Volume 21 Issue 2

Traditional and modern approaches were compared for determining oak sawlog volume under operational conditions. Manual measurement combined with cubic formulas (Huber, Smalian, Newton, and Hossfeld), standardized volume tables (STN 48 0009:2017), computed tomography (CT) scanning, mobile applications (iFOVEA Pro, Timbeter, LogStack LiDAR, and 3D Scanner App), and a handheld mobile laser scanner were evaluated. CT scanning provided a highly detailed geometric benchmark for comparative assessment, but it should not be interpreted as a measure of true solid volume. The Hossfeld model and Newton’s formula showed the closest agreement with CT‑derived volumes. Among bulk‑pile methods, iFOVEA Pro and LogStackLiDAR demonstrated the most balanced combination of internal consistency, speed, and operational usability. Timbeter and the 3D Scanner App showed lower detected volumes; however, these deviations cannot be interpreted as systematic without further analysis. Manual measurement remained accurate but was time‑consuming and sensitive to operator variability, whereas handheld laser scanning provided high‑fidelity results at the cost of increased time and expertise. Limitations of the study include a small sample size, the limited number of repeated measurements, and the absence of testing under variable environmental conditions. Future developments will likely focus on AI‑based log segmentation, GIS integration, and cloud platforms enabling real‑time data sharing.

This study investigated a next-generation recycling strategy for high-density fibreboard (HDF) based on controlled fibre recovery using thermo–hydro–microwave (THM) synergy. Industrial HDF boards with a target density of 900 kg/m³ were treated in saturated steam at 110 °C and 0.2 MPa for 30 min, followed by two microwave treatment cycles of 30 s each at 800 W. The treated material was subsequently disintegrated in hot water (80 °C), and the recovered wood fibres were separated, dewatered, and dried. The recycled fibres were used to manufacture single-layer and three-layer laboratory-scale HDF panels with different recycled fibre contents. The panels were evaluated for selected mechanical and physical properties, including bending performance, internal bond strength, dimensional stability, surface water absorption, and density profile. The results confirmed that the applied THM-assisted recycling process enabled effective fibre recovery while limiting excessive fibre shortening. Three-layer HDF panels containing 40 to 50% recycled fibres exhibited properties comparable to or exceeding those of reference panels. The findings demonstrate the potential of thermo–hydro–microwave-assisted recycling as a viable approach for closed-loop HDF production and improved material circularity.

The mapping relationship was investigated between Color-Material-Finish (CMF) and style imagery, using Neo-Chinese armchairs as the research object. Within a Kansei Engineering (KE) framework, key style imagery features of Neo-Chinese armchairs were extracted by combining the Semantic Differential (SD) method and Principal Component Analysis (PCA), based on evaluations from a panel of design experts. Existing CMF configurations were systematically categorized and coded, with standardized digital samples generated using Rhino 3D and Keyshot software. Quantitative Theory Type I (QTTI) was then employed to establish the CMF-style imagery mapping framework. Results demonstrated that CMF significantly shapes style imagery: Different CMF combinations can shift stylistic perceptions toward “modern” or “traditional,” and modulate the intensity of “Zen-inspired” qualities—though they cannot eliminate such attributes entirely. Notably, individual CMF categories may exert contrasting effects on different imagery dimensions. This research addresses two core questions: (1) Which specific CMF components influence the style imagery of Neo-Chinese armchairs, and (2) How do these components operate mechanistically? Furthermore, qualitative CMF design strategies are proposed for Neo-Chinese furniture. The findings provide a theoretical basis for furniture designers to align CMF decisions with user cognitive expectations and a methodological reference for style mapping studies across broader design disciplines.

