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Professor Sung Yong Kim Elected as the Chair of PICES MONITOR
< Professor Sung Yong Kim > Professor Sung Yong Kim from the Department of Mechanical Engineering was elected as the chair of the Technical Committee on Monitoring (MONITOR) of the North Pacific Marine Science Organization (PICES). PICES is an intergovernmental marine science organization that was established in 1992 through a collaboration between six North Pacific nations including South Korea, Russia, the United States, Japan, China, and Canada to exchange and discuss research on the Pacific waters. Its headquarters is located in Canada and the organization consists of seven affiliated maritime science and marine technology committees. Professor Kim was elected as the chair of the technical committee that focuses on monitoring and will be part of the Science Board as an ex-officio member. His term will last three years from November 2019. Professor Kim was recognized for his academic excellence, expertise, and leadership among oceanographers both domestically and internationally. Professor Kim will also participate as an academia civilian committee member of the Maritime and Fisheries Science and Technology Committee under the Korean Ministry of Oceans and Fisheries for two years from December 18, 2019. He stated, “I will give my full efforts to broaden Korean oceanography research by participating in maritime leadership positions at home and abroad, and help South Korea become a maritime powerhouse.” (END)
New IEEE Fellow, Professor Jong Chul Ye
Professor Jong Chul Ye from the Department of Bio and Brain Engineering was named a new fellow of the Institute of Electrical and Electronics Engineers (IEEE). IEEE announced this on December 1 in recognition of Professor Ye’s contributions to the development of signal processing and artificial intelligence (AI) technology in the field of biomedical imaging. As the world’s largest society in the electrical and electronics field, IEEE names the top 0.1% of their members as fellows based on their research achievements.Professor Ye has published more than 100 research papers in world-leading journals in the biomedical imaging field, including those affiliated with IEEE. He also gave a keynote talk at the yearly conference of the International Society for Magnetic Resonance Imaging (ISMRM) on medical AI technology. In addition, Professor Ye has been appointed to serve as the next chair of the Computational Imaging Technical Committee of the IEEE Signal Processing Society, and the chair of the IEEE Symposium on Biomedical Imaging (ISBI) 2020 to be held in April in Iowa, USA. Professor Ye said, “The importance of AI technology is developing in the biomedical imaging field. I feel proud that my contributions have been internationally recognized and allowed me to be named an IEEE fellow.”
KAIST and Google Jointly Develop AI Curricula
KAIST selected the two professors who will develop AI curriculum under the auspices of the KAIST-Google Partnership for AI Education and Research. The Graduate School of AI announced the two authors among the 20 applicants who will develop the curriculum next year. They will be provided 7,500 USD per subject. Professor Changho Suh from the School of Electrical Engineering and Professor Yong-Jin Yoon from the Department of Mechanical Engineering will use Google technology such as TensorFlow, Google Cloud, and Android to create the curriculum. Professor Suh’s “TensorFlow for Information Theory and Convex Optimization “will be used for curriculum in the graduate courses and Professor Yoon’s “AI Convergence Project Based Learning (PBL)” will be used for online courses. Professor Yoon’s course will explore and define problems by utilizing AI and experiencing the process of developing products that use AI through design thinking, which involves product design, production, and verification. Professor Suh’s course will discus“information theory and convergence,” which uses basic sciences and engineering as well as AI, machine learning, and deep learning.
