Forest stand ingrowth is a critical component of the dynamic growth process of forest stands， essential for maintaining biodiversity and community structure stability in forest resources. Based on data from 61 plots established at the Maoer Mountain Experimental Forest Farm， this study considered factors such as stand characteristics and biodiversity. Through Kendall-Tau-b correlation coefficient analysis and selection of the most suitable variables considering multicollinearity among variables， models for ingrowth were constructed using Poisson， negative binomial （NB）， zero-inflated， and Hurdle models. The contribution rate of variables was analyzed using hierarchical partitioning to identify key factors influencing the ingrowth model. The results showed that stand density （K）， arithmetic mean diameter at breast height （d）， Simpson's index， and mean stand height （MH） were significant factors affecting the number of ingrowth trees per hectare （Nn）. Comparing models using AIC， BIC， and Loglike criteria， it was found that ZINB and HNB significantly outperformed other models. The Vuong test further revealed that the negative binomial models and their composite models （ZINB， HNB） performed better than Poisson models and their composites （ZIP， HP） in fitting the ingrowth quantity of natural forests in Maoer Mountain， with the ZINB model slightly outperforming the HNB model. Therefore， the ZINB model was the optimal model for fitting the ingrowth quantity of natural forest stands in Maoer Mountain， a conclusion also supported by ten-fold cross-validation. Additionally， hierarchical partitioning analysis indicated that the Simpson's index and mean stand height （MH） contributed most to the count and zero parts， respectively， of the optimal ingrowth model （ZINB）. The natural forest ingrowth model constructed by this research has a certain statistical reliability and can be used for ingrowth prediction in the Maoer Mountain area， providing a scientific basis for local natural forest regeneration management.

The mixed broad-leaved forest at Maoer Mountain is characterized by its complex stand structure， rich species diversity， and intricate interactions between tree species， all of which influence height to crown base. Therefore， accurately constructing a height to crown base model can provide important reference value for guiding forest management and improving the accuracy of growth prediction. In this study， 28 representative sample plots of mixed broad-leaved forests in Maoer Mountain area were selected， and 18 tree species widely distributed in the sample plots were divided into groups according to the variability coefficient of tree species diameter， average tree species diameter， average height-diameter ratio， proportion of tree species， classification of soft broad-leaved and hard broad-leaved species， and shade tolerance of tree species， thereby solving the problem of tree species complexity and the insufficiency of sample size of a single tree species in model construction. In this study， Wykoff model was adopted as the basic model. At the same time， factors such as stand， competition and species diversity were considered to express the mixed degree and competition of stand， and a generalized model of height to crown base was established. At the same time， the difference of height to crown base among plots and tree species groups was considered to build a model of subbranch height mixed effect. The influence of sampling on the result was analyzed by using the method of ‘remaining one’. The results showed that the common tree species in Maoer Mountain could be divided into four tree species groups. Besides DBH and tree height， the average height of dominant trees， the sum of the section area of intra-species larger than that of the target trees and the Shannon index had significant effects on height to crown base. Considering the mixed effects of plots and tree species groups， the basic and generalized subbranch height models had high fitting accuracy R ² of 0.638 and 0.627， and RMSE of 1.880 and 1.909， respectively. In addition， when one sample was randomly selected from each tree group in each plot to correct the mixed model， compared with the fixed effect model， the MAE and MAPE of the generalized height to crown base model decreased by 7.51% and 13.51%， respectively， showing better prediction effect without wasting manpower， material and financial resources. This study analyzed the effects of various tree species and ecological functions on the growth of height to crown base in Maoer Mountain， and provided some reference for predicting the height to crown base of different tree species in broad-leaved mixed forest in Maoer Mountain.

