Sogang University Corp.

A joint research team in computer science, economics, and geography has developed an artificial intelligence (AI) technology to measure grid-level economic development within six-square-kilometer regions. This AI technology is applicable in regions with limited statistical data (e.g., North Korea), supporting international efforts to propose policies for economic growth and poverty reduction in underdeveloped countries. The research team plans to make this technology freely available for use to contribute to the United Nations' Sustainable Development Goals (SDGs). The United Nations reports that more than 700 million people are in extreme poverty, earning less than two dollars a day. However, an accurate assessment of poverty remains a global challenge. For example, 53 countries have not conducted agricultural surveys in the past 15 years, and 17 countries have not published a population census. To fill this data gap, new technologies are being explored to estimate poverty using alternative sources such as street views, aerial photos, and satellite images. The paper published in Nature Communications demonstrates how artificial intelligence (AI) can help analyze economic conditions from daytime satellite imagery. This new technology can even apply to the least developed countries -- such as North Korea -- that do not have reliable statistical data for typical machine learning training. The researchers used Sentinel-2 satellite images from the European Space Agency (ESA) that are publicly available. They split these images into small six-square-kilometer grids. At this zoom level, visual information such as buildings, roads, and greenery can be used to quantify economic indicators. As a result, the team obtained the first ever fine-grained economic map of regions like North Korea. The same algorithm was applied to other underdeveloped countries in Asia: North Korea, Nepal, Laos, Myanmar, Bangladesh, and Cambodia. The key feature of their research model is the "human-machine collaborative approach," which lets researchers combine human input with AI predictions for areas with scarce data. In this research, ten human experts compared satellite images and judged the economic conditions in the area, with the AI learning from this human data and giving economic scores to each image. The results showed that the Human-AI collaborative approach outperformed machine-only learning algorithms. The research was led by an interdisciplinary team of computer scientists, economists, and a geographer from KAIST & IBS (Donghyun Ahn, Meeyoung Cha, Jihee Kim), Sogang University (Hyunjoo Yang), HKUST (Sangyoon Park), and NUS (Jeasurk Yang). Dr Charles Axelsson, Associate Editor at Nature Communications, handled this paper during the peer review process at the journal. The research team found that the scores showed a strong correlation with traditional socio-economic metrics such as population density, employment, and number of businesses. This demonstrates the wide applicability and scalability of the approach, particularly in data-scarce countries. Furthermore, the model's strength lies in its ability to detect annual changes in economic conditions at a more detailed geospatial level without using any survey data. This model would be especially valuable for rapidly monitoring the progress of Sustainable Development Goals such as reducing poverty and promoting more equitable and sustainable growth on an international scale. The model can also be adapted to measure various social and environmental indicators. For example, it can be trained to identify regions with high vulnerability to climate change and disasters to provide timely guidance on disaster relief efforts. As an example, the researchers explored how North Korea changed before and after the United Nations sanctions against the country. By applying the model to satellite images of North Korea both in 2016 and in 2019, the researchers discovered three key trends in the country's economic development between 2016 and 2019. First, economic growth in North Korea became more concentrated in Pyongyang and major cities, exacerbating the urban-rural divide. Second, satellite imagery revealed significant changes in areas designated for tourism and economic development, such as new building construction and other meaningful alterations. Third, traditional industrial and export development zones showed relatively minor changes. Meeyoung Cha, a data scientist in the team explained, "This is an important interdisciplinary effort to address global challenges like poverty. We plan to apply our AI algorithm to other international issues, such as monitoring carbon emissions, disaster damage detection, and the impact of climate change." An economist on the research team, Jihee Kim, commented that this approach would enable detailed examinations of economic conditions in the developing world at a low cost, reducing data disparities between developed and developing nations. She further emphasized that this is most essential because many public policies require economic measurements to achieve their goals, whether they are for growth, equality, or sustainability. The research team has made the source code publicly available via GitHub and plans to continue improving the technology, applying it to new satellite images updated annually. The results of this study, with Ph.D. candidate Donghyun Ahn at KAIST and Ph.D. candidate Jeasurk Yang at NUS as joint first authors, were published in Nature Communications under the title "A human-machine collaborative approach measures economic development using satellite imagery."

Researchers identify the most promising advancements and greatest challenges of artificial mitochondria and chloroplasts. The team describes the components required to construct synthetic mitochondria and chloroplasts and identifies proteins as the most important aspects for molecular rotary machinery, proton transport, and ATP production. The authors believe it is important to create artificial cells with biologically realistic energy-generation methods that mimic natural processes; replicating the entire cell could lead to future biomaterials. Energy production in nature is the responsibility of chloroplasts and mitochondria and is crucial for fabricating sustainable, synthetic cells in the lab. Mitochondria are not only "the powerhouses of the cell," as the middle school biology adage goes, but also one of the most complex intracellular components to replicate artificially. In Biophysics Reviews, by AIP Publishing, researchers from Sogang University in South Korea and the Harbin Institute of Technology in China identified the most promising advancements and greatest challenges of artificial mitochondria and chloroplasts. "If scientists can create artificial mitochondria and chloroplasts, we could potentially develop synthetic cells that can generate energy and synthesize molecules autonomously. This would pave the way for the creation of entirely new organisms or biomaterials," author Kwanwoo Shin said. In plants, chloroplasts use sunlight to convert water and carbon dioxide into glucose. Mitochondria, found in plants and animals alike, produce energy by breaking down glucose. Once a cell produces energy, it often uses a molecule called adenosine triphosphate (ATP) to store and transfer that energy. When the cell breaks down the ATP, it releases energy that powers the cell's functions. "In other words, ATP acts as the main energy currency of the cell, and it is vital for the cell to perform most of the cellular functions," said Shin. The team describes the components required to construct synthetic mitochondria and chloroplasts and identifies proteins as the most important aspects for molecular rotary machinery, proton transport, and ATP production. Previous studies have replicated components that make up the energy-producing organelles. Some of the most promising work investigates the intermediate operations involved in the complex energy-generating process. By connecting the sequence of proteins and enzymes, researchers have improved energy efficiency. One of the most significant challenges remaining in trying to reconstruct the energy production organelles is enabling self-adaptation in changing environments to maintain a stable supply of ATP. Future studies must investigate how to improve upon this limiting feature before synthetic cells are self-sustainable. The authors believe it is important to create artificial cells with biologically realistic energy-generation methods that mimic natural processes. Replicating the entire cell could lead to future biomaterials and lend insight into the past. "This could be an important milestone in understanding the origin of life and the origin of cells," Shin said.