‘Carrier-Resolved Photo-Hall’ to Push Semiconductor Advances
(Professor Shin and Dr. Gunawan (left)) An IBM-KAIST research team described a breakthrough in a 140-year-old mystery in physics. The research reported in Nature last month unlocks the physical characteristics of semiconductors in much greater detail and aids in the development of new and improved semiconductor materials. Research team under Professor Byungha Shin at the Department of Material Sciences and Engineering and Dr. Oki Gunawan at IBM discovered a new formula and technique that enables the simultaneous extraction of both majority and minority carrier information such as their density and mobility, as well as gain additional insights about carrier lifetimes, diffusion lengths, and the recombination process. This new discovery and technology will help push semiconductor advances in both existing and emerging technologies. Semiconductors are the basic building blocks of today’s digital electronics age, providing us with a multitude of devices that benefit our modern life. To truly appreciate the physics of semiconductors, it is very important to understand the fundamental properties of the charge carriers inside the materials, whether those particles are positive or negative, their speed under an applied electric field, and how densely they are packed into the material. Physicist Edwin Hall found a way to determine those properties in 1879, when he discovered that a magnetic field will deflect the movement of electronic charges inside a conductor and that the amount of deflection can be measured as a voltage perpendicular to the flow of the charge. Decades after Hall’s discovery, researchers also recognized that they can measure the Hall effect with light via “photo-Hall experiments”. During such experiments, the light generates multiple carriers or electron–hole pairs in the semiconductors. Unfortunately, the basic Hall effect only provided insights into the dominant charge carrier (or majority carrier). Researchers were unable to extract the properties of both carriers (the majority and minority carriers) simultaneously. The property information of both carriers is crucial for many applications that involve light such as solar cells and other optoelectronic devices. In the photo-Hall experiment by the KAIST-IBM team, both carriers contribute to changes in conductivity and the Hall coefficient. The key insight comes from measuring the conductivity and Hall coefficient as a function of light intensity. Hidden in the trajectory of the conductivity, the Hall coefficient curve reveals crucial new information: the difference in the mobility of both carriers. As discussed in the paper, this relationship can be expressed elegantly as: Δµ = d (σ²H)/dσ The research team solved for both majority and minority carrier mobility and density as a function of light intensity, naming the new technique Carrier-Resolved Photo Hall (CRPH) measurement. With known light illumination intensity, the carrier lifetime can be established in a similar way. Beyond advances in theoretical understanding, advances in experimental techniques were also critical for enabling this breakthrough. The technique requires a clean Hall signal measurement, which can be challenging for materials where the Hall signal is weak due to low mobility or when extra unwanted signals are present, such as under strong light illumination. The newly developed photo-Hall technique allows the extraction of an astonishing amount of information from semiconductors. In contrast to only three parameters obtained in the classic Hall measurements, this new technique yields up to seven parameters at every tested level of light intensity. These include the mobility of both the electron and hole; their carrier density under light; the recombination lifetime; and the diffusion lengths for electrons, holes, and ambipolar types. All of these can be repeated N times (i.e. the number of light intensity settings used in the experiment). Professor Shin said, “This novel technology sheds new light on understanding the physical characteristics of semiconductor materials in great detail.” Dr. Gunawan added, “This will will help accelerate the development of next-generation semiconductor technology such as better solar cells, better optoelectronics devices, and new materials and devices for artificial intelligence technology.” Profile: Professor Byungha Shin Department of Materials Science and Engineering KAIST email@example.com http://energymatlab.kaist.ac.kr/
KAIST Alumnus NYU Professor Supports Female AI Researchers
