This paper presents a finite-difference method (FDM)-based heat-transfer model for predicting black-ice formation on asphalt pavements and establishes decision criteria using only meteorological data. Black ice is a major cause of winter road accidents and forms under specific surface temperature and moisture conditions; however, its accurate prediction remains challenging owing to dynamic environmental interactions. The FDM incorporates thermodynamic properties, initial pavement-temperature profiles, and surface heat-transfer mechanisms, i.e., radiation, convection, and conduction. Sensitivity analysis shows the necessity of a 28-d stabilization period for reliable winter predictions. Black-ice prediction logic evaluates the surface conditions, relative humidity, wind speed, and latent-heat accumulation to assess phase changes. Field data from Nonsancheon Bridge were used for validation, where a maximum prediction accuracy of 64% is indicated in specific cases despite the overestimation of surface temperatures compared with sensor measurements. These findings highlight the challenges posed by wet surface conditions and prolonged latent-heat retention, which extend the predicted freezing duration. This study provides a theoretically grounded methodology for predicting black ice on various road structures without necessitating additional measurements. Future studies shall focus on enhancing the model by integrating vehicle-induced heat effects, solar radiation, and improved weather-prediction data while comparing the FDM with machine-learning approaches for performance optimization. The results of this study offer a foundation for developing efficient road-safety measures during winter.

급성 림프구성 백혈병의 항암치료는 메토트렉세이트, 6-머캅토퓨린, 빈크리스틴, 아스파라기나제와 같은 약제를 기반으로 한다. 아스파라기나아제 관련 췌장염은 최대 18%의 발병률을 보이는 것으로 알려져 있으며, 급성 발병 및 만성 합병증으로 백혈병에 대한 항암 치료를 중단하는 주요 원인이다. 백혈병 환자에서 항암제에 의한 췌장주위 체액저류를 치료한 사례는 다양하다. 최근 WON (벽으로 둘러 쌓인 괴사) 배액을 위해 내강 인접 금속 스텐트(LAMS)의 사용이 증가하였다. 전기 소작술로 강화된 전달 시스템을 통해 스텐트 배치가 더 간단하고 빨라졌으며 전체 절차가 간소화되고 잠재적으로 절차 시간이 단축되었다. 따라서 다양한 질환의 내시경 배액술에 LAMS를 사용하면 좋은 결과가 보고되고 있다. 본 논문에서는 급성 림프구성 백혈병을 앓고 있는 성인 환자의 L-아스파라기나제 유발 급성 췌장염 및 췌장 가성낭종을 치료하기 위해 hot-system LAMS를 시행한 사례를 논의하고자 한다.

This study aimed to assess the global and domestic efforts regarding the reduction of environmental-impact-factor emissions in the production and construction processes of concrete pavements. By utilizing internationally commercialized programs, this study sought to calculate the environmental impact factors generated by specific domestic concrete-pavement projects and identify areas for improvement. This study evaluated the global and domestic efforts of environmental impact reduction by focusing on the production and construction of concrete pavements. This study calculated the environmental impact factors for five cases using internationally commercialized software. The analysis revealed that, during the production and construction of concrete pavements, Portland cement production is a dominant cause of global warming, smog, acidification, and non-carcinogenic factors, whereas aggregate production is a dominant cause of ozone depletion, eutrophication, carcinogenicity, respiratory issues, environmental toxicity, and fossil-fuel depletion. This study analyzed the environmental impact factors of material mix and process during concrete pavement production and construction using foreign life-cycle inventory (LCI) databases. The environmental impact of each input material was identified. In the future, if an LCI and life-cycle impact assessment (LCIA) database for domestic road pavement materials is established and analyzed based on the conditions presented in this study, it is expected to lay the foundation for the development of environmentally friendly materials.

This study aimed to assess the current global efforts to reduce greenhouse gas emissions and understand the domestic scenario, particularly focusing on pavement-marking works during road-construction projects. Using internationally commercialized programs, this study aimed to calculate carbon emissions from these projects, identify areas that require further action or improvement, and propose strategies to address them. This study assessed the current global efforts to reduce greenhouse gas emissions and understand the domestic scenario, particularly focusing on pavement-marking works during road-construction projects. Using internationally commercialized programs, this study aimed to calculate carbon emissions from these projects, identify areas that require further action or improvement, and propose strategies to address them. Carbon dioxide emissions from pavement-marking projects were estimated. For a 5,746 m2 construction project, a total of 96.637 was emitted; for a 5,032 m2 project with four types of markings, 89.840 was emitted. A project involving five types of markings, traffic controls, and safety measures resulted in 6.662 emissions. On average, 16.8 was emitted per 1 m2, with 17.8 for the four types and 9.3 for the five types of markings. This study is significant because it calculated the carbon dioxide emissions from domestic pavement-marking works. The use of unit price data is convenient, and for more accurate calculations, expanding environmental product declaration (EPD) certified items and accelerating the establishment of a domestic life-cycle inventory (LCI) are recommended.

