Wood and its products are widely used in people’s daily life， including furniture manufacturing， interior decoration， construction and other fields. The total annual output value of the wood industry has exceeded 3 trillion yuan， which is an important basic industry of the national economy. However， there are still problems such as low added value of products， weak innovation ability of enterprise， and decentralized production supply chain in the process of wood industry development. Starting towards the new and green departure， incubating new technologies and materials for functional wood manufacturing， and cultivating new productivity in the wood industry are important directions to boost the wood industry development and accumulatively achieve low-carbon transformation. This paper focuses on the frontier development direction of wood science， discusses the important quality productivity of wood industry， and clarifies the importance of scientific and technological innovation for wood industry development. Furthermore， this paper puts forward four directions for the future development of wood science， including the micro in-depth， the macro expansion， the extreme conditions， and the comprehensiveness， and summarizes the newly representative research results， sorts out the development status and trend of the representative wood new products and functional new materials， and lays the foundation for further proposing the development path of new quality productivity in China's wood industry.

The moisture content of forest floor litter is a key factor in forest fire occurrences， and its accurate detection is crucial for fire prevention. Near-infrared spectroscopy （NIRS） can directly invert moisture content from spectral data， enabling rapid detection of litter moisture content. However， spectral characteristics differ between fuel types due to variations in light intensity data at different wavelengths， requiring separate detection models for litter from different tree species to match specific light intensity-moisture content inversion relationships. Collecting and labeling spectral data across different forest stands is time-consuming， limiting the practical application of the spectral method. To address this issue， this study proposes a moisture content detection method for forest floor litter based on Bi-LSTM （Bidirectional Long Short-Term Memory） transfer learning. By transferring the trained model parameters to new models， we avoid training models from scratch， thereby improving model learning efficiency and reducing the data required for training. The study demonstrates that the Bi-LSTM method surpasses the traditional inversion approach using LSTM in terms of detection accuracy. Specifically， the mean absolute error （MAE） for Quercus mongolica and Larix gmelinii is reduced by 0.62% and 0.87%， respectively， while the mean squared error （MSE） is reduced by 0.28% and 0.70%， respectively. Moreover， the Bi-LSTM-based transfer learning approach significantly lessens the reliance on labeled NIR spectral data. With a target domain sample size of 300 and a source domain sample size of 1 000， the detection model record an MAE of 3.27%， an MSE of 1.10%， and an R² of 0.918. When compared to models without source domain training， the MAE and MSE show reductions of 2.36% and 1.02%， respectively， and an increase in R² of 0.114. A comparative analysis before and after implementing transfer learning reveals that this methodology offers a novel strategy to diminish the time cost associated with modeling moisture content in spectral litter and to enhance the practical application of spectral detection.

By conducting multi-point regional trials on introduced Kazakhstan birch， analyzing its adaptability， screening out good family lines， and providing a theoretical basis for the selection and application of introduced birch family lines. In this study， we analyzed the genetic variation patterns of tree height， diameter at breast height （DBD）， volume of timber， straightness and other traits in 23 11-year-old Kazakhstani birch family lines and two Chinese birch family lines of the Mao'ershan seed source， which were planted in Daqing， Heilongjiang Province， Shangzhi， Heilongjiang Province， and Jinzhou City， Liaoning Province， and fitted a mixed linear model with heteroscedasticity by using the software package R-language ASReml4.0. Best linear unbiased prediction （BLUP） method was used to obtain the breeding values of each family line at different test locations， and combined with Genotype main effects and genotype × environment interaction （GGE） bisplot plots for comprehensive evaluation and selection of each participant and family line. In the mixed-effects model with location as a fixed effect， it was found that the environmental effects were significant among locations， and growth traits reached significant differences （P<0.05， （Z ratio）>1.5） among locations and among family lines within the same test site； family line No. 17 in the Daqing test site had the highest preservation rate and breeding value， and had better salt tolerance； the GGE biplot based on the BLUP method showed that the introduced birch fast-growing property of No.3 family line was the best， and the stability of No.9 family line was the strongest. Based on the comprehensive ranking of the stability and rapidity of each family line， four excellent family lines， No.20， 9， 7 and 24， were selected according to the 30% selection rate and combined with the genetic gain of the volume of each family line.

In order to solve the problems of conventional radiative cooling devices limited by the theoretical cooling power limit of 150 W/m² and by the inhibition of radiant power by low-temperature condensate on the radiant surface and the intrinsic water under high humidity conditions， an asymmetric functional structure design based on unidirectional liquid transport proposes a passively cooled wood （REW） with radiative refrigeration and evaporative cooling integrated in series. The wood is delignified by a sodium chlorite solution to enhance its hydrophilicity； then a hydrophobic silica/epoxy solution with high reflectivity and infrared emission properties is coated on the top of the hydrophilic wood to form a hydrophobic radiative cooling layer， while the hydrophilic wood at the bottom serves as an evaporative cooling layer. By virtue of the asymmetric wetting design with unidirectional water transport， low-temperature condensate can be spontaneously transported through the radiation-cooling layer to the evaporative-cooling layer for evaporative cooling， whereas the native water in the evaporative-cooling layer is unable to pass through the radiation-cooling layer to inhibit radiation. As a result， based on the tandem integration of radiant-evaporative cooling， the REW achieves a maximum cooling power of 214 W/m² during daytime， and 172 W/m² even at high humidity of 80%， which is more than 2.8 times higher than that of radiant cooling alone. The potential application of REW in energy-efficient cooling of buildings is demonstrated through building models， providing a universal optimisation strategy for expanding the practical application of passive cooling and new insights into the functional utilisation of wood resources.

The Larix gmelinii forests is one of the most important forest types in Northeast China， playing a crucial role in maintaining the stability of the forest ecosystem in the region. The woody plants in the permanent plot of Larix gmelinii forests in Northeast China were taken as the research object. Through field plot investigation， diversity index and spatial structure characteristics calculation， combined with variance partitioning analysis and redundancy analysis， this study explored the characteristics and influencing factors of tree species diversity in Larix gmelinii forests. The results showed that the average tree species richness of Larix gmelinii forests was 10.75 in Northeast China， Simpson index was 0.72， Shannon-Wiener index was 1.69， and Pielou evenness index was 0.76. The average mingling intensity was 0.57， indicating that the forest stands were moderately mixed. The uniform angle index was 0.54， and the forest stands were clustered. The breast diameter dominance was 0.51， indicating that the forest growth was in a moderate state. The spatial structure characteristics of forest stands and geoclimatic conditions jointly explained 35.9% of the changes in tree species diversity， followed by spatial structure characteristics， with an explanatory rate of 29.2%. Simple term effects showed that spatial structure and geoclimatic indicators such as mingling intensity， latitude， annual mean temperature， and annual mean precipitation were the main influencing factors of tree species diversity changes. The research results will provide theoretical basis and data support for the formulation of management strategies for Larix gmelinii forests in Northeast China.

