3 results about "Laboratory results" patented technology

According to one aspect, there is provided a computer-implemented system for evaluating and improving strain production using three integrated components: a prototype system for evaluating candidate strains, an optimization system for enhancing strain performance, and a scale-up system for adapting laboratory results to commercial-scale parameters. The system employs artificial intelligence models trained on data from all three components. The system continuously receives performance data from these systems to update its AI models. Using these updated models, the system generates modified operational parameters for the prototype, optimization, and scale-up systems, creating a feedback loop for continuous process improvement.

This invention proposes a method for determining the source of large-particle slag inclusions at defects in rolled steel plates, belonging to the field of iron and steel metallurgy technology. The method includes: determining the optimal tracer and its content through laboratory experiments; based on the laboratory results, adding the optimal tracer to the slag in a predetermined manner at a specific process stage in industrial steelmaking, controlling the addition ratio to ensure tracer effectiveness; finally, collecting samples from the defects in the rolled steel plates and analyzing the inclusion composition using an electron microscope; if tracer elements are detected, the inclusion is determined to originate from the process stage where the tracer was added. This invention, by combining laboratory pre-screening with industrial testing, improves the accuracy and efficiency of inclusion source location, providing key support for steelmaking process optimization.

An explainable artificial intelligence system for early prediction of missed severe diagnoses in intensive care unit patients is provided. The system comprises a data processing module configured to receive and process electronic health record data including patient demographics, vital signs, laboratory results, and diagnostic codes. A machine learning module with ensemble, deep learning, and large language models analyzes processed data to predict likelihood of severe diagnoses including acute myocardial infarction, acute pulmonary embolism, bacterial pneumonia, aortic dissection, meningitis, spinal abscess, bacterial endocarditis, and arterial thromboembolism. An explainability module generates feature importance rankings and provides interpretable explanations for predictions.