Gwangju Institute of Science and Technology

Gwangju Institute of Science and Technology Researchers Develop Injectable Bioelectrodes with Tunable Lifetimes

Retrieved on:

Thursday, May 11, 2023

Tissue, GIST, Gel, DSM-IV codes, ICH, Gwangju Institute of Science and Technology, Electromyography, Electrode, Small, Fish hydrolysate, Prepolymer, Skin, Medical device, Silk

GWANGJU, South Korea, May 11, 2023 /PRNewswire/ -- Implantable bioelectrodes are electronic devices that can monitor or stimulate biological activity by transmitting signals to and from living biological systems. Such devices can be fabricated using various materials and techniques. But, because of their intimate contact and interactions with living tissues, selection of the right material for performance and biocompatibility is crucial. In recent times, conductible hydrogels have attracted great attention as bioelectrode materials owing to their flexibility, compatibility, and excellent interaction ability. However, the absence of injectability and degradability in conventional conductive hydrogels limits their convenience of use and performance in biological systems.

Key Points:

In recent times, conductible hydrogels have attracted great attention as bioelectrode materials owing to their flexibility, compatibility, and excellent interaction ability.
However, the absence of injectability and degradability in conventional conductive hydrogels limits their convenience of use and performance in biological systems.
Against this backdrop, researchers from Korea have now developed graphene-based conductive hydrogels possessing injectability and tunable degradability, furthering the design and development of advanced bioelectrodes.
The study was led by Professor Jae Young Lee from Gwangju Institute of Science and Technology (GIST) and was published in the Small journal on 24 February 2023.

Gwangju Institute of Science and Technology and MIT Researchers Develop a Natural and Comfortable "Seamless-walk" Virtual Reality Locomotion System

Retrieved on:

Wednesday, April 12, 2023

GIST, Virtual reality, MIT CSAIL, Pressure, Seamless, Artificial Intelligence Laboratory, Fed, Gwangju Institute of Science and Technology, MIT, Privacy, Associate, CSAIL, Movement, Locomotion, Pai, Walking, System, Video game, VR

GWANGJU, South Korea, April 12, 2023 /PRNewswire/ -- Urban real-world environments have limited physical space for foot-based locomotion and present challenges to natural VR locomotion (since virtual environments are much larger than the corresponding real-world environment), a fact that has been noted in past studies (Mandal 2013; Pai and Kunze 2017). To compensate for this challenge, efficient virtual reality (VR)-based locomotion techniques have been proposed to enable natural and immersive locomotion experiences akin to walking in large, virtual environments. However, the VR locomotion systems often require attaching an equipment to the body or video-recording the user's body pose. This leads to discomfort caused by equipment size and discontinuous adjustment as well as privacy concerns related to capturing the entire body without blind spots. Against this backdrop, researchers from Gwangju Institute of Science and Technology (GIST), Korea in collaboration with researchers from Massachusetts Institute of Technology Computer Science and Artificial Intelligence Laboratory (MIT CSAIL), USA developed a novel foot-based VR locomotion system, called "Seamless-walk," that offers a more natural and comfortable locomotion experience without requiring the use of any walking equipment or a video of the user's body pose during walking or interaction with objects using their hands.

Key Points:

To compensate for this challenge, efficient virtual reality (VR)-based locomotion techniques have been proposed to enable natural and immersive locomotion experiences akin to walking in large, virtual environments.
However, the VR locomotion systems often require attaching an equipment to the body or video-recording the user's body pose.
In their recent article published online on 17 January 2023 in the journal Virtual Reality , the researchers, led by Dr. Kyung-Joong Kim, Associate Professor at GIST, have detailed the development of the VR locomotion system.
At the same time, it does not compromise the overall VR experience, outperforming existing VR locomotion methods.

Gwangju Institute of Science and Technology Researchers Correlate Arctic Warming to Extreme Winter Weather in Midlatitude and its Future

Retrieved on:

Thursday, April 6, 2023

GIST, Uncertainty, Climate, Doctor of Philosophy, One half, ECMWF, Temperature, Gwangju Institute of Science and Technology, Canadian Foundation for Climate and Atmospheric Sciences, Atmospheric science, Professor, WACC, George C. Marshall European Center for Security Studies, Weather, Prognosis, Agriculture, Arctic Ocean, Arctic

GWANGJU, South Korea, April 6, 2023 /PRNewswire/ -- Pictures of melting glaciers and stranded polar bears on shrinking sea ice in the Arctic are perhaps the most striking images that have been used to highlights the effects of global warming. However, they do not convey the full extent of the consequences of warmer Arctic. In recent years, there has been growing recognition of the Arctic's role in driving extreme weather events in other parts of the world. While the Arctic has been warming at a rate twice as fast as the global average, winters in the midlatitude regions have experienced colder and more severe weather events. For instance, the winter of 2022-2023 saw record-breaking cold temperatures and snowfall in Japan, China, and Korea. Similarly, many parts of Eurasia and North America have experienced severe cold snaps, with heavy snowfall and prolonged periods of sub-zero temperatures.