The identification of dominant tree species is an important part of forestry resource surveys. Improving the accuracy of dominant tree species identification has significant practical implications for conducting forest resource surveys and related research. Using the Google Earth Engine （GEE） cloud platform， we obtained Sentinel-2 time series images for the Huodong mining area from January to December 2023. The annual growth trajectory features of dominant tree species were constructed based on the CCDC algorithm and the NDFI index. A dominant tree species hierarchical identification method combining "trajectory features + spectral features + texture features" of long-time series remote sensing images was proposed. A control group of "spectral features + texture features" was set up， and hierarchical classification and random forest classification algorithms were used to identify 7 dominant tree species （Pinus tabuliformis， Quercus wutaishansea， Betula playphylla， Larix principis-rupprechtii， Platycladus orientalis， Populus davidiana， and poplars spp.） in the Huodong mining area. The results showed that： 1） The NDFI index can effectively distinguish between deciduous forests and evergreen forests； 2） The dominant tree species identification based on "trajectory features + spectral features + texture features" performed well， with an overall classification accuracy of 79.6% and a Kappa coefficient of 0.742 in the study area， which was 7.3% higher than the control group.

Fraxinus mandshurica is one of the precious broadleaf tree species in Northeast China. It has high economic and ecological value.There′s a shortage of F. mandshurica resources and a structural deficiency in resilient cultivars. It′s particularly important to select and breed F. mandshurica with excellent cold resistance in the context of global climate change. The F. mandshurica in Xiaoxing'an Mountains of Heilongjiang Province were selected as the reseach objects. Dendrochronology and wood anatomy were used to compare the radial growth of 52 F. mandshurica families（Families 1-77 in total）. The relationship between the radial growth and xylem anatomical characteristics of F. mandshurica and main climatic factors was clarified. The response of F. mandshurica families to low temperature event was studied. The results showed that there were significant differences in average annual radial growth of F. mandshurica families in Xiaoxing'an Mountains. The radial growth of No. 56， 46 and 38 F. mandshurica families were higher， the values were 4.07 mm， 3.82 mm and 3.71 mm， respectively. The radial growth of F. mandshurica was constrained by temperature and precipitation during the growing season. The radial growth was significantly negatively correlated with the temperature in October of the previous year， positively correlated with the temperature from January to April， and negatively correlated with the precipitation from March to April. Temperature was the primary climatic factor affecting xylem anatomical features of F. mandshurica families in Xiaoxing'an Mountains. Under low temperature stress， the ring width（RW）， mean vessel area （MVA）， total vessel area（TVA） and theoretical hydraulic conductivity（Kh） decreased by 25.5%， 38.2%， 21.8% and 55.1%， compared with non-low temperature years， while vessel density（VD） increased by 64.1%. There was difference in radial growth among F. mandshurica families under low temperature stress. No. 39， 70 and 36 families had stronger resistance to cold， the values of resistance were greater than 1. The recovery of No. 57， 17 and 70families were better， the values of recovery were higher than 1.63. The No.70 F. mandshurica family in Xiaoxing'an Mountains had excellent growth and stronger cold resistance， which could be used as an excellent F. mandshurica family for directional cultivation.

In order to realize the high-value and large-scale utilization of metallurgical solid waste materials， the soil curing agent was synergistically prepared by using metallurgical solid waste materials （blast furnace slag， fly ash， calcium carbide slag and gypsum）， and the effects of different solid waste materials on the mechanical properties of the cured soil were investigated by orthogonal ratio optimization experiments and the optimal parameter formulations were obtained. The mechanical properties （unconfined compressive strength， splitting strength， delayed molding） and weathering properties （water stability， freeze-thaw cycle） of the solidified soil with solid waste-based curing agent （SWC） were systematically studied and compared with PO 42.5 silicate cement （OPC）. The results showed that： calcium carbide slag played an important role in the curing agent system， and the appropriate gypsum dosage had a positive effect on the strength growth； the strength of cured soil increased with the increase of SWC dosage and the age of curing， and the 7 day unconfined compressive strength of the cured soil at 4% dosage was more than 1 MPa， and the splitting strength was basically comparable to that of the OPC cured soil； the SWC cured soil showed a longer allowable delay in construction， and there was no trend of decreasing strength within 12 h after mixing； the water stability coefficient was more than 90% when the dosage was more than 5%， and the anti-freezing coefficient under the same dosage was increased by 3%-5% compared with that of cement， so it had good water stability and anti-freezing property； X-ray Diffraction （XRD） and Scanning Electron Microscope （SEM） analyses showed that SWC cured soils generate expansionary hydration products， calcite alumina （AFt） and hydrated calcium silicate （C-S-H）， during the hydration process， and that calcite crystals played a significant role in improving the cleavage strength of cured soils and increasing the resistance to cracking and deformation.

The optimization of stand spatial structure is a key issue in achieving sustainable forest management. Traditional optimization methods often exhibit low efficiency in handling complex spatial relationships and large-scale data. This study proposed a stand spatial structure optimization method based on Graph Attention Networks （GAT）. An integrated spatial structure evaluation system was established using the entropy-weighted matter-element analysis method， and a graph neural network model was constructed based on stand data from the Tanglin Forest Farm of the Xinqing Forestry bureau in northern Yichun，Heilongjiang Province. The model was applied to perform multi-objective optimization analysis of stand spatial structure. Experimental results showed that at a 25% harvesting intensity， the integrated spatial structure index improved from 4.336 to 7.256. The GAT model demonstrated superior performance in capturing complex spatial relationships and optimizing multi-objective tasks. This study provides an innovative and intelligent approach for optimizing stand spatial structure and managing forests， contributing to the enhancement of forest ecosystem health and stability.

This study aims to clarify the spatial distribution characteristics of ridge plant belts on soil water-holding capacity and soil structure in sloping farmland， providing a scientific basis for optimizing ridge plant belt configurations and soil and water conservation measures in Northeast China's black soil region. Sloping farmland with ridge plant belts was selected as the research object （Ridge 1： ridge spacing of 12.5 m； Ridge 2： ridge spacing of 19.5 m）， and sloping farmland was selected as the control. The uniform spatial point sampling method was obtained using basic physical property indicators in the surface layer （0-15 cm）， and to quantify the differences in the spatial distribution characteristics of soil water-holding capacity and soil structure in sloping farmland with different spacing of ridge plant belts. The result showed that， the sloping farmland with ridge construction showed a significant increase in total porosity， capillary porosity， saturated water-holding capacity， field capacity， and capillary water-holding capacity， with a relatively uniform distribution across the slope. In addition， compared to the Ridge 2， the soil of Ridge 1 showed an increase of 0.96-1.11 times in total porosity， 1.21-1.31 times in capillary porosity， 1.03-1.25 times in saturated water-holding capacity， 1.22-1.78 times in field capacity， and 1.33-1.52 times in capillary water-holding capacity， respectively. The soil mechanical stable aggregate mass fraction， MWD （mean weight diameter）， water-stable aggregate mass fraction， and GMD （geometric mean diameter） in the sloping farmland with ridge showed significant improvements across all fields. Compared to the controls， the sloping farmland with ridge increased by 1.01-1.15 times， 0.94-1.61 times， 1-1.17 times， and 1.05-1.55 times， respectively. This indicated that the sloping farmland with ridge effectively improved soil structure compared to the control. Moreover， compared to the sloping farmland with Ridge 2， the soil mechanical stable aggregate mass fraction， MWD， water-stable aggregate mass fraction， and GMD in the sloping farmland with Ridge 1 increased by 1.08-1.14 times， 0.95-1.28 times， 1.07-1.15 times， and 1.14-1.40 times， respectively. Constructing ridges can improve water-holding capacity and soil structure characteristics， with a more significant improvement effect observed in relatively small distances smaller distances between ridges.

