Modeling epidemics: A primer and Numerus Model Builder implementation

Epidemiological models are dominated by compartmental models, of which SIR formulations are the most commonly used. These formulations can be continuous or discrete (in either the state-variable values or time), deterministic or stochastic, or spatially homogeneous or heterogeneous, the latter often embracing a network formulation. Here we review the continuous and discrete deterministic and discrete stochastic formulations of the SIR dynamical systems models, and we outline how they can be easily and rapidly constructed using Numerus Model Builder, a graphically-driven coding platform. We also demonstrate how to extend these models to a metapopulation setting using NMB network and mapping tools. Keywords: SIR, SEIR models, Stochastic simulation, Dynamic networks, Compartmental model

Deep Learning Segmentation of Satellite Imagery Identifies Aquatic Vegetation Associated with Snail Intermediate Hosts of Schistosomiasis in Senegal, Africa

Schistosomiasis is a debilitating parasitic disease of poverty that affects more than 200 million people worldwide, mostly in sub-Saharan Africa, and is clearly associated with the construction of dams and water resource management infrastructure in tropical and subtropical areas. Changes to hydrology and salinity linked to water infrastructure development may create conditions favorable to the aquatic vegetation that is suitable habitat for the intermediate snail hosts of schistosome parasites. With thousands of small and large water reservoirs, irrigation canals, and dams developed or under construction in Africa, it is crucial to accurately assess the spatial distribution of high-risk environments that are habitat for freshwater snail intermediate hosts of schistosomiasis in rapidly changing ecosystems. Yet, standard techniques for monitoring snails are labor-intensive, time-consuming, and provide information limited to the small areas that can be manually sampled. Consequently, in low-income countries where schistosomiasis control is most needed, there are formidable challenges to identifying potential transmission hotspots for targeted medical and environmental interventions. In this study, we developed a new framework to map the spatial distribution of suitable snail habitat across large spatial scales in the Senegal River Basin by integrating satellite data, high-definition, low-cost drone imagery, and an artificial intelligence (AI)-powered computer vision technique called semantic segmentation. A deep learning model (U-Net) was built to automatically analyze high-resolution satellite imagery to produce segmentation maps of aquatic vegetation, with a fast and robust generalized prediction that proved more accurate than a more commonly used random forest approach. Accurate and up-to-date knowledge of areas at highest risk for disease transmission can increase the effectiveness of control interventions by targeting habitat of disease-carrying snails. With the deployment of this new framework, local governments or health actors might better target environmental interventions to where and when they are most needed in an integrated effort to reach the goal of schistosomiasis elimination

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The (n,gamma) cross section of Au-197 has been measured at n_TOF in the resolved resonance region, up to 5 keV, with the aim of improving the accuracy in an energy range where it is not yet considered standard. The measurements were performed with two different experimental setup and detection techniques, the total energy method based on C6D6 detectors, and the total absorption calorimetry based on a 4 pi BaF2 array. By comparing the data collected with the two techniques, two accurate sets of neutron-capture yields have been obtained, which could be the basis for a new evaluation leading to an extended cross-section standard. Overall good agreement is found between the n_TOF results and evaluated cross sections, with some significant exceptions for small resonances. A few resonances not included in the existing databases have also been observed

Nuclear astrophysics at n-TOF facility, CERN

The innovative feature of the n-TOF facility at CERN, in the two experimental areas, (20 m and 200 m flight paths), allow for an accurate determination of the neutron capture cross section for radioactive samples or for isotopes with small neutron capture cross section, of interest for Nuclear Astrophysics. This contribution presents an overview on the astrophysical program carried on at the n-TOF facility, the main results and their implications

Nuclear data measurements at the upgraded neutron time-of-flight facility n-TOF at CERN

Applications of nuclear data like neutron-induced reaction cross sections are related to research fields as stellar nucleosynthesis, the study of nuclear level densities and strength functions, and also play a key role in the safety and criticality assessment of existing and future nuclear reactors, in areas concerning radiation dosimetry, medical applications, transmutation of nuclear waste, accelerator-driven systems and fuel cycle investigations. The evaluations in nuclear data libraries are based both on experimental data and theoretical models. CERN's neutron time-of-flight facility n-TOF has produced a considerable amount of experimental data since it has become fully operational with the start of its scientific measurement programme in 2002. While for a long period a single measurement station (EAR1) located at 185 m from the neutron production target was available, the construction of a second beam line at 20 m (EAR2) in 2014 has substantially increased the measurement capabilities of the facility. An outline of the experimental nuclear data activities at CERN's neutron time-of-flight facility n-TOF will be presented

Serpentine differentiation and polyploid evolution in \kur{Knautia arvensis} agg. (Dipsacaceae).

Evolutionary history of an intricate polyploid complex Knautia arvensis agg. in Central Europe has been studied using combined approach of molecular (AFLP, DNA-sequencing) and cytological (flow cytometry, karyology) techniques. Possible evolutionary scenario has been suggested for the whole complex and for the serpentine population in particular, based on critical assessment of results obtained from the different methods

Study of the photon strength functions and level density in the gamma decay of the n + 234U reaction

The accurate calculations of neutron-induced reaction cross sections are relevant for many nuclear applications. The photon strength functions and nuclear level densities are essential inputs for such calculations. These quantities for 235U are studied using the measurement of the gamma de-excitation cascades in radiative capture on 234U with the Total Absorption Calorimeter at n_TOF at CERN. This segmented 4π gamma calorimeter is designed to detect gamma rays emitted from the nucleus with high efficiency. This experiment provides information on gamma multiplicity and gamma spectra that can be compared with numerical simulations. The code DICEBOXC is used to simulate the gamma cascades while GEANT4 is used for the simulation of the interaction of these gammas with the TAC materials. Available models and their parameters are being tested using the present data. Some preliminary results of this ongoing study are presented and discussed