A KAIST alumnus and an associate professor at New York University (NYU), Dr. Kyunghyun Cho donated 3,000 USD to the KAIST Graduate School of AI to support female AI researchers. Professor Cho spoke as a guest lecturer at the 2019 Samsung AI Forum on November 4 and received 3,000 USD as an honorarium. He donated this honorarium to the KAIST Graduate School of AI with a special request to support the school’s female PhD students attending the 2020 International Conference on Learning Representations (ICLR), where he serves as a program co-chair. Professor Cho received his BS degree from KAIST’s School of Computing in 2009 and is now serving as an associate professor at NYU’s Computer Science Department and Center for Data Science. His research mainly covers machine learning and natural language processing. Professor Cho said that he decided to make this donation because “In Korea and even in the US, women in science, technology, engineering, and mathematics (STEM) lack opportunities and environments that allow them to excel.” Professor Song Chong, the Head of the KAIST Graduate School of AI, responded, “We are so grateful for Professor Kyunghyun Cho’s contribution and we will also use funds from the school in addition to the donation to support our female PhD students who will attend the ICLR.” (END)
AI to Determine When to Intervene with Your Driving
(Professor Uichin Lee (left) and PhD candidate Auk Kim) Can your AI agent judge when to talk to you while you are driving? According to a KAIST research team, their in-vehicle conservation service technology will judge when it is appropriate to contact you to ensure your safety. Professor Uichin Lee from the Department of Industrial and Systems Engineering at KAIST and his research team have developed AI technology that automatically detects safe moments for AI agents to provide conversation services to drivers. Their research focuses on solving the potential problems of distraction created by in-vehicle conversation services. If an AI agent talks to a driver at an inopportune moment, such as while making a turn, a car accident will be more likely to occur. In-vehicle conversation services need to be convenient as well as safe. However, the cognitive burden of multitasking negatively influences the quality of the service. Users tend to be more distracted during certain traffic conditions. To address this long-standing challenge of the in-vehicle conversation services, the team introduced a composite cognitive model that considers both safe driving and auditory-verbal service performance and used a machine-learning model for all collected data. The combination of these individual measures is able to determine the appropriate moments for conversation and most appropriate types of conversational services. For instance, in the case of delivering simple-context information, such as a weather forecast, driver safety alone would be the most appropriate consideration. Meanwhile, when delivering information that requires a driver response, such as a “Yes” or “No,” the combination of driver safety and auditory-verbal performance should be considered. The research team developed a prototype of an in-vehicle conversation service based on a navigation app that can be used in real driving environments. The app was also connected to the vehicle to collect in-vehicle OBD-II/CAN data, such as the steering wheel angle and brake pedal position, and mobility and environmental data such as the distance between successive cars and traffic flow. Using pseudo-conversation services, the research team collected a real-world driving dataset consisting of 1,388 interactions and sensor data from 29 drivers who interacted with AI conversational agents. Machine learning analysis based on the dataset demonstrated that the opportune moments for driver interruption could be correctly inferred with 87% accuracy. The safety enhancement technology developed by the team is expected to minimize driver distractions caused by in-vehicle conversation services. This technology can be directly applied to current in-vehicle systems that provide conversation services. It can also be extended and applied to the real-time detection of driver distraction problems caused by the use of a smartphone while driving. Professor Lee said, “In the near future, cars will proactively deliver various in-vehicle conversation services. This technology will certainly help vehicles interact with their drivers safely as it can fairly accurately determine when to provide conversation services using only basic sensor data generated by cars.” The researchers presented their findings at the ACM International Joint Conference on Pervasive and Ubiquitous Computing (Ubicomp’19) in London, UK. This research was supported in part by Hyundai NGV and by the Next-Generation Information Computing Development Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT. (Figure: Visual description of safe enhancement technology for in-vehicle conversation services)
