University of Seoul

Cubic boron nitride (c-BN) and hexagonal boron nitride (h-BN) are known for their transparency and high 10B density, which provides a large thermal-neutron cross-section, yet their potential for space neutron shielding has not been explored. The fabrication of transparent c-BN films remains challenging, and the chemical vapor deposition growth of h-BN beyond 70nm, or with precise thickness control and high uniformity, has not been reported except by our group. Here, we present a space window design integrating an h-BN-based neutron shielding layer with advanced ceramic bulletproof layers and a γ-ray shielding layer. By incorporating C and O into h-BN, sp2-sp3 hybridized BN (HBN) reduces the refractive index mismatch with the SiO2 substrate, achieving 90.9% transmission at 550nm at 11.9µm thickness and enabling stable, transparent growth up to 79.2µm with minimized thermal expansion mismatch. The optically optimized HBN (B0.39N0.39C0.06O0.16) shows reduced boron content, but the enriched formation of 63.4% c-BN, with its higher boron density, compensates for this loss. The resultant density is 3.01g cm-3, evaluated from neutron-shielding probability, and HBN achieves the same neutron-shielding efficiency as h-BN at 3% reduced thickness.

In recent times, slicing methods have yielded a successful outcome in generative models for image, sound, and text data, primarily focusing on joint distributional learning. However, we have identified a critical limitation of the slicing approach for tabular data: it struggles to capture marginal distributional patterns, which are significant for effective tabular data synthesis. To tackle this problem, we introduce a new measure of discrepancy, the mixture Cramer–Wold distance. This measure enables us to capture both marginal and joint distributional patterns simultaneously, striking a balance between the two aspects, and we provide theoretical foundations for its application. Leveraging the power of the mixture Cramer–Wold distance, we present CWDAE (Cramer–Wold Distributional AutoEncoder), a generative model that demonstrates notable performance in generating synthetic tabular data. Furthermore, our model offers the flexibility to adjust the level of data privacy to meet specific needs easily.

ABSTRACT Growing concerns about electronic waste underscore the need for materials that combine high performance with environmental sustainability. Here, we report an organic thin‐film transistor (OTFT) that incorporates a water‐processed gum arabic (GA) dielectric, a natural, biodegradable resin derived from Acacia senegal , to enable eco‐friendly device fabrication. The GA dielectric forms defect‐free films directly from aqueous solution and exhibits a dielectric constant of approximately 27 at 1 kHz. By optimizing GA concentration, we obtain uniform and stable dielectric layers that substantially enhance charge transport in dinaphtho[2,3‐b:2′,3′‐f]thieno[3,2‐b]thiophene (DNTT) semiconductors, yielding p‐type OTFTs operating at ±3 V with high mobilities up to 20.72 cm 2 V −1 s −1 and negligible hysteresis. Comparative analyses show that GA facilitates improved molecular ordering of DNTT and suppresses trap formation, outperforming conventional PMMA dielectrics. Upon immersion in water, the GA layer dissolves rapidly (within 30 s), leaving the substrate pristine and fulfilling key criteria for transient electronics. This combination of outstanding electrical performance and complete aqueous degradability highlights the potential of GA for scalable fabrication of green, high‐performance electronic devices designed to disappear on demand, supporting urgent efforts toward sustainable and transient electronic technologies.

