As an important industry of the national economy, the development of furniture manufacturing industry is very rapid. In particular, with the development of panel furniture industry, wood-based panels have become a necessary choice for furniture material for modern families in recent years. As a new particleboard material, in order to be more widely used in the furniture industry, it is not enough to have the characteristics of environmental protection. The material should also have excellent appearance and dimensional stability, so as to change people’s dependence on traditional wood-based panels. In this study, the rice straw particleboard (RSP) substrate was veneered by Betula sp. and Cyclobalanopsis glauca. In the process of veneering, different RSP specimens were treated by different sanding thicknesses and moisture contents of the RSP substrate, glue spread, species and thickness of veneer. The dimensional stability of different RSP specimens after veneering was analyzed. Based on the same variables, the change in the panel dimension and warp degree of the specimens of RSP which the sanding thickness was 0.2 mm were higher than the specimens with a sanding thickness of 0.6 mm. The dimensional stability of specimens of Cyclobalanopsis glauca veneer was better than that of Betula sp. veneer. A certain degree of change within the appropriate moisture content had a little effect on dimensional stability of veneered RSP. The greater of the amount of glue, the worse the dimensional stability of veneered RSP. The thinner the veneer, the worse was the dimensional stability of the veneered RSP.

A new design adhesive mixed with flame retardant was developed by an optimized and modified dedicated flame retardant and selected at a suitable proportion between the adhesive and flame retardant as well as the coating amount of the adhesive. The new design adhesive was applied to ecological board production, and the flame-retardant properties of products were examined. The dipping and peeling properties, surface bonding strength, and formaldehyde emission reached the national standard GB/T 34722-2017, the flame retardancy meets the requirements of GB/T 8626-2017, GB/T 20284-2006, GB/T20285-2006, and it also reaches the B1-C level (the nonflammable level in the flame retardant level). This study not only has theoretical guidance but also has strong practical value to provide a basis and data support for the research and development of flame-retardant ecological boards.

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To explore the effects of roughness on the tactile properties of rice straw particleboard (RSP), the surface roughness and psychological tactile and visual experiments were conducted for RSP substrates with 0.76 g/cm3 and 0.55 g/cm3 densities after sanding with sandpaper (mesh 180#, 360#, and 600#). The effects of different sandpaper types, sanding time, and density of RSP on the surface roughness were analyzed. The experimental results are as follows. The sanding treatment had significant influence on the surface roughness characterization parameters Ra and Rpv of the RSP specimens. Surface roughness differences between the 180# and 360# mesh-prepared samples were obvious. The tactile and visual psychological values of the 360# and 600# mesh-sanded specimens were higher, and the psychological quantities of untreated and 180# mesh-sanded specimens were lower. After comparing the samples with sanding treatment of sandpaper 0∼180#, the change in surface roughness of RSP with a density of 0.76 g/cm3 was smaller than that of the specimen with a density of 0.55 g/cm3. The psychological quantity difference of RSP specimens with a density of 0.55 g/cm3 was evident. When the sanding time was 1 min., the values of the roughness characterization parameters Ra and Rpv increased slightly. After 3 min. sanding, the Ra and Rpv values stabilized. When the sanding time was 5 min, the roughness was essentially unchanged. With the change in sanding time, the measured values of the tactile psychological quantity varied greatly and the measured values of the visual psychology were very close. For the RSP substrates with higher density, the surface roughness was less after sanding with a smoother surface and better tactile properties. There were significant differences between the surface roughness of the RSPs before and after sanding. After manual sanding over the same time span, the surface roughness evaluation parameter values decreased with an increase of mesh size of the sandpaper, and the tactile properties were improved. The longer the sanding time, the smaller the difference in the surface roughness parameter values, and the smaller the difference between the tactile psychological quantity and the visual psychological quantity. To expand the research scope of RSP products, this study investigates not only the physical and chemical properties but also the subjective feelings when using the RSP products. This will provide analytical methods and design guidelines for the consideration of environmental factors in furniture and interior design.

Deep learning has achieved great success in remote sensing image change detection (CD). However, most methods focus only on the changed regions of images and cannot accurately identify their detailed semantic categories. In addition, most CD methods using convolutional neural networks (CNN) have difficulty capturing sufficient global information from images. To address the above issues, we propose a novel symmetric multi-task network (SMNet) that integrates global and local information for semantic change detection (SCD) in this paper. Specifically, we employ a hybrid unit consisting of pre-activated residual blocks (PR) and transformation blocks (TB) to construct the (PRTB) backbone, which obtains more abundant semantic features with local and global information from bi-temporal images. To accurately capture fine-grained changes, the multi-content fusion module (MCFM) is introduced, which effectively enhances change features by distinguishing foreground and background information in complex scenes. In the meantime, the multi-task prediction branches are adopted, and the multi-task loss function is used to jointly supervise model training to improve the performance of the network. Extensive experimental results on the challenging SECOND dataset demonstrate that our SMNet obtains 71.95% and 20.29% at mean Intersection over Union (mIoU) and Separated Kappa coefficient (Sek), respectively, which proves the effectiveness and superiority of the proposed method.