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As an initiative to increase students' interest in microelectronics and as an effort to introduce more leaming situations involving so-called "soft skills '' like presentations and team management into the curriculum, a novel project-oriented advanced-level microelectronics course has been offered. The common goal of all students in this one semester course is to design, implement, and verif4' an, ASIC, starting with a rough specification, using state-o$ the-art software tools and a standard cell design flow, down to the final layout specification and manufacturing. Students start to work in groups on sub-modules of the whole design, which requires them to coordinate their work, plan new tasks as the project progresses, and assume new responsibilities to succeed. A special presentation skills class helps students to prepare the mandatory final oral presentation. Lessons learned from two holdings of the course are summarized.

We applied a queuing model to inform ventilator capacity planning during the first wave of the COVID-19 epidemic in the province of British Columbia (BC), Canada. The core of our framework is a multi-class Erlang loss model that represents ventilator use by both COVID-19 and non-COVID-19 patients. Input for the model includes COVID-19 case projections, and our analysis incorporates projections with different levels of transmission due to public health measures and social distancing. We incorporated data from the BC Intensive Care Unit Database to calibrate and validate the model. Using discrete event simulation, we projected ventilator access, including when capacity would be reached and how many patients would be unable to access a ventilator. Simulation results were compared with three numerical approximation methods, namely pointwise stationary approximation, modified offered load, and fixed point approximation. Using this comparison, we developed a hybrid optimization approach to efficiently identify required ventilator capacity to meet access targets. Model projections demonstrate that public health measures and social distancing potentially averted up to 50 deaths per day in BC, by ensuring that ventilator capacity was not reached during the first wave of COVID-19. Without these measures, an additional 173 ventilators would have been required to ensure that at least 95% of patients can access a ventilator immediately. Our model enables policy makers to estimate critical care utilization based on epidemic projections with different transmission levels, thereby providing a tool to quantify the interplay between public health measures, necessary critical care resources, and patient access indicators.

We present a queue model to inform ventilator capacity management under different COVID-19 pandemic scenarios. Our model was used to support ventilator capacity planning during the first wave of the COVID-19 epidemic in British Columbia (BC), Canada. The core of our framework is an extended Erlang loss model, which incorporates COVID-19 case projections, along with the proportion of cases requiring a ventilator, the delay from symptom onset to ventilation, non-COVID-19 ventilator demand, and ventilation time. We implemented our model using discrete event simulation to forecast ventilator utilization. The results predict when capacity would be reached and the rate at which patients would be unable to access a ventilator. We further determined the number of ventilators required to meet a performance indicator target for ventilator access. We applied our model to BC by calibrating to the BC Intensive Care Unit Database and by using local epidemic projections. Epidemic scenarios with and without reduced transmission, due to social distancing and other behavioral changes, were used to link public health interventions to operational impacts on ventilator utilization. The results predict that reduced transmission could potentially avert up to 50 deaths per day by ensuring that ventilator capacity would likely not be reached. Without reduced transmission, an additional 181 ventilators would be required to meet our performance indicator target that 95% of patients can access a ventilator immediately. Our model provides a tool for policy makers to quantify the interplay between public health interventions, necessary critical care resources, and performance indicators for patient access.