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 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.

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.

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.

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 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.

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.

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.

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.

In order to explore the potential of L-band full-polarization SAR data to estimate forest aboveground biomass （AGB）， five polarimetric scattering ratio parameters （R₁， R₂， R₃， R₄， R₅） were constructed based on the canopy-ground scattering component of Unmmaned Aero Vehicle Synthetic Aperture Radar （UAVSAR） data of the AfricaSAR project. Calculating the Radar Vegetation Index （RVI）， and 21 polarimetric decomposition parameters were extracted by four model-based decompositions， including six-component and seven-component decomposition. Finally， all features were merged and the random forest feature importance was used to screen out the optimal feature combination， and random forest （RF）， support vector machine regression （SVR）， K-nearest neighbor regression （KNN） were used to estimate forest AGB of Lope， The Gaboneses Repbulic， Africa， with different feature combinations. The results showed that the polarimetric scattering ratio parameters， bulk scattering （Vol） and RVI had high sensitivity to forest AGB， and the correlation between R₂ and AGB was 0.823， and the optimal feature combination was Vol， polarimetric scattering ratio parameters and RVI. Machine learning models with different feature combinations had shown good performance， the coefficient of determination （R²） of the machine learning model based on the polarimetric decomposition parameters was bigger than 0.800， and the root mean square error （RMSE） was less than 88.000 Mg/hm²， and the best effect was the RF model based on the optimal feature combination， which increased R² by 0.144 and decreased RMSE by 30.327 Mg/hm² compared with the polarimetric decomposition parameters alone. The polarimetric scattering ratio parameter had certain potential in the estimation of forest AGB， the introduction of RVI improved the accuracy of the model， the model-based decomposition was suitable for forest AGB estimation， and the machine learning model based on feature screening can better invert forest AGB， and there was no obvious saturation point when the AGB reached 400 Mg/hm².

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.

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.

In order to accurately obtain forest canopy height information， accurately estimate forest aboveground biomass， and evaluate forest carbon sink capacity， this study constructed 30 long time series feature variables based on ground measurements， multi-source remote sensing data， and digital elevation models， combined with machine learning algorithms （ML）， to invert the forest canopy height in Lishui City， Zhejiang Province. The study revealed that terrain factors had no significant impact on the inversion of forest canopy height， while vegetation factors related to the red and green bands were strongly correlated with forest canopy height. Adding long time series feature factors can help improve the accuracy of ML algorithm in inverting forest canopy height. The performance improvement of CNN was particularly significant， achieving an optimal coefficient of determination （R ²） increase of 0.39 and a root mean square error （RMSE in the formula， it is denoted as R _MES） decrease of 4.15. Random forest had the highest inversion accuracy among the four ML algorithms （R ²=0.79， R _MSE=1.65）， greater than support vector machine （R ²=0.65， R _MSE=1.97）， extreme gradient ascent method （R ²=0.76， R _MSE=1.81） and convolutional neural networks （R ²=0.71， R _MSE=1.83）.

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.

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.

To reveal the evolution characteristics of vegetation cover in Nanchang City and identify the main climate drivers affecting its change trends， in order to provide guidance for its long-term stable and benign development and active response to the subsequent climate change. In this paper， NDVI monthly data and ten meteorological driving factors such as temperature， precipitation and pressure of Nanchang City from 2000 to 2020 were used to study the importance of different drivers by using single sample K-S test， Friedmann test， Kendall harmony coefficient test and random forest analysis. The results showed that， 1） the NDVI of Nanchang City showed a fluctuating downward trend from 2000 to 2020， the peak appeared in 2000， while the trough occurred in 2010. 2） The overall vegetation cover of the city showed a spatial distribution pattern of high perimeter and low center， with the NDVI values in the north and the west being relatively high and decreasing at a slow rate， and the NDVI values of the central areas of the city such as the East Lake District and the Castle Peak Lake District being relatively low and with an obvious downward trend. 3） The great average value of the vegetation cover appeared in August each year， which was most significantly affected by temperature and least affected by wind direction， with a certain time lag influenced by precipitation， so the study of vegetation cover change should be determined by combining the driving effect of multiple elements.

