6 results about "Dependable computing" patented technology

The invention discloses a synthetic lethal prediction method based on non-generality forgetting and low-rank interaction multi-mode adaptive fusion, and relates to the technical field of bioinformatics, computational biology and drug target screening. The method comprises the following steps: firstly, constructing a multi-view gene embedding expression based on a gene graph structure, a hypergraph structure and a knowledge graph; then constructing a difference vector and an interaction vector for any gene pair so as to characterize the difference and potential complementary relationship between the genes; multi-source information fusion is realized by using a deep learning model containing interactive attention, a forgetting gate and a multi-modal fusion mechanism; on this basis, multi-task optimization is carried out through joint classification loss, modal interaction loss and redundancy suppression loss, and optimal discrimination is realized through a dynamic threshold search strategy; and finally, outputting a sorting result of the candidate synthetic lethal gene pairs according to the prediction score. According to the method, the accuracy and generalization ability of synthetic lethal relationship prediction can be effectively improved, and a high-reliability calculation auxiliary tool is provided for anti-cancer drug target screening and gene therapy strategy design.

The application discloses a multi-modal graph fusion driven spatial transcriptome region identification method, and belongs to the field of biomedical informatics and computational biology. The method comprises the following steps: step 1, data acquisition; step 2, data processing; step 3, multi-modal adjacency matrix generation; step 4, differentiable multi-modal graph structure fusion; step 5, variational graph autoencoder training; and step 6, clustering. By constructing a spatial domain identification framework based on multi-modal graph fusion and variational autoencoder, the application significantly improves the accuracy, continuity and biological interpretability of spatial transcriptome clustering analysis, effectively solves the deficiencies of existing methods in multi-modal fusion, graph structure optimization and feature discriminability, and provides a more reliable computing tool for in-depth revealing of tissue spatial heterogeneity in the fields of brain science, tumor microenvironment analysis and other frontier biomedical researches.

The invention discloses a spatial transcriptome region identification method driven by multi-modal graph fusion, and belongs to the field of biomedical informatics and computational biology. The method comprises the following steps: step 1, data acquisition; 2, data processing; step 3, generating a multi-modal adjacent matrix; 4, carrying out the structure fusion of the differentiable multi-modal graph; step 5, variational graph self-encoder training is carried out; and step 6, clustering. According to the method, the spatial domain recognition framework based on multi-modal graph fusion and the variational autoencoder is constructed, so that the accuracy, continuity and biological interpretability of spatial transcriptome clustering analysis are remarkably improved, and the defects of an existing method in the aspects of multi-modal fusion, graph structure optimization and feature discrimination are effectively overcome; and a more reliable calculation tool is provided for deeply revealing tissue spatial heterogeneity in the field of advanced biomedical research such as brain science, tumor microenvironment analysis and the like.

The invention relates to the technical field of power equipment multi-physical field calculation and intelligent operation and maintenance, and discloses a power equipment electromagnetic loss rapid prediction method, which comprises the following steps: performing physical driving region decomposition on a three-dimensional model of equipment, and constructing a working condition and node-level loss sample set by using a high-fidelity electromagnetic solver; for grid nodes of each region, constructing a prior enhanced feature vector containing geometric and physical interaction items, and training a dual-stage gradient boosting decision tree proxy model; exporting the model as a general format, integrating the model into a heat flow solver, deducing heat source distribution in real time in coupling calculation, and executing online calibration according to prediction uncertainty. According to the method, node-level high-precision prediction of electromagnetic loss is realized through physical prior feature enhancement and double-stage modeling, time-consuming full-order finite element iteration is replaced, electromagnetic heat flow coupling calculation is compressed from hour level to second level, and efficient and reliable calculation support is provided for digital twinning and real-time operation and maintenance of power equipment.

A system and methods are disclosed for end-to-end provable guarantees for safety critical computer platforms. The technical problem addressed by this invention is the need to provide assurance that a complex safety-critical computer platform behaves as intended and is secure, particularly in safety-critical contexts where errors or malicious attacks can have devastating effects. Despite advances in methodologies and toolchains for high-assurance software production, there remains a significant gap between theoretical guarantees and practical implementation at the operation instruction level (e.g., Assembly language instruction, byte-code instruction, digital circuit state-machine instruction etc.). The present invention overcomes this limitation by providing a system for verifying key security properties for safety-critical computer platforms at the operation instruction level without reliance on custom generation pipelines or certified compilers, thereby enabling the practical development of more secure and reliable computer platforms.

The invention discloses a localized environment-based cross-department comprehensive supervision platform and a data processing method. The system architecture comprises an infrastructure layer, a data layer, a support layer, an application layer, a display layer and a security processing module. The infrastructure layer provides safe and reliable computing storage network resources by adopting a creative cloud and an e-government affair external network; the data layer stores and manages supervision data through a supervision basic library and a supervision thematic library; the support layer provides unified tool support and data support capabilities. According to the invention, a localized unified platform is constructed, the problems of cross-department data sharing and business collaboration are solved, interconnection and on-demand sharing, efficient collaboration and accurate differentiated supervision of supervision data are realized, the supervision efficiency and accuracy are improved, and the whole-process data security is guaranteed through multi-level security measures.