With the popularization of automated production lines， the pressing process of tea cakes has become particularly important for product quality. However， the quality control of Pu'er mini tea cakes produced by automated production lines often falls short of those made by hand. Therefore， the detection of the appearance quality of tea cakes after production by automated lines has become an urgent issue to be addressed. To this end， this study proposes an automated quality detection algorithm for Pu'er tea cakes based on machine vision. The algorithm comprehensively applies various image processing techniques， including Otsu threshold segmentation and Canny edge detection， and introduces multiple algorithm optimization strategies to improve detection accuracy and efficiency. The algorithm can automatically complete the detection and evaluation of the appearance quality of tea cakes and transmit the results in real-time to a Programmable Logic Controller （PLC）. Experimental results show that the algorithm can accurately identify appearance defects of tea cakes， with an average computational accuracy of 95.75%， demonstrating high robustness and reliability. It is suitable for quality control in automated production lines and has a wide range of application prospects， especially in the intelligent transformation of the tea production industry， where it has significant reference value.

Plant residues are an important source of forest soil carbon pool， and changes in soil carbon flux in woodland are closely related to soil carbon pool and carbon cycle. However， current studies on soil organic carbon stability mainly focus on farmland soil. In order to clarify the influence of exogenous carbon input changes on the stability of soil organic carbon， an indoor constant temperature culture experiment was set to study the litter species （Cherry， YH； Sycamore， WT； Poplar， YS）， additive amount （0， 2%， 4% and 6%）， particle size （2 mm， D； 0.25mm， X） as variables， 18 different treatments and 2 controls were concluded. The changes of soil CO₂ release， soil organic carbon content and mineralization intensity under different factors and their interactions were analyzed. Results showed that different litters had significant effects on soil total CO₂ release， and cherry blossom and poplar were more likely to promote soil total organic carbon mineralization. The highest total CO₂ release rate was observed in YHX6 treatment， and the cumulative total CO₂ release of YHD6 treatment was 4.37 times that of CK1. Compared with CK1， the potential mineralizable organic carbon C _p value of 6% added dose was significantly increased. The dynamic changes of soil total organic carbon accumulation mineralization over time can be fitted by the first-order kinetic equation， and the fitting results showed that exogenous carbon input accelerated soil carbon turnover， while litters in small particle size， 6% addition amount and YH type yielded the highest total soil organic carbon turnover rate. WTX2 significantly decreased total organic carbon mineralization intensity， which was only 1.67%. Organic carbon intensity in soil with small particle size was lower than that of large particle size treatment. Therefore， sycamore leaves in small particle size and added with low addition amount can be applied to increase the stability of soil organic carbon and promote the retention of carbon in soil in regional soil organic carbon management.

Soluble solids content （SSC） is a key indicator for assessing the internal quality of fruits. This study proposes a non-destructive detection method based on hyperspectral image fusion to predict the SSC of blueberries. Three widely used wavelength dimensionality reduction algorithms are employed： Monte Carlo uninformative variable elimination （MC-UVE）， Competitive Adaptive Reweighted Sampling （CARS）， and Successive Projections Algorithm （SPA）， to identify optimal wavelengths. Additionally， a strategy integrating Local Binary Patterns （LBP） and Gray Level Co-occurrence Matrix （GLCM） is proposed for feature extraction. Using spectral features， image features， and fused features， Partial Least Squares （PLS）， Backpropagation Neural Network （BPNN）， and Support Vector Machine （SVM） models are developed for SSC prediction. The results demonstrate that the BPNN model， utilizing spectral features extracted via the CARS algorithm and image features derived from the LBP+GLCM algorithm， yields the highest prediction accuracy. The model's coefficient of determination （R {}_p^{\mathrm{2}}） is 0.926 1， while the Root Mean Square Error of Prediction （RMSEP） is 0.364 1. This study indicates that hyperspectral image fusion technology holds significant potential for the non-destructive prediction of blueberry SSC.

The production of plywood consumes a large amount of energy. In order to improve economic efficiency， support carbon peak and carbon neutrality goals， it is necessary to improve energy efficiency and reduce energy consumption. This article took 5 heat exchange logistics in the production process of plywood as the research object， used pinch point technology to analyze the heat exchange network under existing production conditions， and proposed optimization and improvement plans. Aspen Plus was applied to establish a heat exchange network in the plywood production process， calculate the stream flow rate and physical property data of each side line， divide the temperature range， determine the minimum heat transfer temperature difference， and calculate the pinch point temperature. The traditional pinch point method determined the minimum heat transfer temperature difference ∆ T_{\mathrm{m}\mathrm{i}\mathrm{n}} to be 9 ℃. After considering carbon emissions， the minimum heat transfer temperature difference ∆ T_{\mathrm{m}\mathrm{i}\mathrm{n}} was adjusted to 7 ℃， and the average pinch point temperature was 116.5 ℃. The pinch point temperature was used to analyze and diagnose the phenomenon of crossing pinch points in the heat exchange network， accurately located the bottleneck position of the heat exchange network， adjusted the improperly configured cold and hot stream heat exchangers， and achieved the goal of optimizing the entire heat exchange network. After optimization， the usage of cold and hot utilities in the system decreased by 862 465.0 kW and 202 642.0 kW respectively， significantly reducing the energy consumption of the equipment.

In the customization process of passive wooden window manufacturing， reducing material waste during frame cutting is key to cost reduction. This problem is modeled as a one-dimensional cutting stock problem. To address the issue of traditional genetic algorithms where the individual encoding method tends to lead to the destruction of cutting patterns and low exploration efficiency during iterations， a new individual encoding method is proposed to maintain the integrity of cutting patterns throughout the evolutionary process. Additionally， a heuristic strategy and a correction strategy are introduced for individual correction and population evolution. Simulation results show that for different test cases， the average material utilization rate， excluding the last remnants， exceeds 99%， with some improvements in the length of the last remnants compared to other algorithms. For two sets of real production data from enterprises， the proposed algorithm achieves the theoretical lower bound， with average utilization rates （excluding the last remnants） of 99.49% and 99.66%， respectively， outperforming the results of the company's existing software. This demonstrates the algorithm's potential to effectively reduce costs and provide practical solutions in engineering applications.

