Properties of flame-retardant leaf fiber cement-based composites at high temperatures.

Flame retardant modification of leaf fibers was carried out to solve the technical problem of poor fire resistance of plant fibers and improve the utilization rate of urban fallen leaves in building materials. The modification scheme adopts three flame retardants, i.e., ammonium polyphosphate (APP), magnesium hydroxide (MH), and aluminum hydroxide (ATH), and two covering layers, i.e., pure acrylic polymer lotion and water glass (Na2O · nSiO2) solution. The modified leaf fiber's combustion behavior and pyrolysis properties were tested and analyzed. The physical and mechanical characteristics, as well as the thermal insulation qualities, of leaf fiber cement-based composites (LFCC) were studied at high temperatures. The findings revealed that the three flame retardants had an effect on the chemical structure of leaf fibers. In comparison to leaf fibers without flame-retardant modification, flame-retardant-modified leaf fibers have a much greater thermal stability. and its LOI is greater than 27.0%, which is a fire-retardant material. Except for the sample with water glass as the modified cover layer, at high temperatures, the composite flame-retardant fiber LFCC's mass-loss rate is lower compared with fibers without flame-retardant modification or fibers modified with only one kind of flame-retardant. In the composite flame-retardant modified fiber LFCC, the samples with better strength at high temperature are those with ATH replacing 30% and 50% MH. The thermal conductivity of LFCC is negatively correlated with the range of temperature change.

Detecting Pest-Infested Forest Damage through Multispectral Satellite Imagery and Improved UNet+.

Plant pests are the primary biological threats to agricultural and forestry production as well as forest ecosystem. Monitoring forest-pest damage via satellite images is crucial for the development of prevention and control strategies. Previous studies utilizing deep learning to monitor pest-infested damage in satellite imagery adopted RGB images, while multispectral imagery and vegetation indices were not used. Multispectral images and vegetation indices contain a wealth of useful information for detecting plant health, which can improve the precision of pest damage detection. The aim of the study is to further improve forest-pest infestation area segmentation by combining multispectral, vegetation indices and RGB information into deep learning. We also propose a new image segmentation method based on UNet++ with attention mechanism module for detecting forest damage induced by bark beetle and aspen leaf miner in Sentinel-2 images. The ResNeSt101 is used as the feature extraction backbone, and the attention mechanism scSE module is introduced in the decoding phase for improving the image segmentation results. We used Sentinel-2 imagery to produce a dataset based on forest health damage data gathered by the Ministry of Forests, Lands, Natural Resource Operations and Rural Development (FLNRORD) in British Columbia (BC), Canada, during aerial overview surveys (AOS) in 2020. The dataset contains the 11 original Sentinel-2 bands and 13 vegetation indices. The experimental results confirmed that the significance of vegetation indices and multispectral data in enhancing the segmentation effect. The results demonstrated that the proposed method exhibits better segmentation quality and more accurate quantitative indices with overall accuracy of 85.11%, in comparison with the state-of-the-art pest area segmentation methods.

Study on improvement of prefolded energy absorption device to constant resistance and its mechanical properties.

When the rock burst occurs, energy absorption support is an important method to solve the impact failure. To achieve constant resistance performance of energy absorption device, as an important component of the support, the mechanical properties of one kind of prefolded tube is analyzed by quasi-static compression test. The deformation process of compression test is simulated by ABAQUS and plastic strain nephogram of the numerical model are studied. It is found that the main factors affecting the fluctuation of force-displacement curve is the stiffness of concave side wall. The original tube is improved to constant resistance by changing the side wall. The friction coefficient affects the folding order and form of the energy absorbing device. Lifting the concave side wall stiffness can improve the overall stiffness of energy absorption device and slow down the falling section of force-displacement curve. It is always squeezed by adjacent convex side wall in the process of folding, with large plastic deformation. Compared with the original one, the improved prefolded tube designed in this paper can keep the maximum bearing capacity (Pmax), increase the total energy absorption (E), improve the specific energy absorption (SEA), and decrease the variance (S2) of force-displacement curve.