This paper presents an adaptive neuron model utilizing information of local samples—the AUILS neuron model. Differing from traditional neuron models, the AUILS neuron model fully employs the experience samples information within the local range and well embodies the association and analogy functions of cerebrum. The neural network, which is simple in structure and fast in learning speed, can realize the nonlinear mapping relationship between multi-input and multi-output. Through investigating the properties of AUILS, we build a method based on the neuron model for loss prediction in the road traffic accident, which takes full advantage of expert experiences to estimate the loss value according to the accident number, deaths and wounds. It is verified that using the AUILS model, expert experiences can be well expressed in the prediction results.

In order to conquer the localization of the multi-layered feed forward neural networks, this paper presents a kind of neuron models utilizing information of local samples-the UILS neuron model, including an adaptive neuron model and a self-organization neuron model. Differing from traditional models, it fully employs the experience samples information within the local range and well embodies the association and analogy functions of cerebrum. Through investigating the properties of UILS and learning algorithm, we build a method based on the neuron model for forecasting the management state of enterprise according to the economic and technology indexes. It is verified that using the UILS model, expert experiences can be well expressed in the forecasting results.

Transportation ontology is the main means of knowledge sharing for Intelligent Transportation Systems (ITS). In order to handle the uncertainty of transportation knowledge, this paper proposes a new series of fuzzy ontology models. Fuzzy ontology introduces fuzzy concepts like values of property and focuses on the semantic relationships between fuzzy concepts, including set relations, order relations, equivalence relations and semantic association relations. Taking the traffic accident analysis system as an example, this paper builds the transportation knowledge ontology and knowledge sharing system with Web Ontology Language (OWL), which shows that these fuzzy ontology models facilitate knowledge management for ITS on the Semantic Web.

Under data-intensive and computation-intensive research paradigms, research data and software have become increasingly important scholarly outputs and key components of open science. The American Geophysical Union (AGU), a highly influential publisher in the Earth, space, and environmental sciences, has actively promoted policies and practices for data and software sharing. This study combines policy analysis with journal case studies to examine the evolution, major elements, and journal-level practices of the AGU’s data and software sharing policies. The findings show that the AGU has gradually expanded its policy focus from data sharing to integrated data and software sharing, establishing a policy framework centered on availability statements, data and software citation, and trusted repository recommendations. Evidence from the selected journal samples indicates that data availability statements have become common practice. In the sample, 98.2% of 227 articles from Earth and Space Science (ESS) and 96.4% of 503 articles from the Journal of Geophysical Research: Space Physics (JGR: Space Physics) included data availability statements. In addition, software availability statements and formal citations to data and software have shown increasing visibility over time. The AGU’s policy framework suggests that scholarly publishers can promote data and software sharing by clarifying policy requirements, improving author guidance, standardizing citation practices, and strengthening infrastructure support. These findings provide practical guidance for scholarly publishers, especially Chinese scholarly publishers, to improve open science policies and strengthen the standardization of data and software sharing.