Research Articles

Six oak species are native to Korea, but a visible cork layer is present only in Quercus variabilis. This study presents a chemical analysis approach—including colorimetric analysis, FT-IR, and Py-GC/MS—to compare the cork composition of six Korean oak species: Quercus acutissima, Q. aliena, Q. dentata, Q. mongolica, Q. serrata, and Q. variabilis. The analysis focuses on suberin and lignin, the main cork components, by identifying their pyrolysis products. Methanolysis with 3% NaOCH₃ extracted suberin monomers, revealing approximately 20% suberin content in all species except Q. variabilis, which showed around 40%. Py-GC/MS differentiated suberin-derived fatty acids—found exclusively in cork tissue—from other fatty acids present in the rhytidome. Q. variabilis exhibited 2–8 times higher levels of suberin-derived fatty acids and abundant lignin monomers, mainly guaiacyl (G) units. In contrast, lignin monomers were undetectable in Q. acutissima, Q. aliena, and Q. dentata, suggesting either very low levels or concentrations below the instrument’s detection limit. Syringyl (S) monomers were also absent in Q. mongolica and Q. serrata. These findings suggest that lignin composition, along with visual cork layer assessment, can help evaluate the cork potential of Korean oak species and identify viable substitutes for commercial cork.

To enhance consumers’ preference for the appearance of cork flooring, this work employed the semantic differential method to explore the relationship between consumers’ visual perceptions and their preferences. First, a collection of cork flooring product images was assembled, and a lexicon of perceptual vocabulary describing the visual characteristics of cork flooring was developed. Subsequently, a survey based on the semantic differential method and preference questionnaires was completed. A regression model was established to analyze the relationship between the scores of perceptual vocabulary and consumer preferences. The results indicated that the perceptual terms “light” and “warm” had a significant positive impact on consumer preferences. Furthermore, the study explored the relationship between the granularity, grain arrangement, and color of cork flooring samples and the perceptual vocabulary, revealing distribution patterns of the samples in terms of “light” and “warm” characteristics. The findings suggest that increasing the saturation and brightness of cork flooring surface colors, reducing granularity, and enhancing the rhythmic arrangement of patterns can improve consumers’ preference for the appearance of cork flooring.

Microporous-structured Poly(L-lactic acid) (PLLA) spheres were fabricated using a microfluidic shearing method. Poly(vinyl alcohol) (PVOH) solution is used as the continuous phase, and an emulsion containing matrix materials (PLLA), stabilizer (polysorbate 80) and pore-forming agents (gelatin) dissolved in dichloromethane (DCM) were emulsified to be dispersed phase for the generation of droplets, which were eventually transformed into microspheres. An ice water bath environment was shown to be necessary to produce stable microspheres. In addition, it was found that the mechanical stirring emulsification method was more conducive to microstructure construction than the ultrasonic emulsification. By regulating the amount of pore-forming agent (2.5 wt%, 5 wt%, and 7.5 wt% gelatin), three types of micro pores (small: 2.9±0.8 µm, middle: 46.9±14.9 µm, and large: 127.3±55.3 µm) on surface with different inner structure were obtained. Finally, it was demonstrated that the proposed method is general enough to prepare various other polymer microspheres such as PNAGA (Poly-N-acryloyl glycinamide) and PCL (Polycaprolactone).

In the important coastal marsh wetland ecosystem, the soil organic carbon content is subject to multiple environmental factors in Yancheng. The research objectives were to explore the effects of sampling time, soil depth, and vegetation types on soil organic carbon content by screening the core data of related literature and analyzing with the descriptive statistics, Kendall’s consistency test, Spearman’s Rank correlation, and principal component analysis (PCA). The analysis showed that the effects of the factors on soil organic carbon content were significantly different. The plant growth was vigorous, resulting in relatively high soil organic carbon content (80 to 50 t/ha·yr), especially in the spring and summer seasons. In addition, the organic carbon content of the topsoil (0 to 20 cm) was significantly higher than that of the soil at depths below 20 cm, showing higher levels (20 to 50 t/ha·yr). Among different vegetation types, the carbon storage capacity of Spartina alterniflora showed superior performance compared to other vegetation (10 to 25 t/ha·yr). The results provide a scientific basis for the assessment and protection of the carbon storage function of wetlands, intending to promote the sustainable management of wetland ecosystems.

A machine learning (ML)-based framework was developed for predicting and optimizing the antioxidant activity of Ainsliaea acerifolia water extracts. while the response surface methodology (RSM) is deficient in modeling nonlinear interactions. In this study, three machine learning (ML) algorithms, Extreme Gradient Boosting (XGB), Random Forest (RF), and Support Vector Machine (SVM), were evaluated using extraction variables (temperature, time, and solvent-to-solid ratio) along with flavonoid and polyphenol content as input features. Among the models evaluated, the XGB model showed the most advanced antioxidant prediction capabilities, as evidenced by its R² of 0.9835 and RMSE of 2.52 on the test data set. The biological significance of the features was explored using SHAP analysis, revealing flavonoid content and extraction temperature as key contributors. A graphical user interface (GUI) was developed to facilitate real-time prediction, enhancing accessibility for researchers and industrial users. This approach improves operational efficiency by optimizing extraction conditions, predicting antioxidant activity from data including flavonoids and polyphenols, and reducing reagent usage. This study highlights the potential of ML as a sustainable alternative for natural product optimization and lays the groundwork for future research that integrates bioactivity prediction with formulation design.

