Malignant tumours represent a significant public health issue posing a grave threat to human life and health, garnering considerable attention in the field of biomedical science in recent years. Early screening and diagnosis of tumours provide patients with valuable treatment time, constituting a crucial measure in tumour prevention and management. Surface-enhanced Raman spectroscopy (SERS), with its advantages of ultra-high sensitivity, high precision, and multiplexing capabilities, has been widely applied in the detection of tumour markers. This paper examines SERS-based biosensors for three distinct tumour markers: prostate-specific antigen (PSA) for prostate cancer screening, alpha-fetoprotein (AFP) for primary liver cancer detection, and chromogranin A (CgA) for neuroendocrine tumour diagnosis. Compared to SERS technology, alternative early-stage tumour marker detection methods—such as chemiluminescent immunoassays, enzyme-linked immunosorbent assays, and real-time quantitative PCR—face limitations in widespread clinical adoption due to their higher costs, longer analysis times, and greater operational complexity. To address current clinical application challenges, future advancements in SERS-based biosensor detection of tumour markers will primarily be achieved through innovative improvements to the biosensor substrate.

Show more

The popularization speed of generative artificial intelligence technology is accelerating. The Retrieval Augmented Generation (RAG) system, with its feature of enhancing output accuracy by integrating external knowledge bases, has been widely applied in fields such as intelligent question answering and professional document generation. This study first conducted a correlation analysis and then carried out comparative experiments using multiple machine learning algorithms. The results showed that the KELM-LSTM-Transformer algorithm proposed in this paper demonstrated significant advantages in the comparison: Its accuracy rate, recall rate, precision rate and F1 value all exceed 99.5%, with some indicators approaching 100%, and the AUC is also close to 100%. This algorithm not only significantly outperforms relatively weak algorithms such as AdaBoost, decision tree, and KNN, but also outperforms medium and high-performance algorithms like GBDT, ExtraTrees, SVM, and XGBoost. Even compared with the outstanding random forest and multi-head attention LSTM, its core classification index still maintains a higher level, and the AUC index has a particularly prominent advantage, fully demonstrating its superiority in complex feature capture and sequence information modeling. This achievement provides an efficient algorithmic solution for the performance optimization of the RAG system and also offers a reference for technical research in related fields. It is of great significance for promoting the precise application of generative AI in professional scenarios.

Show more

To improve the accuracy of imputing missing maintenance engineering data, an ensemble learning method integrating three algorithms—XGBoost, Support Vector Machine (SVM), and Multilayer Perceptron (MLP)—is proposed. This method constructs three base learners (XGBoost, SVM, and MLP) to establish the nonlinear mapping relationship between maintenance measures and multi-dimensional influencing factors through supervised learning. A soft voting ensemble strategy based on probability weighting is adopted to optimize the comprehensive decision-making effect of the model output, and the imputation performance of each model is systematically evaluated. The research results show that the proposed ensemble learning method achieves an accuracy of 99% in missing data imputation, which is significantly superior to the single models MLP (54%), SVM (72%), and XGBoost (77%). This verifies the effectiveness and superiority of the method in imputing missing maintenance data.

Show more

This paper develops a local discontinuous Galerkin (LDG) method based on generalized numerical fluxes for solving traveling wave solutions of the modified Buckley-Leverett equation. To achieve efficient computation, the original equation is reformulated into a first-order system by introducing auxiliary variables, followed by spatial discretization using the DG method, while the explicit third-order Runge-Kutta method is adopted for temporal discretization. Based on the antisymmetry of the discrete spatial operator, the stability of the scheme under the energy norm is rigorously established. Numerical experiments demonstrate the robustness of the proposed method in handling convection-dominated problems with Riemann initial data, confirming its capability to accurately capture the shock structures.

Show more