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.

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.

To study the identification of burned areas and post fire vegetation restoration in the Daxing'an Mountains region， based on Landsat TM remote sensing images from 2006 to 2020， Google Earth Engine was used to write code. The research background was the 2006 forest fire in the Nayuan Forest Farm in the Songling District of the Daxing'an Mountains region. The differential normalized burned ratio （dNBR） data was used to identify the burned areas， and the severity was classified into mild， moderate， severe， and extremely severe levels. Based on the Enhanced Vegetation Index （EVI） values of burned areas， methods such as univariate linear regression analysis， Mann-Kendall mutation test for climate diagnosis and Theil-Sen media trend analysis for treud analysis were used to analyze the vegetation restoration characteristics of burned areas from 2006 to 2020， and to explore the process of vegetation restoration in the Daxing'an Mountains region. The results showed that， 1）Based on dNBR， the burned areas in the study area was 2 488.7 hm²， with 23.5%， 9.6%， 35.2%， and 31.7% of the areas affected by mild， moderate， severe， and extremely severe fires， respectively. Severe and extremely severe areas of excessive fire were distributed in the western and eastern parts of the burned area， and the severity of excessive fire gradually decreased from the central to the southern and northern parts. The EVI values decreased by about 30%， 40%， 58%， and 67% compared to before the fire， respectively. 2）The recovery rate of EVI in forest burned areas with different intensities showed extremely severe， severe， moderate， mild. During the vegetation restoration process， the EVI value of the burned areas gradually increased. Mild and moderate burned areas can recover 6-8 years after the fire， while the recovery of severely burned areas required 14 years. 3）During the restoration process of burned areas， there were fewer EVI mutation points in forested areas compared to grasslands， indicating stronger stability of forest ecosystems compared to irrigated grasslands. There were also certain differences in the mutation situation of forest burning sites with different intensities， and the mutation time point in the control area lagged behind the burning sites.

When the tractor is undertaking the forestry carrying operation， due to the complex road conditions in the forest， it mostly runs at a low speed， has a high steering demand， and often has a load unit， so it pays more attention to its driving stability and steering sensitivity. In this paper， CarSim software is used to build the physical model and load unit model of the tractor， the control strategy of the tractor is designed based on Simulink software combined with the two-degree-of-freedom dynamic model， and the two-degree-of-freedom model of tractor is established. The braking torque feedback control is carried out by combining the yaw rate and the side deflection angle of centroid with the fuzzy control theory. In addition， the proportional control strategy of front and rear wheel angle is introduced according to the four-wheel steering mode of the tractor. Under the CarSim-Simlulink co-simulation platform， combined with the complex road conditions of forestry operations， the low-speed driving and steering experiments of tractor under different control strategies are carried out. The results show that the control strategy adopted in this paper can effectively reduce the steering radius of the tractor under complex road conditions， increase the steering speed， and limit the side deflection angle of centroid to a small range. The tractor has high stability， the steady state value of yaw rate is closer to the ideal state， and the steering is more sensitive.

By introducing the local density factor into the taper equation， five Larix olgensis plantations in Mengjiagang Forest Farm， Jiamusi City， Heilongjiang Province， were scanned by TLS. The tree height， diameter at breast height and diameter at different relative heights of Larix olgensis were extracted from the collected point cloud data. Based on Kozak variable exponential taper equation， a taper equation with local density factor was constructed， and the fitting result was R_{adj}^{\mathrm{2}}=0.935， RMSE=0.735 6， Bias=0.869 3. The research showed that： the taper equation model with local density factor had good fitting accuracy， Compared with the traditional Kozak taper equation model and the taper equation model with global density factor， the taper equation model with local density factor had better fitting effect. The calculation range of the local density factor of the Larix olgensis taper equation was positively correlated with tree height and diameter at breast height. The experimental results can provide a theoretical basis for scientific and reasonable timber production in Mengjiagang Forest Farm， and preliminarily reveal the influence of local density factor on the trunk shape of Larix olgensis.

