The use of hand disinfectant appears to have become a common practice for infectious as well as non-infectious individuals across the world for years now. Recently, a strong push to enforce the usage of hand disinfectants after touching any surface in public places due to the pressure of Covid-19 pandemic was seen worldwide. Many varieties of hand disinfectants for killing bacteria and viruses have been in use across the world. However, most of them use flammable solvents including alcohol as the main disinfectant.
A significant fraction of wet organic waste streams within typical municipalities include food wastes (collected by many as source separated organics), and sludges or biosolids. In Canada, such wastes are mostly going to landfill or being land applied to recover some nutrient value. Anaerobic digestion (AD) for disposal of organic waste is an attractive option to recover renewable bioenergy (as methane or hydrogen), with additional environmental benefits from preventing such wastes from going to the landfill.
Ground movement can impose excessive deformation violating pertinent pipeline limit states. Currently, the integrity assessment of pipelines subjected to soil movement is generally performed by analyzing the stresses and/or strains in pipelines using various engineering techniques, including finite element analysis (FEA). However, given the wide variability of the pipe and soil engineering properties, using deterministic approaches alone may be inadequate.
Our proposal continues our SFPE International research project on Anthropometric Data and Movement Speeds but directs the project’s goal to enhancing fire engineering resource development and research dissemination in Quebec. Our proposal is multi-staged. We primarily focus on the finalization of the compilation of existing emergency movement data which can be used by various engineering consultancies conducting ASET / RSET analyses or alternative solutions for egress management.
Work improvement is critical for performance increase in business environments. It is used to identify bottlenecks and inefficiencies in the manufacturing and other production processes, and to improve work performance by removing non-value-added activities. To conduct work improvement, the Lean Manufacturing concept is often used along with the Value Stream Mapping (VSM), a tool for visualizing the production processes and productivity metrics.
The proposed project aims to improve the prediction of mineral resources for better decision making throughout a mining project, that is, to decide whether to reject or to process extracted material using machine learning algorithms. This will help maximize profit for mining companies while minimizing environmental impact as the correct material will be processed more often. The use of machine learning algorithms has become popular recently due to the ability to learn important features from a large amount of data.
Concrete is a major construction material used worldwide responsible for the production of roughly 7% of total global carbon dioxide emissions. The extent of its environmental impact relates to the energy embodied in extraction and transportation of concrete aggregates with a direct link to the amount of Portland cement (PC) used to bind the raw materials. Recent advances in design protocols, packing models, and geopolymers are increasingly being used to minimize concrete’s carbon footprint and to produce PC free mixes.
Perfluoroalkyl and Polyfluoroalkyl substances (PFAS) are anthropogenic compounds with unique properties and wide applications. The consequence of using such persistent chemicals is widespread contamination reported for groundwater, soil, sediment, and wastewater, especially in industrialized countries such as Canada. The endocrine-disrupting and likely carcinogenic nature of PFAS have resulted in strict regulations on PFAS in drinking water.
Bicycle and pedestrian counts are important data for the planning and design of safe roads. However, these data need to be inspected for quality, a time-consuming task. Part of this project is to make this project simpler, quicker and more accurate. Installing pedestrian and bicycle counters across an entire city road network is not financially viable. Therefore, a good option is to estimate counts at the network scale, using knowledge from a handful of pedestrian and bicycle counters (strategically placed) and trip data from users who willingly share their position from their smartphones.
This research project explores the application of an artificial intelligence-based monitoring system comprised of image-based sensors and processing algorithms to detect, identify, and monitor the incoming presence of wet wipes and nonwovens in urban drainage systems in near real-time to pre-empt the effects of the damages caused by users’ disposal of these products in toilets. The AI-based system, to be employed in a number of monitoring locations simultaneously, will be used to establish a library of detected materials to identify and categorize incoming products (e.g.