The effects of ethephon （ETH）， ethephon inhibitor 1-methylcyclopropene （1-MCP） and cysteine （CYS） on the yield and main quality indexes of rubber tree latex were analyzed， and the dosage threshold was calculated. An optimized orthogonal experimental design was used to analyze 14 treatments with three factors （ETH， 1-MCP， CYS） and four levels （four concentrations of each reagent） for applying rubber tree cut surfaces. Key indicators such as rubber latex yield， dry rubber content， molecular weight， initial plasticity value， plasticity retention index and Mooney viscosity were measured， and the correlation between the indicators was analyzed. The results showed that there were significant differences in yield and dry rubber content of 14 treated rubber trees. Correlation analysis showed that the number-average molecular weight was positively correlated with the weight-average molecular weight and Mooney viscosity， and the correlation coefficient was 0.71 and 0.83， respectively， and the correlation coefficient was negative with the polydispersity indexes， and the correlation coefficient was -0.91. Initial plasticity value was positively correlated with Mooney viscosity， and the correlation coefficient was 0.73. The polydispersity indexes was negatively correlated with Mooney viscosity with the correlation coefficient -0.89. The regression equations based on dry rubber content index were established respectively. The maximum concentration of ethephon， 1-MCP and CYS were 0.15%， 1.08% and 0.41 g/L， respectively. The optimized orthogonal test method can effectively calculate the threshold of the regulator and provide theoretical and practical guidance for the subsequent experiments.

Rare and endangered plants have formed a stable association with their associated tree species in the long-term evolution process， but whether this association is related to allelopathy produced by litters is still unknown. Taking the unique rare and endangered plant Davidia involucrata Baill. and its associated species Cornus controversa Hemsl. in China as research objects， the allelopathic effects of water extracts from different types of litter （branch litter， leaf litter， and their mixture litter） at natural concentrations on each other's seedlings were studied. The results revealed that， 1）the water extract from the branch litter of C. controversa significantly promoted the basal diameter and root length growth of D. involucrata seedlings， and increased the contents of chlorophyll a and b， total chlorophyll， leaf mass fraction of N， and P， while the extracts from leaf litter and branch-leaf mixture litter did not show significant promotional effects. 2）The water extract from the branch litter of D. involucrata significantly promoted the basal diameter growth of C. controversa seedlings and increased the contents of chlorophyll a and b， and total chlorophyll， whereas the extracts from leaf litter and branch-leaf mixture litter significantly reduced the contents of chlorophyll a and b， total chlorophyll， leaf mass fraction of N. 3）The allelopathic effect index indicated that different litter types from C. controversa had allelopathic promotional effects on D. involucrata seedlings， the intensity from large to small was branch， branch-leaf mixture， and leaf； the allelopathic effects of different litter types from D. involucrata on C. controversa seedling were varied， showing promotion by branch litter， and inhibition by leaf litter and branch-leaf mixture litter， with the mixture litter exhibiting stronger inhibitory effects. These findings suggest that the water extracts from different types of litter from D. involucrata and C. controversa have distinct allelopathic effects on each other's seedling growth， and the extracts from the two types of branch litter exhibit the greatest allelopathic promotional effects on seedlings.

Under the background of new quality productivity， blockchain has brought new opportunities for transformation and upgrading of traditional industries with its transparency and traceability， and also opened up a new path for carbon emission reduction cooperation in wood supply chain. In view of this， this paper constructs an evolutionary game model of China-Russia wood supply chain， and studies the operating evolution law of the four parties-Russian wood suppliers， China wood processing manufacturers， wood products distribution retailers and the government in the carbon emission reduction cooperation mechanism under the blockchain platform. The results show that the initial strategy probability of supply chain members， consumers' low-carbon preference and the increase of manufacturers' carbon emission reduction can effectively encourage the main members of the wood supply chain to actively participate in the carbon emission reduction cooperation mechanism. At the same time， the government reward and punishment mechanism will also affect the final stable result of the game system. The greater the punishment for hitchhiking， the better the game system will reach the ideal state， while excessive subsidies will weaken the possibility of the system reaching the optimal equilibrium. The research results provide theoretical inspiration and reference for carbon emission reduction cooperation in China-Russia wood supply chain.

In order to solve the problem that the target detection algorithm is prone to leakage detection and insufficient detection accuracy in pedestrian detection in forest areas， a forest pedestrian target detection algorithm based on improved YOLOv8 is proposed. The C2f_DWRSeg module is used to replace the C2f module， and the number of initial convolutional channels is expanded so that the network can extract multi-scale features more efficiently. A reconstructed detector head is proposed to increase the complexity of the convolution layer during training， and a single branch structure is used in inference， so as to enrich the feature representation of the network and maintain efficient inference speed； add CGA， the convolution attention mechanism module， before feature fusion， to reduce the amount of calculation； use the Focaler-ShapeIoU loss function to replace the CIoU loss function to make up for the shortcomings of the boundary box regression method and further improve the detection ability. Experimental results show that compared with benchmark model， the improved algorithm mAP50 has increased by 2%， mAP50-95 has increased by 2.4%， and FPS has increased by 4.33%. It proves that the improved algorithm can be better applied to the task of pedestrian detection in forest areas.

To expand the application of wood in the field of outdoor reflective materials， a series of calcium carbonate/eucalyptus wood composite materials with different calcium carbonate （CaCO₃） contents were prepared by hot pressing method using the mineralization method of alternating vacuum impregnation of eucalyptus wood with calcium chloride and sodium bicarbonate to mimic the organic/inorganic composite structure of natural pearl layer. The effects of CaCO₃ on the structure and properties of the mineralized materials were investigated through structural characterization and performance tests. The results showed that CaCO₃ could enhance the mechanical properties of the mineralized material， improve its thermal stability and reflective ability to sunlight， and reduce the temperature at which the material was heated by light. When the mineralization time was lower than 3 days， the tensile strength increased with the increase of CaCO₃ content， up to 18.35 MPa， and the fracture process showed multiple fracture； when the mineralization time reached 4 days， the material's reflectance of sunlight reached more than 80 %， which reduced the temperature of the original wood by 10 ℃ compared with the original wood itself. This environmentally friendly material has a certain application potential in the field of outdoor reflective insulation field.