In order to investigate the self-heating thermal runaway problem of ternary lithium-ion battery used in a certain forest plant protection machine， the structural composition of the battery cell， the heat generation mechanism of each side reaction and the three heat transfer modes of heat radiation， heat conduction and convective heat transfer between the battery cell， end plate， heat insulation pad， box body and the environment were first explored. Then based on the thermal runaway modeling method of the equivalent circuit model， the thermoelectric characteristic model of the 280 Ah large-capacity battery module was established in the Amesim one-dimensional simulation software by the physical parameters of the battery cell， the side reaction mechanism， and the exothermic heat transfer model.The heat transfer characteristics of the battery during self-heating thermal runaway and the path of thermal runaway propagation in the module were analyzed and simulated.Finally， according to the national standard GB 38031—2020， the self-heating experiment of the battery module was carried out to obtain the change of the cell temperature with time， and the specific position of the thermal runaway of the trigger cell was confirmed by CT scanning. The simulation and experimental results showed that the established model had high accuracy and reliable performance.The maximum error of the thermal runaway temperature of the cell was 11.3%， and the maximum error of the thermal runaway trigger time was 4.2%.It can provide technical reference for the design and development of battery thermal management， such as the prediction of thermal runaway temperature and the safety of preventing thermal diffusion of battery pack.

Pine wilt disease， as a world quarantine disease， seriously endangers pine plants and threatens forest ecological security. However， there are currently no effective measures to control and eliminate it. To suit people's needs for supplying diverse ecological services to the forest， high-intensity （60% logging intensity） and low-intensity （15% logging intensity） thinning measures were used in Pinus massoniana forests to harvest wood infected with pine wilt disease. The alterations in bacterial and fungal composition in the soil of P. massoniana forests impacted by the disease were analyzed using high-throughput sequencing. The objective was to investigate the alterations in the organization of the soil microbial community in P. massoniana forests impacted by thinning-induced pine wilt disease and assess the effects on the soil microenvironment. The soil bacterial and fungal populations of P. massoniana woods were investigated using the high-throughput sequencing technique. The findings demonstrated that the bacterial and fungal populations in the soil varied significantly among the three stands. The high thinning measure forest group （H-CK） had the greatest Shannon diversity index and Chao1 index， and the diversity and richness of the soil microbial community in the H-CK group were considerably higher than those in the low thinning measure forest group （L-CK） and control group （CK）. Actinobacteriota was the marker bacterial community and Leotiomycetes was the marker fungus community in the L-CK group. In the H-CK group， Chloroflexi was the marker bacterial community and Dothideomycotes was the marker fungal community. The high-intensity thinning H-CK group's soil organic matter content rose by 13% as compared to the control group， according to an analysis of soil physical and chemical properties. The H-CK group's total nitrogen content increased to 1.12 g/kg and its alkaline nitrogen content was 64.15 mg/kg as soil organic matter buildup increased. The forest's soil fertility was enhanced as a result， with a significant increase in both the total and accessible nitrogen content of the soil （P<0.05）. Steadily impoving forest quality， strengthening the disease resistance and boosting the ecological function of the current， pure Pinus massoniana forest that has been harmed by pine wilt disease by progressively transforming it into a multilayer， unevenly aged needle broad-leaved mixed forest.