VESTAP, a climate decision support tool offered by the South Korean government, supports sector-specific vulnerability assessments for local and regional governments based on the IPCC AR4 framework. As a multi-indicator, spatially based assessment tool, VESTAP conceptualizes vulnerability as a composite of climate exposure, sensitivity, and adaptive capacity, with sub-indicators derived from administrative-unit-level data and aggregated using weighted sums. However, the current assessment of building vulnerability to flooding has limited capacity to reflect the highly localized distribution of key risk factors driving pluvial flooding, which tend to emerge heterogeneously within administrative units. As a result, improvements in spatial discrimination for priority area identification and in the explanatory power of assessment results are required.Effective climate adaptation requires the precise identification of vulnerable areas to support priority setting. Pluvial flooding, the focus of this study, occurs within urban areas due to insufficient drainage capacity and topographic water retention during heavy rainfall, with risk factors often concentrated at highly localized scales. It is widely recognized that administrative-unit-level assessments are insufficient for identifying flood-prone areas under such conditions. Recent studies have addressed this limitation by quantifying the spatial distribution of risk factors at finer spatial units-such as parcels, households, and grids-and by integrating physical models with socio-economic indicators to better capture spatial heterogeneity.This study aims to improve a government-offered vulnerability assessment for Suwon, South Korea, by enhancing spatial resolution and indicator composition to provide actionable information for local adaptation planning. Compared to a baseline setup (S0), five improvement strategies are introduced: (1) region-specific adjustment of indicator weights, (2) incorporation of nationally available spatial explanatory datasets, (3) addition of literature-based key indicators, (4) transition from simple aggregation to an overlapping analytical approach, and (5) integration of municipality-produced datasets. The results of each pilot application are quantitatively compared in terms of changes in the spatial patterns of vulnerable areas, and their validity is evaluated through consistency with observed flood damage records and recovery and prevention investment histories. In addition, interviews with local government officials are conducted to assess practical relevance and usability.By diagnosing the existing vulnerability assessment framework for pluvial flooding in light of theory and prior research, and by comparing pilot application results, this study examines the potential for improving spatial sensitivity and assessment validity. The findings provide more precise and explainable evidence to support priority setting and resource allocation for local climate adaptation planning.

As urban temperatures rise at an unprecedented pace due to climate change, cities worldwide are experiencing increasing infrastructure damage and heat-related health impacts. In response, many cities are developing heat-specific adaptation plans and broader resilience strategies. While a wide range of heat mitigation and management measures has been proposed, it remains unclear how these measures are translated into concrete planning targets, how much adaptation progress has been achieved to date, and whether cities are following an appropriate adaptation pathway. The lack of a standardized approach for defining and evaluating quantitative heat adaptation targets poses a major barrier to effective urban heat adaptation planning and implementation.To address this gap, this study proposes a methodology for quantitatively setting urban heat adaptation targets. Using the latest urban structure data, we diagnose the current thermal environment and project future thermal conditions under a sustainability-oriented target pathway (SSP1) and a high-emission reference pathway (SSP5), assuming the urban structure remains unchanged. By comparing the diagnosed current thermal environment with the heat level associated with the SSP1 target pathway, we quantify the heat risk reduction required for the city to reach a sustainable adaptation state.The proposed framework enables discussion of necessary concrete adaptation measures by linking the quantified adaptation target to required physical and spatial changes in urban form. Through real-world urban application, we demonstrate how this methodology can diagnose a city's current adaptation pathway, define measurable heat adaptation targets, and support iterative updates as urban structure and adaptation interventions evolve.This approach contributes to effective urban heat adaptation planning by providing a framework for defining and updating quantitative adaptation targets, which can ultimately be linked to more effective evaluation and implementation of urban heat adaptation strategies.This work was supported by Korea Environment Industry &Technology Institute (KEITI) through "Climate Change R&D Project for New Climate Regime.", funded by Korea Ministry of Climate, Energy and Environment. (MCEE) (RS-2022-KE002102)

Public demand for ecologically healthy rivers and water-friendly spaces has grown over time, increasing the need for planning and application at the regional scale. Accordingly, incorporating citizens’ needs into management plans has become increasingly important. This study aimed to identify citizens’ preferences for the ecological and spatial features of stream waterfronts. We conducted a survey using 30 images of stream waterfronts that are open access, asking respondents to rate each image on a 7-point scale (1 = very low to 7 = very high). A total of 235 responses were collected. The evaluation features were selected based on findings from previous monitoring studies. In addition, generative AI (ChatGPT 5.2) was used to generate representative stream waterfront images reflecting the observed feature preferences (e.g., best case vs. worst case). Further studies for enhancing the training process by revising the criteria and adding more images are required. The results are expected to support stream waterfront design and discharge management by linking these preferences with holistic planning approaches.