This paper presents a study of positioning system that provides advanced information services based on Wi-Fi and Bluetooth Low Energy (BLE) technologies. It uses Wi-Fi for rough positioning and BLE for fine positioning. It is designed for use in public transportation system stations and terminals where the conditions are “hostile” or unfavourable due to signal noise produced by the continuous movement of passengers and buses, data collection conducted in the constant presence thereof, multipath fading, non-line of sight (NLOS) conditions, the fact that part of the wireless communication infrastructure has already been deployed and positioned in a way that may not be optimal for positioning purposes, variable humidity conditions, etc. The ultimate goal is to provide a service that may be used to assist people with special needs. We present experimental results based on scene analysis; the main distance metric used was the Euclidean distance but the Mahalanobis distance was also used in one case. The algorithm employed to compare fingerprints was the weighted k-nearest neighbor one. For Wi-Fi, with only three visible access points, accuracy ranged from 3.94 to 4.82 m, and precision from 5.21 to 7.0 m 90% of the time. With respect to BLE, with a low beacon density (1 beacon per 45.7 m2), accuracy ranged from 1.47 to 2.15 m, and precision from 1.81 to 3.58 m 90% of the time. Taking into account the fact that this system is designed to work in real situations in a scenario with high environmental fluctuations, and comparing the results with others obtained in laboratory scenarios, our results are promising and demonstrate that the system would be able to position users with these reasonable values of accuracy and precision.
Purpose The purpose of this paper is to examine whether the appearance of cyberloafing at work, that is, the use of the company’s internet connection for personal purposes, may be due to a workplace that lacks mindfulness and compassion. The authors first hypothesize that supervisors’ mindfulness is related to the mindfulness of their direct followers, and that both are related to employees’ compassion at work. The authors also hypothesize that compassion mediates the link between supervisors’ and followers’ mindfulness and cyberloafing, and that empathic concern mediates the link from compassion to cyberloafing. Design/methodology/approach A questionnaire was distributed to followers working in groups of three with the same leader in all of the 100 banks in London (UK). Supervisors and their direct reports (n=100) and 100 triads of followers (n=300) participated. The authors applied structural equation modeling (SEM) for analyses. Findings Results showed that supervisors’ and followers’ mindfulness were significantly related to each other and to compassion at work, but compassion acted as a mediator only in the case of supervisors’ mindfulness. Empathic concern mediated the compassion-cyberloafing link. Research limitations/implications The study could suffer from mono-method/source bias and specificities of banks and their work processes can raise concerns about the generalizability of the results. Practical implications Findings suggest that mindfulness training may facilitate compassion at work, which, in turn, will restrain the occurrence of cyberloafing at work. Originality/value This is the first study to analyze how and why employees refrain from harming their organizations out of compassion.
In road-based mass transit systems, travel time is a key factor in providing quality of service. This article proposes a method of predicting travel time for this type of transport system. This method estimates travel time by taking into account its historical behaviour, represented by historical profiles, and the current behaviour recorded on the public transport vehicle for which the prediction is to be made. The model uses the k-medoids clustering algorithm to obtain historical travel time profiles. A relevant feature of the model is that it does not require recent travel time data from other vehicles. For this reason, the proposed model may be used in intercity transport contexts in which service planning is carried out according to timetables. The proposed model has been tested with two real cases of intercity public transport routes and from the results obtained we may conclude that, in general, the average error of the predictions is around 13% compared to the observed travel time values.
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