Monte Carlo Analysis of Optical Interactions in Reflectance and Transmittance Finger Photoplethysmography

Photoplethysmography (PPG) is a non-invasive photometric technique that measures the volume changes in arterial blood. Recent studies have reported limitations in developing and optimising PPG-based sensing technologies due to unavailability of the fundamental information such as PPG-pathlength and penetration depth in a certain region of interest (ROI) in the human body. In this paper, a robust computational model of a dual wavelength PPG system was developed using Monte Carlo technique. A three-dimensional heterogeneous volume of a specific ROI (i.e., human finger) was exposed at the red (660 nm) and infrared (940 nm) wavelengths in the reflectance and transmittance modalities of PPG. The optical interactions with the individual pulsatile and non-pulsatile tissue-components were demonstrated and the optical parameters (e.g., pathlength, penetration depth, absorbance, reflectance and transmittance) were investigated. Results optimised the source-detector separation for a reflectance finger-PPG sensor. The analysis with the recorded absorbance, reflectance and transmittance confirmed the maximum and minimum impact of the dermis and bone tissue-layers, respectively, in the formation of a PPG signal. The results presented in the paper provide the necessary information to develop PPG-based transcutaneous sensors and to understand the origin of the ac and dc components of the PPG signal

Artificial intelligence applications in the agrifood sectors

Food security is one of the priorities of every country in the World. However, different factors are making it difficult to meet global targets on food security. Some unprecedented shocks are encumbering food security at the global level. Various interventions have been applied toward food security and artificial intelligence is one of the modern methods that is being used in various stages of the food system. In this paper, the application of artificial intelligence in the whole food production ecosystem ranging from crop production, livestock production, harvesting/slaughtering, postharvest management, food processing, food distribution, food consumption and food waste management is assessed. The objective of this research is to assess the application of artificial intelligence systems in all the stages of food systems. A systematic review was conducted by analyzing 110 articles after the screening of 450 articles based on the inclusion and exclusion criteria. The results indicated that various artificial intelligence algorithms are being applied to all the stages of the food system from crop/livestock production up to food or agro-waste management