Key Points:

In recent years, there has been growing recognition of the Arctic's role in driving extreme weather events in other parts of the world.
While the Arctic has been warming at a rate twice as fast as the global average, winters in the midlatitude regions have experienced colder and more severe weather events.
For instance, the winter of 2022-2023 saw record-breaking cold temperatures and snowfall in Japan, China, and Korea.
They observed that lower winter temperatures in East Asia and North America are usually accompanied by warmer Arctic Sea temperatures.

Gwangju Institute of Science and Technology Researchers Develop a Novel Thermoelectric Generator Inspired from Zebra Skin

Retrieved on:

Thursday, March 9, 2023

Science Advances, IR, TEG, Environment, Radiation, Silicon, Eye, PEDOT, Gwangju Institute of Science and Technology, Chris Coghill, Sunlight, Electricity, GIST, Temperature, PLCL, PSS, Skin, Science

GWANGJU, South Korea, March 9, 2023 /PRNewswire/ -- Thermoelectric generators (TEG) are devices that can convert temperature gradients to electricity. Such devices are extremely useful for generating electricity for remote sensors that cannot be connected to the main electricity grid. A conventional TEG is composed of one side(top or bottom) that radiates heat to cool down and the other side that absorbs heat from the sun or the environment. This, in turn, generates out-of-plane temperature gradient, which is converted into electricity. However, such requirements often make for designs that are bulky, complex, and inefficient. This, in turn, makes TEGs hard to integrate with other components or systems, limiting their applications in renewable energy systems.

Key Points:

Such devices are extremely useful for generating electricity for remote sensors that cannot be connected to the main electricity grid.
This, in turn, makes TEGs hard to integrate with other components or systems, limiting their applications in renewable energy systems.
In a new study, the researchers, led by Professor Young Min Song from Gwangju Institute of Science and Technology (GIST), reported a new flexible, lightweight, and biodegradable TEG that gets its inspiration from an unlikely place – zebra skin.
Essentially, the design uses a pattern resembling black-and-white zebra stripes to create a high in-plane temperature gradient for generating electricity.

GIST Researchers Develop "AMP-BERT": A New AI-based "Finder" of Antimicrobial Peptides

Retrieved on:

Friday, February 24, 2023

GWANGJU, South Korea, Feb. 24, 2023 /PRNewswire/ -- Over the last few decades, antimicrobial resistance has become a major public health concern globally. This has led to a search for alternative methods of treating microbial infections. One such innovation is the discovery of antimicrobial properties of certain peptides. Antimicrobial peptides (AMPs) are short peptides found in most animals, plants, and microorganisms as a natural defense against infections. AMPs combat harmful bacteria via a nonspecific mechanism that prevents them from developing antimicrobial resistance. Despite these exceptional abilities, research on AMPs is being hindered because the existing systems for identifying candidate AMPs are like a black box, where the outputs are not easily interpretable for further analysis.

Key Points:

One such innovation is the discovery of antimicrobial properties of certain peptides.
Antimicrobial peptides (AMPs) are short peptides found in most animals, plants, and microorganisms as a natural defense against infections.
AMPs combat harmful bacteria via a nonspecific mechanism that prevents them from developing antimicrobial resistance.
The important peptide subregion information provided by this prediction platform can also be used to optimize the antibiotic efficiency of peptides.

Novel Protective Layer for Catalysts Developed by GIST Scientists Improves Life and Performance

Retrieved on:

Thursday, January 19, 2023

Gwangju Institute of Science and Technology, Oxygen, Carbon, Hydrogen, Wind, TPP, Life, Angewandte Chemie, Research, Aluminium

GWANGJU, South Korea, Jan. 19, 2023 /PRNewswire/ -- Green hydrogen is hydrogen fuel that is produced using environment-friendly methods. Water electrolysis is one of the primary methods of producing green hydrogen. Here, electrical energy generated using renewable energy sources, such as solar panels or wind energy, is used to drive a water-splitting reaction in an electrochemical cell to produce hydrogen and oxygen. However, this reaction requires the use of expensive catalysts, which increases the cost of green hydrogen, making it unviable. Using nickel–iron (Ni–Fe) catalysts at the anode is a possible solution, but these catalysts tend to corrode and get deactivated due to the oxygen released at the anode of the water-splitting cell.

Key Points:

However, this reaction requires the use of expensive catalysts, which increases the cost of green hydrogen, making it unviable.
So, we developed an Ni–Fe-based catalyst with a tetraphenylporphyrin (TPP) protection layer.
The TPP layer was able to minimize the dissolution of Fe during the oxygen evolution reaction and thus, increase the life and performance of the catalyst."
The TPP protection layer was also seen to increase the redeposition of Fe atoms back on to the catalyst.

