Guodong Li

Chaotic systems have been widely investigated for color image encryption because of their nonlinear dynamics, initial-condition sensitivity, and pseudorandom behavior. However, locating numerically robust parameter regions in high-dimensional hyperchaotic systems remains difficult, while many DNA-based schemes employ fixed or weakly varying rules. This study proposes a color image encryption scheme combining a 6D Lorenz–Sprott system optimized by particle swarm optimization (PSO), symbol-level feedback-dependent DNA transformation, and bidirectional cross-channel chained diffusion. The second-largest Lyapunov exponent is used as the optimization objective to locate parameter sets with at least two positive exponents. The selected system has the Lyapunov spectrum (0.8118, 0.2736, −0.0013, −4.8957, −8.5499, −12.6746) and retains two positive exponents under refined numerical settings and ±1% single-parameter perturbations. One hundred independently initialized sequences satisfy all 15 categories of the NIST SP 800-22 test suite. Tests on six images and three secret keys achieve exact reconstruction in all 18 cases. Across 180 randomly located one-bit plaintext perturbations, the mean NPCR and UACI are 99.6089% and 33.4554%, respectively. For the tested Baboon case, perturbing any initial-state component by approximately 10⁻¹⁴ prevents meaningful plaintext recovery. Ablation results further demonstrate the contributions of dynamic DNA transformation and bidirectional diffusion. The proposed scheme therefore provides reproducible hyperchaotic parameter modulation and strong empirical statistical and differential performance.

Accurate viral protein function annotation is essential for genomic surveillance, yet conventional retrieval-augmented generation (RAG) pipelines often fragment biological evidence into fixed-length text chunks, disrupting relationships among ORFs, annotations, structural domains, sequence motifs, residue mappings, and model-derived attention evidence. We propose ViroBioTree, a tree-structured biological evidence retrieval framework for downstream viral protein evidence review rather than a new primary annotation classifier. Built as an evidence organization layer on ViralMultiNet-derived ORF-level predictions and annotations, ViroBioTree converts sequence, annotation, structure, and attention evidence into typed biological nodes and traceable edges, then performs deterministic multi-channel recall, evidence-aware reranking, balanced TopK selection, rule-based verification, and node-cited report generation. In a demo benchmark, ViroBioTree achieved its strongest deterministic proxy performance on structure-explanation tasks, with Precision@K = 1.0, Recall@K = 1.0, and diversity = 0.52; these values reflect expected node-type and tag agreement rather than independent biological correctness. A bounded full-scale SARS-CoV-2 index contained 39,800 ORF rows, 80,000 attention records, 199,418 nodes, and 495,886 edges. In a stratified full20k diagnostic evaluation, ViroBioTree showed task-dependent advantages over LlamaIndex vector retrieval for conflict detection, evidence retrieval, and structure explanation, while LlamaIndex remained competitive or stronger for annotation-rich function annotation. A cross-family Influenza A Virus (IAV) diagnostic audit showed that the schema can represent IAV evidence namespaces while explicitly exposing missing formal ORF inputs, missing attention evidence, and unavailable residue/PDB assertions. Supplementary robustness, external sanity-check, diversity-risk, expert-evaluation, domain-tool positioning, and cross-family audit analyses supported traceability, report quality, and conservative evidence handling, but also showed that stable Precision@K under query perturbation does not necessarily imply stable retrieved evidence sets. ViroBioTree operates offline and deterministically, but does not address raw-read assembly, base calling, primary ORF prediction, or wet-lab validation. Its results should be interpreted as proxy and expert-reviewed evidence for traceable viral protein evidence retrieval and report generation rather than as direct validation of biological function annotation.

Accurate functional annotation of viral proteins is essential for genomic surveillance, yet rapid viral evolution causes “functional drift” that challenges conventional sequence-only models. These models often lack interpretability and struggle with fragmented sequences from complex environmental samples such as wastewater. We developed ViralMultiNet, a structure-aware multimodal framework that integrates multi-scale k-mer encodings (4–7-mers) with functional semantic embeddings derived from UniProt annotations. Using a curated Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) dataset of 66,011 samples from wastewater metagenomics (NCBI SRA: SRX28474964), we implemented gated multimodal fusion and triple knowledge distillation to transfer structural insights from a teacher to a student model. Model performance was evaluated via 5-fold cross-validation and external validation on emerging variants. Training efficiency was optimized using Low-Rank Adaptation and Flash Attention. ViralMultiNet achieved robust classification performance with a macro F1 score of 0.921 ± 0.004, accuracy of 0.928 ± 0.003, and AUC of 0.983 in cross-validation. The distilled student model matched teacher performance within a negligible margin (<0.003 F1 difference) while reducing training time by 40.4% (from 94.3 to 56.2 minutes per epoch). Interpretability analysis revealed that model attention peaks consistently aligned with experimentally validated functional domains of the SARS-CoV-2 Spike protein, including the receptor-binding domain (residues 319–541), S1/S2 cleavage site (681–685), and fusion peptide (816–835). ViralMultiNet offers a scalable, interpretable solution for viral protein function prediction. Its ability to generalize across variants and map attention to critical biological regions supports deployment in wastewater-based early warning systems, enhancing global pandemic preparedness.

In the digital economy era, digital inclusive finance represents a paradigmatic reconstruction of key economic information flows. This study integrates multi-source panel data of 27 cities in the Yangtze River Delta from 2011 to 2023. By constructing an economic geography composite spatial weight matrix and a nonlinear spatial panel model, this study analyzes the impact of the diffusion of digital inclusive financial information on industrial structure upgrading. The results show that: (1) digital financial inclusion exerts a significant direct effect and spatial spillover effect on industrial structure; (2) the local effect exhibits a “U-shaped” curve with an accelerating characteristic on the right side; the spatial spillover effect demonstrates an “inverted U-shaped” curve, revealing the transformation law and threshold effect of the diffusion and aggregation of digital financial information benefits; (3) digital payment and digital credit constitute the core information flows driving the coordinated upgrading of industries; and (4) entrepreneurial activity exerts a partial mediating effects, and exhibits a spatial mediating effect, while the technological innovation only demonstrates a significant local mediating effect. The findings provide quantitative evidence to support the optimization of the digital financial information ecosystem and the realization of coordinated industrial upgrading in the Yangtze River Delta.