Highly Uniform and Low Hysteresis Pressure Sensor to Increase Practical Applicability
< Professor Steve Park (left) and the First Author Mr. Jinwon Oh (right) > Researchers have designed a flexible pressure sensor that is expected to have a much wider applicability. A KAIST research team fabricated a piezoresistive pressure sensor of high uniformity with low hysteresis by chemically grafting a conductive polymer onto a porous elastomer template. The team discovered that the uniformity of pore size and shape is directly related to the uniformity of the sensor. The team noted that by increasing pore size and shape variability, the variability of the sensor characteristics also increases. Researchers led by Professor Steve Park from the Department of Materials Science and Engineering confirmed that compared to other sensors composed of randomly sized and shaped pores, which had a coefficient of variation in relative resistance change of 69.65%, their newly developed sensor exhibited much higher uniformity with a coefficient of variation of 2.43%. This study was reported in Small as the cover article on August 16. Flexible pressure sensors have been actively researched and widely applied in electronic equipment such as touch screens, robots, wearable healthcare devices, electronic skin, and human-machine interfaces. In particular, piezoresistive pressure sensors based on elastomer‐conductive material composites hold significant potential due to their many advantages including a simple and low-cost fabrication process. Various research results have been reported for ways to improve the performance of piezoresistive pressure sensors, most of which have been focused on increasing the sensitivity. Despite its significance, maximizing the sensitivity of composite-based piezoresistive pressure sensors is not necessary for many applications. On the other hand, sensor-to-sensor uniformity and hysteresis are two properties that are of critical importance to realize any application. The importance of sensor-to-sensor uniformity is obvious. If the sensors manufactured under the same conditions have different properties, measurement reliability is compromised, and therefore the sensor cannot be used in a practical setting. In addition, low hysteresis is also essential for improved measurement reliability. Hysteresis is a phenomenon in which the electrical readings differ depending on how fast or slow the sensor is being pressed, whether pressure is being released or applied, and how long and to what degree the sensor has been pressed. When a sensor has high hysteresis, the electrical readings will differ even under the same pressure, making the measurements unreliable. Researchers said they observed a negligible hysteresis degree which was only 2%. This was attributed to the strong chemical bonding between the conductive polymer and the elastomer template, which prevents their relative sliding and displacement, and the porosity of the elastomer that enhances elastic behavior. “This technology brings forth insight into how to address the two critical issues in pressure sensors: uniformity and hysteresis. We expect our technology to play an important role in increasing practical applications and the commercialization of pressure sensors in the near future,” said Professor Park. This work was conducted as part of the KAIST‐funded Global Singularity Research Program for 2019, and also supported by the KUSTAR‐KAIST Institute. Figure 1. Image of a porous elastomer template with uniform pore size and shape (left), Graph showing high uniformity in the sensors’ performance (right). Figure 2. Hysteresis loops of the sensor at different pressure levels (left), and after a different number of cycles (right). Figure 3. The cover page of Small Journal, Volume 15, Issue 33. Publication: Jinwon Oh, Jin‐Oh Kim, Yunjoo Kim, Han Byul Choi, Jun Chang Yang, Serin Lee, Mikhail Pyatykh, Jung Kim, Joo Yong Sim, and Steve Park. 2019. Highly Uniform and Low Hysteresis Piezoresistive Pressure Sensors Based on Chemical Grafting of Polypyrrole on Elastomer Template with Uniform Pore Size. Small. Wiley-VCH Verlag GmbH & Co. KgaA, Weinheim, Germany, Volume No. 15, Issue No. 33, Full Paper No. 201901744, 8 pages. https://doi.org/10.1002/smll.201901744 Profile: Prof. Steve Park, MS, PhD firstname.lastname@example.org http://steveparklab.kaist.ac.kr/ Assistant Professor Organic and Nano Electronics Laboratory Department of Materials Science and Engineering Korea Advanced Institute of Science and Technology (KAIST) http://kaist.ac.kr Daejeon 34141, Korea Profile: Mr. Jinwon