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The ongoing coronavirus pandemic requires more effective disinfection methods. Disinfection using ultraviolet light (UV), especially longer UVC wavelengths, such as 254 and 270/280 nm, has been proven to have virucidal properties, but its adverse effects on human skin and eyes limit its use to enclosed, unoccupied spaces. Several studies have shown the effectiveness of blue light (405 nm) against bacteria and fungi, but the virucidal property at 405 nm has not been much investigated. Based on previous studies, visible light mediates inactivation by absorbing the porphyrins and reacting with oxygen to produce reactive oxygen species (ROS). This causes oxidative damage to biomolecules, such as proteins, lipids, and nucleic acids, essential constituents of any virus. The virucidal potential of visible light has been speculated because the virus lacks porphyrins. This study demonstrated porphyrin-independent viral inactivation and conducted a comparative analysis of the effectiveness at 405 nm against other UVC wavelengths. The betacoronavirus 1 (strain OC43) was exposed to 405, 270/280, 254, and 222 nm, and its efficacy was determined using a median tissue culture infectious dose, i.e., TCID50. The results support the disinfection potential of visible light technology by providing a quantitative effect that can serve as the basic groundwork for future visible light inactivation technologies. In the future, blue light technology usage can be widened to hospitals, public places, aircraft cabins, and/or infectious laboratories for disinfection purposes.

The lighting requirements are subjective and one light setting cannot work for all. However, there is little work on developing smart lighting algorithms that can adapt to user preferences. To address this gap, this paper uses fuzzy logic and reinforcement learning to develop an adaptive lighting algorithm. In particular, we develop a baseline fuzzy inference system (FIS) using the domain knowledge. We use the existing literature to create a FIS that generates lighting setting recommendations based on environmental conditions i.e. daily glare index, and user information including age, activity, and chronotype. Through a feedback mechanism, the user interacts with the algorithm, correcting the algorithm output to their preferences. We interpret these corrections as rewards to a Q-learning agent, which tunes the FIS parameters online to match the user preferences. We implement the algorithm in an aircraft cabin mockup and conduct an extensive user study to evaluate the effectiveness of the algorithm and understand its learning behavior. Our implementation results demonstrate that the developed algorithm possesses the capability to learn user preferences while successfully adapting to a wide range of environmental conditions and user characteristics. and can deal with a diverse spectrum of environmental conditions and user characteristics. This underscores its viability as a potent solution for intelligent light management, featuring advanced learning capabilities.