Aboveground biomass reflects the growth of vegetation and the magnitude of carbon storage， and the accuracy of this parameter is crucial for carbon cycle research and climate change mitigation. In this study， a new idea of using deep learning to realize banana canopy detection segmentation and aboveground biomass estimation was proposed. Firstly， the deep learning algorithm YOLOv8s-seg was used as the basic framework improvement， and UAV remote sensing images were applied to realize banana canopy detection segmentation. Then， the canopy coverage area of banana trees was extracted， combined with the measured aboveground biomass data for fitting， and the aboveground biomass estimation model of banana was established by linear regression， K-Nearest Neighbor （KNN）， support vector machine， random forest and XGBoost （eXtreme Gradient Boosting）. Finally， the model estimation results were compared and analyzed to determine the optimal model. The results showed that the improved YOLOv8s-seg model can quickly and effectively detect and segment banana canopies. Through verification， it was found that the fitting effect and prediction error of the aboveground biomass estimation model based on XGBoost were better than those of other models， with R² of 0.881 4， root mean square error （RMSE） of 231.37 kg， and mean absolute error （MAE） of 140.47 kg， which could predict the aboveground biomass more accurately and was more suitable for the inversion of the aboveground biomass of bananas， which further verified the feasibility of using UAV and deep learning methods to extract canopy information to estimate the aboveground biomass.

By exploring the species diversity and soil physical and chemical properties of four typical forest types （natural broad-leaved mixed forest， artificial broad-leaved mixed forest， artificial broad-leaved pure forest， and artificial coniferous and broad-leaved mixed forest） in Zengcheng District， Guangzhou City， based on their origins and stand types， we analyzed their species diversity differences and the relationship between them and soil environmental factors， providing a theoretical basis for improving the quality of natural forests accurately. Using the typical quadrat survey method and statistical analysis methods， we calculated the Magalef richness index， Shannon-Wiener index， and Simpson dominance index to analyze the differences in species composition， dominance， and species diversity of the arbor and shrub layers in typical forest communities in Zengcheng District， and conducted correlation analysis with soil environmental factors. A total of 193 species belonging to 82 families and 153 genera were surveyed in the study area. The dominant species and their importance values of the four forest types were as follows： natural broad-leaved mixed forest （Machilus thunbergii， 6.36%）， artificial broad-leaved mixed forest （M. thunbergii， 5.52%）， artificial broad-leaved pure forest （Eucalyptus， 29.54%）， and artificial coniferous and broad-leaved mixed forest （Canarium， 4.73%）. The dominant species in natural and artificial broad-leaved mixed forests showed an inverted J-shaped distribution， while artificial coniferous and broad-leaved mixed forests and broad-leaved pure forests showed a normal distribution. The four forest types had significant differences in species diversity， soil nitrogen， and phosphorus content. Among them， the artificial broad-leaved pure forest had the lowest species diversity in the arbor layer and the highest species diversity in the shrub layer. The soil nutrient content of the four forest types varied significantly， and there was a significant correlation between species diversity and soil physical and chemical properties. Forests of different origins and stand types in the region require different management measures.

To investigate the effects of different stand ages on the litter and soil water conservation ability in apple tree plantation， and clarify the water characteristics of apple tree plantation， is of great significance for establishing a reasonable orchard management system， consiolidating and expanding the achievements of poverty alleviation and realizing rural revitalization. Taking apple tree plantation of different stand ages （4， 6， 15 and 30 a） in Linyi County， Yuncheng City as the research object， this paper quantitatively described and studied the differences of litter and soil water conservation ability in apple tree plantation through field investigation and laboratory test. The results showed that： the total storage of the litter about the four stand ages in apple tree plantation ranged from 0.82 to 2.09 t/hm²， the maximum water holding capacity ranged from 4.91 to 13.09 t/hm²， and the water conservation capacity ranged from 3.36 to 9.38 t/hm². With the increase of tree age， these parameters increased first and then decreased， and the maximum was 15 years old. There were good logarithmic and power function relationships between water holding capacity， water absorption rate and soaking time of litter layer respectively. Within the range of 0 to 60 cm， as the tree age increased， the soil bulk density decreased gradually， the total porosity and the maximum water holding increased gradually， and the maximum was 30 a. With the deepening of soil layer， the soil bulk density of apple tree plantation of different stand ages increased first and then decreased. Except 4 a， the total soil porosity of the remaining trees decreased gradually， but the difference between different soil layers was not significant. The maximum soil water holding capacity of 30 a orchard gradually decreased with the deepening of the soil layer， and the other trees showed a trend of decreasing first and then increasing at the age of 0 to 10 cm soil layer， and the minimum water holding capacity at 20 to 40 cm soil layer. According to the coordinate comprehensive evaluation method， the performance of litter and soil water conservation capacity of different stand ages was the strongest at the age of 15 years， the second at the age of 30 years， and the weakest at 4 a years.

