Short-time traffic flow prediction is essential for intelligent traffic management. By accurately predicting traffic conditions in the near future, it helps to alleviate congestion, improve road efficiency, reduce accidents, and support timely traffic control. Short-time traffic flow exhibits uncertainty and randomness, and this paper proposes an SVR model for short-time traffic flow prediction on non-main and branch roads, using correlations between associated roads to improve accuracy. Association Rule Analysis: First, we use Pearson correlation to identify strongly correlated roads. This step helps in understanding the relationships between different roads and their traffic patterns. SVR Model Construction: Second, based on the identified correlations, we construct an SVR model using traffic data from the target road and its associated roads. The model parameters are optimized using grid search and cross-validation to ensure the best performance. Simulation and Evaluation: Third, we conduct simulation experiments using real traffic data from Xi’an city. The performance of our model is evaluated using metrics such as root mean square error (RMSE), mean absolute error (MAE), and mean relative error (MRE). Simulation experiments show that our model outperforms existing methods. Specifically, our model achieved an RMSE of 11.422, an MAE of 7.017, and an MRE of 0.139. In comparison, other models tested in our study, such as LSTM, Random Forest, and Gradient Boosting Decision Tree (GBDT), had higher error values. For instance, the LSTM model had an RMSE of 14.5, an MAE of 8.2, and an MRE of 0.165; the Random Forest model had an RMSE of 13.8, an MAE of 7.8, and an MRE of 0.152; and the GBDT model had an RMSE of 13.2, an MAE of 7.5, and an MRE of 0.148. These results demonstrate that our proposed SVR model, combined with association rules, is highly effective in predicting short-time traffic flow on non-main and branch roads, which are often overlooked in existing research.

Barcodes are ubiquitous in manufacturing and logistics, but defects can reduce decoding efficiency and disrupt the supply chain. Existing studies primarily focus on a single barcode type or rely on small-scale datasets, limiting generalizability. We propose Y8-LiBAR Net, a lightweight two-stage framework for multi-type barcode defect detection. In stage 1, a YOLOv8n backbone localizes 1D and 2D barcodes in real time. In stage 2, a dual-branch network integrating ResNet50 and ViT-B/16 via hierarchical attention performs three-class classification on cropped regions of interest (ROIs): intact, defective, and non-barcode. Experiments conducted on the public BarBeR dataset, covering planar/non-planar surfaces, varying illumination, and sensor noise, show that Y8-LiBARNet achieves a detection-stage mAP@0.5 = 0.984 (1D: 0.992; 2D: 0.977) with a peak F1 score of 0.970. Subsequent defect classification attains 0.925 accuracy, 0.925 recall, and a 0.919 F1 score. Compared with single-branch baselines, our framework improves overall accuracy by 1.8–3.4% and enhances defective barcode recall by 8.9%. A Cohen’s kappa of 0.920 indicates strong label consistency and model robustness. These results demonstrate that Y8-LiBARNet delivers high-precision real-time performance, providing a practical solution for industrial barcode quality inspection.

The construction and enhancement of ecological niches are essential for small and medium-sized enterprises (SMEs), with digital platforms serving as key carriers for achieving niche improvement. However, SMEs encounter a “distinctiveness–conformity” paradox when leveraging digital platforms: they are expected to sustain differentiation to attract resource tilt while simultaneously integrating into the platform ecosystem to obtain a sense of belonging and complementary resources. Grounded in optimal distinctiveness theory, this study analyzes questionnaire data from 383 Chinese SMEs embedded in digital platforms. Results show that digital platform capabilities (integration and reconfiguration) enhance SMEs ecological niches through organizational agility and platform eco-embeddedness. Polynomial regression and response surface analyses reveal that balanced improvement in organizational agility and eco-embeddedness significantly strengthens niche enhancement, whereas imbalance between the two weakens it. This research clarifies how SMEs leverage digital platform capabilities to advance their ecological niches, offering theoretical and practical insights for achieving strategic balance between distinctiveness and conformity in digital platform ecosystems.