The aim of the study was to investigate the possibility of using infrared thermography (IRT) as a tool for monitoring the physiological condition of dairy cows. The goal was to monitor cows at normal temperatures. As physiological parameters began to change with the onset of heat stress, the study focused on a group with temperature and humidity index (THI) values below 72. The study monitors the surface temperature at various points on the body, rectal temperature (RT) and Frequency of Breathing. Those parameters were chosen because they give the most accurate data on physiology and can be objectively measured. Thermography images of the udder surface temperature (UST), leg surface temperature (LST) and shoulder surface temperature (SST) of the cows were taken. Surface temperatures were extracted from the photos using specialized software. The data were processed and analyzed statistically. The correlation coefficient between UST and SST was 0.90, between RT and Frequency of Breathing was 0.65, between LST and UST it was 0.45 and between LST and SST - 0.39. In conclusion, it can be said that IRT can be used to monitor the physiological condition of dairy cows.

The paper explores the shadows created by moving humans in two types of Forward Scatter (FS) systems, which use GPS signals and sound signals. The comparative analysis of results is based on the correlation analysis and focused on establishing of relationships between different types of shadows. The results show whether it is possible to use sound barriers for detection of moving objects.

Cognitive stress, also known as mental workload, constitutes a central topic within the field of psychophysiology due to its role in modulating attention, autonomic regulation, and stress reactivity. Furthermore, it bears direct relevance to practical monitoring systems that employ non-invasive sensing techniques. This study investigates whether a multimodal, non-invasive measurement setup can detect systematic physiological differences between Resting periods and short episodes of cognitive load within the same individuals. Additionally, it explores the capacity of such a system to differentiate tasks characterized by varying cognitive demands. A sequential, within-subject protocol was employed, comprising five consecutive phases (rest 1, Stroop, rest 12, subtraction, rest 3), during which five modalities were recorded concurrently: EEG, heart rate (HR), galvanic skin response (GSR), facial surface temperature, and oxygen saturation (SpO₂). Beyond phase-wise inspection of time-series data, an exploratory assessment of similarity across participants was conducted using correlation coefficients. The maximum cross-participant correlations observed were 0.88 (HR), 0.90 (GSR), 0.83 (facial temperature), and 0.77 (SpO₂); however, these correlations were used only as exploratory descriptors of inter-individual similarity and did not imply a significant phase effect. For inferential analysis, phase-wise epoch means were evaluated through one-factor repeated-measures ANOVA. The heart rate exhibited a robust main effect of phase (F(4, 32) = 10.5862, p_GG = 0.01044, ηp² = 0.5696), with higher HR observed during cognitive load epochs (e.g., 77.841 ± 11.777 bpm at rest 1 versus 83.926 ± 14.532 bpm during subtraction). The relatively large standard deviation reflects variability between subjects rather than variability within epochs. Regarding processed baseline-referenced GSR, the omnibus phase effect was not statistically significant under the conservative Greenhouse–Geisser correction; therefore, GSR was interpreted as exploratory in this dataset. Facial temperature and SpO₂ likewise did not show statistically significant omnibus phase effects under Greenhouse–Geisser correction (e.g., SpO₂: p_GG = 0.1209). EEG-derived measures provide supplementary central evidence of task engagement; entropy variations within an approximate dynamic range of 0.2 to 0.8 were observed, and the α/θ ratios demonstrated nearly a twofold distinction between rest and cognitive load epochs across different leads.

This paper presents a study focused on utilizing neural networks (NN) to forecast the lifespan of electronic components. These components are susceptible to deterioration over time caused by aging, environmental factors, and other issues. Accurately predicting the remaining useful life of these components is crucial for enhancing reliability and minimizing downtime. Earlier, we introduce a neural network-based approach to estimate the life expectancy of electronic components with correct data. This study presents a logical extension, where the same NN is try against “noisy” data. Moreover, by simulating real-world variations and uncertainties, the model becomes more adaptable and capable of handling unforeseen circumstances in electronic component lifespan prediction. This noise-aided training approach ensures that the neural network is not overly reliant on ideal or noise-free data, making it more reliable and resilient in practical applications.