Oh, MS email@example.com http://steveparklab.kaist.ac.kr/ Researcher Organic and Nano Electronics Laboratory Department of Materials Science and Engineering Korea Advanced Institute of Science and Technology (KAIST) http://kaist.ac.kr Daejeon 34141, Korea Profile: Prof. Jung Kim, MS, PhD firstname.lastname@example.org http://medev.kaist.ac.kr/ Professor Biorobotics Laboratory Department of Mechanical Engineering Korea Advanced Institute of Science and Technology (KAIST) http://kaist.ac.kr Daejeon 34141, Korea Profile: Joo Yong Sim, PhD email@example.com Researcher Bio-Medical IT Convergence Research Department Electronics and Telecommunications Research Institute (ETRI) https://www.etri.re.krDaejeon 34129, Korea (END)
Chem-E-Car Team to Vie for World Title
Team KAItalyst, composed of KAIST undergraduate students, celebrated victory in the regional qualifying rounds of the 2019 International Chem-E-Car Competition held at KAIST’s Main Campus in Daejeon on July 20. The high finish in the national rankings qualified the team for a trip to the world finals to be held in Orlando, Florida, USA, in November. The Chem-E-Car Competition involves designing and building a shoebox-sized model car that is powered and controlled by chemical reactions. University students from all over the world have been actively participating in this competition since the competition was introduced by the American Institute of Chemical Engineers (AIChE) in 1999. KAIST first entered the competition in 2014, won the world finals in 2016, and then received the Most Consistent Award in 2017 and 2018. In recognition of KAIST’s consistently outstanding performance in the competition, AIChE asked KAIST to host this year’s regional competition for the first time in Korea. Although a number of Korean university student teams have shown great interest in participating in this regional competition, most were not able to successfully implement their technology, and only two teams each from KAIST and Seoul National University (SNU) joined the competition. Each team collaborated to fabricate a chemically powered model car that could carry a payload, and travel any distance between 15 and 30 meters. The weight of the payload and the travelling distance were randomly set an hour before the competition started, to require the participating teams adapt and perform calculations in a short period of time. The goal was to stop travelling exactly at the randomly chosen distance. The car closest to the finish line at the end of the race earned the highest amount of points. Precise control over chemical reactions was key to landing directly on the mark. Team KAItalyst, consisting of six KAIST undergraduate students majoring in chemical and biomolecular engineering and mechanical engineering, beat their SNU rivals by stopping their car 1.5 meters closer to the goal at the end of the 22.5 meter-long race. Team KAItalyst loaded vanadium redox flow batteries onto their car to stabilize its output, and further increased the accuracy and velocity of chemical reactions through iodine clock reactions. 200 USD was awarded to Team KAItalyst, and 100 USD in prize money went to the SNU team. KAItalyst team leader Jee-Hyun Hong said, “This was the first time for us to develop and drive our own chemically-powered model car, and we learned a lot from the challenges we faced,” Hong continued, “We will step up our efforts to perform better in the upcoming international competition.” The world finals will be held during the AIChE Fall Meeting in Orlando, Florida in November. Students from over 50 universities worldwide including the Georgia Institute of Technology and Carnegie Mellon University will compete against each other. The first, second, and third prizes at the finals will be 2,000, 1,000, and 500 USD respectively. Professor Dong-Yeun Koh of the KAIST Chemical and Biomolecular Engineering Department who advised Team KAItalyst remarked, “I hope this year’s regional competition that KAIST held for the first time as a Korean university will be a possible starting point for more Korean universities to participate and compete in the future.” (END)
'Flying Drones for Rescue'