Due to the decreasing supply of petroleum based plastics resources， the development of biomass resources has attracted extensive attention of researchers recently. The research on environmental friendly antibacterial packaging materials has increasingly become a new trend. Nanocellulose （CNC） derived from biomass has attracted particular attention. In recent years， nanocellulose has been widely used in packaging materials， medical fields， filter media and other fields. It is a good substrate， modifier and additive. Polylactic acid （PLA） is an environmentally friendly and renewable polymer， which can be widely used in the field of food packaging to replace petroleum based plastics. However， its high brittleness and poor antibacterial properties limit the application of PLA in the field of packaging. In this study， two kinds of polylactic acid/carvacrol/nanocelluluse-zinc oxide （PLA/CRV/CNC-ZnO） composite films with antibacterial effect were successfully prepared by solution casting and electrospinning. The effects of CRV and CNC-ZnO hybrid on the properties of the composite films were studied. At the same time， two kinds of composite films were applied to strawberry preservation experiment to explore their preservation effect on strawberry. The PLA/CRV20%/CNC-ZnO3% composite film prepared by solvent casting method and F-PLA/CRV20%/CNC-ZnO3% composite film prepared by electrospinning method were used to freshen strawberries in the same environment， and the fresh-keeping effect of strawberries was evaluated by sensory evaluation， weight loss rate and other tests， and the effects of packaging films prepared by different processes on the fresh-keeping effect of strawberries were compared and analyzed. The results showed that both casting film and spinning film had better preservation effect on strawberry， and the preservation effect of F-PLA/CRV20%/CNC-ZnO3% spinning film on strawberry was better than PLA/CRV20%/CNC-ZnO3% casting film， and the shelf life of strawberry was successfully extended to 10 days at 25 ℃. This study has a certain guiding role in expanding the application of PLA composite film in antibacterial food packaging.

As the main part of plant photosynthesis， chlorophyll plays an important role in monitoring vegetation growth and evaluating the capacity of carbon sequestration. Remote sensing technology， which is a high efficiency and low cost earth observation technology， can realize the estimation of chlorophyll content by leaf reflection spectrum characteristics. However， the accuracy of chlorophyll content estimation will be reduced due to the influence of leaf water content and leaf cell structure on leaf spectrum. Solar-induced chlorophyll fluorescence （SIF） is a technology of detecting the fluorescence information of chlorophyll excited fluorescence signal and the variation of fluorescence signal is directly related to chlorophyll content， which has great potential in chlorophyll content estimation. In this study， the fluorescence sensitive band of chlorophyll content was determined by sensitivity analysis using the fluorescence radiation transfer model SCOPE （Soil Canopy Observation， Photo-chemistry and Energy fluxes） and the chlorophyll content estimation model was established based on the fluorescence spectrum. Finally， the robustness of the model was verified by the measured data. The results showed that 700 nm and 730 nm were the highest and lowest sensitive bands of chlorophyll content， respectively. The band of 760 nm was the highest correlated band of chlorophyll content. The chlorophyll content estimation model were established based on the indices of fluorescence ratio of these three bands. SIF₇₆₀/SIF₇₀₀ had the best modeling accuracy with the R² of 0.998 1 and root mean square error （RMSE） of 0.043 5 μg/cm². The accuracy of SIF₇₀₀/SIF₇₃₀ and chlorophyll content （Cab） was the lowest in three models with R² and RMSE 0.904 8 and 0.088 6 μg/cm²， respectively. Independent samples of measured data were used to verify the above three estimated methods. SIF₇₆₀/SIF₇₃₀ had the best estimation results， with RMSE of 0.210 8 μg/cm²， followed by SIF₇₀₀/SIF₇₃₀， with RMSE of 0.345 4 μg/cm². But estimated model by using SIF₇₀₀/SIF₇₃₀ showed an overall overestimating results. The estimated results of SIF₇₆₀/SIF₇₀₀ showed a different results compared with measured data with RMSE of 0.743 5 μg/cm². In summary， the ratio vegetation index calculated by SIF₇₆₀/SIF₇₃₀ showed higher accuracy of modeling and excellent robustness of chlorophyll content estimation. Relevant studies provide technical references for the estimation of leaf biochemical parameters by chlorophyll fluorescence remote sensing technology.

In view of the limitations of existing indicators， in order to better evaluate the high and low temperature performance of warm mix asphalt binder， different amounts of Sasobit and Evotherm 3G warm mix agent were selected to be mixed with 70^# matrix asphalt and SBS modified asphalt to prepare modified asphalt. Two parameters of complex shear modulus G^* and phase angle δ were obtained and analyzed by dynamic shear rheological （DSR） test. The rutting factor G^*/sinδ， improved rutting factor G^* /（sinδ）⁹ index and critical temperatureT_{G\mathrm{*}/\mathrm{s}\mathrm{i}\mathrm{n}\delta }， improved critical temperatureT_{G\mathrm{*}/{(\mathrm{s}\mathrm{i}\mathrm{n}\delta )}^{\mathrm{9}}} index were used to comprehensively evaluate and analyze the high temperature performance of modified asphalt. Two parameters of creep stiffness modulus S and creep rate m were obtained and analyzed by low temperature bending rheological （BBR） test. The k index was established and the creep compliance J（t） index was introduced to evaluate the low temperature performance of modified asphalt. The test results and data analysis showed that： G^* /（sinδ）⁹ was more accurate than G^*/sinδ in evaluating the high temperature performance of warm mix asphalt， and T_{G\mathrm{*}/{(\mathrm{s}\mathrm{i}\mathrm{n}\delta )}^{\mathrm{9}}} was suitable for the high temperature performance evaluation of SBS warm mix asphalt， and there was no obvious difference for 70^# matrix asphalt. The k index can distinguish the difference of low temperature performance between matrix asphalt and modified asphalt， and the J（t） index can well reflect the low temperature creep performance of warm mix asphalt. Finally， the weight analysis of high and low temperature indexes was carried out by analytic hierarchy process （AHP）， and the weight values of T_{G\mathrm{*}/{(\mathrm{s}\mathrm{i}\mathrm{n}\delta )}^{\mathrm{9}}} and J（t） were the largest. It was suggested that T_{G\mathrm{*}/{(\mathrm{s}\mathrm{i}\mathrm{n}\delta )}^{\mathrm{9}}} was used to evaluate the high temperature performance of warm mix asphalt， and J（t） was used to evaluate the low temperature performance of warm mix asphalt.

This paper aims to compare the differences of burning properties of 12 typical tree species in Xiaoxing’anling，and to provide reference for the selection and allocation of fireproof forest tree species. In the study， the 12 kinds of shrubs in the Xiaoxing’anling region of Heilongjiang Province are taken as the main research subjects， with their stems， branches， and leaves measured separately for the combustibility indicators. The hierarchical analysis method （AHP） is then applied to construct a hierarchical model of the combustibility of shrub plants， determining the weights of each index of combustibility， calculating the combustibility values of different parts of the shrub， and confirming the weight ratio of each part of the shrub in combination with expert scoring methods to arrive at the combustibility ranking of 12 kinds of shrub. It has been revealed by the research results that： in terms of the whole plant， Rosa davurica has the strongest combustibility， while Lonicera japonica has the weakest. Compared the combustibility of the stem， the most easily ignited is Sambucus williamsii and the hardest to ignite is Lonicera japonica. Compared the branch combustibility， the most easily ignited is Sambucus williamsii and the hardest to ignite is Lonicera japonica. Compared the combustibility of leaves， those of Acanthopanax senticosus are most easily ignited and those of Tamarix ramosissima are the hardest to be ignited. The results can provide a theoretical basis for the shrub′s combustibility and a support for predicting forest fire behavior， and combustible materials management.

