In resistance training, the use of predicting proximity to momentary task failure (MF, i.e., maximum effort), and repetitions in reserve scales specifically, is a growing approach to monitoring and controlling effort. However, its validity is reliant upon accuracy in the ability to predict MF which may be affected by congruence of the perception of effort compared with the actual effort required. The present study examined participants with at least 1 year of resistance training experience predicting their proximity to MF in two different experiments using a deception design. Within each experiment participants performed four trials of knee extensions with single sets (i.e., bouts of repetitions) to their self-determined repetition maximum (sdRM; when they predicted they could not complete the next repetition if attempted and thus would reach MF if they did) and MF (i.e., where despite attempting to do so they could not complete the current repetition). For the first experiment (n = 14) participants used loads equal to 70% of a one repetition maximum (1RM; i.e., the heaviest load that could be lifted for a single repetition) performed in a separate baseline session. Aiming to minimize participants between day variability in repetition performances, in the second separate experiment (n = 24) they used loads equal to 70% of their daily isometric maximum voluntary contraction (MVC). Results suggested that participants typically under predicted the number of repetitions they could perform to MF with a meta-analytic estimate across experiments of 2.0 [95%CIs 0.0 to 4.0]. Participants with at least 1 year of resistance training experience are likely not adequately accurate at gauging effort in submaximal conditions. This suggests that perceptions of effort during resistance training task performance may not be congruent with the actual effort required. This has implications for controlling, programming, and manipulating the actual effort in resistance training and potentially on the magnitude of desired adaptations such as improvements in muscular hypertrophy and strength.

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The digitalization of education is a continuously developing process, aimed at the use of information and communication technologies. However, engineering education utilizes several forms of learning, with laboratory experiments being one of them. The use of digital learning materials (DLM) in labs is still limited due to numerous existing factors. This study investigates students' acceptance of using DLM during laboratory exercises in three universities located in Turkey, Poland, and Bulgaria. A questionnaire was prepared, and a survey was conducted among 625 learners. They were divided into eight categories, based on their engineering area and country. The survey results demonstrate that there is a strong correlation between the students' opinion on DLM and the use of DLM by their lab instructors, which means that their acceptance could be increased if more DLM are integrated in lab courses. The analysis of the questionnaire results also showed that there is a significant difference in the students' opinion on DLM, depending on the engineering area. Students of Food, Chemistry, and Electrical engineering rated the use of DLM quite high (above 4.0 out of 5), followed by the Civil engineering students with average results between 3.5 and 4.0. Respondents involved in Textile, Bio, and Machine engineering were the most skeptical (<3.5). Furthermore, their opinions covered a wide range from "Strongly disagree" to "Strongly agree."

The article considers the methods, procedures and results of experimental studies of the main egg quality indicators. The offered express methods and the automated installation provide definition of the weight, the form and density of egg. Based on the results of experimental studies of egg parameters, the express method of determining the volume of the egg through the area of the longitudinal section and the small diameter of the egg is substantiated. The express method for density determination by direct mass measurement and volume calculation gives minimal absolute error and provides a six time increase in performance, compared to the direct method.