Both data ferrying with disruption-tolerant networking (DTN) and mobile cellular base stations constitute important techniques for UAV-aided communication in situations of crises where standard communication infrastructure is unavailable. For optimal use of a limited number of UAVs, we propose providing both DTN and a cellular base station on each UAV. Here, DTN is used for large amounts of low-priority data, while capacity-constrained cell coverage remains reserved for emergency calls or command and control. We optimize cell coverage via a novel optimal transport-based formulation using alternating minimization, while for data ferrying we periodically deliver data between dynamic clusters by solving quadratic assignment problems. In our evaluation, we consider different scenarios with varying mobility models and a wide range of flight patterns. Overall, we tractably achieve optimal cell coverage under qualityof-service costs with DTN-based data ferrying, enabling large-scale deployment of UAV swarms for crisis communication.

Training helps meet specific skill deficits in employees' performance. Successful organizations and managers realize the importance of human resources; trained human resources are key to maintaining a competitive advantage in today's constantly changing global environment. An efficiently implemented training program leads to better employee performance. This study aims to test for important training-related variables that significantly affect the performance of bank employees in urban Lahore. Using earlier studies on training and job performance, we identify key variables and analyze them through a questionnaire-based survey carried out among 75 local consumer bank employees at various managerial levels. It is evident from our findings that a proper needs assessment, the extent of a training program's effectiveness, investment by the host organization, and the provision of training programs all significantly affect employees' job performance. This study provides managers with an insight into important aspects of designing training programs to ensure higher employee productivity.

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Scheduling decisions in parallel queuing systems arise as a fundamental problem, underlying the dimensioning and operation of many computing and communication systems, such as job routing in data center clusters, multipath communication, and Big Data systems. In essence, the scheduler maps each arriving job to one of the possibly heterogeneous servers while aiming at an optimization goal such as load balancing, low average delay or low loss rate. One main difficulty in finding optimal scheduling decisions here is that the scheduler only partially observes the impact of its decisions, e.g., through the delayed acknowledgements of the served jobs. In this paper, we provide a partially observable (PO) model that captures the scheduling decisions in parallel queuing systems under limited information of delayed acknowledgements. We present a simulation model for this PO system to find a near-optimal scheduling policy in real-time using a scalable Monte Carlo tree search algorithm. We numerically show that the resulting policy outperforms other limited information scheduling strategies such as variants of Join-the-Most-Observations and has comparable performance to full information strategies like: Join-the-Shortest-Queue, Join-the-Shortest-Queue(d) and Shortest-Expected-Delay. Finally, we show how our approach can optimise the real-time parallel processing by using network data provided by Kaggle.

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