(Video Credit: ⓒNASA JPL) < Team USRG and Professor Shim (second from the right) > Having recently won the AI R&D Grand Challenge Competition in Korea, Team USRG (Unmanned System Research Group) led by Professor Hyunchul Shim from the School of Electrical Engineering is all geared up to take on their next challenges: the ‘Defense Advanced Research Projects Agency Subterranean Challenge (DARPA SubT Challenge)’ and ‘Lockheed Martin’s AlphaPilot Challenge’ next month. Team USRG won the obstacle course race in the ‘2019 AI R&D Grand Challenge Competition’ on July 12. They managed to successfully dominate the challenging category of ‘control intelligence.’ Having to complete the obstacle course race solely using AI systems without any connection to the internet made it difficult for most of the eight participating teams to pass the third section of the race, and only Team USRG passed the long pipeline course during their attempt in the main event. They also demonstrated, after the main event, that their drone can navigate all of the checkpoints including landing on the “H” mark using deep learning. Their drone flew through polls and pipes, and escaped from windows and mazes against strong winds, amid cheers and groans from the crowd gathered at the Korea Exhibition Center (KINTEX) in Goyang, Korea. The team was awarded three million KRW in prize money, and received a research grant worth six hundred million KRW from the Ministry of Science and ICT (MSIT). “Being ranked first in the race for which we were never given a chance for a test flight means a lot to our team. Considering that we had no information on the exact size of the course in advance, this is a startling result,” said Professor Shim. “We will carry out further research with this funding, and compete once again with the improved AI and drone technology in the 2020 competition,” he added. The AI R&D Grand Challenge Competition, which was first started in 2017, has been designed to promote AI research and development and expand its application to addressing high-risk technical challenges with significant socio-economic impact. This year’s competition presented participants with a task where they had to develop AI software technology for drones to navigate themselves autonomously during complex disaster relief operations such as aid delivery. Each team participated in one of the four tracks of the competition, and their drones were evaluated based on the criteria for each track. The divisions were broken up into intelligent context-awareness, intelligent character recognition, auditory intelligence, and control intelligence. Team USRG’s technological prowess has been already well acclaimed among international peer groups. Teamed up with NASA JPL, Caltech, and MIT, they will compete in the subterranean mission during the ‘DARPA SubT Challenge’. Team CoSTAR, as its name stands for, is working together to build ‘Collaborative SubTerranean Autonomous Resilient Robots.’ Professor Shim emphasized the role KAIST plays in Team CoSTAR as a leader in drone technology. “I think when our drone technology will be added to our peers’ AI and robotics, Team CoSTAR will bring out unsurpassable synergy in completing the subterrestrial and planetary applications. I would like to follow the footprint of Hubo, the winning champion of the 2015 DARPA Robotics Challenge and even extend it to subterranean exploration,” he said. These next generation autonomous subsurface explorers are now all optimizing the physical AI robot systems developed by Team CoSTAR. They will test their systems in more realistic field environments August 15 through 22 in Pittsburgh, USA. They have already received funding from DARPA for participating. Team CoSTAR will compete in three consecutive yearly events starting this year, and the last event, planned for 2021, will put the team to the final test with courses that incorporate diverse challenges from all three events. Two million USD will be awarded to the winner after the final event, with additional prizes of up to 200,000 USD for self-funded teams. Team USRG also ranked third in the recent Hyundai Motor Company’s ‘Autonomous Vehicle Competition’ and another challenge is on the horizon: Lockheed Martin’s ‘AlphaPilot Challenge’. In this event, the teams will be flying their drones through a series of racing gates, trying to beat the best human pilot. The challenge is hosted by Lockheed Martin, the world’s largest military contractor and the maker of the famed F-22 and F-35 stealth fighters, with the goal of stimulating the development of autonomous drones. Team USRG was selected from out of more than 400 teams from around the world and is preparing for a series of races this fall, beginning from the end of August. Professor Shim said, “It is not easy to perform in a series of competitions in just a few months, but my students are smart, hardworking, and highly motivated. These events indeed demand a lot, but they really challenge the researchers to come up with technologies that work in the real world. This is the way robotics really should be.” (END)
Newly Identified Meningeal Lymphatic Vessels Answers the Key Questions on Brain Clearance