Gwangju Institute of Science and Technology Researchers Embrace Uncertainty to Make Microgrids Better

Retrieved on:

Wednesday, December 7, 2022

ANN, Electricity, Probability, Distributed generation, Natural Energy Laboratory of Hawaii Authority, Uncertainty, ENS, Climate change, Fossil fuel, Gwangju Institute of Science and Technology, Research, Renewable energy, Risk management

GWANGJU, South Korea, Dec. 7, 2022 /PRNewswire/ -- Microgrids are crucial for integrating renewable energy into the electrical grid. But current microgrid models ignore the inherent uncertainty of renewable energy supply. Now, scientists from Gwangju Institute of Science and Technology have developed a new optimization model that incorporates possible variations in future power outputs to arrive upon an optimal scheduling decision and reduce operational costs and load shedding.

Key Points:

Now, scientists from Gwangju Institute of Science and Technology have developed a new optimization model that incorporates possible variations in future power outputs to arrive upon an optimal scheduling decision and reduce operational costs and load shedding.
Models that guide the operation of microgrids, such as scheduling load shedding etc., are key to their efficient functioning.
To address this, a research team from Gwangju Institute of Science and Technology, South Korea, has developed a new two-stage stochastic optimization model to minimize operating costs and load shedding.
Thus, improving the efficiency and integration of microgrids will bring us one step closer to energy security and stability," concludes Dr. Kim.

Researchers from Gwangju Institute of Science and Technology Develop an Intelligent Observer for Esports

Retrieved on:

Friday, November 25, 2022

Expert system, Human, Associate, Observation, ROCI, CNN, Part, StarCraft, Gwangju Institute of Science and Technology, Video game, Medical imaging, Esports

GWANGJU, South Korea, Nov. 25, 2022 /PRNewswire/ -- Human game observers are a vital part of the Esports industry. They use extensive domain knowledge to decide what to show to the spectators. However, they may miss important events, necessitating the need for automatic observers. Researchers from South Korea have recently proposed a framework that utilizes an object detection method, Mask R-CNN, and human observational data to find the 'Region of Common Interest' in StarCraft—a real-time strategy game.

Key Points:

GWANGJU, South Korea, Nov. 25, 2022 /PRNewswire/ -- Human game observers are a vital part of the Esports industry.
Esports, already a billion-dollar industry, is growing, partly because of human game observers.
Recently, researchers from South Korea, led by Dr. Kyung-Jong Kim, Associate Professor in Gwangju Institute of Science and Technology, have proposed an approach to overcome these problems.
As services such as multi-screen transmission continue to grow in Esports, the proposed automatic observer will play a role in these deliverables.

A Novel Multi-Modal Image Retrieval System by Researchers from Gwangju Institute of Science and Technology

Retrieved on:

Wednesday, November 9, 2022

Motivation, Technology, Gwangju Institute of Science and Technology, MIT, Doctor of Philosophy, Deep learning, Algorithm, Database, Perceptron, Architecture, Internet, CBIR, Information science, DL, BERT, Online shopping

GWANGJU, South Korea, Nov. 9, 2022 /PRNewswire/ -- With the amount of information on the internet increasing by the minute, retrieving data from it is like trying to find a needle in a haystack. Content-based image retrieval (CBIR) systems are capable of retrieving desired images based on the user's input from an extensive database. These systems are used in e-commerce, face recognition, medical applications, and computer vision. There are two ways in which CBIR systems work—text-based and image-based. One of the ways in which CBIR gets a boost is by using deep learning (DL) algorithms. DL algorithms enable the use of multi-modal feature extraction, meaning that both image and text features can be used to retrieve the desired image. Even though scientists have tried to develop multi-modal feature extraction, it remains an open problem.

Key Points:

Content-based image retrieval (CBIR) systems are capable of retrieving desired images based on the user's input from an extensive database.
DL algorithms enable the use of multi-modal feature extraction, meaning that both image and text features can be used to retrieve the desired image.
To this end, researchers from Gwangju Institute of Science and Technology have developed DenseBert4Ret, an image retrieval system using DL algorithms.
Additionally, the user can make changes to the query image and retrieve the amended image from the inventory," concludes Prof. Jeon.

Gwangju Institute of Science and Technology Researchers Design AI-based Model that Predicts Extreme Wildfire Danger

Retrieved on:

Friday, October 21, 2022

Knowing when and where a widespread fire could happen in advance can improve fire prevention and resource allocation.

Key Points:

Knowing when and where a widespread fire could happen in advance can improve fire prevention and resource allocation.
Scientists have now applieda deep learning algorithm to enhance the prediction of wildfire danger in the Western United States.
"We tried numerous approaches to integrate machine learning with traditional weather forecast models to improve forecasts of wildfire risks.
"The fire danger forecasts could be improved further using constant development in both Earth System Models and recent AI developments," he adds.