5results about How to "High scientific value" patented technology

The invention discloses a confocal laser microscope detection method for micro-plastic surface biofilm components and biomass, and belongs to the field of micro-plastic pollution monitoring. The method comprises the following steps: carrying out stratified sampling and polymer component identification on micro-plastic in an environmental water body; the method comprises the following steps: marking algae, blue-green algae, bacteria and extracellular polymeric substances in a biological membrane through specific fluorescent staining, carrying out high-resolution three-dimensional imaging by utilizing a confocal laser microscope, and realizing batch quantitative analysis of each component of the biological membrane by adopting ImageJ software. The technical problems that an existing detection method is insufficient in parameter dimension, evaluation result deviation is caused only based on partial component quantification, and the influence of the overall structure of the biological membrane on the micro-plastic migration behavior cannot be truly reflected are solved. According to the method, the biomass and spatial distribution of each component of the biological membrane can be comprehensively and accurately quantified, the accuracy of analyzing the vertical migration mechanism of the micro-plastic is improved, and multi-dimensional data support is provided for ecological risk assessment.

The application discloses a method for constructing a lung cancer xenograft tumor model, which comprises the following steps: S1, constructing an asthma model by using a sensitizing agent causing asthma on a rodent; S2, establishing the lung cancer xenograft tumor model by transplanting lung cancer cells from the rodent into the asthma model. The lung cancer xenograft tumor model constructed by the application can simulate the findings of clinical research in basic research, and improve the scientific value and application value of the lung cancer xenograft tumor model.

The application provides a land use and cover grid data downscaling method and system based on AlphaEarth Foundations and machine learning in future years, which identifies coarse scale pixels in which land classes change in future years by comparing coarse scale data in future years and a base year; maps the coarse scale pixels to fine scale data in the base year to determine a target area; counts the number of fine scale pixels of each land class in the target area to obtain the net increase of fine scale pixels of each land class in future years; in the target area, generates a conversion probability corresponding to each land class for each fine scale pixel by using a land class conversion probability prediction model; constructs an optimization model with the sum of the conversion probabilities corresponding to the land classes to which all fine scale pixels in the target area are assigned as an objective function, and solves the optimization model to obtain an optimal land class assignment result of the fine scale pixels in the target area; and fuses the optimal land class assignment result with the fine scale data in the base year to generate fine scale land use and cover grid data in future years after downscaling.

This invention relates to the field of medical and health information processing technology, specifically disclosing an early risk stratification and warning system and method for diabetic nephropathy. The system includes a data fusion preprocessing module, a multi-scale causal network construction module, a dynamic risk trajectory calculation module, and a stratified warning decision-making module, connected sequentially. By fusing multimodal data to construct an interpretable causal network and utilizing a network-constrained prediction model, it achieves dynamic trajectory prediction of future renal function indicators and comprehensive risk indices. Based on the prediction results, it triggers different levels of risk warnings and outputs decision support information, thereby achieving early, dynamic, and causally interpretable risk stratification and warning.

This application relates to the field of underwater robot navigation and environmental perception technology, and discloses a positioning method and system for underwater environments. The positioning method includes: acquiring flow velocity profile data, temperature data, salinity data, and depth data at sampling points; obtaining normalized flow field feature vectors and physical feature vectors corresponding to each sampling point; calculating the cosine similarity of the normalized flow field feature vectors and the physical feature similarity of the normalized three-dimensional physical feature vectors; weightedly fusing the cosine similarity and physical feature similarity to obtain a fusion similarity; combining the fusion similarity with preset constraint rules to obtain position revisit constraint factors; adding the position revisit constraint factors to the factor graph to construct a complete factor graph model; and using the iSAM2 algorithm to solve the factor graph to obtain the globally optimized position sequence of the underwater vehicle. This method can correct cumulative drift errors and effectively ensure the reliability of the AUV's trajectory in visually and acoustically limited scenarios.