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Mosses are proven bioindicators of living environments. It is known that mosses accumulate pollutants from precipitates and, to some lesser extent, from the substrate. In this study, the effects of cesium (Cs) on the physiological traits of acrocarp polytrichaceous Catherine's moss (Atrichum undulatum Hedw.) were studied under controlled, in vitro conditions. Cesium can be found in the environment in a stable form (133Cs) and as a radioactive isotope (134Cs and 137Cs). Belonging to the same group of elements, Cs and potassium (K) share various similarities, due to which Cs can interfere with this essential element and thus possibly alter the plant's metabolism. Results have shown that Cs affects the measured physiological characteristics of A. undulatum, although the changes to antioxidative enzyme activities were not drastic following Cs treatments. Therefore, the activities of antioxidative enzymes at lower pH values are more the consequence of pH effects on enzymatic conformation than simply the harmful effects of Cs. Moreover, Cs did not affect the survival of plants grown on the solid substrate nor plants grown in conditions of light and heavy rain simulation using Cs with variable pH, indicating that Cs is not harmful in this form for the studied species A. undulatum. [ABSTRACT FROM AUTHOR]

Dimensional analysis is a tool used to understand and analyze various problems in engineering. This analysis is useful for calculating dimensionless parameters and gives an answer as to which group of parameters affects the problem. Dimensional analysis can be represented using Buckingham's p-theorem. This paper provides an overview of the literature on the application of dimensional analysis and Buckingham's p theorem for various processing processes. The review period is observed in the period from 2007 until today. The main focus remains of the production process, performance and parameters of process, dimension and years of publication. Review work in this area aims to provide ready-made information in one place that can be very useful to future researchers in this direction of research. [ABSTRACT FROM AUTHOR]

The radiation status of the national park (NP) Djerdap (Eastern Serbia) is characterized using bioindicators (mosses). Mosses (16 species, 156 samples) were collected randomly within the area of NP Djerdap during the month of June, periodically from 2015 to 2019. Samples were collected in the regions of Dobra, Donji Milanovac, and Tekija. Within the mosses, the presence of 40 K and 137Cs is regarded as a good indicator of radionuclides present in the environment due to their easy interchangeability with 39 K highly present in living moss organisms. The activity concentrations of gamma ray–emmiting radionuclides in samples were determined using high-resolution gamma ray spectrometry, an HPGe-ORTEC/Ametek detector. The determining activity concentrations of 40 K and 137Cs in collected samples were as follows: for 40 K from 31.4 to 721 Bq kg−1and for 137Cs varied from 2.6 to 908 Bq kg−1. The average activity concentrations of 40 K and 137Cs (Bq kg−1) in mosses in the period 2015–2019 were the lowest in moss samples collected in the Dobra region, and the highest in the area of Donji Milanovac. The most prevalent collected moss species within the Djerdap National Park was Hypnum cupressiforme. The determined activity concentrations in H. cupressiforme of 137Cs for the whole region of NP Djerdap for all 5 years was 78.1 ± 70.3 Bq kg−1 and of 40 K was 181 ± 86.1 Bq kg−1. [ABSTRACT FROM AUTHOR]

In recent years, trends have been towards modeling machine processing using artificial intelligence. Artificial neural network (ANN) and multiple regression analysis are methods used to model and optimize the performance of manufacturing technologies. ANN and multiple regression analysis show high reliability in the prediction and optimization of machining processes. In this paper, machining parameters such as spindle speed, feed rate and depth of cut were used in end milling process to minimize surface roughness. The influence of the parameters on the surface roughness was investigated using an artificial neural network and multiple regression analysis, and results are compared with the measured results. [ABSTRACT FROM AUTHOR]