Volume 20 Issue 3

Leguminous wood occupies an important position in the market of cultural and high-end wood. Accurate identification and classification of its species is crucial for the development of the industry. However, existing studies are still deficient in classification methods under small sample conditions. This paper uses hyperspectral image data and combines models such as support vector machine (SVM), random forest (RF), logistic regression (LR), and one-dimensional convolutional neural network (1-CNN). The synthetic minority oversampling technique (SMOTE) data enhancement technology was introduced to classify and recognize 18 common legume woods. After data processing, the classification accuracy of the traditional models was improved by about 5% on average, with the SVM model reaching 98.86%; the accuracy of the 1-CNN model was increased to 97.67% after adding the first-order derivative transform and Savitzky-Golay filtering, it reached 98.89% after further adding the SMOTE.

This study examined the feasibility and benefits of children completing woodworking projects at home in Chinese families, using online video tutorials and parental guidance. A survey assessed family interest and gathered background information, selecting 36 Chinese families with children aged 7 to 12 for an experiment on making traditional Luban locks. The projects were divided into two levels: a basic, video-assisted three-post lock completed by children with parental help; and an advanced, six-post lock, requiring families to find resources independently. Results indicate that the majority of families showed strong interest in parent-child woodworking (76.7%) and successfully completed the basic project (94.4%). However, only a small minority managed to complete the advanced project (8.3%). The study indicates that while children aged 7 to 12 are in a concrete operational stage of cognitive development, success in these projects isn’t solely age-dependent. Parent-child woodworking in Chinese families appears feasible and beneficial for cognitive growth when projects are age-appropriate. Findings suggest designing tasks within a child’s zone of proximal development with corresponding resources, offering insights for family-based learning approaches.

Hybrid epoxy composites reinforced with Luffa acutangula fiber (LAF) and Sal wood sawdust (SWD) were examined for their tribological and acoustic properties. A consistent 20 wt% LAF was employed throughout all composites, with the SWD content adjusted to 0%, 5%, 15%, and 25%.  The engineered composites underwent assessment for wear loss, coefficient of friction (CoF), sound absorption coefficient, and noise reduction coefficient. The results demonstrated a notable reduction in wear loss with the addition of SWD up to 15 wt%, with the 20FL/15SWD sample exhibiting the lowest wear at 0.32%. In a similar manner, the CoF decreased to 0.26 for the identical composition, indicating an ideal equilibrium between filler dispersion and fiber-matrix interaction. The enhancement of sound absorption and noise reduction coefficients was observed with increased SWD content, reaching peaks of 0.23 and 0.13, respectively for the 20FL/15SWD composite. The enhancements observed can be linked to the superior void-filling capacity and interfacial bonding facilitated by the SWD particles. Nonetheless, a high concentration of SWD (25 wt%) led to a minor decrease in performance attributed to particle agglomeration. The findings indicate that the 20FL/15SWD composite demonstrates enhanced tribo-acoustic performance, positioning it as a strong contender for applications requiring noise insulation and wear resistance.

Improper disposal of agricultural residues is becoming a looming environmental issue as a major contributor to pollution and depletion of natural resources. The current study aimed to evaluate the proximate compositions, phytochemical profile along with antioxidant properties of the residues of hemp (Cannabis sativa) and parthenium (Parthenium hysterophorus) as sustainable resources. The standard protocol of AOAC was followed for proximate analysis. Phytochemical profiling was done to identify key bioactive compounds through qualitative assays. Their antioxidant activity was assayed by DPPH (2,2-diphenyl-1-picrylhydrazyl) free radical scavenging. Hemp recorded 50.2% more fibers than parthenium at 27.5% making it a better candidate for bio-material development. Future in vivo studies are recommended to elucidate the metabolic effects of these plants and their potential health benefits. Phytochemical analysis revealed the presence alkaloids, flavonoids, tannins, steroids, coumarins, and sterols, thus showing their bioactivity and possible health benefits. The antioxidant activity was significantly increased in hemp residues (0.1 ± 0.13 mg Trolox/g) compared to parthenium (0.057 ± 0.21 mg Trolox/g), whereas the activity from aerial parts was found lower. This demonstrates the wider application potentials of these residues in the industrial and pharmaceutical sectors as eco-friendly substitutes for conventional materials.

Four Vision Transformer (ViT)-based models were optimized to classify microscopic wood images. The models were DeiT, Google ViT, BeiT, and Microsoft Swin Transformer. Training was performed on a set enriched with data augmentation techniques. The generalization ability of the model was strengthened by increasing the number of images for each class with data augmentation. The dataset used in the study consisted of 112 different species belonging to 30 families, 37 of which were coniferous and 75 were angiosperms. The samples had been softened, cut into thin sections, colored with the triple staining method, and imaged with fixed magnification. The Google ViT model was the most successful, with 99.40% accuracy. The DeiT model, which stood out with its data efficiency, ranked second with 98.51% accuracy, while the BEiT and Microsoft Swin Transformer models reached 96.43% and 98.21% accuracy, respectively. The Microsoft Swin Transformer model required the least training time. Data augmentation techniques improved the performance of all models by 3% to 5%, thus increasing the resistance of the models to overfitting and providing more robust predictions. It was found that ViT-based models gave superior performance in microscopic wood image classification tasks and that data augmentation significantly improved model performance.