(Figure: Schematic images of location and features of meningeal lymphatic vessels and their changes associated with ageing.) Just see what happens when your neighborhood’s waste disposal system is out of service. Not only do the piles of trash stink but they can indeed hinder the area’s normal functioning. That is also the case when the brain’s waste management is on the blink. The buildup of toxic proteins in the brain causes a massive damage to the nerves, leading to cognitive dysfunction and increased probability of developing neurodegenerative disorders such as Alzheimer's disease. Though the brain drains its waste via the cerebrospinal fluid (CSF), little has been understood about an accurate route for the brain’s cleansing mechanism. Medical scientists led by Professor Gou Young Koh at the Graduate School of Medical Science and Engineering have reported the basal side of the skull as the major route, so called “hotspot” for CSF drainage. They found that basal meningeal lymphatic vessels (mLVs) function as the main plumbing pipes for CSF. They confirmed macromolecules in the CSF mainly runs through the basal mLVs. Notably, the team also revealed that the brain’s major drainage system, specifically basal mLVs are impaired with aging. Their findings have been reported in the journal Nature on July 24. Throughout our body, excess fluids and waste products are removed from tissues via lymphatic vessels. It was only recently discovered that the brain also has a lymphatic drainage system. mLVs are supposed to carry waste from the brain tissue fluid and the CSF down the deep cervical lymph nodes for disposal. Still scientist are left with one perplexing question — where is the main exit for the CSF? Though mLVs in the upper part of the skull (dorsal meningeal lymphatic vessels) were reported as the brain’s clearance pathways in 2014, no substantial drainage mechanism was observed in that section. “As a hidden exit for CSF, we looked into the mLVs trapped within complex structures at the base of the skull,” says Dr. Ji Hoon Ahn, the first author of this study. The researchers used several techniques to characterize the basal mLVs in detail. They used a genetically engineered lymphatic-reporter mouse model to visualize mLVs under a fluorescence microscope. By performing a careful examination of the mice skull, they found distinctive features of basal mLVs that make them suitable for CSF uptake and drainage. Just like typical functional lymphatic vessels, basal mLVs are found to have abundant lymphatic vessel branches with finger-like protrusions. Additionally, valves inside the basal mLVs allow the flow to go in one direction. In particular, they found that the basal mLVs are closely located to the CSF. Dr. Hyunsoo Cho, the first author of this study explains, “All up, it seemed a solid case that basal mLVs are the brain’s main clearance pathways. The researchers verified such specialized morphologic characteristics of basal mLVs indeed facilitate the CSF uptake and drainage. Using CSF contrast-enhanced magnetic resonance imaging in a rat model, they found that CSF is drained preferentially through the basal mLVs. They also utilized a lymphatic-reporter mouse model and discovered that fluorescence-tagged tracer injected into the brain itself or the CSF is cleared mainly through the basal mLVs. Jun-Hee Kim, the first author of this study notes, “We literally saw that the brain clearance mechanism utilizing basal outflow route to exit the skull. It has long been suggested that CSF turnover and drainage declines with ageing. However, alteration of mLVs associated with ageing is poorly understood. In this study, the researchers observed changes of mLVs in young (3-month-old) and aged (24~27-months-old) mice. They found that the structure of the basal mLVs and their lymphatic valves in aged mice become severely flawed, thus hampering CSF clearance. The corresponding author of this study, Dr. Koh says, “By characterizing the precise route for fluids leaving the brain, this study improves our understanding on how waste is cleared from the brain. Our findings also provide further insights into the role of impaired CSF clearance in the development of age-related neurodegenerative diseases.” Many current therapies for Alzheimer’s disease target abnormally accumulated proteins, such as beta-amyloid. By mapping out a precise route for the brain’s waste clearance system, this study may be able to help find ways to improve the brain’s cleansing function. Such breakthrough might become quite a sensational strategy for eliminating the buildup of aging-related toxic proteins. “It definitely warrants more extensive investigation of mLVs in patients with age-related neurodegenerative disease such as Alzheimer’s disease prior to clinical investigation,” adds Professor Koh.
