ABSTRACT
The air quality of the living area influences human health to a certain extent. Therefore, it is particularly important to detect the quality of indoor air. However, traditional detection methods mainly depend on chemical analysis, which has long been criticized for its high time cost. In this research, a rapid air detection method for the indoor environment using laser-induced breakdown spectroscopy (LIBS) and machine learning was proposed. Four common scenes were simulated, including burning carbon, burning incense, spraying perfume and hot shower which often led to indoor air quality changes. Two steps of spectral measurements and algorithm analysis were used in the experiment. Moreover, the proposed method was found to be effective in distinguishing different kinds of aerosols and presenting sensitivity to the air compositions. In this paper, the signal was isolated by the forest, so the singular values were filtered out. Meanwhile, the spectra of different scenarios were analyzed via the principal component analysis (PCA), and the air environment was classified by K-Nearest Neighbor (KNN) algorithm with an accuracy of 99.2%. Moreover, based on the establishment of a high-precision quantitative detection model, a back propagation (BP) neural network was introduced to improve the robustness and accuracy of indoor environment. The results show that by taking this method, the dynamic prediction of elements concentration can be realized, and its recognition accuracy is 96.5%.
ABSTRACT
The need of supporting meditation through digital technology has been increased especially after COVID-19. By combining the olfactory experience provided by the ambient incense connected with virtual reality technology, we propose to bring the aesthetic and affective aspects of smell to the users for meditation in the digital era. TranScent aims to provide users a hybrid composition of sensory experiences that transcends the spatial and temporal characteristics in their surroundings. It lets the users meditate with the incense burnt in the real world while immersing in the audiovisual virtual environment. Rather than emphasizing on the mobility in fast pace, it focuses on giving users the stillness atmosphere for meditation practice through olfactory art with virtual reality. © 2021 Copyright held by the owner/author(s). Publication rights licensed to ACM.
ABSTRACT
Background and Objectives: The COVID-19 pandemic has highlighted the risk of airborne transmission of infections in health-care facilities such as dental clinics. In this experimental study, methods to control airborne particles in a simulated dental clinic setting were measured and compared using a low cost and convenient technique. Materials and Methods: Particles representing inhalable airborne particles were generated using smoke from incense sticks and their concentration measured by handheld particle sensors whereas using different engineering controls for the particle removal in dental clinic equivalent settings. Measurements were made at short (<3 ft) and intermediate (between 3 and 6 ft) distance from the source. The particle filtration through surgical masks and N95 masks was also studied. Results: Natural ventilation, by keeping windows open, can reduce intermediate range particles (removal of 4.7% of ambient particles/min). However, in closed facilities without natural ventilation, particle removal by air purifier combined with overhead fan or with high volume evacuators was found most suitable for intermediate range particles (25.9%/min) and for short range particles (27.6%/min), respectively. N95 masks were found to filter out 99.5% of the generated PM 2.5 particles. Conclusions: Potentially inhalable airborne particles can persist in the air of a dental clinic. The use of N95 masks and environmental controls is essential for the dental team's safety. The choice of an engineering control is governed by multiple factors explained in the study. Smoke particles generated by incense sticks and measurement by handheld particle sensors are low-cost methods to estimate the effectiveness of airborne particle controls.