To treat dyeing and finishing industrial wastewater effluents by an advanced oxidation process, the decomposition kinetics of methylene blue by nanofine sol TiO 2 /UV was systematically studied in two batch slurry reactors. The factors of study include agitation speed, recirculation flow rate, initial dissolved oxygen concentration, initial methylene blue concentration, reaction temperature, TiO 2 dosage, and UV light intensity. By using the network reduction technique, the general rate equation has been developed from considering the reaction mechanism. The rate equation uses a systematic reaction network reduction technique to avoid the need of more restrictive assumptions commonly used, such as a rate-determining step or equilibrium steps. The resulting rate equation is applicable to a wider range of reaction conditions. After applying the resulting rate equation to predict the transient concentrations of other dye compounds, we expect it can be applied for the kinetics of photodecomposition of other organic compounds.
Location services, fundamentally, rely on two components: a mapping system and a positioning system. The mapping system provides the physical map of the space, and the positioning system identifies the position within the map. Outdoor location services have thrived over the last couple of decades because of well-established platforms for both these components (e.g. Google Maps for mapping, and GPS for positioning). In contrast, indoor location services haven't caught up because of the lack of reliable mapping and positioning frameworks. Wi-Fi positioning lacks maps and is also prone to environmental errors. In this paper, we present DLoc, a Deep Learning based wireless localization algorithm that can overcome traditional limitations of RF-based localization approaches (like multipath, occlusions, etc.). We augment DLoc with an automated mapping platform, MapFind. MapFind constructs location-tagged maps of the environment and generates training data for DLoc. Together, they allow off-the-shelf Wi-Fi devices like smartphones to access a map of the environment and to estimate their position with respect to that map. During our evaluation, MapFind has collected location estimates of over 105 thousand points under 8 different scenarios with varying furniture positions and people motion across two different spaces covering 2000 sq. Ft. DLoc outperforms stateof-the-art methods in Wi-Fi-based localization by 80% (median & 90 th percentile) across the two different spaces. CCS CONCEPTS • Networks → Location based services; • Computing methodologies → Robotic planning; Supervised learning; • Information systems → Sensor networks.
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