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Real-time visual object tracking (VOT) may suffer from performance degradation and even divergence owing to inaccurate noise statistics typically engendered by non-stationary video sequences or alterations in the tracked object. This paper presents a novel adaptive Kalman filter (AKF) algorithm, termed AKF-ALS, based on the autocovariance least square estimation (ALS) methodology to improve the accuracy and robustness of VOT. The AKF-ALS algorithm involves object detection via an adaptive thresholding-based background subtraction technique and object tracking through real-time state estimation via the Kalman filter (KF) and noise covariance estimation using the ALS method. The proposed algorithm offers a robust and efficient solution to adapting the system model mismatches or invalid offline calibration, significantly improving the state estimation accuracy in VOT. The computation complexity of the AKF-ALS algorithm is derived and a numerical analysis is conducted to show its real-time efficiency. Experimental validations on tracking the centroid of a moving ball subjected to projectile motion, free-fall bouncing motion, and back-and-forth linear motion, reveal that the AKF-ALS algorithm outperforms a standard KF with fixed noise statistics. [ABSTRACT FROM AUTHOR]

Titanium dioxide (TiO2) is a known photocatalyst with a capability of decomposing organic substances. However, the photocatalysis of the pure TiO2 is not effective for the indoor environment due to a lack of the ultraviolet irradiation inside a building. Doping TiO2 with substance such as C, N, or metal can extend the threshold of the absorption spectrum to the visible spectrum region. Thus, doped-TiO2 is able to decompose volatile organic compounds (VOCs) under an indoor environment. To date, most experimental works reported on photocatalytic kinetics were conducted inside small-scale devices. The performance of air purification function under the actual indoor application scenery need to be further clarified. For this purpose, it is crucial to predict the performance of autogenous air quality improvements by visible light-driven photocatalyst for the actual applications. This work has developed a model to evaluate the performance of functional coating with photocatalyst in removing VOCs. Factors such as the effects of coating designs and indoor ambient conditions on the air purification efficiency were studied. This work demonstrates that doped-TiO2 photocatalytic coating is effective to improve the indoor air quality. [ABSTRACT FROM AUTHOR]

Nitrogen-doped TiO2 has a great potential as a photocatalyst under visible light irradiation with applications in the removal of air and water pollutants, and the treatment of bacterial contaminations. In this study, nitrogen-doped TiO2 nanoparticles were synthesized via the sol–gel method and a post-annealing heat treatment approach. The effects of annealing treatment on the photocatalyst crystalline size and degree of crystallinity were analyzed. Methylene blue dye was used as the model water contaminant for the evaluation of the photoactivity of the synthesized nitrogen-doped TiO2 nanoparticles. The degradation of methylene blue was attributed to three mechanisms, i.e., adsorption, photocatalysis, and direct light photolysis. A kinetic model was developed to distinguish the impact of these three different mechanisms on the removal of contaminants. Adsorption and photocatalysis are heterogeneous processes for removing water organic contaminants. The characterization analysis demonstrates that they are relevant to the microstructures and surface chemical compositions of nitrogen-doped TiO2 photocatalysts. The processing–structure–performance relationship helped to determine the optimal processing parameters for nitrogen-doped TiO2 photocatalyst to achieve the best performance. While we used methylene blue as the model contaminant, the generalized quantitative model framework developed in this study can be extended to other types of contaminants after proper calibration. [ABSTRACT FROM AUTHOR]

Air pollution has become a serious public health problem and there is evidence that air pollution affects the incidence of allergic rhinitis. To further investigate the effect of ambient air pollutants on the severity of allergic rhinitis symptoms, a prospective follow-up study in patients with allergic rhinitis was conducted. A total of 167 allergic rhinitis patients with a mean age of 35.4 years, who were visiting the hospital, were enrolled. The daily symptom severity of allergic rhinitis and the concentrations of six air pollutants, including PM 2.5 , PM 10 , SO 2 , CO, O 3 and NO 2, were collected through follow-up investigations. The impact of ambient air pollutants on symptom severity was assessed via multi-pollutant models. Among several typical ambient air pollutants, we observed correlations of allergic rhinitis symptoms with PM 2.5 , PM 10 , CO, SO 2 and NO 2 , whereas O 3 showed no such correlation. Specifically, PM 2.5 and PM 10 were significantly associated with sneezing and nasal blockage. NO 2 was significantly correlated with symptoms of rhinorrhea, itchy nose and itchy eyes. CO was significantly linked to sneezing and nasal blockage symptoms. These air pollutants not only had a direct impact on allergic rhinitis symptoms but also exhibited a lagging effect. This study indicates that short-term exposure to air pollutants is associated with exacerbation of nasal symptoms in patients with allergic rhinitis, leading to a decline in their quality of life.

Titanium dioxide (TiO 2 ) is a well-known photocatalyst in the applications of water contaminant treatment. Traditionally, the kinetics of photo-degradation rates are obtained from experiments, which consumes enormous labor and experimental investments. Here, a generalized predictive model was developed for prediction of the photo-degradation rate constants of organic contaminants in the presence of TiO 2 nanoparticles and ultraviolet irradiation in aqueous solution. This model combines an artificial neural network (ANN) with a variety of factors that affect the photo-degradation performance, i.e., ultraviolet intensity, TiO 2 dosage, organic contaminant type and initial concentration in water, and initial pH of the solution. The molecular fingerprints (MF) were used to interpret the organic contaminants as binary vectors, a format that is machine-readable in computational linguistics. A dataset of 446 data points for training and testing was collected from the literature. This predictive model shows a good accuracy with a root mean square error (RMSE) of 0.173., (Copyright © 2020 Elsevier Inc. All rights reserved.)