Xinjiang is an important forest and fruit industry base in China， and the characteristic forest and fruit industry is an important part of regional economic development. In order to prevent fruit tree diseases from restricting the development of forest and fruit industry， a MobileNet-V2 NAM fruit tree leaf classification and disease identification model was designed in this study. It incorporated a lightweight normalization-based attention module to improve the model's sensitivity to feature information and make the model focus on salient features. At the same time， L1 regularization was added to the loss function to penalize the sparsity of the weights and suppress the non-significant weights. The experimental results showed that： in leaf classification， the model performed well in the classification results of self-built， Plant Village， and mixed datasets， with the accuracy rates reaching 97.05%， 98.73%， and 94.91%， respectively， and had good generalization ability. In disease identification， the MobileNet-V2 NAM model achieved a recognition accuracy of 94.55%， which was higher than the AlexNet， VGG16 classic CNN models， and the number of parameters of the model was only 3.56M. MobileNet-V2 NAM has good accuracy while maintaining a low amount of model parameters， provides technical support for embedding deep learning models into mobile devices.

Microwave curing technology of concrete can significantly improve the early strength of concrete， but there is no uniform design standard for microwave curing method， resulting in great differences in material properties after curing. To explore the influence of microwave curing methods， this study employed the response surface methodology to systematically analyze the influence of microwave curing method on the compressive strength of cement mortar. The relationship model between key microwave curing parameters and material properties was established， and the optimal microwave curing method was determined when the heating temperature was under 40℃ and the heating power was 1 000 W for 30 min. Chemical components and microstructure impacts were studied using X-ray diffraction （XRD）， mercury intrusion porosimetry （MIP）， and scanning electron microscope （SEM）， and it was revealed that a reasonable microwave curing method can promote the hydration degree of mortar and form a denser structure. The temperature variation in microwave curing process was analyzed by combining experimental data and computer simulation， and it was found that microwave curing temperature had hot spot effect. It is concluded that microwave curing temperature is the most important factor affecting the strength of mortar.

Taking the typical frozen soil area of Da Xing'an Ling Huzhong as example， the spatial distibution law of soil carbon storage in the suface layer （0-15， 15-30 cm） was studied. Through field investigation， sampling and indoor anlysis， the reflectance of seven bands based on Landsat 8 OLI images was modeled using the Random Forest （RF） algorithm to analyze the spatial distribution of surface soil organic carbon in the permafrost region of the Da Xing′an Ling. The highest correlation variable was selected among 13 remote sensing variables， and six environmental variables of altitude， ARVI， VIGreen， EVI， OSAVI， NDVI were identified as the highest correlation variables and could be used as independent variables. The results showed that with a leaf value of 5 and a tree value of 900， the training and test sets of the prediction model were closest， indicating that the model stability was the best at this time. The fitting accuracy of training set R ² of 0-15，15-30 cm soil layer was 0.23 and 0.43， the fitting accuracy of test set R ² was 0.35 and 0.24. Using this parameter to predict the spatial distribution characteristics of soil carbon stock in the surface layer （0-15， 15-30 cm）， 0-15 cm and 15-30 cm average soil carbon density was 7.40 kg/m² and 14.80 kg/m²， respectively. The distribution of soil carbon content in 0-15 cm soil layer gradually increased from north to south， and there was no significant difference in soil carbon distribution in 15-30 cm soil layer， but slightly higher in the south than in the north.

Dioryctria is a kind of pest that seriously harms conifer species and seriously affects the health and growth of conifer trees. The larvae of Dioryctria feed on the leaves of coniferous trees and build nests in coniferous trees. Gradually， they destroy the leaf tissues， causing the needles to turn yellow and eventually leading to the withering of the trees. In addition， larvae may also erode the bark of trees， resulting in bark flaking and trunk exposure， leaving trees vulnerable to other pests， germs and natural elements， increasing the vulnerability of trees and reducing their viability. In order to assist the ground treatment of trees eaten by Dioryctria， the YOLOv8s target detection algorithm was adopted to realize the detection and recognition of the trees eaten by Dioryctria. By using C2f-GAM and dynamic detection head to build a model （YOLOv8-DM）， the detection ability of YOLOv8s against Dioryctria moth trees was improved. The experimental results showed that YOLOv8-DM could effectively identify Dioryctria moth trees with an average accuracy of 84.8%. Compared with other target detection algorithms， YOLOv8-DM has higher average precision.

In order to lessen the environmental issues caused by the accumulation of titanium gypsum and to also alleviate the current situation of road material shortage， titanium gypsum and silane coupling agent-treated titanium gypsum were used to partially or completely replace limestone mineral powder. Additionally， the effects of titanium gypsum and modified titanium gypsum on the conventional and rheological properties of asphalt mastic were investigated. First， a laser particle size meter and a scanning electron microscope were used to examine the micro-morphology and particle size distribution of titanium gypsum， modified titanium gypsum， and limestone mineral powder. Subsequently， asphalt mortar containing varying amounts of titanium gypsum and modified titanium gypsum dosage was prepared. The asphalt mortar's three main indicators （penetration， ductility， and softening point）， as well as its high temperature and low temperature rheological performances， fatigue resistance， were tested using conventional testing methods， temperature scanning test， frequency scanning test， multiple stress creep recovery test （MSCR）， linear amplitude scanning test （LAS）， and bending beam rheological test （BBR）. The findings indicated that： titanium gypsum and modified titanium gypsum had a wider particle size range and a rougher surface. The addition of titanium gypsum and modified titanium gypsum decreased the asphalt mortar's ductility， penetration， unrecoverable creep flexibility （Jnr）， fatigue life （N _f）， creep rate （m） and phase angle （δ）， while the softening point， complex modulus （G*）， resilience （R） and stiffness modulus （S） were increased. In other words， performance at high temperatures was enhanced while low temperatures and weariness were decreased. The low-temperature performance was decreased and the high-temperature performance and elastic recovery indexes of modified titanium gypsum outperformed those of titanium gypsum at the same temperature. The determination of the viability of utilizing titanium gypsum in the creation of asphalt mixtures provides a path forward for the resource's exploitation.

Cracks in wood have a non-negligible effect on the propagation characteristics of stress waves. It is significant to investigate the energy change law for the crack depth identification in wood. Pinus sylvestris var. mongholica Litv. wood was used as the specimen in the study， and different numbers of cracks were made on different specimens. The depth of cracks was gradually increased from 0 to 90 mm with an increment of 10 mm each time. Firstly， an acoustic emission （AE） sensor was used to collect the stress wave generated on the surface of the specimen by the pencil lead breaking， and its time-frequency characterization was performed. Secondly， the energy attenuation model of the stress wave in different frequency bands was established based on discrete wavelet analysis. The results showed that， the percentage of AE signals with frequency components of 125 to 250 kHz in the transient wave decreased significantly with increasing crack depth. As the number of cracks increased， the coefficient indicating the attenuation degree in the energy attenuation model after wavelet reconstruction of the AE signal increased from 0.73 to 1.09. In addition， with the increase of crack depth， the energy decay of the wavelet signal with frequency components of 31.25 to 62.5 kHz was obviously slow. And the crack region determined by the energy attenuation model can cover the location of the actual crack.