This research explored the static structural behavior of the Song Dynasty ‘Five-tier Outer Eave Column-head Dougong Bracket’ in the Main Hall of Chuzu Convent. Finite element analysis (FEA) was employed. Relying on an orthotropic constitutive model and Pinus sylvestris’ mechanical properties, a refined ANSYS model was built (assessed in line with GB/T criteria), with the Hill yield criterion to evaluate wood plasticity. For assessing strength, deformation and energy dissipation, simulations were carried out involving Z-axis vertical monotonic static loading and X/Y-axis horizontal low-count reciprocating loading. The findings showed a vertical ultimate bearing capacity of 342 kN along the Z-axis. Stress concentrations peak at 18.8 MPa specifically at the capital block-Ludou junction, a significant concern. Horizontal loading resulted in symmetrical hysteresis loops, exhibiting peak thrusts: 750 kN (Y-axis) and 597 kN (X-axis). Ductility coefficients (4.98/Y, 3.67/X) and equivalent viscous damping coefficients (0.121/Y, 0.149/X) were identified. The vertical behavior followed a tri-linear stiffness degradation model, with the horizontal response adhering to multi-linear restoring force models. The findings confirm FEA as an efficient, reliable method to assess Dougong mechanical behavior, providing crucial knowledge for ancient wooden building upkeep.

A close relationship exists among cultural expression, craftsmanship transmission, and user experience in intangible cultural heritage (ICH) furniture. However, the modernization of ethnic cultural products requires systematic understanding of users’ latent cultural and emotional needs. Using the Yi ethnic lacquerware chair as a focus, this study proposes a user-centered design framework integrating cultural genes, emotional resonance, and design innovation. User requirements were identified from 49,671 valid online comment records using natural language processing and Grounded Theory, complemented by semi-structured interviews with 12 experts. User requirements were classified using the Kano model and weighted via the Analytic Hierarchy Process. Quality Function Deployment was used to translate prioritized user needs into technical characteristics, while the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) was applied to evaluate alternative design schemes. It was found that cultural symbolism, emotional value, and narrative expression exerted stronger influences on user preference than functional attributes. Technical priorities include culturally themed form expression, sustainable application of Yi lacquerware craftsmanship, and digital integration of ethnic patterns. The proposed NLP–Kano–AHP–QFD–TOPSIS system reduces subjectivity in design decision-making and offers a transparent and transferable approach for ethnic furniture design, ICH product development, and sustainable cultural innovation.

INTRODUCTION

Traditional fiber component analysis combining Herzberg and Graff “C” staining methods achieves high accuracy but relies on manual fiber length measurement using an ImageJ software, making it cumbersome and subjective. This study developed a MATLAB-based image preprocessing approach utilizing HSV color space transformation and color threshold segmentation to achieve precise extraction of different fibers from stained microscopic images. Experiments employed four two-component and two three-component mixed slurry samples to compare accuracy and efficiency against the ImageJ method. Optimal color rendering was attained with saturation and lightness gain factors of 1.5 and 1.1 after Herzberg staining and 2.0 and 1.1 after Graff “C” staining. The new method matched ImageJ’s accuracy while significantly improving processing efficiency. Applied to commercial cigarette paper, it accurately identified fiber components, consistent with raw material data. Integrating staining techniques with image recognition maintains analytical precision while substantially boosting detection speed. This approach provides an efficient high-throughput solution for cigarette paper fiber analysis with clear industrial application potential.

Lignin is one of the most abundant biopolymers in lignocellulosic biomass, yet its efficient quantification remains a significant challenge for biorefineries due to the time-consuming nature and limitations of traditional wet-chemical analysis methods. This study aimed to develop a rapid and accurate approach for quantifying lignin concentration in alkali extracts of steam-exploded woody biomass by integrating UV-visible spectroscopy with machine learning algorithms as a practical complement to the conventional Klason lignin assay (modified ASTM D1106-56). UV-visible spectral data were collected and subjected to outlier removal using the Isolation Forest algorithm, followed by various preprocessing techniques and feature selection via the SelectKBest algorithm to optimize inputs for four regression models: Extra Trees, Random Forest, XGBoost, and Support Vector Regression. The combination of Baseline Correction and Standard Normal Variate (SNV) was the optimal preprocessing method, while the selection of the top 150 characteristic wavelengths effectively maximized information retention. Among the models evaluated, the Extra Trees (ET) regressor exhibited superior generalization capability and stability, achieving a test coefficient of determination (R²) of 0.803 and a Mean Absolute Percentage Error (MAPE) of 4.0%, significantly outperforming SVR and XGBoost, which suffered from overfitting and underfitting, respectively.

