Innovative Projects Realized

Synthesis of Novel Coumarin Derivatives with Anti-Neuroinflammatory Activity

Working with Reinforcement Learning for Robot Manipulation

Why Galaxies Spin

Our galaxy the Milky Way and our orbiting companion Andromeda are the dominant members of a small collection of galaxies, the Local Group, which is about 3 million light years across. Recently, it was recognized that the Local Group is simply a mass concentration in a thin “Local Sheet” of galaxies spanning 35 million light years. Furthermore, the largest members of the Sheet encircle us. This unusual environment may have played a role in guiding the evolution of the Local Group, and the Local Group may have influenced the evolution of the Local Sheet. The proposed research is aimed at understanding this interplay. Specifically, the spin of galaxies in analogues of the Local Sheet will be measured to study how spin axes are aligned, which in turn will enable us to learn how the Milky Way and its neighbours in the Local Sheet started spinning in the first place.

Disturbed Repolarisation-Relaxation Coupling: A Novel Mechanism of Systolic Mechano-Arrhythmogenesis

Thermo-economic analysis of Carnot Batteries

Machine Learning in Chemical Kinetic Modeling for Sustainable Aviation Fuels

Modelling the effect of pathway perturbations using Reactome

THIS IS A GENERIC TEXT PUT IN PLACE AS THERE WAS NO PROJECT OVERVIEW

Synthesis of Cationic tungsten(VI) species stabilized by cyanoborate anions

Cyanoborate anions, negatively charged B–CN compounds, have been well established as important building blocks in metal organic frameworks, room-temperature ionic liquids (RTIL’s), and part of metal complexes. RTIL are particularly important in green chemistry, because of absence of any toxic vapours, and in photovoltaic cells, to name only a few applications. Certain cationic species, such as WF5+ and BF2+ can only be prepared with stabilizing nitrogen-bases, which coordinate to the cations donating electron density. Furthermore, WOF3+ and MoOF3+ species have not been reported in the literature, but unpublished work has shown that they are assessable using nitrogen bases to stabilize these highly electron-poor cations. Herein, it is proposed to use cyanoborate anions to stabilize BF2+, WF5+, MoOF3+ and WOF3+ centres resulting in unique RTIL’s with large anions. In addition, the resulting compounds’ fluorinating properties would be of interest.

Internship in the field of Artificial Intelligence in the context of Stream Processing

Mechanisms driving early T follicular helper cell fate decisions

Automated and smart manufacturing system for Resistance temperature detectors (RTDs) fabrication

The research project aims to revolutionize the manufacturing of temperature sensors, specifically Resistance Temperature Detectors (RTDs), through cutting-edge automation and advanced welding techniques. Think of RTDs as the devices ensuring your oven maintains the right cooking temperature. Our approach involves using smart sensors and robotics, guided by artificial intelligence, to create a highly efficient and precise manufacturing system. Imagine a robot delicately handling a thin platinum wire to craft a sensor that meets strict temperature standards. This innovation benefits our partner, Dynatherm, a leader in sensor manufacturing. The automated system will boost their production, reduce manual labor, and enhance the reliability of their instruments. Ultimately, our project promises a significant leap forward in the efficiency and quality of temperature sensor manufacturing, benefitting industries reliant on accurate temperature measurements.

Using Digital Twins and Predictive Analytics to Enhance Chicken Production Inventory Management

In today’s global market for chicken, producers of broiler hatching eggs face challenges in managing production
efficiently. Traditional methods struggle with complex record-keeping and a lack of real-time monitoring and
prediction capabilities. To overcome these challenges, we propose a digital twin framework—a virtual copy of the
chicken production system. This framework uses synthetic data generated by models like Generative Adversarial
Network and transformers to simulate realistic egg production scenarios. The framework includes layers for the
physical environment, virtual replication, data storage, analytics using Machine Learning, and user interaction. By
integrating digital twin technology, we aim to enhance data management, real-time monitoring, and control in
chicken production, optimizing efficiency and decision-making for producers.