Model-driven mitigation measures for reopening schools during the COVID-19 pandemic

Reopening schools is an urgent priority as the COVID-19 pandemic drags on. To explore the risks associated with returning to in-person learning and the value of mitigation measures, we developed stochastic, network-based models of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission in primary and secondary schools. We find that a number of mitigation measures, alone or in concert, may reduce risk to acceptable levels. Student cohorting, in which students are divided into two separate populations that attend in-person classes on alternating schedules, can reduce both the likelihood and the size of outbreaks. Proactive testing of teachers and staff can help catch introductions early, before they spread widely through the school. In secondary schools, where the students are more susceptible to infection and have different patterns of social interaction, control is more difficult. Especially in these settings, planners should also consider testing students once or twice weekly. Vaccinating teachers and staff protects these individuals and may have a protective effect on students as well. Other mitigations, including mask wearing, social distancing, and increased ventilation, remain a crucial component of any reopening plan.

Co-Evolution of Communication and System Performance in Engineering Systems Design: A Stochastic Network-Behavior Dynamics Model

The socio-technical perspective on engineering system design emphasizes the mutual dynamics between interdisciplinary interactions and system design outcomes. How different disciplines interact with each other depends on technical factors such as design interdependence and system performance. On the other hand, the design outcomes are influenced by social factors such as the frequency of interactions and their distribution. Understanding this co-evolution can lead to not only better behavioral insights, but also efficient communication pathways. In this context, we investigate how to quantify the temporal influences of social and technical factors on interdisciplinary interactions and their influence on system performance. We present a stochastic network-behavior dynamics model that quantifies the design interdependence, discipline-specific interaction decisions, the evolution of system performance, as well as their mutual dynamics. We employ two datasets, one of student subjects designing an automotive engine and the other of NASA engineers designing a spacecraft. Then, we apply statistical Bayesian inference to estimate model parameters and compare insights across the two datasets. The results indicate that design interdependence and social network statistics both have strong positive effects on interdisciplinary interactions for the expert and student subjects alike. For the student subjects, an additional modulating effect of system performance on interactions is observed. Inversely, the total number of interactions, irrespective of their discipline-wise distribution, has a weak but statistically significant positive effect on system performance in both cases. However, excessive interactions mirrored with design interdependence and inflexible design space exploration reduce system performance. These insights support the case for open organizational boundaries as a way for increasing interactions and improving system performance.

QoS-aware Resource Allocation for Live Streaming in Edge-Clouds Aided HetNets Using Stochastic Network Calculus

Mobile edge computing (MEC) is a key feature of next generation mobile networks aimed at providing a variety of services for different applications by performing related processing tasks closer to the user equipment. Edge clouds can be installed as an interface between the cellular networks and the core to provide the required services based on the known concept of the MEC networks. Nonetheless, the problem of green networking will be of great importance in such networks. This paper presents an energy-efficient stochastic network calculus (SNC) framework to control MEC data flows. In accordance with the entrance processes of different QoS-class data flows, closed-form problems were formulated to determine the correlation between resource utilization and the violation probability of each data flow. Also, in the access layer, this paper proposes a dynamic user association and resource allocation approach which maximizes the overall energy efficiency of cache-enabled cellular networks in addition to provide the superior fairness level for UEs. In this energy-cooperative approach, the power can be shared among the cells using a grid network. This model also performs routing in the multi-hop backhaul to efficiently use the existing infrastructure of small cell networks for simultaneous dual-hop transmissions. The simulation results exhibit that the proposed approach can effectively increase the user throughput and the total power efficiency while guaranteeing the acceptable fairness level for uniform and hotspot UE distribution models. It also proved that the energy utilization index and the system data rate can be significantly improved.