European Journal of Wood and Wood Products最新文献

Bamboo and bamboo products, known for their hygroscopic nature, exhibit sensitivity to different loading rates in dry conditions. However, with the increasing prevalence of heavy and extended precipitation events due to global warming, there is still a lack of investigation on the response of bamboo strips to various loading rates after being attacked by moisture. In this study, the bending properties of bamboo strips after exposure to extremely high-humidity environments were investigated to assess their responses to varying loading rates. Throughout different exposure durations, bamboo strips exhibited varying moisture contents up to 112% and evident volumetric moisture expansion. After moisture exposure, bamboo strips exhibited heightened sensitivity in flexural strength and modulus to loading rates, with the most pronounced sensitivity observed at 33.9% moisture content. In addition, a linear relationship was established between flexural strength loss and volumetric expansion rather than moisture content. Notably, the samples exhibited greater sensitivity in strength loss to moisture expansion under lower loading rates. These findings preliminarily elucidate how loading rates impact the mechanical properties of bamboo strips across varying moisture levels, contributing to understanding the deterioration in the properties of engineered bamboo products when exposed to high humidity and rainy environments with potential collision events.

Agarwood oil is considered to be one of the costliest essential oils, produced by hydro-distillation of agarwood chips from resin impregnated wood from Aquilaria spp. Prior to distillation, the traditional production process involves a prolonged (up to 90 days) soaking of the wood in water filled basins. This natural fermentation step exposes the wood to interactions with different types of microorganisms like in mixed culture fermentation. Reports have suggested a definite role of fermentation on the qualitative and quantitative properties of the distilled oil. However, these studies relied on single organism led fermentation. The present study explores the consortia approach as a method to enhance the aroma of the oil, simulating natural fermentation. Enzyme activity based screening and co-culture interaction studies were employed to develop three (3) fungal and seven (7) microbial (bacteria-fungi) two-member consortia from microorganisms isolated from traditional agarwood fermentation basins located in Assam, India. The effect of consortia on agarwood oil was validated by fermentation with agarwood chips. Among the fungal consortia, 20PW (Pupureocillium lilacinum) with PG120 (Penicillium aethiopicum) and among bacteria- fungi consortia NH7 (Microbacterium oxydans) with PG120 (Penicillium aethiopicum), showed promising results. GC–MS analysis revealed qualitative enhancement of oil by all consortia compared to the control for key aroma compounds such as Agarospirol, Guaiol, 10S, 11SHimachala-3(12), 4-diene and Aristol-1(10)-en-9-ol. This is the first report where microbial consortia were used for the fermentation of agarwood chips, which enhance the quality of agarwood essential oil.

Lightweight structures are of paramount importance in engineering applications. Optimal designs combining various optimization techniques can maximize desired mechanical characteristics while minimizing undesired static or dynamic behaviors. However, these optimized structures usually have low safety factors, which makes it necessary to consider uncertainties in project design to ensure their reliability. This paper presents a systematic approach to quantify uncertainties in an optimized structural member of an unmanned aerial vehicle (UAV) wing used in remote sensing. The UAV wing structural member was optimized using the Multi-Objective Genetic Algorithm, and balsa wood, a lightweight and ecological material with high variability in mechanical properties, was used for its manufacture. To analyze the structural integrity of the UAV wing, the present study quantified parametric uncertainties in material properties and manufacturing processes using stochastic models. A probabilistic approach was adopted, which revealed a 37% reduction in the structure's safety coefficient. Various conclusions were drawn from this research, which highlights the importance of considering uncertainties in the design of optimized structures to ensure their reliability.

Veneer knots are the main indicator of plywood quality. Existing veneer knot identification algorithms have a high identification accuracy rate of over 90%. However, the convolutional neural network (CNN) model is complex and requires laborious data labeling. The localization algorithm produces veneer knot bounding boxes, except for the Mask Region-based CNN (Mask R-CNN) model, which is not accurate and has error transmission. Additionally, the calculation of defect size (area and diameter) has not been addressed. This paper proposes a parallel structured fusion algorithm. One branch employs a classical simple CNN, specifically the Inception V3 network, to identify veneer knot defects. The other branch proposes an improved K-means clustering algorithm to localize veneer knot defects. After identification and localization are achieved, the actual area of the defect is calculated. The proposed method for identifying veneer knot defects has an accuracy rate of 99.61%. The size accuracy localization rate is 94%, with an under-sized localization rate of 2%, an over-sized localization rate of 3%, and the knot localization error rate is 1%. Additionally, the algorithm also calculates the area and diameter of the knot, with a mean absolute error of the diameter of 3.23 mm. This paper presents an algorithm with low complexity, fast speed, high accuracy, and no error transmission, making it suitable for identifying and localizing other defects.