In this study, a negative substrate bias voltage is used to tune the structural, morphological, mechanical and electrochemical properties of TiAlN coatings fundamental for protective coating applications. TiAlN thin films have been deposited on glass, (001)Si and stainless steel substrates by RF magnetron sputtering at a power density of 4.41 W/cm2. The deposition rate was determined from X-ray reflectivity measurements to 7.00 ± 0.05 nm/min. TiAlN films used in this work were deposited for 60 min to yield a film thickness of 420 nm. Structural analysis has shown that TiAlN coating forms a cubic (fcc) phase with orientations in (111), (200), (220) and (222) planes. The deposited coatings present maximum hardness (H = 37.9 GPa) at −75 V. The dependence of hardness and Young's modulus and corrosion resistance on microstructure has been established. Electrochemical studies by potentiodynamic polarization in aggressive environment (3.5 wt.% NaCl) have revealed that stainless steel substrate with TiAlN coating exhibits excellent corrosion resistance.
As reported by numerous World Health Organization studies, fall incidents are considered one of the leading causes of loss of autonomy, injuries, and even deaths, and this is not only among elderly people but also in other categories such as workers. Fall incidents also have a considerable impact on the budget allocated to the care of people suffering from the effects of falls. This work presents a comprehensive review of state-of-the-art fall detection technologies considering the most powerful machine learning methodologies, both classical formalism (shallow methods), and approaches based on deep learning formalism. The authors reviewed the most recent and effective methods for fall detection and presented the used sensors, cameras, applied pre-treatments, generated attributes, and algorithms used in this field of application. The present work is completed by a discussion presenting some limitations that need to be analyzed and taken into account to further improve the quality of fall detection and reduce their impacts.
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