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.

In early spring， the sap of Betula platyphylla was easy to collect and had a high yield. In order to explore the effect of temperature on the change of Betula platyphylla sap and the change of sap component concentration in different developmental periods， the natural secondary forest of B. platyphylla in Mao'ershan Experimental Forest， Northeast Forestry University in Heilongjiang Province was selected. In 2022 and 2023， sap of B. platyphylla was collected to calculate the spring sap flow rate. The concentration of carbohydrates， amino acids， total triterpenes， soluble proteins， pH， potassium， calcium， sodium and magnesium in the sap of B. platyphylla was determined at different developmental periods in 2023， and the variation patterns of sap components were analyzed. The B. platyphylla sap component concentration extracted from two consecutive years was compared with that extracted in the same year， in order to explore whether continuous extraction of sap had an effect on B. platyphylla. The results showed that sap of B. platyphylla production showed an overall trend of increasing and then decreasing from April to June， with the highest production in April and May. The increase in temperature promoted sap production. The concentration of total triterpenes， carbohydrates， amino acids and protein fractions in the sap showed a trend of increasing， then decreasing and then increasing during the whole period of phenological development. All of them were the lowest during the vigorous growth period. The pH value of sap showed an overall decreasing trend with the development of the phenology， and it was acidified continuously before the complete leaf expansion. The ion concentration showed a trend of increasing and then decreasing. Continuous sap extraction did not significantly affect the total triterpenes， amino acids and protein concentration of sap， but decreased the carbohydrate concentration and pH. The research results provided scientific guidance for the collection of sap of B. platyphylla in the eastern mountainous of Heilongjiang Province.

Helan Mountain is located in the arid and semi-arid region of northwest China. Systematic analysis of the composition characteristics of soil fungal communities in this area is of great significance for clarifying the fungal diversity and community functions in forest ecosystems of arid and semi-arid regions. The Illumina Miseq high-throughput sequencing technology was used to analyze the fungal diversity， community composition， and their responses to changes of soil physicochemical properties in three main constructive tree species—Picea crassifolia， Pinus tabuliformis and Betula platyphylla in Helan Mountain. The results revealed that a total of 276， 353 and 433 fungal OTUs were obtained from rhizosphere soil with 60 shared OTUs. All fungal OTUs belonged to 17 phyla， 42 classes， 107 orders， 210 families and 326 genera. At the phylum level， Basidiomycota and Ascomycota were dominant， with relative abundances of 58.7% and 35.7%， respectively. At the genus level， the top five dominant genera were Sebacina（6.7%）， Tomentela（6.5%）， Inocybe（6.1%）， Geminibasidium（3.7%） and Penicillium（2.9%）. Significant differences were observed in the composition of dominant fungi community at the genus level among different tree species. The genera with the highest relative abundance in the rhizosphere soil are Geminibasidium（10.8%）， Inocybe（16.9%）， and Sebacina（9.0%） respectively. RDA was used to analysis the driving factors of fungal community composition difference in the rhizosphere soil of the main constructive tree species in Helan Mountain. Soil total nitrogen content （TN） and soil water content （SWC） exert significant influences on the composition of fungal communities， accounting for explanation of 41.6% and 39.1%， respectively. This study enriches the biological information of fungal resources in the rhizosphere soil of the main tree species in Helan Mountain， and can provide theoretical basis for the excavation and research of fungal resources in forest ecosystems in arid and semi-arid areas.

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.

