Grants and Contributions

Acentury will participate with consortium partners to research and create a process for creating “digital twins” for communication networks, also known as “Network Digital Twins” or NDTs.
The NDT will replicate a physical network, allowing for testing, optimization and simulation of network activity in a virtual environment. The purpose of the NDT / LAMTA integration is for operators to use "LAMTA NDT" to test their networks virtually, without always needing a physical lab.
Acentury will leverage our expertise in network element configuration, deployment and testing to generate customer specific NDTs that support a wide range of use cases.

Software development and demonstration project to enhance a route optimization platform that plans and optimizes delivery routes, tracks delivery progress, and automates customer notifications with estimated delivery time of arrivals. Improved algorithms will reduce time spent on the road and fuel consumption by at least 20% for delivery trucks through increased routing efficiency and applied AI technologies.

This project will develop, in collaboration with one of AGE-WELL National Innovation Hubs for agetech, hydration sensor capabilities that integrates with the Firm's platform to support safe, secure, and healthy independent aging.

This project aims to develop engineered hostPseudomonas aeruginosa strains with undesired prophages, phage defense systems, and virulence factors to streamline and enhance phage production. The work will begin with bioinformatic analyses to identify suitable candidate strains, followed by allelic exchange and targeted mutagenesis to remove unwanted elements. The engineered mutants will be evaluated for growth, fitness, and phage susceptibility relative to their parental strains. These optimized strains are expected to improve phage yield, simplify production workflows, reduce the number of required host strains, and prevent contamination from endogenous prophages.

The broader adoption of phage therapy for antimicrobial resistant infections is challenging due to the significant time required to screen for phage candidates, propagate them, purify them, and perform the required quality controls. This procedure can take upwards of 25 days and requires highly trained personnel. To overcome this challenge, this collaboration aims to develop a phage engineering platform and design phages capable of infecting a broader range of clinicalP. aeruginosa isolates to facilitate the deployment of phage therapy.

The overarching goal of this project is to develop and implement a MLdriven platform that integrates comprehensive omics and structural data to predict phage-host interactions with high precision, thus leveraging AI and ML to predict and optimize phage-host interactions with unprecedented accuracy. The primary goal of this platform is to provide accurate forecast phage therapy (PT) outcomes for existing phage-bacteria combinations by streamlining the selection of effective phage cocktails. This will significantly reduce the need for extensive lab testing, leading to more efficient, personalized and scalable PT applications.

In-situ sensing technologies with machine learning–based data interpretation for Cone Penetration Testing (CPT) systems to lower costs, improve safety, and minimize environmental impact while delivering more accurate subsurface models - reducing geotechnical risk.

The project goal is to integrate a number of complementary clean technology initiatives aimed at reducing greenhouse gas (GHG) emissions, improving energy efficiency, and minimizing waste. The focus is on particularly labour-intensive processes in the production facility. Each component of the project involves tailored engineering and process development to fit the firm's existing infrastructure and operational constraints.

This project will create and test a portable radio-wave imaging system that measures key soil properties with high precision. Combining multiple sensing technologies into one mobile unit, it will support smarter field management practices. Advancing from prototype to field-ready, the system will be evaluated in real-world conditions to generate useful data that help optimize crop production and promote sustainability.

The ES-PEMS Technology Development Project focuses on advancing and validating next-generation predictive emissions monitoring technology for the power generation sector. This project is not intended as a commercial deployment but rather as a structured R&D initiative designed to improve accuracy, standardization, and optimization capabilities through iterative testing, data acquisition, and model refinement at diverse demonstration sites.