On the basis of a semi-realistic SIR microsimulation for Germany and Poland, we show that the R ₀ parameter interval for which the COVID-19 epidemic stays overcritical but below the capacity limit of the health care system to reach herd immunity is so narrow that a successful implementation of this strategy is likely to fail, which is in contrast to results obtained from classical differential equation models. Our microsimulation is based on official census data and involves household composition and age distribution as the main population structure variables. Outside household contacts are characterised by an out-reproduction number R ^* which is the only free parameter of the model. For a subcritical domain we compute the time till extinction and prevalence as a function of the initial number of infected individuals and R ^* . For the Polish city of Wrocław we also discuss the combined impact of testing coverage and contact reduction. For both countries we estimate R ^* for disease progression until 20th of March 2020.

We consider iterative learning control of linear repetitive processes in a setting, which is motivated by laser cladding processes that are controlled with the help of cameras and/or infra-red (IR) cameras. We develop a gradient-like learning procedure that is based on the Frechet derivative of a control quality criterion. Then, we prove its convergence. We also provide local bounds on parameter uncertainty for which the convergence of the learning process is still retained. The proposed approach is extensively tested using a very accurate model of the gantry robot. Finally, an example of the laser cladding process is discussed. Images from a camera and IR camera allow us to design a proper temperature profile, for which a laser power control signal is calculated by simulations of the the repetitive process.

The use of low-emission combustion technologies in power boilers has contributed to a significant increase in the rate of high-temperature corrosion in boilers and increased risk of failure. The use of low quality biomass and waste, caused by the current policies pressing on the decarbonization of the energy generation sector, might exacerbate this problem. Additionally, all of the effects of the valorization techniques on the inorganic fraction of the solid fuel have become an additional uncertainty. As a result, fast and reliable corrosion diagnostic techniques are slowly becoming a necessity to maintain the security of the energy supply for the power grid. Non-destructive testing methods (NDT) are helpful in detecting these threats. The most important NDT methods, which can be used to assess the degree of corrosion of boiler tubes, detection of the tubes’ surface roughness and the internal structural defects, have been presented in the paper. The idea of the use of optical techniques in the initial diagnosis of boiler evaporators’ surface conditions has also been presented.

Our target application is a laser additive manufacturing (LAM) process that suffers from too wide end points. To overcome this drawback, we propose to extend an algorithm of iterative learning of optimal control (ILOC) to nonlinear systems. We demonstrate its application for a LAM process, not only by simulations but also by comparing the results of 3-D metallic printing with and without learning. Extending previously published results, we provide proof of convergence of an ILOC algorithm for a wide class of nonlinear systems.