Lignin-based adhesives are promising eco-friendly alternatives to petroleum-derived resins, but they face challenges in bonding strength and water resistance. This study presents a dual chemical modification strategy—phenolation and hydroxymethylation—to enhance the reactivity and polarity of industrial lignin. The modified lignin was blended with polyvinyl alcohol (PVOH) and epoxy resin to formulate adhesives suitable for engineered wood applications. Among the tested formulations, the PVOH-lignin adhesive reached a tensile shear strength of 7.8 MPa with 85% strength retention after water immersion, while the epoxy-lignin adhesive achieved 9.5 MPa with enhanced thermal resistance. Structural characterization using FTIR and 1H-NMR confirmed the successful introduction of functional groups. To reduce energy consumption, a low-temperature and low-pressure curing process based on microwave-assisted heating and mechanical stirring was implemented, reducing energy use by 40% compared to conventional heating methods. Life cycle assessment (LCA) results indicated a 40% lower carbon footprint and 50% reduction in volatile organic compound (VOC) emissions relative to petroleum-based adhesives. These results demonstrate the feasibility of using chemically modified lignin in high-performance adhesive systems for engineered wood while improving environmental sustainability.

The inlaying of molten metal into artificial or natural wood cracks represents an innovative combination of metal and wood. After cooling and solidifying, a unique texture is formed, offering a rich aesthetic experience rooted in its intricate decorative texture. This study systematically summarizes the process steps of molten metal inlaid in wood, including material selection, pretreatment, casting, cooling, and sanding. It elaborates on the aesthetic characteristics and decorative potential of this novel material through three aspects, including visual expression, tactile difference, and cultural value. Selected design cases of molten metal inlaid in wood for home furnishings are analyzed to assess the product design feasibility through the lens of craft aesthetics. The research further provides a guiding direction for its design application in household products.

The aim of this study was to investigate the effects of different doses and application methods of Trinexapac-ethyl (TE) and Paclobutrazol (PAC) on the growth, development, and flowering characteristics of safflower (Carthamus tinctorius L.) plants. This study will contribute to revealing and expanding the potential of this species, which is known to have high drought, cold, and salinity tolerance, in the ornamental plants sector. In the experiment, two safflower cultivars (Olas and Dinçer), two different plant growth inhibitors (TE and PAC), two different application methods (foliar and soil), and different doses (TE: foliar- 0, 4, 6, 8, and 12 ppm; PAC: soil- 0, 25, and 50 mg/pot, foliar- 0, 50, 100, and 500 ppm) were studied. Based on the results obtained, the use of TE and PAC, which are plant growth inhibitors, is seen as a suitable alternative to use ‘Olas’ and ‘Dinçer’ cultivars of safflower plant as ornamental plants in landscape designs and to expand their use for this purpose. It was determined that PAC application at a dose of 500 ppm in the form of foliar spray was an appropriate application especially in terms of suppressing height growth, and the plants treated with TE had a wider plant width compared to those treated with PAC.

Softness is a critical yet subjective characteristic of hygiene paper products such as facial tissues. In this study, softness values were obtained from the authors’ previous research using the Interval Scale Value (ISV) method, involving panelists’ round-robin pairwise comparisons. A machine-learning approach was developed to predict softness from one-dimensional power spectral density (1D-PSD) spectra of surface roughness profiles. Using seven commercial samples and an optimized multilayer perceptron model, a achieved high predictive performance (R² = 0.860) was achieved without additional measurements such as tensile modulus or surface friction. This work highlights the potential of combining spectral analysis and machine learning for objective softness evaluation.

Corn husk, a significant by-product of the corn deep-processing industry, is currently utilized as coarse feed, yielding very low economic benefits while consuming high amounts of energy and water. This study focused on the high-value utilization of corn husk resources. A two-step enzymatic hydrolysis and combined microbial fermentation was adopted to produce corn husk microbial protein feed. The true protein content was increased by 103% through yeast proliferation. To explore the key driving factors affecting yeast proliferation, a quantitative polymerase chain reaction (qPCR) was adopted to analyze the succession of yeast communities during the fermentation of corn husks. Redundancy analysis (RDA) and variance inflation factor (VIF) were applied to examine the relationship between physicochemical factors and yeast microbial community. The results revealed that, in terms of fermentation time, the uppermost driving factors influencing yeast abundance is moisture content; in terms of contribution, both cellulose content and moisture content serve as the most significant driving factors for yeast proliferation. This research revealed that microbial-enzyme synergy can significantly increase the true protein content of feed, and the key driving factors identified further provide theoretical references for the controllable yeast fermentation.