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Assessing Environmental Hazards and Vulnerabilities in Underprivileged Communities of the Red River Delta area (Vietnam)

Ensuring environmental equity and justice are pressing concerns in developing nations, where marginalized populations frequently bear a disproportionate burden of environmental hazards. This research aims to explore the distribution of environmental hazards and assess the vulnerability of low-income communities in the Red River Delta area in Northern Vietnam. Through a comprehensive risk assessment framework, the study will evaluate the types and levels of environmental hazards present in these communities, as well as the socio-economic, demographic, and environmental factors that contribute to their heightened vulnerability. The findings will inform policy interventions and strategies to address environmental inequalities and promote environmental justice in the region.

Drivers of Cost-Effective Resilience: A Household-Level Study in Vietnam

Investigate how Vietnamese households respond to exogenous shocks such as power cuts and property damage by: - Identify the resilience tactics employed, - Assess their cost-effectiveness, - Determine the predictors of this cost-effectiveness

Research and Development of Autonomous EV Chargers Integrating with a Digital Twin System

Autonomous EV chargers with robotic systems and smart grid capabilities will provide efficient, user-friendly charging while enhancing grid stability. The project focuses on research and development, testing and validating a virtual system that integrates digital twins, autonomous chargers, and battery storage. Digital twins will simulate and optimize urban mobility for future demands. This initiative aims to reduce emissions, lower costs, and improve urban energy grids, creating a resilient and sustainable mobility ecosystem.

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

Principal Investigators & Key Members:

Nguyen Ngoc Doanh, PhD

Urban development in Vietnam and other developing countries has led to increased greenhouse gas emissions. Smart transportation, enhanced by AI and ML, can reduce these emissions by implementing real-time congestion pricing based on sensor data. This research will create digital twins of transportation and air quality in Southeast Asian cities, simulating various policy scenarios to encourage sustainable transportation. The goal is to develop economic mechanisms and policies that promote shifts to electric vehicles, transit, and ridesharing, ultimately reducing pollution and congestion. Innovation lies in using AI/ML to design dynamic, real-time congestion pricing and understand public opinions on sustainable transportation.

Data-driven Optimization of High-energy Na-ion Battery Materials

Principal Investigators & Key Members:

Phung Thi Viet Bac, PhD

Sodium-ion batteries are emerging as a promising technology in energy storage and electric mobility, poised to potentially replace lithium-ion batteries in the near future. Our research is at the forefront of this exciting field, focusing on developing novel, sustainable materials for both cathodes and anodes. By synthesizing innovative layered oxide composites and carbon-based materials, we aim to create high-performance, cost-effective sodium-ion batteries. Our approach combines cutting-edge experimental techniques with advanced computational methods, including machine learning and density functional theory simulations. This allows us to optimize materials and processes, bringing us closer to our goal of demonstrating full-cell prototypes with energy density and cycle life comparable to current lithium-ion technologies. Join us as we explore the future of large-scale energy storage and contribute to a more sustainable electric future.

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.

Green Serverless Computing for Resource-Efficient AI Training

Principal Investigators & Key Members:

Kok-Seng Wong, PhD

With the increasing demand for artificial intelligence (AI) applications, there is a need for efficient, sustainable computation solutions for AI model training. Traditional server-based architectures often consume substantial energy and resources, leading to environmental concerns and high operational costs. Serverless computing offers a promising approach by providing on-demand computing resources and scaling capabilities. This research explores the feasibility of leveraging serverless computing for efficient AI training, focusing on minimizing energy consumption and resource utilization.

Smart Functional Textiles for the Environment

Principal Investigators & Key Members:

Nguyen Dang Tung, PhD

The core of this research lies in engineering fibers and textiles that can serve as portable, flexible sensing/energy generation/energy storage devices. A major objective is to create these solar textiles that will have the capability to power a wide range of devices, particularly those connected to the Internet of Things (IoT). IoT devices, which range from home appliances to healthcare monitors, require continuous power to collect and transmit data. By embedding solar energy harvesting and storage capabilities directly into the fabric, these textiles will provide a sustainable energy solution, reducing reliance on traditional power sources and batteries. In parallel, flexible sensing devices on fibers and textiles will used in environmental monitoring (e.g. water pollution and water level).

Development of energy-efficient perovskite light-emitting diodes and solar cells

Principal Investigators & Key Members:

Le Van Quynh, PhD

This project is aimed at developing semiconductor technology of producing a new generation of light-emitting diodes for energy efficiency and flexible solar panels for remote areas. The project focus on making real devices that will be used in the renewable energy through real solar cells, light-emitting diodes (LEDs) based on emerging materials such as semiconductor nanocrystals, mxenes and perovskite semiconductors. The project allows VinUni students to expose with the latest technologies in semiconductor, electrical engineering, mechanical engineering, computer science and materials science.

