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The effects of air pollution on the general public received much attention recently. Personal exposure and deposition fraction of aerosol particles were studied in Vilnius, Lithuania, focusing on individuals working in an office and driving to work. Aerosol monitoring in the urban background was found to give an indication of the minimum concentrations of particulate matter (PM) expected at urban roads, as these correspond to the lowest PM concentrations measured there. In March 2021, PM2.5 concentrations at the urban background monitoring station reached values above the annual limit of 5 μg/m3 the World Health Organization in 50% of cases. Our study shows significant differences in exposure to air pollution in a car cabin and in a modern office. According to the multiple-path particle dosimetry model, the exposure of the person in the office is about 14 times lower than driving a car, where the minute deposition dose for PM1 is 0.072 µg/min for the period when the PM2.5 concentration in the urban background reaches 10 µg/m³. Compared to the PM2.5 mass concentration at the urban background station, the mean PM2.5 concentration in the vehicle reaches values that are 2–3 times higher. During the working day, when driving takes less than 10% of the time considered (commuting plus working), PM exposure during driving accounts for about 80% of the PM exposure caused by PM concentration in the office. [ABSTRACT FROM AUTHOR]

Recommender systems analyze conditions and user behaviors to recommend proportional services to users. Since the aim of such systems is to provide the most appropriate services, it appears essential to use filtering techniques to limit recommender items. In this study, spatial criteria such as distance, movement direction, visibility, and topological relationships were employed as filtering tools to provide the right items. Our model creates appropriate items for better recommendation based on spatial relationships between users and the surrounding service sites. This method demonstrates that the number of recommended items can be limited by considering the shortest distance from the service centers intended by users and taking user direction into account. Moreover, appropriate service centers can be proposed with respect to user visibility. In this study, topological relationships between user location and near places were used as spatial filters, too. Further, if these filters can interact with the environment in the same way as humans, it can be expected the recommendation process to improve. Thus, our model uses the fuzzy approach to help the system to perceive the uncertainty of the spatial linguistic terms. To evaluate the performance and effectiveness of our proposed spatial filtering model, we conduct several experiments on real datasets that were obtained from tracking the users' location through GPS. Considering the actual conditions, this system solved the cold start problem using spatial filtering model. Experimental results show that 68% of test users considered our recommendations as relevant in new item cold start problem. Moreover, results reveal that compared with an LA-LDA model, using spatial filtering in cold start item problem is more robust. [ABSTRACT FROM AUTHOR]

A pervasive computing environment is characterized by computing capabilities and effective use of smart spaces. Many critical elements of pervasive computing, such as wireless networks are now viably commercial products. Ambient services are known as one of the main types of pervasive information services in which suitable information is presented to the users based on their surrounding geographical environments. Increase in the use of such services may cause service domain overlapping in which many of the users are located in several domains simultaneously. The aim of this paper is to present the best information service composition to such users by considering spatio-temporal contexts and analyses. User's location, direction and speed were regarded as the most effective spatial contexts. Spatio-temporal topological relationships as well as influence ability relation were used to model the topological context. The proposed methodology was implemented in three nested service domains. Based on the results, composition of the proposed services and the period for service update were described as "good" by 75 and 80 percent of the users, respectively. [ABSTRACT FROM AUTHOR]