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 explore the effect of chemical treatment on adventitious bud formation of P. koraiensis top pruned seedlings， 5-year-old P. koraiensis bare root seedlings were used as experimental materials. After top pruning at the beginning of the growing season， different chemicals were sprayed every 15 days （GA₃ （200， 500， 1 000 mg/L）， 6-BA （100， 300， 500 mg/L）， KMnO₄ （0.1%， 0.2%， 0.3%））. Using spray water treatment as a control， the formation of adventitious buds of P. koraiensis seedlings treated with different chemicals was investigated. The results showed that the chemical treatment （except 1 000 mg/L GA₃ and 0.1% KMnO₄） increased the incidence of adventitious buds of P. Koraiensis top pruned seedlings. Compared with the control， the sprouting rate increased by 5.72% to 48.58%， and the number of sprouting strip increased by 15.37% to 175.94%. The suitable concentration of GA₃ to promote P. koraiensis top pruned seedlings was 200 mg/L， which can increase the sprouting rate by 34.28%， and the number of sprouting strip by 33.63%.The suitable concentration of 6-BA to promote P. koraiensis truncated seedlings was 500 mg/L， which can increase the sprouting rate by 48.58%， and the number of sprouting strip by 175.94%. The suitable concentration of KMnO₄ to promote P. koraiensis top pruned seedlings was 0.2%， which can increase the sprouting rate by 42.85%， and the number of sprouting strip by 95.18%. The better effect of promoting adventitious bud formation can be obtained by using 500mg/L 6-BA to treat P. koraiensis top pruned seedlings.

Direct-injection SBS modified asphalt has advantages such as easy quality control， low cost， and minimal pollution. It can address the issue of unstable quality in conventional wet-process SBS modified asphalt and has been gradually widely applied in recent years. This study investigated the aging resistance performance of direct-injection SBS modified asphalt at different dosages through short-term aging. By conducting temperature scanning tests， mid-temperature fatigue performance tests， low-temperature rheological property tests， and atomic force microscope tests on the aged direct-injection modified asphalt， the aging resistance performance of the SBS modified asphalt was studied from macroscopic to microscopic scales. The macroscopic experimental results indicated that the direct-injection SBS modified asphalt exhibited an increase in elastic components and fatigue life after short-term aging. During the aging process， the Guolu direct-injection SBS modified asphalt showed a secondary development phenomenon. The microscopic test results revealed that， after short-term aging， both conventional wet-process SBS modified asphalt and Liaohe direct-injection SBS modified asphalt exhibited reduced roughness due to changes in the content of the four components and the degradation of SBS. In contrast， the Guolu direct-injection SBS modified asphalt exhibited increased surface roughness due to further swelling development， consistent with the conclusions drawn from the macroscopic tests.

This study focused on the natural birch forest （Betula platyphylla） in the Daxing'an Mountains of Yakeshi Forestry Administration in Inner Mongolia. Using 198 sample plots， the relationship between carbon density in the natural birch forest stand and various stand variables was analyzed. A basic model for predicting carbon density was established， incorporating site conditions （grass birch forest， rhododendron-vaccinium birch forest， hazelnut birch forest） as dummy variables to predict stand carbon density across different forest types. This paper offered insights and methodologies for constructing carbon density models and advancing forest carbon sequestration in forestry research. The results showed that the determination coefficient （R²） for the basic model of carbon density in the natural birch forest was 0.703， root mean square error （R_MSE） was 8.615 t/hm^-2， Akaike information criterion （AIC） was 841.206， and Bayesian Information Criterion （BIC） was 851.071. After site conditions were introduced as dummy variables， R² increased to a maximum of 0.818， and RMSE were all less than or equal to 8.241 t/hm^-2， indicating that the model had good stability and the predicted parameters were more accurate. AIC for the dummy variable model was less than or equal to 541.431， and BIC was less than or equal to 550.320. The dummy variable model can reflect the change of carbon density under different site conditions， and both the fitting and testing of the model show that it is suitable for the prediction of forest carbon density in the study area， which provides a reference for the estimation of natural birch forest carbon density.

