Water challenges are becoming increasingly complex, spanning issues of scarcity, pollution, ecosystem degradation, and public health. Addressing these challenges requires advances across multiple disciplines, from engineering and environmental science to microbiology, chemistry, and policy. Nature Water was launched to serve as a leading platform for publishing impactful research that advances fundamental understanding of water systems and supports sustainable water management.
In this presentation, I will introduce Nature Water and its positioning within the global water research landscape. The presentation outlines journal’s scope, highlighting key areas of interest across water quality, treatment technologies, resource recovery, hydrology, and governance. I will also discuss the journal’s editorial expectations and selectivity criteria, including conceptual advance, broad relevance, and mechanistic insight. Finally, emerging directions where the journal seeks to expand and encourage contributions that push the boundaries of water research will be highlighted.

This presentation introduces the fundamental directions, program structures, and implementation strategies of the 2026 Basic Research Programs in the Division of Life Sciences, organized by the National Research Foundation of Korea (NRF). The Division aims to foster creative and challenging research to strengthen the nation’s science and technology competitiveness, with a focus on expanding researcher-centered support and ensuring the stability of the research ecosystem.

The major programs include: (1) Individual Basic Research supporting early-career, mid-career, and leading researchers; and (2) Group Research such as research centers and collaborative projects. Special emphasis is placed on assisting early-career researchers in establishing their research capacity, while mid-career researchers are provided with stable, long-term support. Leading researchers are encouraged to undertake large-scale projects that can generate world-class outcomes.

Research funding will be differentiated according to career stage and project characteristics. Evaluation criteria will prioritize creativity, challenge, and potential academic contributions, with greater weight placed on long-term impact and scholarly value rather than short-term results. To improve the research environment, measures will be implemented to enhance flexibility in research fund management, reduce administrative burdens, and strengthen transparency and research integrity, thereby enabling researchers to focus on their scientific work.

Finally, strategic investment will be expanded in future-oriented fields such as artificial intelligence and biohealth, which are recognized as national priorities. Through these initiatives, the NRF seeks to ensure that basic research not only advances academic excellence but also contributes to social and industrial innovation.

The transition from traditional mass production to hyper-personalized cosmetics requires a fundamental shift in how subjective sensory elements and biological data are processed. This study presents a comprehensive end-to-end framework that digitizes human sensory experiences and biological skin phenotypes to drive advanced AI-predictive models for customized formulation. First, we successfully objectified visual evaluation, such as color, into a robust relational database. Using this digitized data, we developed an AI-assisted predictive model capable of highly accurate smart color matching and initial formulation input recommendations. Building upon this AI prediction model, we integrated quantitative biological skin phenotyping with our Digital Formulation (DF) architecture to design truly hyper-personalized color cosmetics solutions tailored to individual consumer needs. Finally, to translate these digital blueprints into physical realities, we seamlessly connected our algorithmic designs with an advanced automated manufacturing system utilizing robotic arms. This multidisciplinary convergence of sensory science, digital formulation, and mechanical automation not only minimizes trial-and-error in R&D but also establishes a highly efficient, scalable, and on-demand pipeline for next-generation customized cosmetics.