The low-temperature bonding performance at the asphaltmastic -aggregate interface plays a crucial role in determining the low-temperature crack resistance and water stability of asphalt mixtures in cold regions. This study employs a pull-off test coupled with ImageJ software for detailed image analysis of the failure surfaces. It focuses on two key metrics： bonding strength and bonding failure ratio， to evaluate the bond performance of asphalt， asphalt mortar， and asphalt concrete with limestone aggregates under varying low temperatures （-10， -20， and -30 ℃） and in wet conditions. The results reveal that the asphalt-aggregate interface achieves the highest bonding strength at -20 ℃. However， the bonding failure ratio increases as temperatures decrease. Notably， the presence of water can lead to a 49% reduction in low-temperature bonding strength， resulting in a continuous detachment of the interface and an increase in the bonding failure ratio by 44%. Furthermore， an optimal powder-binder ratio can enhance the low-temperature bonding strength of asphalt mortar； specifically， a ratio of 1.2 yields a bonding strength that is 1.44 times greater than that of the asphalt matrix. Consistency in the low-temperature bonding strength results is further corroborated through bending creep stiffness tests. The incorporation of mineral powder and fine aggregates modifies the contact characteristics at the interface， leading to significant alterations in the failure location and bonding failure ratio. Failures in asphalt mortar typically occur within the mortar itself， while those in asphalt concrete predominantly occur at the interface， with the bonding failure ratios of asphalt concrete being substantially higher than those of asphalt mortar.

Aiming at the bottleneck problem of insufficient adaptability of traditional defect detection methods in automated wood processing industry， research on intelligent detection technology based on deep learning is carried out， and a dataset covering multi-species wood characteristics and typical defect types is proposed. Applying object detection technology to defect detection， using dilation wise residual （DWR） module to optimize C2f module， and proposing task aligned dynamic detection head （TADDH） and feature focusing spread pyramid network （FSPN） to impove YOLOv8 algorithm （DFT-YOLO）. The experimental results showed that a significant improvement in accuracy， reaching 96.8%， which was 7.9 higher than the original model. On the average accuracy of the key evaluation indicators mAP50 and mAP50-95， the impoved model reached 93.8% and 75.2%， respectively， increasing by 6.8% and 17.5%， respectively. While improving the detection accuracy， the number of parameters of the model had decreased by approximately 1/6 （16.2%）. The impoved model can provide a lightweight detection method for wood defects.

To solve the problem that the target detection algorithm is prone to leakage and lacks detection accuracy in detecting wood surface defects， this paper proposes an improved YOLOv8 model （YOLOv8-CBW， C， B and W are abbreviations for CondSiLU， BiFPN and Wise-IoU） and constructs a self-made dataset containing various wood defects. By optimizing the original YOLOv8 algorithm and combining CondConv （conditional convolution） with SiLU （sigmoid-weighted linear unit） to form the CondSiLU module instead of the traditional convolution module， the flexibility of feature extraction is improved； the bidirectional feature pyramid network （BiFPN） is introduced to enhance the multi-scale feature fusion capability； and the Wise-IoU （weighted intersection over union） loss function replaces the CIoU （complete intersection over union） to improve the adaptability and generalization performance of the model to low-quality samples. The experimental results show that the improved YOLOv8-CBW model improves the mAP50 （mean average precision at IoU threshold 0.50） and mAP50-95（mean average precision over IoU thresholds from 0.50 to 0.95） by 3.7% and 3.9%， respectively， compared with the YOLOv8 model， and it shows higher precision and stability in complex wood defect detection tasks. The research in this paper provides new ideas for wood defect detection tasks and has good practical application prospects.

To explore the differences of community characteristics， species diversity and their coupling relationship with environment in different cold temperate coniferous forests in Daxing'an Mountains， and to provide theoretical basis and data support for the scientific management and biodiversity protection of cold temperate coniferous forests in this area. Taking the typical Larix gmelinii forest， Pinus sylvestris var. mongolica forest， Picea jezoensis forest， Picea koraiensis forest， and Pinus pumila var. pumila forest in Daxing'an Mountains as the research objects， the characteristics of tree layer， shrub layer and herb layer were investigated respectively， and the diversity index， evenness index and richness index were calculated. Redundancy analysis （RDA） was used to explore the coupling relationship between community characteristics and species diversity of five populations. The results showed that the plant species in the cold temperate coniferous forest were poor. The overall tree layer tree species type was single. The average DBH and average tree height of Pinus sylvestris var. mongolica were the highest in the five coniferous forest associations. Vaccinium vitis-idaea was the main species in the shrub layer of five coniferous forests. Deyeuxia purpurea， Pyrola asarifolia subsp. incarnata and Maianthemum bifolium were the main species in the herb layer. These five populations had significantly different diversity characteristics and had significant differences in coupling relationships. The diversity index was relatively concentrated on the left side of the second axis， and was positively correlated with tree height， DBH and number of species in each layer， but negatively correlated with altitude， canopy density， shrub layer coverage and herb layer height. Five typical cold temperate coniferous forests in the Daxing 'an Mountains have significantly different community structure and species diversity characteristics.

In response to the complex diversity of surface defects in plywood veneers and the difficulties in feature extraction， as well as the large number of parameters and computational costs of deep learning-based defect detection algorithms， which makes effective application on devices with lower computing power challenging， a detection model for surface defects （live knots， dead knots， holes， cracks， and notches） in veneers based on an improved YOLOv8n is constructed. To enhance the detection accuracy and lightweight performance of the model， improvements are made to the plywood veneer surface defect detection model. First， a new efficient attention mechanism （coordinate attention， CA） is adopted， which can enhance the accuracy of feature extraction and the network's spatial information perception ability while avoiding excessive computational burden； secondly， a novel structure based on partial convolution （PConv） is proposed——CSPPC （CSP（coross stage partial） pyramid convolution）， it to improve computational efficiency and the fusion capability of multi-scale features； finally， an improved weighted intersection over union loss function——WIoUv3， it is introduced， which enhances the model's localization accuracy and robustness. Experimental results show that the improved YOLOv8 model （CP-YOLOv8） performs excellently in the task of detecting surface defects in plywood veneers， achieving an average precision mean （mAP） of 93.8%， an increase of 0.9% over the original model， while reducing the model's floating-point operations （GFLOPs） and parameter count to 7.2 G and 2.58 M， respectively， a reduction of 0.9 G and 0.42 M， which can fully meet practical application needs and provide an efficient， accurate， and lightweight solution for quality inspection of plywood veneers.