A vitrimer composite based on epoxidised palm oil (EPO) was reinforced with nanofibrillated cellulose (NFC) and palm kernel shell (PKS)-derived activated carbon as complementary bio-based fillers. The loadings of NFC and activated carbon were varied to examine their influence on the thermal, mechanical, chemical, and healing-reprocessing behaviour of the EPO vitrimer network. The synergistic interaction between these fillers preserves dynamic bond exchange within the vitrimer network, enabling effective thermal welding in which the welded interface becomes seamless after treatment. This dynamic network behaviour was further reflected in dynamic mechanical and creep-recovery analyses, which revealed the influence of filler content on network mobility. A higher filler content (5 wt% of each filler) enhanced stiffness but restricted network rearrangement, leading to incomplete strain recovery. In contrast, the composite containing 4 wt% of each filler achieved complete strain recovery (100%) while exhibiting strong viscoelastic damping behaviour (tan d ~ 1.36), indicating efficient molecular relaxation during the glass-transition process. FESEM showed improved interfacial continuity within the synergistic system, where NFC extends between activated carbon particles and the vitrimer matrix to maintain local network integrity and facilitate stress transfer. The rigid carbon phase also limits solvent diffusion within the matrix, contributing to improved solvent resistance of the vitrimer composite.

Acute myeloid leukemia (AML) is a clinically challenging malignancy with limited effective therapies and significant toxicity. This study evaluated the anti-leukemic potential of ethanolic extract of Smilax china L. root in HL-60 cells and a xenograft mouse model. The extract markedly reduced HL-60 cell viability in a dose- and time-dependent manner (IC₅₀ = 65.2 µg/mL at 24 h). Mechanistic analyses showed strong induction of apoptosis, increased Bax expression, decreased Bcl-2 levels, and an elevated Bax/Bcl-2 ratio. Cell-cycle arrest at the G2/M phase further confirmed its antiproliferative activity. The extract also significantly inhibited HL-60 cell migration and invasion. In vivo treatment of HL-60 xenograft-bearing mice (5 and 10 mg/kg) resulted in substantial suppression of tumor growth with minimal systemic toxicity. Tumor tissues exhibited reduced TNF-α, IL-1β, and IL-6 levels, indicating an additional anti-inflammatory effect. These findings demonstrate that Smilax china L. root extract exerts multi-target anti-leukemic actions by inducing apoptosis, blocking cell-cycle progression, suppressing metastasis-related behavior, and down-regulating inflammatory cytokines. Smilax china shows therapeutic promise as a complementary strategy for AML and warrants further investigation of its active constituents.

Traditional wood drying systems suffer from significant control inaccuracies, excessive temperature fluctuations, and suboptimal energy efficiency, leading to wood defects and economic losses. This study introduces a Machine Learning–Enhanced Adaptive Network-Based Fuzzy Inference System with Proportional-Integral-Derivative control (ML-ANFIS-PID). The proposed system incorporates Long Short-Term Memory (LSTM) networks for temporal pattern recognition, Convolutional Neural Networks (CNN) for temporal feature extraction, and adaptive fuzzy inference for real-time control parameter optimization. The results of experimental validation on ayous wood show that the performance was significantly improved: 98.4% temperature prediction accuracy, 42-second rise time (8.7% faster than traditional ANFIS-PID), 0.02% overshoot (50% less than traditional PID), 58-second settling time (5% better than conventional PID), and remarkably low 0.04°C steady-state error (96% lower than traditional PID). Also, the ML-ANFIS-PID system attained 23.7% less energy use, 18.4% less drying duration, and 31.2% less defect rate with high-quality wood produced under the tested ayous wood conditions. These results demonstrated significant performance improvement compared with classical and adaptive PID-based controllers under controlled experimental conditions.