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This paper presents a machine learning and image segmentation based advanced quality assessment technique for thin refill friction stir spot welded (RFSSW) joints. In particular, the research focuses on developing a predictive support vector machines (SVM) model. The purpose of this model is to facilitate the selection of RFSSW process parameters in order to increase the shear load capacity of joints. In addition, an improved weld quality assessment algorithm based on optical analysis was developed. The research methodology includes specimen preparation stages, mechanical tests, and algorithmic analysis, culminating in a machine learning model trained on experimental data. The results demonstrate the effectiveness of the model in selecting welding process parameters and assessing weld quality, offering significant improvements compared to standard techniques. The optimized SVM model, employing the radial basis function (RBF) kernel, achieved a lower root mean square error of 257.9 and a high correlation coefficient of 0.95, indicating a strong linear relationship between the predicted and actual shear load capacities. This research not only proposes a novel approach to optimizing welding parameters but also facilitates automatic quality assessment, potentially revolutionizing and spreading the application of the RFSSW technique in various industries. [ABSTRACT FROM AUTHOR]

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Copyright of Przeglad Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The mechanisms by which yeast cells respond to environmental stress include the production of heat shock proteins (HSPs) and the reduction of oxidative stress. The response of yeast exposed to aflatoxins B2+G1 (AFB2+G1), ochratoxin A (OTA), and zearalenone (ZEA) in aerobic conditions was studied. After 72 h of yeast cultivation in media contaminated with mycotoxins, the growth of yeast biomass, the level of malondialdehyde, and the activity of superoxide dismutase, glutathione S-transferase and glutathione peroxidase were examined; the expression profile of the following heat shock proteins was also determined: HSP31, HSP40, HSP60, HSP70, and HSP104. It was demonstrated that at the tested concentrations, both AFB2+G1 and ZEA inhibited yeast biomass growth. OTA at a concentration of 8.4 [µg/L] raised the MDA level. Intensified lipoperoxidation and increased activity of SOD and GPx were observed, regardless of the level of contamination with ZEA (300 µg/L or 900 µg/L). Increased contamination with AFB2+G1 and OTA caused an increase in the production of most HSPs tested (HSP31, HSP40, HSP70, HSP104). ZEA contamination in the used concentration ranges reduced the production of HSP31. The response of yeast cells to the presence of mycotoxin as a stressor resulted in the expression of certain HSPs, but the response was not systematic, which was manifested in different profiles of protein expression depending on the mycotoxin used. The tested mycotoxins influenced the induction of oxidative stress in yeast cells to varying degrees, which resulted in the activation of mainly SOD without GST mobilization or with a small involvement of GPx. [ABSTRACT FROM AUTHOR]

Aim of the study was to evaluate the effect of supplementation of high gravity media with mineral compounds and myo-inositol, at concentration which would be obtained as a result of degradation of phytates present in raw material during alcoholic fermentation. The process of alcoholic fermentation was conducted under laboratory conditions in a 72 h system at 37°C with the use of S. cerevisiae D-2 strain. Calcium chloride proved to be the most effective of all supplements tested. Final ethanol concentration increased by 1.2% v v−1 and the yield of process increased by ca. 7 dm−3 ethanol 100 kg−1 of starch in comparison with control. Selective supplementation with KH2PO4, ZnSO4 and MgSO4 also increased the ethanol concentration, but the effect was accompanied by a deterioration in composition of volatile products. The hydrolysis of phytate complexes with microbial phytases can be an alternative solution to supplementation of HG mashes presented in this work.

Copyright of Przeglad Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This article presents an original approach to improve the results of tomographic reconstructions by denoising the input data, which affects output images improving. The algorithms used in the research are based on autoencoders and Elastic Net - both related to artificial intelligence or machine-learning developed controllers. Due to the reduction of unnecessary features and removal of mutually correlated input variables generated by the tomography electrodes, good quality reconstructions of tomographic images were obtained. The simulation experiments proved that the presented methods could be effective in improving the quality of reconstructed tomographic images.

The following paper presents a key role and potential of Industrial Internet of Things (IIoT) in industrial applications as a solution for monitoring and maintaining manufacturing assets. IIoT is particularly important due to progressing computerisation of hardware resources leading to development of a virtualised model of autonomous real-time production management. Adequately article presents case study of IIoT use in production environment – both methodical and analytic approach is presented.

Copyright of Przeglad Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)