Global responses to the COVID-19 pandemic have largely been suboptimal due to significant underdevelopment of infrastructure, human capital and analytics in pandemic prevention, preparedness, and response (PPR). In particular, epidemic nowcasting has been universally challenging because it requires distilling informative or actionable insights from diverse range of real-world data which are often biased. Misinterpretation, misrepresentation or otherwise misuse of these nowcasts will fuel infodemics, as we have learned to our detriment during the COVID-19 pandemic. We will discuss some lessons learned from COVID-19 and how we can strengthen pandemic PPR in the Age of Information.
The Longyou Caves represent an important historical site in China that has undergone periodic water level changes over several centuries. The ground water flow through the intact rock and fractures is an important factor in the geotechnical assessment of the site. The Environmental Geomechanics Laboratory at McGill University has focused on the development of innovative theoretical approaches and experimental facilities for wide range of rocks including Indiana Limestone, the Cobourg Limestone, the Vermont Granite and the Lac du Bonnet Granite, using both steady state and transient techniques. In this Teck Talk, Professor Selvadurai will present a range of experimental techniques, their theoretical interpretations that can be used to estimate of fluid transport processes through intact rocks that can be described by Darcy’s law. The theoretical and experimental techniques are used to determine the intact permeability of Longyou claystone recovered from the site.
Emerging infectious diseases, such as COVID-19 and pandemic influenza, have a significant impact on the healthcare system and the society. Rapid diagnostic tests are essential for guiding patient management and infection control measures, which lead to improvement in patient outcome and prevent outbreaks in the community and in hospitals. In recent years, fully automated testing has greatly reduced the complexity of diagnostic testing and shortened the turn-around time. Despite their potential benefits, several challenges need to be addressed. In this talk, Professor To will present the advances in rapid diagnostic testing, and will discuss about the hurdles in implementing these novel technologies in real-life settings.
By integrating sensing, memory and processing functionalities, biological nervous systems are energy and area efficient. Emulating such capabilities in artificial systems is, however, challenging and is limited by the device heterogeneity of sensing and processing cores., Here, we present a universal solution to simultaneously perform multi-modal sensing, memory and processing using organic electrochemical transistors. The device has a vertical traverse architecture and a crystalline–amorphous channel that can be selectively doped by ions to enable two reconfigurable modes: volatile receptor and non-volatile synapse. As a volatile receptor, the device is capable of multi-modal sensing, and as a non-volatile synapse, it is capable of 10-bit analogue states, low switching stochasticity and good state retention. Homogeneous integration of such devices enables functions such as conditioned reflex and real-time cardiac disease diagnose via reservoir computing, illustrating the promise for future edge AI hardware.
Miniature optoelectronic sensors which have features of convenient, reliable, economic, ultra sensitive, and capable of real-time measurement are highly desirable nowadays. However, currently reported optical and electronic sensing devices are still hindered with complex optical components and bulky equipment. Hence, we hope to further minimize the volume of the sensing system and get rid of the dependence on complex, expensive and bulky sensing components. In particular, we demonstrate a micro-scale III-nitride chip that integrates a light emitter (LED) and a photodetector (PD) together, realizing the emission and detection of signals in a single miniature chip. Thus, we have applied the device into some classical sensing, such as pressure, salinity content and cell activities sensing. Additionally, we also conduct integration on the diamond based quantum sensing system, and demonstrate a compact chip architecture (sub ~mm3 volume) being capable of on-chip quantum sensing.