Fast Uncertainty Quantification of Spent Nuclear Fuel with Neural Networks

The accurate calculation and uncertainty quantification of the characteristics of spent nuclear fuel (SNF) play a crucial role in ensuring the safety, efficiency, and sustainability of nuclear energy production, waste management, and nuclear safeguards. State of the art physics-based models, while reliable, are computationally intensive and time-consuming. This paper presents a surrogate modeling approach using neural networks (NN) to predict a number of SNF characteristics with reduced computational costs compared to physics-based models. An NN is trained using data generated from CASMO5 lattice calculations. The trained NN accurately predicts decay heat and nuclide concentrations of SNF, as a function of key input parameters, such as enrichment, burnup, cooling time between cycles, mean boron concentration and fuel temperature. The model is validated against physics-based decay heat simulations and measurements of different uranium oxide fuel assemblies from two different pressurized water reactors. In addition, the NN is used to perform sensitivity analysis and uncertainty quantification. The results are in very good alignment to CASMO5, while the computational costs (taking into account the costs of generating training samples) are reduced by a factor of 10 or more. Our findings demonstrate the feasibility of using NNs as surrogate models for fast characterization of SNF, providing a promising avenue for improving computational efficiency in assessing nuclear fuel behavior and associated risks

A novel haemocytometric covid-19 prognostic score developed and validated in an observational multicentre european hospital-based study

COVID-19 induces haemocytometric changes. Complete blood count changes, including new cell activation parameters, from 982 confirmed COVID-19 adult patients from 11 European hospitals were retrospectively analysed for distinctive patterns based on age, gender, clinical severity, symptom duration and hospital days. The observed haemocytometric patterns formed the basis to develop a multi-haemocytometric-parameter prognostic score to predict, during the first three days after presentation, which patients will recover without ventilation or deteriorate within a two-week timeframe, needing intensive care or with fatal outcome. The prognostic score, with ROC curve AUC at baseline of 0.753 (95% CI 0.723-0.781) increasing to 0.875 (95% CI 0.806-0.926) on day 3, was superior to any individual parameter at distinguishing between clinical severity. Findings were confirmed in a validation cohort. Aim is that the score and haemocytometry results are simultaneously provided by analyser software, enabling wide applicability of the score as haemocytometry is commonly requested in COVID-19 patients

A new species of Varanus (Anguimorpha: Varanidae) from the early Miocene of the Czech Republic, and its relationships and palaeoecology

Skeletal remains of a new early Miocene (Ottnangian, MN 4 mammal zone) monitor lizard, Varanus mokrensis sp. nov., are described from two karst fissures in the Mokrá-Western Quarry (1/2001 Turtle Joint; 2/2003 Reptile Joint), Czech Republic, providing the first documented example of a European varanid for which osteological data permit a well-supported assignment to the genus Varanus. The new species is morphologically similar to the Recent Indo-Asiatic varanids of the Varanus bengalensis group. It differs from all other Varanus species on the basis of a single autapomorphy and a combination of 11 characters. As a distinguishing feature of V. mokrensis, the parietal and squamosal processes of the postorbitofrontal form a narrowly acute angle. The teeth show distinct, smooth cutting edges along the mesial and distal margins of the apical portion of their crowns. This feature is not observed in most extant Asiatic Varanus species and may represent a plesiomorphic condition. The results of parsimony phylogenetic analyses, with and without character reweighting, reveal poor resolution within Varanus. A Bayesian analysis shows V. mokrensis to be closely related to extant representatives of the Indo-Asiatic Varanus clade, with close affinities to the V. bengalensis species group. The topology of the Bayesian tree supports the hypothesis that Miocene monitors from Mokrá are representatives of a lineage that is ancestral to the well-defined clade of extant African varanids, including the early Miocene V. rusingensis. In addition, our results support a Eurasian origin for the varanid clade. The extant African Varanus species probably originated in the late Oligocene. The radiation of African varanids probably occurred during the late Oligocene to early Miocene time interval. The occurrence of Varanus in the early Miocene of Mokrá-Western Quarry corresponds to the warm phase of the Miocene Climatic Optimum. Remains of a diverse aquatic and heliophobe amphibian fauna at the 2/2003 Reptile Joint site indicate more humid conditions than those at the 1/2001 Turtle Joint site

Mechanisms of Plastic Deformation in Collagen Networks Induced by Cellular Forces

Contractile cells can reorganize fibrous extracellular matrices and form dense tracts of fibers between neighboring cells. These tracts guide the development of tubular tissue structures and provide paths for the invasion of cancer cells. Here, we studied the mechanisms of the mechanical plasticity of collagen tracts formed by contractile premalignant acinar cells and fibroblasts. Using fluorescence microscopy and second harmonic generation, we quantified the collagen densification, fiber alignment, and strains that remain within the tracts after cellular forces are abolished. We explained these observations using a theoretical fiber network model that accounts for the stretch-dependent formation of weak cross-links between nearby fibers. We tested the predictions of our model using shear rheology experiments. Both our model and rheological experiments demonstrated that increasing collagen concentration leads to substantial increases in plasticity. We also considered the effect of permanent elongation of fibers on network plasticity and derived a phase diagram that classifies the dominant mechanisms of plasticity based on the rate and magnitude of deformation and the mechanical properties of individual fibers. Plasticity is caused by the formation of new cross-links if moderate strains are applied at small rates or due to permanent fiber elongation if large strains are applied over short periods. Finally, we developed a coarse-grained model for plastic deformation of collagen networks that can be employed to simulate multicellular interactions in processes such as morphogenesis, cancer invasion, and fibrosis