The Korea Advanced Institute of Science and Technology (KAIST) is a national research university located in Daedeok Innopolis, Daejeon, South Korea. KAIST was established by the Korean government in 1971 as the nation's first public, research-oriented science and engineering institution. KAIST is considered to be one of the most prestigious universities in the nation. KAIST has been internationally accredited in business education, and hosting the Secretariat of the Association of Asia-Pacific Business Schools (AAPBS). KAIST has 10,504 full-time students and 1,342 faculty researchers (as of Fall 2019 Semester) and had a total budget of US$765 million in 2013, of which US$459 million was from research contracts.In 2007, KAIST partnered with international institutions and adopted dual degree programs for its students. Its partner institutions include the Technical University of Denmark, Carnegie Mellon University, the Georgia Institute of Technology, the Technical University of Berlin, and the Technical University of Munich.
Numerical investigations on prediction of flashback of a lean premixed hydrogen flame in an industrial gas turbine combustor were conducted. Three combustion models were tested for prediction capabilities: (i) the flamelet-generated manifold-finite rate (FGM-FR) model, (ii) the flamelet-generated manifold-turbulent flame speed (FGM-ST) model, and (iii) the eddy dissipation concept (EDC) model. Without an additional wall damping model, the three models are unable to predict the flame flashback limit correctly. A wall damping model was applied to modify the reaction rate near the wall. With certain values of the wall damping coefficient, the FGM-FR and FGM-ST models successfully predict the flame flashback limit.
Physics-based simulations are essential in science and engineering, yet creating them typically requires expert knowledge of numerical solvers and governing equations. Large language models (LLMs) offer new possibilities for natural language-based simulation, but they often fail when prompts are vague, incomplete, or multilingual. We present MCP-SIM (Memory-Coordinated Physics-Aware Simulation), a self-correcting multi-agent framework that transforms underspecified prompts into validated simulations and explanatory reports. The system integrates input clarification, code generation, error diagnosis, and multilingual explanation through structured agent collaboration and persistent memory. Rather than relying on one-shot code generation, MCP-SIM emulates expert-like reasoning via iterative plan–act–reflect–revise cycles. Tested on a twelve-task benchmark across diverse physics domains, MCP-SIM achieved 100% success, significantly outperforming baseline LLMs. In addition to numerical accuracy, the system produces interpretable, language-localized reports that explain each simulation’s physical logic. MCP-SIM represents a step toward general-purpose autonomous scientific assistants that simulate, adapt, and teach through natural language.
Lithium–sulfur batteries (LSBs) are attracting significant attention as next‐generation energy storage systems due to their high theoretical specific capacity (1675 mAh g−1), high‐energy density (2600 Wh kg−1), and light weight. Despite these advantages, the practical application of Li–S batteries is impeded by several challenges, such as the low electrical conductivity of sulfur and lithium sulfide and the shuttle effect of lithium polysulfides (LiPSs), which lead to rapid capacity fading. Carbon materials have emerged as strong candidates for addressing these issues, owing to their excellent electrical conductivity, high porosity, and superior physical and chemical stability. Furthermore, the performance of carbon materials can be enhanced by doping with non‐metallic elements or incorporating metallic elements to take advantage of the large surface area. Such modifications enhance the chemical adsorption of LiPSs and improve the catalytic activity for electrochemical reactions, thereby significantly boosting the overall performance of LSBs. In this review, recent advances in carbon materials are systematically categorized according to their dimensional architecture—0D, 1D, 2D, and 3D. This classification reflects the distinct structure‐dependent characteristics of each dimensionality, including surface area, pore structure, electron/ion transport properties, and the accessibility of active sites. It enables comparative evaluation of their roles as sulfur hosts and interlayers and offers insights into the rational design of high‐performance carbon materials for Li–S batteries.
Multimodal machine learning has been widely studied for the development of general intelligence. Recently proposed Perceiver and its variant (Perceiver IO) have shown promising results in addressing diverse types of input modalities with a universal model architecture. However, they have mainly focused on image and text data modalities, and it is unclear whether this kind of universal architecture can also be effective for graph-structured datasets. As the graph data contains topological information, which is lacking in the image and text data, it is non-trivial to devise a universal architecture for graph and other data modalities altogether. In this study, we provide a Graph Perceiver IO (GPIO), a class of Perceiver IO models that addresses graph-structured datasets. We keep the main structure of the GPIO the same as with the Perceiver, which can already handle multiple data modalities, while focusing on how we can extend it to the graph domain. By leveraging positional encoding and output query smoothing, GPIO serves as a general architecture that handles graph-structured data as well as text and image data. Besides, we further propose GPIO+ for the multimodal few-shot classification that incorporates both images and graphs simultaneously. Through extensive experiments covering link prediction, graph classification, node classification, and multimodal text classification, we demonstrate that GPIO and GPIO+ outperform the representative graph neural network baseline models, while requiring lower computational complexity than them.
Urban Air Mobility (UAM) is an emerging mobility service increasingly proposed by cities worldwide. Among its various applications, UAM as an airport shuttle offers particularly strong early-stage commercial potential. However, understanding of the key factors influencing the adoption of UAM as an airport shuttle service remains limited, particularly regarding the role of social-psychological factors and their tolerance thresholds from a nonlinear perspective, where critical points in factors such as time or cost may shift the decision from declination to acceptance. Using a stated-preference survey of 1250 respondents from South Korea, this study identifies the primary determinants of UAM adoption and examines their decision thresholds using a newly proposed hybrid approach that combines automated machine learning (AutoML) and statistical models in a complementary manner. The results show thatt: (1) Previously overlooked social psychological factors, such as individuals seeking time savings, environmental benefits, and openness to new technologies, play adominant role, accounting for 55.4 % of explanatory power in predicting adoption decisions. (2) Threshold effects emerge in airport trip chains, with firstmile and in-vehicle durations under 15 min or over one hour marking critical adoption points; and (3) UAM holds strong substitute potential for car use for long-distance airport access. These findings provide actionable insights for policymakers and service providers aiming to promote UAM adoption, emphasizing the need to align service design and marketing strategies with users' psychological motivations and to improve access environments for UAM connectivity within urban areas.
