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The chemical composition of ginseng varies by several factors such as age, harvest season, and region. Each part of fresh ginseng contains certain valuable bioactive compounds; however, chemical studies of ginseng have focused on the ginsenoside content. In this study, we examine the chemical composition of Panax ginseng Meyer as related to harvest season and specific root parts via untargeted metabolomics using ultra-high performance liquid chromatography-quadrupole-time-of-flight-mass spectrometry (UPLC-QTOF-MS). Multivariate statistical analysis such as partial least squares discriminant analysis (PLS-DA) was used to select significant metabolites distinguishing ginseng harvested in different seasons and various ginseng parts. The selected thirteen metabolites were identified as amino acids, ginsenosides, and fatty acid amides through MS fragment pattern analysis. Additional chemometric analysis revealed chemical consistency between main and fine roots regardless of harvest time, indicating a stable chemical composition in P. ginseng among seasons. Furthermore, most ginsenosides among the thirteen selected metabolites had higher abundance in fine roots than in main roots, while primary metabolites were more abundant in main roots, highlighting a unique chemical composition dependent on the specific root parts. [ABSTRACT FROM AUTHOR]

Long-term cross-correlational structures are examined for pairs of sea surface temperature anomalies (SSTAs) and advective forcing parameters and sea surface height anomalies (SSHAs) and current velocity anomalies (CVAs) in the East/Japan Sea (EJS); all these satellite datasets were collected between 1993 and 2023. By utilizing newly modified detrended cross-correlation analysis algorithms, incorporating local linear trend and local fluctuation level of an SSTA, the analyses were performed on timescales of 400–3000 days. Long-term cross-correlations between SSTAs and SSHAs are strongly persistent over nearly the entire EJS; the strength of persistence is stronger during rising trends and low fluctuations of SSTAs, while anti-persistent behavior appears during high fluctuations of SSTAs. SSTA-CVA pairs show high long-term persistence only along main current pathways: the zonal currents for the Subpolar Front and the meridional currents for the east coast of Korea. SSTA-CVA pairs also show negative long-term persistent behaviors in some spots located near the coasts of Korea and Japan: the zonal currents for the eastern coast of Korea and the meridional currents for the western coast of Japan; these behaviors seem to be related to the coastal upwelling phenomena. Further, these persistent characteristics are more conspicuous in the recent decades (2008~2023) rather than in the past (1993~2008). [ABSTRACT FROM AUTHOR]

In this study, we investigate multiscale auto- and cross-correlation structural characteristics of sea surface temperature (SST) variability using our new methodology, called the multifractal asymmetric cross-correlation analysis (MF-ACCA), incorporating signs of a segment's detrended covariance and linear trend. SST is greatly affected by air–sea interactions and the advection of water masses with a wide range of spatiotemporal scales. Since these force factors are imprinted on SST variability, their features can be revealed in terms of long-range auto- and cross-correlation structures of SST variability via a multifractal analysis. By applying the MF-ACCA methodology to SST variability in the East/Japan Sea, we have found the following features: (1) the auto- and cross-correlation multifractal features are dependent on several parameters, such as the location, linear trends (rising or falling), level of fluctuations, and temporal scales; (2) there are crossover behaviors that are discrete for small scales (less than 1000 days) but continuous for large scales (more than 1000 days); (3) long-range persistence of auto- and cross-correlations is random for large scales during the falling phase; (4) long-range persistence is stronger during the rising phase than during the falling phase; (5) the degree of asymmetry is greater for large scales than for small scales. [ABSTRACT FROM AUTHOR]

This research addresses the challenges faced in collecting and managing repair history information for architectural heritage due to its distribution among different institutions. To overcome this problem, this study proposes a building information modeling (BIM)-based repair history management framework that links non-formal repair details of architectural heritage to BIM. This framework was applied to the case study of the Great Hero Hall of Sudeoksa Temple in Yesan, South Korea. A repair history database in the form of structured data adhering to the historic building information modeling standard was utilized. The proposed framework effectively syncs the repair history database with BIM to preserve architectural heritage and streamline repair history information updates as well as management. Integrated heritage BIM is anticipated to furnish a thorough knowledge base for architectural heritage, comprising non-geometric details and formal information models, thus enhancing building maintenance planning. Ultimately, the benefits and limitations of the suggested framework and methodologies are deliberated for further implementation. [ABSTRACT FROM AUTHOR]