To improve the accuracy of non-destructive estimation of volume of trees with complex trunk shapes， this study uses terrestrial laser scanning （TLS） point cloud data and developes a taper-binary volume equation based on the optimal taper model for plantation Mongolian oak. The study was conducted in a Mongolian oak plantation located at the Urban Forestry Demonstration Base in Harbin. Complete point cloud data were collected via TLS， and tree trunk structural parameters were extracted after processes such as clipping， elevation normalization， filtering， individual tree segmentation， and leaf-branch separation. Based on the trunk shape characteristics of Mongolian oak， six taper equation models were tested （Biging（1984）， Amidon（1984）， Meng Xianyu（1982）， Kozak（2004）-Ⅱ， Zeng Weisheng et al.（1997）， Max and Burkhart（1976））， and the best-fitting model was selected through nonlinear regression to construct the taper-binary volume equation. Results showed that the accuracy of individual tree identification was 95.22%， with the coefficient of determination （R²） for extracted tree height and diameter at breast height （DBH） being 0.97 and 0.98， respectively， when compared to field measurements. The optimal taper model achieved an R² of 0.99 and a root mean square error （RMSE） of 0.38 cm. Residual analysis of the Mongolian oak taper-binary volume equation from this study with the existing volume equation showed the reliability of its estimation results. It can provide important technical support for estimating the volume of trees with complex trunk shapes using TLS point cloud data.

Because the planting mode of Eucalyptus robusta plantation is basically a single mode of multi-generation continuous planting， many ecological problems are caused. Therefore， ensuring that artificial E. robusta forests can achieve long-term and stable sustainable management has become a hot issue in the cultivation technology of artificial E. robusta forests. In order to achieve this goal， adopting appropriate tree species configuring and moderate canopy density is an effective way to adjust the stand structure and enhance the soil quality. In this study， six E. robusta pure forest plots were selected from the West Coast Forestry Farm in Foshan City， China， to study the effects of two species configuration modes （A is 5 species of Theaceae， B is 5 species of Theaceae and 2 deciduous broad-leaved tree species） and three E. robusta canopy density （Y1 is 0%， Y2 is 15%， and Y3 is 30%） on the soil physical and chemical properties， enzyme activities， and soil stoichiometric ratios. Among the different tree species configuration mode， the soil capillary water holding capacity， soil capillary porosity and soil void ratio of three soil layers （0-20， 20-40， 40-60 cm） and the total nitrogen N∶P， catalase activity and urease activity of 0-20 cm soil layer in Y3 plot of mode B were significantly higher than those in Y3 plot of mode A （P<0.05）. In different canopy density， the total phosphorus， available phosphorous， available potassium， catalase and urease activity of the three soil layers in the Y3 plot of the A model were significantly higher than those in the Y1 plot （P<0.05）， and the soil organic carbon， total nitrogen， alkali-hydrolyzable nitrogen， available phosphorous， catalase activity and N∶P of the three soil layers in the Y3 plot of the B model were significantly higher than those in the Y1 plot （P<0.05）. In conclusion， the appropriate tree species configuration mode and canopy density are beneficial to the improvement of soil nutrients and enzyme activities， and the pattern of Theaceae tree species and deciduous broad-leaved tree species under 30% E. robusta canopy density has a good prospect of application in the transformation of South China.

Studying the spatial-temporal evolution and driving factors of forest vegetation in the three parallel rivers region can provide data support for building the ecological safety barrier and promoting the social and economic development in Southwest China. The spatial-temporal evolution pattern of forest vegetation in the three parallel rivers region was explored by geospatial analysis， and the evaluation index system was constructed from the two dimensions of ‘natural-social economy’. And 14 specific evaluation indexes （elevation， slope and annual precipitation， etc.） were selected to measure the driving force of the area change of each vegetation type in the study area. The results were as follows， 1） there were three types of forest vegetation in the study area： evergreen broad-leaved forest， deciduous broad-leaved forest and evergreen needle-leaved forest， among which evergreen needle-leaved forest accounted for more than 84% of the forest vegetation. 2） There were three change track of stablility， mutation and fluctuation， among which the stablility type accounted for 80.35% of the changing area. The spatial distribution of “cold-hot spots” in evergreen broad-leaved forest and deciduous broad-leaved forest was similar， and the “cold-hot spots” in evergreen needle-leaved forest showed a trend of cold spots surrounding hot spots. 3） The factor detection showed that the influence of each factor on evergreen broad-leaved forest was greater than others， and the explanation strength for evergreen needle-leaved forest was small. The spatial distribution of forest vegetation in the three parallel rivers region has obvious spatial heterogeneity， and the interaction effect of elevation and other factors on forest vegetation in the study area is significantly stronger than that of single factor detection.

High-quality and efficient wood cutting processes are of significant importance for improving the utilization rate of wood resources and enhancing the quality of wooden products. Currently， the commonly used steel 65Mn for woodworking circular saw blades has issues with subpar mechanical properties such as hardness and tensile strength. To address this， nickel was added to modify the 65Mn steel. The Ni-modified 65Mn steel was prepared through steps including raw material smelting， casting test bars， forging processing， and heat treatment. Microhardness， impact toughness， and tensile strength tests were conducted on the 65Mn steel before and after modification. Additionally， the organizational differences of 65Mn steel before and after modification were analyzed using Metallographic microscopes and Scanning Electron Microscopes （SEM）. The results of the study indicated that， 1） after adding Ni elements， the tempered structure of 65Mn steel changed from tempered troostite to a mixture of quenched martensite and tempered martensite， and when the mass fraction of Ni reached 0.9%， residual austenite appeared. 2） Under the same heat treatment conditions， the effect of Ni elements on the hardness and tensile strength of the material showed a trend of first increasing and then decreasing， with the impact toughness being lower than that of unmodified 65Mn steel. The hardness and tensile strength of the 65Mn steel with a mass fraction of 0.7% Ni were optimal， increasing by 10.18% and 35.40%， respectively compared to unmodified 65Mn steel （at room temperature）. This research findings provides new ideas and methods for the subsequent improvement of woodworking circular saw blade steels and is expected to promote further development in the wood processing tools industry.

A method for identifying the number of wood cracks based on acoustic emission is proposed for wood crack distances. Firstly， four cracks of 1 mm×9 mm （length×height） are artificially made in sequence on the specimen， and an acousitic emission （AE） signal is generated on one side of the crack by folding the lead， and the sensor is placed on the other side with a signal sampling frequency set to 2 MHz.Then， the number of decomposition layers K and the penalty factor α of the variational modal decomposition （VMD） are determined by the particle swarm algorithm （PSO）， and the original signals are decomposed into the intrinsic mode function （IMF） with different frequencies. tFive groups of signals are then randomly selected for VMD decomposition， and the matrix composed of the decomposed IMFs is subjected to singular value decomposition （SVD） to obtain the corresponding singular value vectors， and then the standard matrix is composed of the five groups of singular value vectors. Finally， from the measured AE signals， the Mahalanobis distance is calculated with the standard matrix， respectively， and the number of cracks is determined based on the principle of minimum discrimination. The results show that the AE signal features can be easily extracted by the PSO-VMD-SVD method and the number of cracks can be discriminated by calculating the Mahalanobis distance， and the correct rate of discrimination is 92%.