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 essential for understanding the Earth's carbon cycle, assessing the impact of human activities on the environment, and guiding efforts to mitigate climate change. By quantifying the amount of carbon stored in ecosystems like forests, soil, and oceans, scientists and policymakers can make informed decisions about land-use planning, conservation efforts, and carbon sequestration strategies. In the context of carbon credits, accurate carbon stock estimation is crucial for ensuring the credibility and effectiveness of carbon offset projects.

An advanced AI-enabled HVAC control system: Towards greener and healthier indoor facilities in Vietnam

Principal Investigators & Key Members:

Le Duy Dung

Heating, Ventilation, and Air Conditioning (HVAC) systems are pivotal in enhancing and maintaining indoor air quality, but consume a significant portion of the building's energy use. This project aims to develop an intelligent energy management platform called V-IndoorSMART for multi-user facilities (e.g. public rooms, labs, etc.) in buildings. The platform is designed to optimize HVAC operation by leveraging modeling, simulation, and AI technologies, ensuring thermal comfort for occupants while conserving energy consumption efficiency and maintaining indoor pollutants at permissible levels.

High-Performance Triboelectric Nanogenerator Based on Recycled Materials for Mechanical Energy Harvesting

As global energy demands rise, the need for emission-free sources is growing. Triboelectric nanogenerators (TENGs), invented in 2012, are key in energy harvesting by converting mechanical energy into electricity. This project focuses on designing a high-performance TENG using polydimethylsiloxane (PDMS) and graphene oxide (GO) synthesized from recycled battery graphite. The GO/PDMS TENG is efficient, reliable, and eco-friendly, capable of powering LEDs, charging capacitors, and driving devices. It can also be integrated into clothing and insoles to harvest energy from movements like walking and running.

Assessing Environmental Hazards and Vulnerabilities in Underprivileged Communities of the Red River Delta area (Vietnam)

Ensuring environmental equity and justice are pressing concerns in developing nations, where marginalized populations frequently bear a disproportionate burden of environmental hazards. This research aims to explore the distribution of environmental hazards and assess the vulnerability of low-income communities in the Red River Delta area in Northern Vietnam. Through a comprehensive risk assessment framework, the study will evaluate the types and levels of environmental hazards present in these communities, as well as the socio-economic, demographic, and environmental factors that contribute to their heightened vulnerability. The findings will inform policy interventions and strategies to address environmental inequalities and promote environmental justice in the region.

Drivers of Cost-Effective Resilience: A Household-Level Study in Vietnam

Investigate how Vietnamese households respond to exogenous shocks such as power cuts and property damage by: - Identify the resilience tactics employed, - Assess their cost-effectiveness, - Determine the predictors of this cost-effectiveness

Research and Development of Autonomous EV Chargers Integrating with a Digital Twin System

Autonomous EV chargers with robotic systems and smart grid capabilities will provide efficient, user-friendly charging while enhancing grid stability. The project focuses on research and development, testing and validating a virtual system that integrates digital twins, autonomous chargers, and battery storage. Digital twins will simulate and optimize urban mobility for future demands. This initiative aims to reduce emissions, lower costs, and improve urban energy grids, creating a resilient and sustainable mobility ecosystem.

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

Principal Investigators & Key Members:

Nguyen Ngoc Doanh, PhD

Urban development in Vietnam and other developing countries has led to increased greenhouse gas emissions. Smart transportation, enhanced by AI and ML, can reduce these emissions by implementing real-time congestion pricing based on sensor data. This research will create digital twins of transportation and air quality in Southeast Asian cities, simulating various policy scenarios to encourage sustainable transportation. The goal is to develop economic mechanisms and policies that promote shifts to electric vehicles, transit, and ridesharing, ultimately reducing pollution and congestion. Innovation lies in using AI/ML to design dynamic, real-time congestion pricing and understand public opinions on sustainable transportation.

Data-driven Optimization of High-energy Na-ion Battery Materials

Principal Investigators & Key Members:

Phung Thi Viet Bac, PhD

Sodium-ion batteries are emerging as a promising technology in energy storage and electric mobility, poised to potentially replace lithium-ion batteries in the near future. Our research is at the forefront of this exciting field, focusing on developing novel, sustainable materials for both cathodes and anodes. By synthesizing innovative layered oxide composites and carbon-based materials, we aim to create high-performance, cost-effective sodium-ion batteries. Our approach combines cutting-edge experimental techniques with advanced computational methods, including machine learning and density functional theory simulations. This allows us to optimize materials and processes, bringing us closer to our goal of demonstrating full-cell prototypes with energy density and cycle life comparable to current lithium-ion technologies. Join us as we explore the future of large-scale energy storage and contribute to a more sustainable electric future.

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.