In forestry research， the bivariate distribution of diameter at breast height （DBH） and tree height is often constructed from identical marginal distributions， yet the actual distributions of DBH and tree height within a forest stand may vary. To mitigate the impact of these differences， based on the data from 115 Larix olgensis stands in Mengjia Gang Forest Farm， Jiamusi City， this study employs the Copula function method， which has low application conditions and a broad range of adaptability， to model the bivariate distribution of DBH and tree height in Larix olgensis plantations. Firstly， we selected six distribution functions-Weibull， G-Weibull， Logistic， Logit-Logistic， Gamma， and Log-Normal- as potential base models. These were then screened and used to construct a Copula-based bivariate distribution model for DBH and tree height， based on the results of the Kolmogorov-Smirnov （K-S） test and semiparametric estimation. The model's fit was further evaluated using the negative log-likelihood （NLL）， the Sn goodness-of-fit statistic， and the likelihood ratio test （LRT）， comparing it with the bivariate logistic distribution function and the bivariate Weibull distribution function. Finally， the predictive capacity of the model was assessed using the Reynolds error index （EI）. The findings indicated that the Copula-based bivariate fitting results and predictive capabilities （E _I=0.318 4） outperformed those of the bivariate Weibull distribution （E _I=0.638 1） and the bivariate logistic distribution （E _I=0.949 0）. This suggests that the Copula method for constructing a bivariate joint distribution model of DBH and tree height can effectively describe the joint distribution in Larix olgensis plantations， making it a viable approach for modeling tree height and DBH distributions.

Forest carbon storage is a critical component of global carbon cycle research and plays a significant role in addressing climate change. This study focused on the northern slope of the Zhangguangcai Mountains in Heilongjiang Province. By combining ground observation data with Landsat TM （thematic mapper）/OLI （operational land imager） data， multiple machine learning models were applied， along with the bootstrap aggregating ensemble learning algorithm， to simulate forest carbon storage. The results showed that from 1990 to 2022， the forest carbon storage in the study area exhibited a significant increasing trend， with an annual average carbon storage of （80.77±0.27） Mg C/hm². The spatial distribution demonstrated notable heterogeneity， with high carbon storage areas concentrated in flat and semi-mountainous regions. Additionally， the mean growing-season temperature was found to have a highly significant positive correlation with forest carbon storage （P<0.01）， indicating that temperature was the primary climatic factor influencing carbon storage changes. This study provides a novel approach for forest carbon storage accurate simulation carbon sink management.

Accurately grasping the spatial distribution of forest cover is crucial for the protection， restoration and sustainable use of forest ecosystems. However， it is no longer possible to efficiently and accurately obtain the changes in complex forest cover at the county scale by relying on low spatial resolution remote sensing images combined with traditional computer classification models. Therefore， this study took the complex forests in Tangyuan County， Jiamusi， Heilongjiang Province as the research object， used the medium spatial resolution satellite remote sensing images of Sentinel-1 and Sentinel-2， and constructed a machine learning model optimized by particle swarm optimization （PSO） to detect the changes in forest cover at the county scale. The K-fold cross validation was used to evaluate the accuracy of the forest cover detection results. The results showed that the support vector machine and random forest machine learning models optimized by particle swarm algorithm had improved the accuracy of forest cover change detection compared with their own models without parameter optimization. The support vector machine model increased by 6.52%， and the random forest model increased by 4.65%. Compared with the current mainstream ESA COVER WORD land cover product， the random forest model optimized by particle swarm algorithm had the highest accuracy， with an overall accuracy of 0.92. The optimized random forest model was also more precise in detecting forest cover changes. By classifying medium spatial resolution remote sensing images through the random forest model of the particle swarm optimization algorithm， we can quickly and accurately grasp the spatial distribution of forest cover at the county scale， and provide data and technical support for the protection， restoration and sustainable utilization of forest ecosystems.

