This paper aims to address the increasingly severe security threats in financial systems by proposing a novel financial attack detection model, Finsformer. This model integrates the advanced Transformer architecture with the innovative cluster-attention mechanism, dedicated to enhancing the accuracy of financial attack behavior detection to counter complex and varied attack strategies. A key innovation of the Finsformer model lies in its effective capture of key information and patterns within financial transaction data. Comparative experiments with traditional deep learning models such as RNN, LSTM, Transformer, and BERT have demonstrated that Finsformer excels in key metrics such as precision, recall, and accuracy, achieving scores of 0.97, 0.94, and 0.95, respectively. Moreover, ablation studies on different feature extractors further confirm the effectiveness of the Transformer feature extractor in processing complex financial data. Additionally, it was found that the model’s performance heavily depends on the quality and scale of data and may face challenges in computational resources and efficiency in practical applications. Future research will focus on optimizing the Finsformer model, including enhancing computational efficiency, expanding application scenarios, and exploring its application on larger and more diversified datasets.

Maize aerial root architecture is a key trait for assessing yield stability due to their critical role in biological nitrogen fixation and mechanical lodging resistance. However, automated identification in field environments remains challenging due to small targets, severe occlusion, and the limitations of traditional destructive measurement methods. To address these issues, we initially established a high-definition maize video dataset (380 GB, 1900 mins) using the Maizrob, and subsequently constructed the first standardized aerial root image library (12 million images) to fill the domain gap. Based on these datasets, a hierarchical identification framework was developed, utilizing YOLOv10 for feature extraction and a novel Occlusion-Aware Multi-Frame Fusion (OMF) strategy for decision-making. This strategy synthesizes multi-view information to specifically mitigate the impact of cluttered backgrounds and visual blocking. Experimental results demonstrate that the OMF strategy significantly improves robustness, raising Cohen's Kappa coefficient from 0.34 to 0.64 in ablation studies. Consequently, the overall framework achieves a plant-level accuracy of 90.7% in complex field scenarios. This performance represents a substantial improvement over traditional single-frame detection models (65.6%-75.5%), confirming the effectiveness of the dataset-supported cascade architecture. This study provides an accurate, non-destructive technical solution for maize phenotyping and offers a reference for the automatic identification of local micro-traits in other crops.

In this paper, we investigate Turing instability and spatiotemporal pattern formation in a toxic-phytoplankton–zooplankton model incorporating Riesz fractional diffusion. By replacing classical Laplacians with fractional-order operators ${(-\Delta )}^{\varphi /2}$ and ${(-\Delta )}^{\psi /2}$, we establish a framework to capture species-specific anomalous diffusion in aquatic ecosystems. Theoretical analysis demonstrates that the fractional orders significantly modulate the Turing bifurcation threshold and the range of unstable modes. Numerical simulations, implemented via a DCT-based fractional finite difference scheme, reveal that: (1) fractional diffusion triggers pattern formation below the classical critical threshold, generating complex mixed structures unattainable in integer-order models; (2) the two fractional orders exert opposing influences on pattern morphology—decreasing the phytoplankton order $\varphi$ promotes a transition from stripes to hexagonal spots. Our results suggest that anomalous diffusion is a key mechanism for ecological pattern selection, providing new insights into the spatiotemporal complexity of plankton populations in heterogeneous environments.