In order to solve the problems in predicting the asphalt aging behavior， such as the large performance deviation between simulated aging asphalt and actual aging asphalt， the lack of scientific evaluation index easy to promote in the practical engineering， properties of natural aging SBS （Styrene Butadiene Styrene Triblock Copolymer） modified asphalt from an in-service expressway were tested by Asphalt Binder Quality Test System （ABQT）. Then， some indexes， including maximum creep deformation， elastic recovery rate， loading deformation rate coefficient and rebound deformation rate coefficient， were proposed to evaluate the changes of asphalt’s properties； meanwhile， the accuracy of evaluating asphalt aging behavior with the above indicators were also analyzed by analyzing the differences between the above indicators and conventional performance indicators in evaluating the asphalt aging properties. On this basis， the nonlinear prediction model for asphalt aging was established based on the above indicators， and the aging rate values of asphalt were obtained to analyze the degradation laws of asphalt properties under natural aging conditions， and ultimately a reasonable preventive maintenance time was proposed. Results showed that， the maximum creep deformation， loading deformation rate coefficient and rebound deformation rate coefficient can effectively characterize the decay process of asphalt aging properties under natural aging conditions. Meanwhile， the nonlinear prediction model for asphalt aging established based on the above indicators had strong applicability， which can achieve bidirectional calculation for asphalt performance and aging time. Furthermore， as the aging time prolonged， the aging rate values corresponding to the maximum creep deformation， loading deformation rate coefficient and rebound deformation rate coefficient all decayed in the form of a cubic function model； and the turning points of aging rate were 6.67 a， 5.56 a， and 6.67 a， respectively. Therefore， considering the safety of asphalt mixture performance， it was suggested that 5.56 a as the threshold for intervention in preventive maintenance measures to restore asphalt performance.

In order to investigate the differences in soil fungal diversity between healthy Pinus sylvestris var.mongolica natural forests and different areas of blight disease-susceptible P.sylvestris forests， this study used healthy natural forests of P.sylvestris in Honghuaerji Nature Reserve as the control， susceptible P.sylvestris in natural forests of Honghuaerji Nature Reserve， and in plantation forests in Tailai County and Zhanggutai as the study objects. The soil fungal community composition was analyzed using lllumina Miseq high-throughput sequencing technology. The results showed that 5 564 fungal Amplicon Sequence Variants （ASV） were obtained from the soil samples of 4 groups from three regions， with higher relative abundance of Basidiomycota and Ascomycota. The abundance and species of dominant genera varied in different soil depths in different regions. The dominant genus of soil fungi in the surface layer of healthy natural forests was Amphinema， and the dominant genus of soil fungi in the 0-10 cm and 10-20 cm depths was Cortinarius. In the understorey soil of susceptible P.sylvestris， the dominant genera of soil fungi in the surface layer， 0-10 cm and 10-20 cm depth of natural forests were the genera of Trichoderma， Talaromyces and Suillus， and the dominant genera of soil fungi in the surface layer， 0-10 cm and 10-20 cm depth of plantation forests in Tailai County were the genera of Delastria， Pseudogymnoascus and Sagenomella， and the dominant genus of soil fungi in the surface layer of Zhanggutai plantation forest was Knufia， while the dominant genus of soil fungi in the 0-10 cm and 10-20 cm depths were Pseudogymnoascus. In addition， the phytopathogenic fungi in the soil fungal community of the susceptible plantation forests were in the percentages of 2.63% and 5.04%， respectively， which were much lower than those of the susceptible natural forests （8.77%） and healthy natural forests （8.11%）. The content of ectomycorrhizal in the soil fungal community of plantation was lower than that of natural forest. It is hypothesised that plantation forests have a lower abundance of exogenous mycorrhizal fungi due to their monoculture， making P. sylvestris less resistant to disease.

