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This study aims to investigate the behavior of the green biomass-derived copper (lignin/silica/fatty acids) complex, copper lignin/silica/fatty acids (Cu-LSF) complex, when incorporated into the nonpolar ethylene propylene diene (EPDFM) rubber matrix, focusing on its reinforcing and antioxidant effect on the resulting EPDM composites.

The structure of the prepared EPDM composites was confirmed by Fourier-transform infrared spectroscopy, and the dispersion of the additive fillers and antioxidants in the EPDM matrix was investigated using scanning electron microscopy. Also, the rheometric characteristics, mechanical properties, swelling behavior and thermal gravimetric analysis of all the prepared EPDM composites were explored as well.

Results revealed that the Cu-LSF complex dispersed well in the nonpolar EPDM rubber matrix, in thepresence of coupling system, with enhanced Cu-LSF-rubber interactions and increased cross-linking density, which reflected on the improved rheological and mechanical properties of the resulting EPDM composites. From the various investigations performed in the current study, the authors can suggest 7–11 phr is the optimal effective concentration of Cu-LSF complex loading. Interestingly, EPDM composites containing Cu-LSF complex showed better antiaging performance, thermal stability and fluid resistance, when compared with those containing the commercial antioxidants (2,2,4-trimethyl-1,2-dihydroquinoline and N-isopropyl-N’-phenyl-p-phenylenediamine). These findings are in good agreement with our previous study on polar nitrile butadiene rubber.

The current study suggests the green biomass-derived Cu-LSF complex to be a promising low-cost and environmentally safe alternative filler and antioxidant to the hazardous commercial ones.

The purpose of this study is to highlight and demonstrate how the study of stress and related responses in construction can best be measured and benchmarked effectively.

A range of perceptual and physiological measures are obtained across different time periods and during different activities in a fieldwork setting. Differences in the empirical results are analysed and implications for future studies of stress discussed.

The results of this study strongly support the use of multiple psychometrics and biosensors whenever biometrics are included in the study of stress. Perceptual, physiological and environmental factors are all shown to act in concert to impact stress. Strong conclusions on the potential drivers of stress should then only be considered when consistent results apply across multiple metrics, time periods and activities.

Stress is an incredibly complex condition. This study demonstrates why many current applications of biosensors to study stress in construction are not up to the task and provides empirical evidence on how future studies can be significantly improved.

To the best of the author’s knowledge, this is the first study to focus explicitly on demonstrating the need for multiple research instruments and settings when studying stress or related conditions in construction.

This paper aims to concentrate on recent innovations in flexible wearable sensor technology tailored for monitoring vital signals within the contexts of wearable sensors and systems developed specifically for monitoring health and fitness metrics.

In recent decades, wearable sensors for monitoring vital signals in sports and health have advanced greatly. Vital signals include electrocardiogram, electroencephalogram, electromyography, inertial data, body motions, cardiac rate and bodily fluids like blood and sweating, making them a good choice for sensing devices.

This report reviewed reputable journal articles on wearable sensors for vital signal monitoring, focusing on multimode and integrated multi-dimensional capabilities like structure, accuracy and nature of the devices, which may offer a more versatile and comprehensive solution.

The paper provides essential information on the present obstacles and challenges in this domain and provide a glimpse into the future directions of wearable sensors for the detection of these crucial signals. Importantly, it is evident that the integration of modern fabricating techniques, stretchable electronic devices, the Internet of Things and the application of artificial intelligence algorithms has significantly improved the capacity to efficiently monitor and leverage these signals for human health monitoring, including disease prediction.