Efficient and accurate tree species identification is critical for the realization of smart forestry. Traditional field survey methods are low efficiency and high cost， while machine learning-based tree species identification approaches often rely on extensive feature extraction and prior knowledge. To address these issues， a tree species identification algorithm based on improved YOLOv10 for UAV imagery is proposed in this paper. The improved architecture integrates lightweight network design and attention mechanisms to enable efficient edge device deployment， providing technical support for digital forest resource management. A UAV imagery dataset was developed for five common tree species （Larix gmelinii， Phellodendron amurense， Juglans mandshurica， Ulmus pumila， and Fraxinus mandshurica） in Northeast China. The backbone network was reconstructed using lightweight convolution （Ghost） for computational complexity reduction. The convolutional block attention module （CBAM） was introduced in the fusion layer to strengthen fine-grained feature extraction through channel and spatial dual dimensional feature calibration. Multi-scale feature fusion was optimized through bidirectional cross-scale connections （BiFPN）， while bounding box regression efficiency was improved using a structured intersection over union （SIoU） loss function. Final deployment validation was conducted on the Jetson Nano embedded platform. The improved YOLOv10 model achieved 91.5% precision and 77.5% mAP@0.5 on the validation set， showing improvements of 4.5% and 3.8% compared to the baseline model， respectively. In practical deployment， the model achieved an inference speed of 43.5 FPS， 35.5% faster than the baseline model， with mAP@0.5 of 75.7%. Results showed that， the improved YOLOv10 algorithm successfully balances identification accuracy and real-time performance in complex forest environments through lightweight architecture and multi-scale feature optimization. The solution demonstrates particular effectiveness in scenarios with dense canopy overlap and variable illumination， offering an embeddable solution for UAV forestry surveys.

Stand diameter distribution is one of the most important indicators reflecting stand structure， aiding in estimating forest biomass， timber yield， and evaluating stand stability. Unmanned aerial vehicle （UAV）-LiDAR technology provides high-precision three-dimensional spatial information， offering a new technical approach for large-scale and continuous predictions of stand diameter distributions. This study utilized UAV-LiDAR derived features and measured diameter distributions as data sources to develop Weibull diameter distribution models using three parameter prediction methods： the parameter prediction method， percentile-based parameter recovery method， and moment-based estimation parameter recovery method. The model’s performance was evaluated through multiple metrics including， R ²， R _MSE， M _AE， E _IP， and E _IR， ultimately identifying the optimal Weibull model for Korean pine planatation diameter distributions. The precision of Weibull parameter predictions based on UAV-LiDAR data showed an R ² ranging from 0.51 to 0.71. The diameter distribution models established by the three parameter prediction methods can predict the diameter distribution of the stand， and the parameter prediction method performed best （E _IR=57.384， E _IP=0.319）. UAV-LiDAR technology effectively addresses the limitations of conventional surveys， such as low data acquisition efficiency and restricted spatial coverage， providing a robust technical foundation for reliable predictions of stand diameter distributions.

Spatial structure units of Pinus sylvestris plantations stands were established using Voronoi diagram and n=4 methods， with subsequent comparision of their colculated stand spatial structure parameters. Both methods were employed to calculate the comprehensive index （Q） of single-tree spatial structure， enabling forest stand optimization and comparison. The goal was to evaluate the advantages of the Voronoi diagram method in calculating forest structure parameters and optimizing stand structure in Pinus sylvestris plantations. Fixed plots of 22-， 31-， and 43-year-old Pinus sylvestris plantations in Mengjiagang Forest Farm， Jiamusi City， Heilongjiang Province， were selected for this study. Forest spatial structure units were determined using both the Voronoi method and the n=4 method. For each sample plot， the size ratio （U）， angular scale （W）， competition index （C _I）， and openness （K） were calculated. A significance test was conducted to assess the differences between the results obtained from the two methods. Based on these parameters， a comprehensive index （Q） of individual tree spatial structure was constructed to guide stand thinning and simulate the effects of stand spatial structure optimization under different thinning intensities （10%， 20%， and 30%）.The results revealed significant differences between the Voronoi method and the n=4 method in calculating W， C _I， and K for Pinus sylvestris plantations（P<0.01）. Using the Q values derived from the Voronoi method， thinning was conducted at intensities of 10%， 20%， and 30%. After thinning， the average DBH of the stand increased by 0.27 cm， 1.03 cm， and 1.47 cm， respectively. Concurrently， U decreased by 7.17%， 24.80%， and 38.93%； W decreased by 27.98%， 55.65%， and 69.35%； C _I decreased by 19.62%， 35.74%， and 47.78%； and K increased by 6.37%， 16.67%， and 28.92%. The Q value increased by 84.91%， 248.12%， and 530.87%， respectively. The simulated changes under the three thinning intensities demonstrated significant improvements in stand parameters and overall spatial structure optimization， with the most pronounced effects observed at the 30% thinning intensity. The Voronoi method is an effective approach for constructing forest spatial units， calculating stand spatial structure parameters， and optimizing stand structure in Pinus sylvestris plantations. This method provides a robust framework for enhancing forest management practices and achieving sustainable stand optimization.