Based on the orthogonal test design method， the expansive mudstone in the cutting of Jingxin expressway in Zhangjiakou city was tested， and the relationship between the expansion rate of mudstone samples under different overlying loads and the soaking time， initial water content and compaction degree was studied， and the sensitivity of various factors was analyzed by the range analysis method. The results showed that the curve of expansion rate with the soaking time presented an S-shaped trend， which can be roughly divided into initial expansion stage， accelerated expansion stage and slow expansion stage. Under the same initial water content， the expansion rate increased with the increase of the compaction degree. Under the same compaction degree， the expansion rate decreased with the increase of initial water content. The range analysis method showed that the soaking time of the sample was the main factor affecting the expansion rate. Water content and compaction degree were the secondary influencing factors， and the influence of water content on the sample expansion rate was obvious under small load， and the load continued to increase， the compaction degree exceeded the influence of water content on the sample expansion rate.

In order to further explore the influence of freezing and thawing on the deformation and strength of loess under the actual working conditions， this paper carries out the compression test of loess after experiencing 0， 1， and 4 pressurized freeze-thaw cycles， respectively. The results show that： after experiencing pressurized freeze-thaw， the frozen loess mostly shows brittle damage mode， and the stress-strain curve is strain-hardening curve. The strength of loess decreases by about 35% after one pressurized freeze-thaw cycle， and the strength decreases by 51% after four pressurized freeze-thaw cycles， which is a nonlinear negative correlation. In the case of the same number of freeze-thaw cycles， with the increase of the water content， the peak stress will be reduced gradually and the strength in the range of water content decreases by nearly 77%. The uniaxial compressive strength of frozen loess shows a significant increase with the increase of initial dry density and the decrease of test temperature， when the temperature decreases from -10℃ to -15℃， the strength increases 1.5 times， and the strength increases 2 times when the temperature decreases from -15℃ to -20℃. Finally， based on the Gaussian function， a strength model of frozen loess is constructed considering the influence of pressurized freeze-thaw cycles， which can better reflect the strain hardening situation of frozen loess.

To explore the changes and main influencing factors of soil active carbon during the autumn freeze-thaw period of three forest types in the permafrost region of the Great Daxing'an Mountains， and to provide assistance for forest soil carbon sink management in the permafrost region.Taking the Larix gmelinii forest （LY）， Pinus sylvestris var. mongolica forest （ZZ） and Betula platyphylla forest （BH） as the research objects， soil samples were taken from different soil layers （0-5， 5-10， and 10-20 cm） to determine the content of soil active carbon components （dissolved organic carbon， microbial biomass carbon， and readily oxidizable organic carbon） from October 10th to November 18th， 2022， and to explore their dynamic characteristics and influencing factors.Results showed that， from October 10th to November 18th， the soil temperature in the Larix gmelinii forest， Betula platyphylla forest and Pinus sylvestris var. mongolica forest all decreased with decreasing temperature， with ranges of -0.49 to 3.71， -2.10 to 2.39， and -1.04 to 3.48 ℃， respectively. The content of dissolved organic carbon （DOC） in different soil layers of three forest types showed a trend of first increasing and then decreasing with the decrease of temperature， the content of microbial biomass carbon （MBC） first decreased and then increased， while the content of readily oxidizable organic carbon （ROC） fluctuated， ranging from 78.75 to 214.32， 101.06 to 988.40， and 1.52 to 13.94 g/kg， respectively. Among them， the soil DOC and ROC contents in the Betula platyphylla forest were significantly higher than those in the Larix gmelinii forest and Pinus sylvestris var. mongolica forest， while the soil MBC content was the highest in the Larix gmelinii forest. The three types of active carbon showed a ‘surface aggregation effect’ in different forest types， and the soil DOC content increased and the soil MBC content decreased during the freeze-thaw period， but the soil ROC content did not change significantly. The changes in soil active carbon content in Larix gmelinii forest were mainly influenced by soil moisture and microbial biomass nitrogen. Ammonium nitrogen was the dominant factor affecting the three types of soil active carbon in Betula platyphylla forest， while soil pH was the main influencing factor for the three types of soil active carbon in Pinus sylvestris var. mongolica forest.Research indicates that， the freeze-thaw effect has a significant impact on the active carbon content of typical forest soils in the permafrost regions of the Great Daxing'an Mountains， increasing soil DOC content and reducing soil MBC content， while the impact on ROC content is relatively small.