The current- and H 2 -producing performances of microbial electrolysis cells (MECs) were predicted by constructing machine learning models based on the previous 76 MEC datasets, making it the largest dataset to date. All models showed high correlation efficiency (R 2 > 0.92) in predicting MEC performances. When the models were constructed separately based on the organic substrate type used in the anode of MECs, the models based solely on acetate-fed MEC data exhibited higher prediction accuracies compared to those on all kinds of substrate or complex substrate-based data. As a results of the feature importance analysis, the applied voltage and cathode surface area were identified as the two most critical factors in the acetate-fed MEC data models. Still low prediction accuracies in the models here seem to be due to several important features which could not be numerically presented and thus not be considered as input variables such as electrode material types. • RF models were constructed to predict current and H 2 productions from MECs. • All models showed high prediction accuracies based on total 76 data points. • The models were constructed separately based on the organic substrate type fed. • The E ap and cathode surface area were critical factors in acetate-fed MEC models. • Non-numerical factors need to be considered such as electrode material types.

Abstract Concrete, as the most widely used construction material, is inextricably connected with human development. Despite conceptual and methodological progress in concrete science, concrete formulation for target properties remains a challenging task due to the ever-increasing complexity of cementitious systems. With the ability to tackle complex tasks autonomously, machine learning (ML) has demonstrated its transformative potential in concrete research. Given the rapid adoption of ML for concrete mixture design, there is a need to understand methodological limitations and formulate best practices in this emerging computational field. Here, we review the areas in which ML has positively impacted concrete science, followed by a comprehensive discussion of the implementation, application, and interpretation of ML algorithms. We conclude by outlining future directions for the concrete community to fully exploit the capabilities of ML models.

Railway infrastructure has been severely damaged by extreme heat, which causes structural stress and thermal expansion. We looked into phase change material (PCM)-integrated heat-resistant paints for improved thermal regulation and coating stability on web, flange, and head surfaces in order to control rapid temperature changes in rail components. An acrylic emulsion paint was mixed with two PCMs (melting points of 28 °C and 45 °C) at 15 wt% and 30 wt%, respectively. A maximum latent heat storage of 79.76 J/g (PCM45-30) was confirmed by differential scanning calorimetry, and rheological tests revealed that the viscosity increased from 1.24 Pa·s to 4.03 Pa·s and the yield stress increased from 9.43 Pa (control) to 172.7 Pa. In comparison to the control, thermal performance tests showed a cooling time of 12 minutes and a peak surface temperature reduction of up to 5 °C. For PCM45-30, a Carreau-Yasuda-based model predicted maximum coating thicknesses of 7.95 mm on a 13° incline, 3.62 mm on a vertical (90°) face, and 1.79 mm on a horizontal (0°) surface. These results highlight the two advantages of PCM integration: significant passive heat reduction and adjustable workability. They also suggest that PCM45-based formulations are good options for long-term thermal protection of high-speed rail profiles.

A performance-based approach is essential for evaluating the long-term durability and performance of concrete. Nondestructive methods, such as electrical resistivity measurements, offer significant advantages in this context due to their simplicity, speed, and cost-effectiveness. This study applied electrical resistivity as a tool to assess key performance factors, including compressive strength and chloride transport properties, in concrete mixtures with and without supplementary cementitious materials (SCMs). The results demonstrated strong correlations between electrical resistivity and the performance factors across all concrete mixtures, highlighting its potential as a reliable evaluation method. While electrical resistivity shows promise for quantifying performance factors, challenges remain in addressing variability and measurement accuracy. Further investigations are recommended to refine the method and enhance its applicability in practical durability assessments.