KAIST-Google Partnership for AI Education and Research
Google has agreed to support KAIST students and professors in the fields of AI research and education. President Sung-Chul Shin and Google Korea Country Director John Lee signed the collaboration agreement during a ceremony on July 19 at KAIST. Under the agreement, Google will fund the Google AI-Focused Research Awards Program, the PhD Fellowship Program, and Student Travel Grants for KAIST. In addition, Google will continue to provide more academic and career building opportunities for students, including Google internship programs. KAIST and Google has been collaborating for years. Professor Steven Whang at the School of Electrical Engineering and Professor Sung Ju Hwang at the School of Computing won the AI-Focused Award in 2018 and conduct their researches on "Improving Generalization and Reliability of Any Deep Neural Networks" and "Automatic and Acitionable Model Analysis for TFX," respectively. Outstanding PhD students have been recognized through the PhD Fellowship Program. However, this new collaboration agreement will focus on research, academic development, and technological innovation in AI. Google plans to support research in the fields of deep learning, cloud machine learning, and voice technologies. Google will fund the development of two educational programs based on Google open source technology each year for two years that will be used in the new AI Graduate School opening for the fall semester. John Lee of Google Korea said, “This partnership lays a solid foundation for deeper collaboration.” President Shin added, “This partnership will not only advance Korea’s global competitiveness in AI-powered industries but also contribute to the global community by nurturing talents in this most extensive discipline.”
Hydrogen-Natural Gas Hydrates Harvested by Natural Gas
A hydrogen-natural gas blend (HNGB) can be a game changer only if it can be stored safely and used as a sustainable clean energy resource. A recent study has suggested a new strategy for stably storing hydrogen, using natural gas as a stabilizer. The research proposed a practical gas phase modulator based synthesis of HNGB without generating chemical waste after dissociation for the immediate service. The research team of Professor Jae Woo Lee from the Department of Chemical and Biomolecular Engineering in collaboration with the Gwangju Institute of Science and Technology (GIST) demonstrated that the natural gas modulator based synthesis leads to significantly reduced synthesis pressure simultaneously with the formation of hydrogen clusters in the confined nanoporous cages of clathrate hydrates. This approach minimizes the environmental impact and reduces operation costs since clathrate hydrates do not generate any chemical waste in both the synthesis and decomposition processes. For the efficient storage and transportation of hydrogen, numerous materials have been investigated. Among others, clathrate hydrates offer distinct benefits. Clathrate hydrates are nanoporous inclusion compounds composed of a 3D network of polyhedral cages made of hydrogen-bonded ‘host’ water molecules and captured ‘guest’ gas or liquid molecules. In this study, the research team used two gases, methane and ethane, which have lower equilibrium conditions compared to hydrogen as thermodynamic stabilizers. As a result, they succeeded in stably storing the hydrogen-natural gas compound in hydrates. According to the composition ratio of methane and ethane, structure I or II hydrates can be formed, both of which can stably store hydrogen-natural gas in low-pressure conditions. The research team found that two hydrogen molecules are stored in small cages in tuned structure I hydrates, while up to three hydrogen molecules can be stored in both small and large cages in tuned structure II hydrates. Hydrates can store gas up to about 170-times its volume and the natural gas used as thermodynamic stabilizers in this study can also be used as an energy source. The research team developed technology to produce hydrates from ice, produced hydrogen-natural gas hydrates by substitution, and successfully observed that the tuning phenomenon only occurs when hydrogen is involved in hydrate formation from the start for both structures of hydrates. They expect that the findings can be applied to not only an energy-efficient gas storage material, but also a smart platform to utilize hydrogen natural gas blends, which can serve as a new alternative energy source with targeted hydrogen contents by designing synthetic pathways of mixed gas hydrates. The research was published online in Energy Storage Materials on June 6, with the title ‘One-step formation of hydrogen clusters in clathrate hydrates stabilized via natural gas blending’. Professor Lee said, “HNGB will utilize the existing natural gas infrastructure for transportation, so it is very likely that we can commercialize this hydrate system. We are investigating the kinetic performance through a follow-up strategy to increase the volume of gas storage. This study was funded by the National Research Foundation of Korea and BK21 plus program. (Figure1. Schematics showing the storage method for hydrogen in a natural gas hydrate using a substitution method and storage method directly from ice to a hydrogen-natural gas hydrate.) (Figure 2. Artificially synthesized and dissociated hydrogen-natural gas hydrates. The Raman spectra of tuned sI and sII hydrate showing the hydrogen clusters in each cage.)
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