Data streams with varying feature spaces have received extensive attention recently, while the common concept drift in them remains underexplored. Unsupervised concept drift detectors can report potential drifts without class labels, making them suitable for practical scenarios where labeling is usually costly and difficult. However, existing unsupervised detectors usually operate under fixed feature spaces. To address this limitation, a Matching Degree Histogram-based unsupervised detector for data streams with Varying Feature Spaces (MDH-VFS) is proposed. Changes in input features are refined into four scenarios, specifying the sources of concept drifts in such data streams. Based on this, MDH-VFS monitors the distribution of each feature independently using the fix-slide windows model. A matching degree-based histogram (MD-Histogram) supporting online updating is proposed to model data distribution. MD-Histogram requires no prior distributions and captures data change more sensitively than traditional histograms. The dissimilarity between two MD-Histograms is measured by the Hellinger distance, and drift is detected using an adaptive thresholding strategy. Both the drift positions and drift features can be reported. Experimental results show that MDH-VFS can not only effectively detect drifts in data streams with varying feature spaces (achieving average F1-score/MCC above 77% and outperforming nine existing detectors with improvements of at least 43%), but also improve the classification performance of downstream learning algorithms (reaching a maximum average accuracy of 88% and yielding up to 7.23% improvement).

Considering the current “Digital Finance- Commercial Bank Risks” is not clear and rarely studied for rural commercial banks. This study builds a two-way fixed effects model based on the annual data of 122 rural commercial banks from 2014-2020year in China and found that digital finance development has an “inverted U-shaped” relationship with the risk-taking of rural commercial banks. Further studies have found that market competition and risk preference for rural commercial banks have moderating and mediating effects. We have found that: (1) During the early stages, there is an increase in competition among rural commercial banks, leading them to adopt more stable operating strategies to ensure operations. which passively increases their risk-taking, thus verifying the left half of the “inverted U-shaped (2) In the future, digital finance is expected to break through an inflection point and competition will weaken. Rural commercial banks will increase their risk preferences to make up for previous profit losses. At this time, they will be better able to accurately identify risks, ultimately reducing their level of risk-taking. This verifies the right half of the “inverted U-shaped Overall, the Internal Mechanism among “Digital Finance Development, Market Competition, Risk Preferences, and Risk-Taking” has been confirmed.

Aiming at problems such as incomplete, inconsistent, and inaccurate requirements that often occur in the process of obtaining high-end equipment functional requirements, this paper presents a requirement quality improvement method. Referring to the agile development theory of requirements engineering, the quality improvement method constructs a functional requirement model based on user stories, defines the concept of functional requirement quality, designs functional requirement quality evaluation criteria, and constructs a functional requirement quality evaluation process. A case study and sensitivity analysis of new energy vehicle requirement development are conducted to confirm the feasibility and effectiveness of the method, and the experimental results show the superiority of the proposed method in improving the quality of high-end equipment functional requirements.