Smart Functional Textiles for the Environment

Smart Functional Textiles for the Environment

Principal Investigators & Key Members:
Nguyen Dang Tung, PhD
This research focuses on creating fibers and textiles that function as portable devices for sensing, energy generation, and storage. The goal is to develop solar textiles capable of powering IoT devices, reducing reliance on traditional power sources. Additionally, flexible sensing textiles will be used for environmental monitoring, such as water pollution detection.
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Advancing Sustainable Electric Vehicle Charging through Green Infrastructure and Smart Charging Techniques

Advancing Sustainable Electric Vehicle Charging through Green Infrastructure and Smart Charging Techniques

Principal Investigators & Key Members:
Do Danh Cuong, PhD.
In today's world, increasing greenhouse gas emissions pose a threat due to global warming. To combat this, transitioning from fossil fuel vehicles to electric vehicles (EVs) is crucial. However, as EV numbers rise, so does energy demand on the grid, causing potential issues. This study proposes renewable EV charging stations to mitigate grid challenges.
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Digital Twin Platform to Empower Communities towards an Eco-friendly and Healthy Future

Digital Twin Platform to Empower Communities towards an Eco-friendly and Healthy Future

Principal Investigators & Key Members:
Nguyen Ngoc Doanh, PhD
Urban development in Vietnam and other developing countries has increased greenhouse gas emissions. This research uses AI and ML to create digital twins of transportation and air quality, simulating policy scenarios to promote sustainable transportation. The goal is to develop policies encouraging shifts to electric vehicles, transit, and ridesharing, using real-time congestion pricing to reduce pollution and congestion.
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Carbon Stock Estimation and Biodiversity Assessment in Vietnam Forests using Remotely Sensed Data and Deep Learning Neural Networks

Carbon Stock Estimation and Biodiversity Assessment in Vietnam Forests using Remotely Sensed Data and Deep Learning Neural Networks

Principal Investigators & Key Members:
Nidal Kamel, PhD.
Estimating carbon stocks is vital for understanding the Earth's carbon cycle and guiding climate change mitigation efforts. By quantifying carbon in forests, soil, and oceans, scientists and policymakers can make informed decisions on land use and conservation. Accurate estimates are also key to ensuring the credibility of carbon offset projects.
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