Dynamic Repositioning of Aerial Base Stations for Enhanced User Experience in 5G and Beyond.

The ultra-dense deployment (UDD) of small cells in 5G and beyond to enhance capacity and data rate is promising, but since user densities continually change, the static deployment of small cells can lead to wastes of capital, the underutilization of resources, and user dissatisfaction. This work proposes the use of Aerial Base Stations (ABSs) wherein small cells are mounted on Unmanned Aerial Vehicles (UAVs), which can be deployed to a set of candidate locations. Furthermore, based on the current user densities, this work studies the optimal placement of the ABSs, at a subset of potential candidate positions, to maximize the total received power and signal-to-interference ratio. The problems of the optimal placement for increasing received power and signal-to-interference ratio are formulated, and optimal placement solutions are designed. The proposed solutions compute the optimal candidate locations for the ABSs based on the current user densities. When the user densities change significantly, the proposed solutions can be re-executed to re-compute the optimal candidate locations for the ABSs, and hence the ABSs can be moved to their new candidate locations. Simulation results show that a 22% or more increase in the total received power can be achieved through the optimal placement of the Aerial BSs and that more than 60% users have more than 80% chance to have their individual received power increased.

Using Pre-stimulus EEG to Predict Driver Reaction Time To Road Events.

The ability to predict a driver's reaction time to road events could be used in driver safety assistance systems, allowing for autonomous control when a driver may be about to react with sup-optimal performance. In this paper, we evaluate a number of machine learning and feature engineering strategies that we use to predict the reaction time(s) of 24 drivers to road events using EEG (Electroencephalography) captured in an immersive driving simulator. Subject-independent models are trained and evaluated using EEG features extracted from time periods that precede the road events that we predict the reaction times for. Our paper has two contributions: 1) we predict the reaction times corresponding to individual road events using EEG spectral features from a time period before the onset of the road event, i.e. we take EEG data from 2 seconds before the event, and 2) we predict whether a subject will be a slow or fast responder compared to other drivers.

Investigating Inclusive Leadership and Pro-Social Rule Breaking in Hospitality Industry: Important Role of Psychological Safety and Leadership Identification.

This study aims to empirically examine the mediating effects of psychological safety and leadership identification on the relationship between inclusive leadership and pro-social rule breaking among hospitality employees. This study analyzes the survey data collected in three waves from 589 employees working in different hotels and restaurants operating in the Northern areas of Khyber Pakhtunkhwa, Pakistan. The scale validity, composite reliability, and hypotheses were assessed through PLS-SEM. The study found that inclusive leadership significantly impacts employees' pro-social rule-breaking. The study also found that leadership identification and psychological safety partially mediate the relationship between inclusive leadership and pro-social rule-breaking. Hospitality leaders can practice inclusive leadership characteristics because it may significantly enhance employee engagement in pro-social rule-breaking. Through their inclusive features, hospitality leaders can improve employees' psychological safety and leadership identification, enhancing frontline employees' pro-social rule-breaking.