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New energy vehicles (NEVs) are the main direction for the development of the global automobile industry. Evaluating and analyzing the competitiveness of new energy vehicle enterprises (NEVEs) is of great significance for promoting their development. In order to explore the current situation of NEVEs in Henan Province, this paper firstly constructs a competitiveness evaluation index system for NEVEs, comprising both quantitative and qualitative indexes. Then, a new definition of consistency, the consistency measure level, and corresponding improvement methods for interval multiplicative preference relations (IMPRs) are proposed. On this basis, fuzzy group decision-making models with IMPRs are constructed to deal with the ambiguity and uncertainty of the decision information, where consistency and consensus are both considered. In our case study, decision results are derived using Lingo 11.0 software. The results of this paper show that the degree of specialization has the greatest impact on the competitiveness of NEVEs, and some NEVEs are deficient in this regard. Related suggestions based on expert evaluation results are also provided. In addition, a comparison with other consistency improvement methods of IMPRs reveals that the methods proposed utilize the original information provided to decision-makers to the utmost degree. [ABSTRACT FROM AUTHOR]

Deep learning is often inadequate for achieving effective dialect recognition in situations where data are limited and model training is complex. Differences between Mandarin and dialects, such as the varied pronunciation variants and distinct linguistic features of dialects, often result in a significant decline in recognition performance. In addition, existing work often overlooks the similarities between Mandarin and its dialects and fails to leverage these connections to enhance recognition accuracy. To address these challenges, we propose the Decoupled Fusion Network (DFNet). This network extracts acoustic private and shared features of different languages through feature decoupling, which enhances adaptation to the uniqueness and similarity of these two speech patterns. In addition, we designed a heterogeneous information-weighted fusion module to effectively combine the decoupled Mandarin and dialect features. This strategy leverages the similarity between Mandarin and its dialects, enabling the sharing of multilingual information, and notably enhance the model's recognition capabilities on low-resource dialect data. An evaluation of our method on the Henan and Guangdong datasets shows that the DFNet performance has improved by 2.64% and 2.68%, respectively. Additionally, a significant number of ablation comparison experiments demonstrate the effectiveness of the method. [ABSTRACT FROM AUTHOR]