To address communication overhead, high confirmation latency, and inadequate network adaptability in high-frequency trading and large-scale node scenarios, this paper proposes a lightweight Byzantine Fault Tolerant Consensus Algorithm (LBFT-NA) based on neighborhood interaction and trust scoring mechanisms. The algorithm reconstructs the traditional network-wide broadcast-based consensus process into a localized proposal, weighted voting, and distributed threshold submission mechanism constrained by neighborhood size, thereby reducing communication complexity during consensus phases. A multi-dimensional trust scoring model is introduced to dynamically evaluate nodes' communication latency, processing capacity, packet loss rate, and historical reliability. The scoring results are mapped into voting weights and valid participation sets, enabling adaptive suppression of low-trust and abnormal nodes. Without relying on any centralized coordinator or fixed leadership structure, LBFT-NA achieves fully distributed consensus through trust-aware neighborhood interactions. Theoretical analysis demonstrates that the algorithm exhibits near-linear communication complexity growth under neighborhood size constraints. Simulation results show that compared to traditional PBFT and its improved subsets, LBFT-NA achieves better trade-offs between security and throughput in high-frequency trading scenarios with malicious nodes, making it suitable for resource-constrained and complex network conditions in consortium blockchain systems.
Path planning for unmanned surface vehicles (USVs) in risk-constrained maritime environments requires the simultaneous consideration of obstacle avoidance, environmental risk, safety clearance, and trajectory executability. Existing sampling-based planners can efficiently explore feasible regions; however, their expansion process is often dominated by geometric metrics and therefore does not fully capture the coupled influence of environmental risk and obstacle clearance. In addition, unconstrained post-smoothing may reduce clearance, violate risk constraints, or increase local heading variation. To address these issues, this paper proposes RCF-RRT*, a risk-clearance cost-field-guided RRT*-based planning framework with feasibility-certified spline refinement. First, a traversal cost field is constructed by integrating an obstacle-distance field with an environmental risk field, and the corresponding cost-to-go information is used to guide tree expansion. Second, an adaptive step-size strategy and hard-constrained point/edge feasibility checks are introduced to reject unsafe candidate nodes and connections under collision, risk, and clearance constraints. Third, a Pareto-dominance parent-selection strategy is designed to improve the tree structure by jointly considering path length, length-normalized risk exposure, normalized turning cost, and clearance margin. Finally, a feasibility-certified and quality-gated spline-refinement mechanism is developed to prevent unsafe or degraded smoothing results from being accepted. Simulation results show that the proposed Pareto parent-selection strategy reduces the average path length by approximately 5.43
Breast cancer is one of the most common malignant tumors,and its molecular typing can effectively guide individualized treatment.However,its clinical detection is relatively complex and time consuming.Fluorescence spectroscopy is convenient and efficient;hence,it is widely used for tumor detection.Meanwhile,studies involving molecular typing are few.In this study,a new method for detecting breast cancer via molecular typing is proposed,which is based on a principal component analysis and support vector machine(PCA-SVM)model combined with fluorescence spectroscopy.A laser-induced fluorescence detection system with a wavelength of 405 nm is constructed and the autofluorescence spectra of breast cancer tissues are measured.The results of Gaussian fitting show that the peak intensity of fluorophores in different subtypes of breast cancer are different.Through further analysis using the PCA-SVM model,the molecular subtypes of breast cancer are identified with an accuracy of 95.0%,a precision of 95.9%,and a balanced F-score of 94.9%.Moreover,the discriminant sensitivities of the four subtypes are 91%,94%,97%,and 100%.This scheme improves the analysis efficiency of high-dimensional spectral data and provides a new direction for the rapid identification of breast cancer via molecular typing.
An environmentally friendly conversion coating is prepared on AZ91D magnesium alloy surface by chemical conversion method using tannic acid as the coating forming agent to improve corrosion resistance. The acid regulating the pH value of the conversion solution is selected by contrast experiment. The pH value， reaction temperature， and conversion time are optimized by the Box-Behnken test， and the optimum process conditions are obtained. The effect of pH value on the corrosion resistance of the conversion coating is also discussed. CuSO4 pitting time and electrochemical experiments are used to judge the corrosion resistance， SEM and EDS are used to characterize the coating surface morphology and element composition of AZ91D magnesium alloy. The results show that the optimum process of tannic acid conversion coating is as follows： tannic acid content 10 g/L， pH value 2.7 （hydrochloric acid regulation）， reaction temperature 41 ℃， conversion time 15 min. The conversion coating has good corrosion resistance， uniform density， and the covering is complete. The coating is mainly composed of tannic acid hydrolysate and Mg2+ chelate. Box-Behnken test result shows that the pH value has a great influence on the corrosion resistance of the conversion coating. When the pH value is too low， the coating is rough and poorly compacted；while when the pH value is too high， the coating is thin and lacks continuity， which cannot completely cover the surface of the magnesium alloy.
