Estimating the effect of timetabling decisions on the spread of SARS-CoV-2 in medium-to-large engineering schools in Canada: an agent-based modelling study.

BACKGROUND: During the COVID-19 pandemic, universities transitioned to primarily online delivery, and it is important to understand what implications the transition back to in-person activities may have on spread of SARS-CoV-2 in the student population. The specific aim of our study was to provide insights into the effect of timetabling decisions on the spread of SARS-CoV-2 in a population of undergraduate engineering students. METHODS: We developed an agent-based modelling simulation that used a Canadian first-year undergraduate engineering program with an enrolment of 180 students in 5 courses of 12.7 weeks in length. Each course involved 150 minutes of lectures and 110 minutes of tutorials or laboratories per week. We considered several online and in-person timetabling scenarios with different scheduling frequencies and section sizes, in combination with surveillance and testing interventions. The study was conducted from May 1 to Aug. 31, 2021. RESULTS: When timetabling interventions were applied, we found a reduction in the mean number of students who were infected and that a containment of widespread outbreaks could be achieved. Timetables with online lectures and small (1/6 class capacity) tutorial or laboratory sections reduced the mean number of students who were infected by 83% and reduced the risk of large outbreaks that occurred with in-person lectures. We also found that spread of SARS-CoV-2 was less sensitive to class size than to contact frequency when a biweekly timetable was implemented (i.e., alternating online and in-person sections on a biweekly basis). Including a contact-tracing policy and randomized testing to the timetabling interventions helped to contain the spread of SARS-CoV-2 further. Vaccination coverage had the largest effect on reducing the number of students who were infected. INTERPRETATION: Our modelling showed that by taking advantage of timetabling opportunities and applying appropriate interventions (contact tracing, randomized testing and vaccination), SARS-CoV-2 infections may be averted and disruptions (case isolations) reduced. However, given the emergence of SARS-CoV-2 variants, transitions from online to in-person classes should proceed cautiously from small biweekly classes, for example, to manage risk.

An Embedded Multi-Agent Systems Based Industrial Wireless Sensor Network.

With the emergence of cyber-physical systems, there has been a growing interest in network-connected devices. One of the key requirements of a cyber-physical device is the ability to sense its environment. Wireless sensor networks are a widely-accepted solution for this requirement. In this study, an embedded multi-agent systems-managed wireless sensor network is presented. A novel architecture is proposed, along with a novel wireless sensor network architecture. Active and passive wireless sensor node types are defined, along with their communication protocols, and two application-specific examples are presented. A series of three experiments is conducted to evaluate the performance of the agent-embedded wireless sensor network.

A Comparison of Alternative Distributed Dynamic Cluster Formation Techniques for Industrial Wireless Sensor Networks.

In this paper, we investigate alternative distributed clustering techniques for wireless sensor node tracking in an industrial environment. The research builds on extant work on wireless sensor node clustering by reporting on: (1) the development of a novel distributed management approach for tracking mobile nodes in an industrial wireless sensor network; and (2) an objective comparison of alternative cluster management approaches for wireless sensor networks. To perform this comparison, we focus on two main clustering approaches proposed in the literature: pre-defined clusters and ad hoc clusters. These approaches are compared in the context of their reconfigurability: more specifically, we investigate the trade-off between the cost and the effectiveness of competing strategies aimed at adapting to changes in the sensing environment. To support this work, we introduce three new metrics: a cost/efficiency measure, a performance measure, and a resource consumption measure. The results of our experiments show that ad hoc clusters adapt more readily to changes in the sensing environment, but this higher level of adaptability is at the cost of overall efficiency.

Development of a finger printing device for use on a mobile robot.

In this paper, we describe the development of a device for fuming fingerprints with cyanoacrylate (Super Glue) to enable police tactical units to obtain fingerprint evidence from suspicious packages using a remote-controlled robot. Through a series of initial experiments and preliminary designs, we show that effective cyanoacylate fuming requires sufficient heat, humidity, and airflow. This work led to the development of a final working prototype, called robot accessory for fuming fingerprint evidence (RAFFE), which is currently being field tested by the Calgary Police Service.