A stepwise sequential assimilation algorithm is proposed based on an optimisation approach for recursive parameter estimation and tracking of radioactive plume propagation in the early stage of a radiation accident. Predictions of the radiological situation in each time step of the plume propagation are driven by an existing short-term meteorological forecast and the assimilation procedure manipulates the model parameters to match the observations incoming concurrently from the terrain. Mathematically, the task is a typical ill-posed inverse problem of estimating the parameters of the release. The proposed method is designated as a stepwise re-estimation of the source term release dynamics and an improvement of several input model parameters. It results in a more precise determination of the adversely affected areas in the terrain. The nonlinear least-squares regression methodology is applied for estimation of the unknowns. The fast and adequately accurate segmented Gaussian plume model (SGPM) is used in the first stage of direct (forward) modelling. The subsequent inverse procedure infers (re-estimates) the values of important model parameters from the actual observations. Accuracy and sensitivity of the proposed method for real-time forecasting of the accident propagation is studied. First, a twin experiment generating noiseless simulated "artificial" observations is studied to verify the minimisation algorithm. Second, the impact of the measurement noise on the re-estimated source release rate is examined. In addition, the presented method can be used as a proposal for more advanced statistical techniques using, e.g., importance sampling.

• Klíčová slova
• Assimilation of measurements, Ill-posed inversion problem, Measurement noise, Radioactivity release, Urgent emergency,
• MeSH
• algoritmy MeSH
• monitorování radiace metody   MeSH
• normální rozdělení MeSH
• počítačová simulace MeSH
• předpověď MeSH
• radioaktivita * MeSH
• teoretické modely * MeSH
• únik radioaktivních látek * MeSH
• Publikační typ
• časopisecké články MeSH

The source term of atmospheric emissions of hazardous materials is a crucial aspect of the analysis of unintended release. Motivated by wildfires of regions contaminated by radioactivity, the focus is placed on the case of airborne transmission of material from 5 dimensions: spatial location described by longitude and latitude in a given area with potentially many sources, time profiles, height above ground level, and the size of particles carrying the material. Since the atmospheric inverse problem is typically ill-posed and the number of measurements is usually too low to estimate the whole 5D tensor, some prior information is necessary. For the first time in this domain, a method based on deep image prior utilizing the structure of a deep neural network to regularize the inversion is proposed. The network is initialized randomly without the need to train it on any dataset first. In tandem with variational optimization, this approach not only introduces smoothness in the spatial estimate of the emissions but also reduces the number of unknowns by enforcing a prior covariance structure in the source term. The strengths of this method are demonstrated on the case of 137Cs emissions during the Chernobyl wildfires in 2020.

• Klíčová slova
• Atmospheric inversion, Chernobyl wildfires, Deep image prior, Deep neural networks, Spatial-temporal source,
• Publikační typ
• časopisecké články MeSH

We build an operational scheme for the quantum state reconstruction based on the fitting of data patterns. Each data pattern corresponds to the response of the measurement setup to a predefined reference state. The set of data patterns can be measured experimentally in the calibration stage preceding to the reconstruction. The quorum of reference states plays the role of a positive operator valued measure in terms of which the reconstruction is done. As the main advantage, the procedure is free of notorious problems with projections into non-normalizable quadrature eigenstates, infinite dimensionality, ill-posed inversion, or imperfect detection.

• Publikační typ
• časopisecké články MeSH

Food of non-animal origin is a major component of the human diet and has been considered to pose a low risk from the point of view of bacteriological safety. However, an increase in the number of outbreaks of illness caused by such pathogens and linked to the consumption of fresh fruit and vegetables have been reported from around the world recently. Salmonella spp., STEC (Shiga toxin producing Escherichia coli) and Listeria monocytogenes are among the most frequently identified agents. Additionally, the transmission of antibiotic resistant strains including also the methicillin resistant S. aureus (MRSA) to humans via the food chain is one of the greatest public health problems being confronted today. Therefore, we focused on the bacterial safety of fruit, vegetables and sprouts on sale in the Czech Republic. One strain (0.3%) of Salmonella Enteritidis phage type PT8, one strain (0.3%) of MRSA and 17 strains (5.0%) of L. monocytogenes were isolated from a total of 339 collected samples. The most problematic commodities were frozen fruit and vegetables (packed and unpacked) and fresh-cut vegetables. Our findings indicate deficiencies in hygiene practices during harvesting, processing and distribution of these commodities. Although sprouts and berries are the most likely to be contaminated by human pathogens, only two samples were positive for the presence of L. monocytogenes.

• Klíčová slova
• Foodborne, Listeria monocytogenes, MRSA, STEC, Salmonella,
• MeSH
• Bacteria izolace a purifikace   MeSH
• bezpečnost potravin * MeSH
• Escherichia coli O157 izolace a purifikace   MeSH
• lidé MeSH
• Listeria monocytogenes izolace a purifikace   MeSH
• methicilin rezistentní Staphylococcus aureus izolace a purifikace   MeSH
• ovoce mikrobiologie   MeSH
• počet mikrobiálních kolonií MeSH
• polymerázová řetězová reakce MeSH
• potravinářská mikrobiologie MeSH
• Salmonella enteritidis izolace a purifikace   MeSH
• semenáček mikrobiologie   MeSH
• shiga-toxigenní Escherichia coli izolace a purifikace   MeSH
• techniky typizace bakterií MeSH
• zelenina mikrobiologie   MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• Geografické názvy
• Česká republika MeSH