남북한 접경지역에서는 발생하고 있는 초국경 감염병 문제와 기후변화에 따른 자연재난 발 생은 국민의 생명과 재산을 위협하는 새로운 안보 이슈로 부상하고 있다. 이러한 감염병과 남 북한 공동 수계에서의 물 부족 문제, 병해충 문제 등은 정치·군사적 배경을 배제하고 남북한 공동 대비책 마련이 절실히 필요한 상황이다. 그러나 남북 관계의 불확실성과 변화로 인해 지 속적인 협력이 이루어지지 않고 있다. 본 연구는 남북한 접경지역에서 발생하는 감염병과 자연재난의 위협을 분석하고 대응방안을 모색하며, 연구의 효용성 제고를 위해 동서독이 분단 상황에서 감염병과 자연재난에 대해 교 류·협력한 사례를 비교 분석한다. 동서독의 분단은 한반도의 분단 상황과는 다소 차이가 있지 만, 분단 상황에서 국민의 삶에 직접적으로 위협이 되는 감염병과 자연재난에 대한 협력을 통 해 공동 대응한 사례는 현재의 남북 상황에 많은 시사점을 제시하고 있다. 동서독은 접경지역 의 상설기구인 ‘접경위원회’를 설치하여 보건·의료, 환경, 자연재해 등 전 분야에 대해 협력체 계를 구축하였고, 1973년 ｢재난공동대응협정｣을 체결하여 접경지역을 중심으로 감염병, 환 경, 재난을 최우선적으로 대응하여 상호 신뢰 구축과 군사적 긴장완화에 기여하였다. 이러한 동서독의 접경지역 협력 사례를 토대로 남북한 접경지역의 감염병과 자연재난에 대 한 구체적인 협력을 통한 신뢰회복과 지속적인 협력 방안을 다음과 같이 제시한다. 첫째, 남 북 접경지역 교류·협력과 관련한 법규의 구체화 및 규범력 부여, 둘째, 남북 접경지역 협력을 위한 상설조직 구축, 셋째, 정부, 지자체, 민간단체가 참여하는 거버넌스형 협력체계 구축. 넷 째, 구속력이 있는 ｢재난 및 보건협정｣체결을 통한 지속적인 협력체계 구축이 필요하다.

This study evaluates indirect carbonation for CO₂ sequestration and high-purity CaCO3 production using FBC fly ash (HFA) and bottom ash (HBA). NH₄Cl extraction showed higher calcium recovery than DI water, with CaCO3 yields of 10.2 wt.% (HFA) and 8.4 wt.% (HBA). CO2 capture capacities were 45.0 kg and 36.8 kg per ton of ash. Vaterite formation occurred under low pH conditions. Carbonation residues had lower compressive strength but improved durability. This process supports sustainable waste management, circular economy principles, and CCUS by utilizing industrial by-products and generating high-purity CaCO3 for environmental and construction applications.

This study presents the development and pilot application of cementitious materials incorporating microencapsulated phase change materials (PCMs) for thermal regulation in rubber-tired Automated Guideway Transit (AGT) systems. Unlike previous PCM studies limited to buildings, this work demonstrates their first use in railway infrastructure. Material tests confirmed that PCM incorporation increased water and superplasticizer demand and reduced compressive strength, with up to a 36.7% loss at 30% replacement. Despite this trade-off, DSC and dynamic heat flow tests showed stable phase-change behavior and a significant heat-buffering effect, delaying internal temperature rise from 20 to 40 °C by up to 80 min. A pilot-scale test section of the K-AGT system was prepared, in which PCM-incorporated concrete was cast only at the top surface in contact with the wheels. This preliminary field application, the first of its kind in railway infrastructure, demonstrated the feasibility of the localized use of PCM-enhanced materials for addressing heat accumulation.

The current- and H 2 -producing performances of microbial electrolysis cells (MECs) were predicted by constructing machine learning models based on the previous 76 MEC datasets, making it the largest dataset to date. All models showed high correlation efficiency (R 2 > 0.92) in predicting MEC performances. When the models were constructed separately based on the organic substrate type used in the anode of MECs, the models based solely on acetate-fed MEC data exhibited higher prediction accuracies compared to those on all kinds of substrate or complex substrate-based data. As a results of the feature importance analysis, the applied voltage and cathode surface area were identified as the two most critical factors in the acetate-fed MEC data models. Still low prediction accuracies in the models here seem to be due to several important features which could not be numerically presented and thus not be considered as input variables such as electrode material types. • RF models were constructed to predict current and H 2 productions from MECs. • All models showed high prediction accuracies based on total 76 data points. • The models were constructed separately based on the organic substrate type fed. • The E ap and cathode surface area were critical factors in acetate-fed MEC models. • Non-numerical factors need to be considered such as electrode material types.

Abstract Concrete, as the most widely used construction material, is inextricably connected with human development. Despite conceptual and methodological progress in concrete science, concrete formulation for target properties remains a challenging task due to the ever-increasing complexity of cementitious systems. With the ability to tackle complex tasks autonomously, machine learning (ML) has demonstrated its transformative potential in concrete research. Given the rapid adoption of ML for concrete mixture design, there is a need to understand methodological limitations and formulate best practices in this emerging computational field. Here, we review the areas in which ML has positively impacted concrete science, followed by a comprehensive discussion of the implementation, application, and interpretation of ML algorithms. We conclude by outlining future directions for the concrete community to fully exploit the capabilities of ML models.