multiple positions to achieve data denoising, enhancing the quality of semantic features. The proposed model considers each possible entity boundary through the GlobalPointer module, and the IDCNNLR semantic extraction module and multi-position attention mechanism are introduced to enhance the model’s semantic extraction capability. Experimental results demonstrate that the proposed model achieves F1 scores of 69.617% and 79.285% on the CMeEE Chinese nested entity recognition dataset and CLUENER2020 Chinese fine-grained entity recognition dataset, respectively. The model exhibits improvement compared to baseline models, and each innovation point shows effective performance enhancement in ablative experiments.

Named Entity Recognition (NER) is a crucial component of Natural Language Processing (NLP). When dealing with the high diversity and complexity of the Chinese language, existing Chinese NER models face challenges in addressing word sense ambiguity, capturing long-range dependencies, and maintaining robustness, which hinders the accuracy of entity recognition. To this end, a Chinese NER model based on multi-level representation learning is proposed. The model leverages a pre-trained word-based embedding to capture contextual information. A linear layer adjusts dimensions to fit an Extended Long Short-Term Memory (XLSTM) network, enabling the capture of long-range dependencies and contextual information, and providing deeper representations. An adaptive multi-head attention mechanism is proposed to enhance the ability to capture global dependencies and comprehend deep semantic context. Additionally, GlobalPointer with rotational position encoding integrates global information for entity category prediction. Projected Gradient Descent (PGD) is incorporated, introducing perturbations in the embedding layer of the pre-trained model to enhance stability in noisy environments. The proposed model achieves F1-scores of 96.89%, 74.89%, 72.19%, and 80.96% on the Resume, Weibo, CMeEE, and CLUENER2020 datasets, respectively, demonstrating improvements over baseline and comparison models.