Development of Methods for Early Detection of Radioactive Tender

• Country: Czech Republic
• Summary: Development of Methods for Early Detection of Radioactive Contamination of the Air as Part of Monitoring the Radiation Situation
• CZT Ref No: 137089998
• Deadline: 13 Apr 2026
• Financier: Self Financed
• Purchaser Ownership: Government
• Tender Value: Refer Document
• Notice Type: Tender
• Document Ref. No.: 150137-2026
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• Description:
• Research on the use of advanced statistical, machine learning and AI methods to increase the sensitivity of the Monitoring Radiation Network (MRS) and accelerate the response of the MRS to radioactive contamination in the air. The main method is the application of predictive modeling based on machine learning and artificial intelligence on data from online sensors and regular laboratory measurements. The benefit is more sensitive detection of anomalies in air contamination monitoring data and thus achieving highly timely detection of air contamination even in trace amounts of radionuclides and predictions at the time of international radiation threats as a result of the conflict in Ukraine and at the same time a more accurate estimate of the local radiation background. The main goals and results are to improve the interpretability of the outputs of monitoring the radiation situation through atmospheric modeling. The need for immediate data availability will be solved by installing a dedicated server for fully autonomous running of the HYSPLIT model with the possibility of remote user access. The result of the project is a modernized sampling device used in monitoring the radiation situation. Specifically, these are modifications of the scintillation modules (for the JL Hunter device) and the expansion of semiconductor gamma spectrometry by other modalities, including alpha/beta detectors (for the JL Snow White device). Listed in detail ZP. Development of methods for early detection of radioactive contamination of the air as part of monitoring the radiation situation Research on the use of advanced statistical and machine learning and AI methods to increase the sensitivity of monitoring boards...
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