An international team of researchers has recently demonstrated for the first time the use of a new lensless ultrafast X-Ray method to image phase transitions. This new method enables the direct observation of the dynamics of quantum materials at the nanoscale. The use of light to produce transient phases in quantum materials is fast becoming a novel way to engineer new properties in them, such as the generation of superconductivity or nanoscale topological defects. However, visualizing the growth of a new phase in a solid is not easy, due in-part to the wide range of spatial and time scales involved in the process. Although in the last two decades scientists have explained light-induced phase transitions by invoking nanoscale dynamics, real space images have not yet been produced and, thus, no one has seen them. In the new study published in Nature Physics, ICFO researchers Allan S. Johnson and Daniel Pérez-Salinas, led by former ICFO Prof. Simon Wall, in collaboration with colleagues from Aarhus University, Sogang University, Vanderbilt University, the Max Born Institute, the Diamond Light Source, ALBA Synchrotron, Utrecht University, and the Pohang Accelerator Laboratory, have pioneered a new imaging method that allows the capture of the light-induced phase transition in vanadium oxide (VO2) with high spatial and temporal resolution. The new technique implemented by the researchers is based on coherent X-ray hyperspectral imaging at a free electron laser, which has allowed them to visualize and better understand, at the nanoscale, the insulator-to-metal phase transition in this very well-known quantum material. The crystal VO2 has been widely used in to study light-induced phase transitions. It was the first material to have its solid-solid transition tracked by time-resolved X-ray diffraction and its electronic nature was studied by using for the first time ultrafast X-ray absorption techniques. At room temperature, VO2 is in the insulating phase. However, if light is applied to the material, it is possible to break the dimers of the vanadium ion pairs and drive the transition from an insulating to a metallic phase. In their experiment, the authors of the study prepared thin samples of VO2 with a gold mask to define the field of view. Then, the samples were taken to the X-ray Free Electron Laser facility at the Pohang Accelerator Laboratory, where an optical laser pulse induced the transient phase, before being probed by an ultrafast X-ray laser pulse. A camera captured the scattered X-rays, and the coherent scattering patterns were converted into images by using two different approaches: Fourier Transform Holography (FTH) and Coherent Diffractive Imaging (CDI). Images were taken at a range of time delays and X-ray wavelengths to build up a movie of the process with 150 femtosecond time resolution and 50 nm spatial resolution, but also with full hyperspectral information. The surprising role of the pressure The new methodology allowed the researchers to better understand the dynamics of the phase transition in VO2. They found that pressure plays a much larger role in light-induced phase transitions than previously expected or assumed. "We saw that the transient phases aren't nearly as exotic as people had believed! Instead of a truly non-equilibrium phase, what we saw was that we had been misled by the fact that the ultrafast transition intrinsically leads to giant internal pressures in the sample millions of times higher than atmospheric. This pressure changes the material properties and takes time to relax, making it seem like there was a transient phase" says Allan Johnson, postdoctoral researcher at ICFO. "Using our imaging method, we saw that, at least in this case, there was no link between the picosecond dynamics that we did see and any nanoscale changes or exotics phases. So, it looks like some of those conclusions will have to be revisited." To identify the role played by the pressure in the process, it was crucial to use the hyperspectral image. "By combining imaging and spectroscopy into one great image, we are able to retrieve much more information that permits us to actually see detailed features and decipher exactly where they come from," continues Johnson. "This was essential to look at each part of our crystal and determine whether it was a normal or an exotic out-of-equilibrium phase-and with this information we were able to determine that during the phase transitions all the regions of our crystal were the same, except for the pressure." Challenging research One of the main challenges the researchers faced during the experiment was to ensure that the crystal sample of VO2 returned to its original starting phase each time and after being illuminated by the laser. To guarantee that this would occur, they conducted preliminary experiments at synchrotrons where they took several crystal samples and repeatedly shone the laser on them to test their capacity to recover back to their original state. The second challenge resided in having access to an X-Ray free electron laser, large research facilities where the time windows to conduct the experiments are very competitive and in-demand because there are only a few in the world. "We had to spend two weeks in quarantine in South Korea due to the COVID-19 restrictions before we got our one shot of just five days to make the experiment work, so that was an intense time" Johnson recalls. Although the researchers describe the present work as fundamental research, the potential applications of this technique could be diverse, since they could "look at polarons moving inside catalytic materials, try imaging superconductivity itself, or even help us understand novel nanotechnologies by viewing and imaging inside nanoscale devices" concludes Johnson.