This study determined the minimum size of a representative molecular structure for use in future dynamic analyses of asphalt binders. The minimum representative size, considering factors such as aging, additive types, and temperature variations, was established using density and radial distribution functions. This approach ensures that the structure reflects temperature-dependent property changes, which are critical characteristics of asphalt binders. In this study, the structure of asphalt-binder molecules was generated using the composition proposed by Li and Greenfield (2014) for AAA1. To assess the appropriateness of the molecular structure size, we generated additional structures, X2 and X3, maintaining the same composition as X1, but with two and three times the number of molecules, respectively, as suggested by Li and Greenfield (2014). Silica and lignin were considered as additives, and the aging conditions examined included unaged, short-term aging, and long-term aging. In addition, 11 temperature conditions were investigated. The density and radial distribution functions were plotted and analyzed. The variables influencing the density and radial distribution functions were set as the aging degree of the asphalt binder (unaged, short-term aging, long-term aging), 11 temperature conditions ranging from 233 to 433 K in 20 K intervals, structure size (X1, X2, and X3), and the presence of additives (no additives, silica, and lignin). For density, clear differences were observed based on the degree of aging, temperature conditions, and presence of additives, whereas the structure size did not significantly affect the density. In terms of radial distribution functions, the X1 structure reflected differences based on the degree of aging and the presence of additives but was limited in exhibiting temperature-dependent variations. In contrast, the X3 structure effectively captured temperature-dependent trends, indicating that the size of the molecular structure is crucial when evaluating energy calculations or physical tensile strength, necessitating careful assessment.

This paper explores a convergent approach that combines advanced informatics and computational science to develop road-paving materials. It also analyzes research trends that apply artificial-intelligence technologies to propose research directions for developing new materials and optimizing them for road pavements. This paper reviews various research trends in material design and development, including studies on materials and substances, quantitative structure–activity/property relationship (QSAR/QSPR) research, molecular data, and descriptors, and their applications in the fields of biomedicine, composite materials, and road-construction materials. Data representation is crucial for applying deep learning to construction-material data. Moreover, selecting significant variables for training is important, and the importance of these variables can be evaluated using Pearson’s correlation coefficients or ensemble techniques. In selecting training data and applying appropriate prediction models, the author intends to conduct future research on property prediction and apply string-based representations and generative adversarial networks (GANs). The convergence of artificial intelligence and computational science has enabled transformative changes in the field of material development, contributing significantly to enhancing the performance of road-paving materials. The future impacts of discovering new materials and optimizing research outcomes are highly anticipated.

PURPOSES : This study aimed to develop a quantitative structure property relationships (QSPR) model to predict the density from the molecular structure information of the asphalt binder AAA1, a non-full connected structure mixed with a total of 12 molecules. METHODS : The partial least squares regression (PLSR) model, which models the relationship between predictions and responses and the structure of these variables, was applied to predict the density of a binder with molecule descriptors. The PLSR model could also analyze data with collinear, noisy, and multiple dimensional independent variables. The density and additive-free AAA1 binder’s molecule systems generated by an asphalt binder’s molecules-related study were used to fit the PLSR model with the molecular descriptors produced using alvaDesc software. In addition to developing the relationship, a systematic feature selection framework (i.e., the V-WSP- and PLSR-modelbased genetic algorithm (GA)) was applied to explore sets of predictors which contributed to predicting the physical property. RESULTS : The PLSR model accurately predicted the density for the AAA1 binder’s molecules using the condition of the temperature and aging level (R2 was 0.9537, RMSE was 0.00424, and MAP was 0.00323 for the test data) and provided a set of features which correlated well to the property. CONCLUSIONS : Through the establishment of the physical property prediction model, it was possible to evaluate the physical properties of construction materials without limited experiments or simulations, and it could be used to comprehensively design the modified material composition.

PURPOSES : As evaluation methods for road paving materials become increasingly complex, there is a need for a method that combines computational science and informatics for new material development. This study aimed to develop a rational methodology for applying molecular dynamics and AI-based material development techniques to the development of additives for asphalt mixtures. METHODS : This study reviewed relevant literature to analyze various molecular models, evaluation methods, and metrics for asphalt binders. It examined the molecular structures and conditions required for calculations using molecular dynamics and evaluated methods for assessing the interactions between additives and asphalt binders, as well as properties such as the density, viscosity, and glass transition temperature. Key evaluation indicators included the concept and application of interaction energy, work of adhesion, cohesive energy density, solubility parameters, radial distribution function, energy barriers, elastic modulus, viscosity, and stress-strain curves. RESULTS : The study identified key factors and conditions for effectively evaluating the physical properties of asphalt binders and additives. It proposed selective application methods and ranges for the layer structure, temperature conditions, and evaluation metrics, considering the actual conditions in which asphalt binders were used. Additional elements and conditions considered in the literature may be further explored, considering the computational demands. CONCLUSIONS : This study devised a methodology for evaluating the physical properties of asphalt binders considering temperature and aging. It reviewed and selected useful indicators for assessing the interaction between asphalt binders, additives, and modified asphalt binders and aggregates under various environmental conditions. By applying the proposed methods and linking the results with informatics, the interaction between asphalt binders and additives could be efficiently evaluated, serving as a reliable method for new material development.