Defense mechanisms were studied for Pinus brutia, a cornerstone Turkish forest tree, against pine processionary moth damage by Thaumetopoea pityocampa (Den. & Schiff.) and Thaumetopoea wilkinsoni Tams 1926 moth species. This research addressed the significance of Pinus brutia in afforestation and breeding. The expression of enzymatic antioxidants (SOD, POD, CAT, APX) and photosynthetic pigments (chlorophylls and carotenoids) at a clonal level in response to insect damage was assessed. Approximately 84 needle samples from 28 Pinus brutia clones from the Antalya Düzlerçamı Brutian Pine Seed Orchard were studied. Samples were collected in February and August 2021 to capture responses during key insect activity periods. These samples were then analyzed for pigment concentrations and antioxidant activities. Statistical analysis revealed that sampling period and clone significantly affected chlorophyll and carotenoid levels. The POD and SOD activities were primarily influenced by the sampling period. However, CAT activity was affected by the number of insect pouches, the period, and the clone. APX activity was significantly impacted by both pouch number and sampling period. These findings offer insights into how seasonal changes and genetic variations modulate P. brutia clones’ defense mechanisms against pine processionary moth infestations, informing future forest management.

Copper oxide (CuO) nanoparticles were prepared using waste-derived cellulose from bamboo biomass as a functional additive. The cellulose, recovered from an alkaline dissolution process, enhanced the dispersion and structural integrity of CuO nanoparticles (NPs). The CuO prepared in the presence of waste cellulose (CuO-C) exhibited a specific surface area of 32 m²/g, compared to 7 m²/g for pure CuO. Scanning electron microscopy (SEM) revealed a feather-like CuO structure influenced by the presence of the waste-derived cellulose matrix. The catalytic activity of CuO-C was tested through the reduction of 4-nitrophenol (4-NP) to 4-aminophenol (4-AP), achieving complete conversion within 15 min. The synthesis cost of CuO-C was approximately RM 3.30 per gram. Antibacterial tests confirmed activity against both Staphylococcus aureus and Klebsiella pneumoniae. These findings demonstrate the feasibility of using a highly alkaline solution from the cellulose dissolution process to produce low-cost CuO with improved catalytic and antibacterial properties.

The usability of wood-based smart furniture interfaces for elderly users remains a critical challenge in aging-in-place solutions. This study aims to explore the most effective visual presentation styles of sofa illustrations in the smart sofa mobile app interface, with the goal of reducing cognitive load and enhancing the interaction experience for elderly users. To achieve this, we evaluated the cognitive responses of elderly users to different visual presentation styles through eye-tracking experiments and correctness analysis. As the results show, the four visual presentation styles exhibited comparable attention levels but diverged in concentration patterns. A 3D modeling schematic focused on peripheral interface areas, whereas physical product schematic and planar schematic emphasized hardware components. Abstract styles increased cognitive resource allocation and prolonged information processing. Pupil diameter and time to first fix (TTFF) data indicated that the 3D schematic imposed the lowest cognitive difficulty and pressure, while physical product schematic and 3D modeling schematic provided superior real-time feedback clarity. Therefore, wood-based smart sofa interface design should address elderly users’ needs by optimizing visual presentation styles, reducing cognitive load and stress, and improving attention. Future research should explore multi-channel human-computer interaction to support smart sofa adoption for aging in place.

Sustainable composites were produced by recycling polystyrene (PS) and pine and poplar fiber residues remaining after lignin extraction. Polystyrene was dissolved in acetone and reinforced with wood fiber residues at 10%, 20% and 30%. The mixture was dried at 100 °C for 2 hr, granulated, and pressed at 190 °C under 5 MPa for 20 minutes. Characterizations of samples were performed according to physical (ASTM D1037 for water resistance and ASTM 7032-21 for density measurement), mechanical (ASTM D638 for tensile, ASTM D790 for flexural), scanning electron microscopy (SEM), X-ray diffraction (XRD), and Fourier transform infrared spectroscopy (FTIR) analyses. The addition of fiber residue increased (up to 6.7%) the density but decreased the water absorption (WA) (up to 22%) and thickness swelling (TSW) (up to 12%). Fiber residue increased tensile strength (TS) by 46 to 167% and flexural strength (FS) by 45 to 82%. However, it decreased tensile modulus (TM) by 0 to 58% and flexural modulus (FM) by 3 to 12% (excluding pine). However, pine fiber residue increased FM by 2 to 19%. SEM analyses revealed homogeneous distribution of fiber residues.

Evidence by which to confirm the location and approximate manufacturing date of document paper is a critical task in forensic investigations, particularly in cases involving suspected forgery or document manipulation. In this study, periodic marks formed during the papermaking process were analyzed using light-transmitted images captured by a two-dimensional lab formation sensor. Step and angle data from the top five intensity peaks were extracted and used to train tree-based classification models. To handle directional symmetry, a modulo 180° transformation was applied to the angle data. The random forest (RF) classifier outperformed decision tree (DT) and extreme gradient boosting (XGB) models, achieving the highest F1 score. Feature importance analysis revealed that the step and angle at the third intensity level were the most discriminative features, likely reflecting structural characteristics of forming fabrics or drainage patterns. A simplified univariate strategy using these features also showed potential for estimating production periods. However, differences between the top and bottom surfaces—particularly in twin-wire systems—introduced classification bias, highlighting the need to separately classify paper sides in forensic datasets. Overall, this study demonstrates the feasibility and limitations of using periodic mark analysis for document dating.