Green Serverless Computing for Resource-Efficient AI Training

Principal Investigators & Key Members:

Kok-Seng Wong, PhD

With the increasing demand for artificial intelligence (AI) applications, there is a need for efficient, sustainable computation solutions for AI model training. Traditional server-based architectures often consume substantial energy and resources, leading to environmental concerns and high operational costs. Serverless computing offers a promising approach by providing on-demand computing resources and scaling capabilities. This research explores the feasibility of leveraging serverless computing for efficient AI training, focusing on minimizing energy consumption and resource utilization.

Smart Functional Textiles for the Environment

Principal Investigators & Key Members:

Nguyen Dang Tung, PhD

The core of this research lies in engineering fibers and textiles that can serve as portable, flexible sensing/energy generation/energy storage devices. A major objective is to create these solar textiles that will have the capability to power a wide range of devices, particularly those connected to the Internet of Things (IoT). IoT devices, which range from home appliances to healthcare monitors, require continuous power to collect and transmit data. By embedding solar energy harvesting and storage capabilities directly into the fabric, these textiles will provide a sustainable energy solution, reducing reliance on traditional power sources and batteries. In parallel, flexible sensing devices on fibers and textiles will used in environmental monitoring (e.g. water pollution and water level).

Development of energy-efficient perovskite light-emitting diodes and solar cells

Principal Investigators & Key Members:

Le Van Quynh, PhD

This project is aimed at developing semiconductor technology of producing a new generation of light-emitting diodes for energy efficiency and flexible solar panels for remote areas. The project focus on making real devices that will be used in the renewable energy through real solar cells, light-emitting diodes (LEDs) based on emerging materials such as semiconductor nanocrystals, mxenes and perovskite semiconductors. The project allows VinUni students to expose with the latest technologies in semiconductor, electrical engineering, mechanical engineering, computer science and materials science.

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 essential for understanding the Earth's carbon cycle, assessing the impact of human activities on the environment, and guiding efforts to mitigate climate change. By quantifying the amount of carbon stored in ecosystems like forests, soil, and oceans, scientists and policymakers can make informed decisions about land-use planning, conservation efforts, and carbon sequestration strategies. In the context of carbon credits, accurate carbon stock estimation is crucial for ensuring the credibility and effectiveness of carbon offset projects.

An advanced AI-enabled HVAC control system: Towards greener and healthier indoor facilities in Vietnam

Principal Investigators & Key Members:

Le Duy Dung

Heating, Ventilation, and Air Conditioning (HVAC) systems are pivotal in enhancing and maintaining indoor air quality, but consume a significant portion of the building's energy use. This project aims to develop an intelligent energy management platform called V-IndoorSMART for multi-user facilities (e.g. public rooms, labs, etc.) in buildings. The platform is designed to optimize HVAC operation by leveraging modeling, simulation, and AI technologies, ensuring thermal comfort for occupants while conserving energy consumption efficiency and maintaining indoor pollutants at permissible levels.

High-Performance Triboelectric Nanogenerator Based on Recycled Materials for Mechanical Energy Harvesting

As global energy demands rise, the need for emission-free sources is growing. Triboelectric nanogenerators (TENGs), invented in 2012, are key in energy harvesting by converting mechanical energy into electricity. This project focuses on designing a high-performance TENG using polydimethylsiloxane (PDMS) and graphene oxide (GO) synthesized from recycled battery graphite. The GO/PDMS TENG is efficient, reliable, and eco-friendly, capable of powering LEDs, charging capacitors, and driving devices. It can also be integrated into clothing and insoles to harvest energy from movements like walking and running.

Assessing Environmental Hazards and Vulnerabilities in Underprivileged Communities of the Red River Delta area (Vietnam)

Ensuring environmental equity and justice are pressing concerns in developing nations, where marginalized populations frequently bear a disproportionate burden of environmental hazards. This research aims to explore the distribution of environmental hazards and assess the vulnerability of low-income communities in the Red River Delta area in Northern Vietnam. Through a comprehensive risk assessment framework, the study will evaluate the types and levels of environmental hazards present in these communities, as well as the socio-economic, demographic, and environmental factors that contribute to their heightened vulnerability. The findings will inform policy interventions and strategies to address environmental inequalities and promote environmental justice in the region.

Drivers of Cost-Effective Resilience: A Household-Level Study in Vietnam

Investigate how Vietnamese households respond to exogenous shocks such as power cuts and property damage by: - Identify the resilience tactics employed, - Assess their cost-effectiveness, - Determine the predictors of this cost-effectiveness

Research and Development of Autonomous EV Chargers Integrating with a Digital Twin System

Autonomous EV chargers with robotic systems and smart grid capabilities will provide efficient, user-friendly charging while enhancing grid stability. The project focuses on research and development, testing and validating a virtual system that integrates digital twins, autonomous chargers, and battery storage. Digital twins will simulate and optimize urban mobility for future demands. This initiative aims to reduce emissions, lower costs, and improve urban energy grids, creating a resilient and sustainable mobility ecosystem.