The complexity and inaccuracy of extracting waveform features from spaceborne full waveform LiDAR data affect the accuracy of forest aboveground biomass （AGB） estimation. To address this problem， this study combined Global Ecosystem Dynamics Investigation （GEDI） LiDAR waveform data with GF-7 stereo imagery data in the Simao region of Yunnan as an example. The digital surface model （DSM） generated by multi-angle stereo geometry was used to accurately locate the starting point of the GEDI waveform and optimize the waveform features. Multiple stepwise regression methods were used to construct biomass estimation models for coniferous， broadleaf and mixed forests at the footprint scale. These models were then extrapolated to the regional scale using a random forest algorithm. The results showed that the biomass estimation accuracy at the footprint scale was significantly improved after optimizing the waveform features. The root mean square error （RMSE） for the coniferous forest was 20.11 Mg/hm²， the coefficient of determination （R²） was 0.89， and the accuracy （ACC） was 82.87%. The RMSE of broadleaf forest was 22.07 Mg/hm² with R² of 0.89 and ACC of 81.77%. The RMSE of the mixed forest was 24.51 Mg/hm² with R² of 0.88 and ACC of 80.54%. Finally， based on the optimized GEDI biomass footprints， the regional forest AGB distribution map was successfully generated at 25 m resolution.

This study estimated the net ecosystem productivity （NEP） of forests in Northeast China based on the MODIS MOD17A3GF dataset， aiming to explore the spatiotemporal coupling relationship between NEP and extreme climate events. By integrating temperature and precipitation data and the extreme climate index calculated by RClimDex， the spatiotemporal variation characteristics of NEP and ten climate factors from 2000 to 2020 were analyzed， and the influence of each climate factor on NEP was evaluated using GeoDetector from two dimensions： factor detection and interaction detection. The results showed that： 1） In the past 21 years， the annual average NEP of forests in Northeast China had shown a slow but continuous upward trend. From 2000 to 2010， the annual average NEP increased by 30.94 gC·m^-2·year^-1， and the increase slowed down from 2010 to 2020， reaching only 9.16 gC·m^-2·year^-1. Spatially， the main growth areas were concentrated in the Greater and Lesser Khingan Mountains. 2） Extreme climate events showed a trend of ‘less cold events， more warm events， and more humid events’， which was specifically manifested in the decrease of the cold persistence index （CSDI）， the increase of the warm persistence index （WSDI）， the significant increase of annual precipitation， the decrease of the number of continuous dryness index （CDD）， and the alleviation of drought in some regions. 3） The annual average temperature， annual precipitation and the number of frost days were the dominant factors of the spatial distribution of NEP （q>0.2）， followed by the continuous dryness index， continuous wet days and the warm persistence index. The daily temperature difference and the number of heavy precipitation days had weaker explanatory power. The interaction of any two climate factors generally had a stronger explanatory power on NEP than the single factor effect. The interaction between annual precipitation and the number of frost days， annual precipitation and annual average temperature， and annual precipitation and the warm persistence index showed high q values in most years. This study reveals the response of forest NEP in Northeast China to climate （especially extreme climate）， emphasizes the importance of evaluating the coupling effects of extreme climate and forest NEP under the context of climate change， and provides a theoretical support for carbon budget regulation and climate adaptation management of forest ecosystems in Northeast China.

Forest fires are characterized by significant danger， widespread impact， and challenges in both reproducing the fire field and allowing personnel to approach it. Virtual reality （VR） technology offers distinct advantages in simulating the spread of forest fires and providing training for firefighting personnel. This paper presents the design and implementation of a virtual simulation system for forest fire fighting， discussing the key principles and technologies behind its design. These include computer simulation， wireless infrared tracking motion capture， and digital visualization technologies. The paper provides a detailed introduction to the system's overall design， as well as its hardware and software configuration. It also analyzes the main functions of the system， such as understanding and operating fire extinguishers， simulating the spread of forest fires， and facilitating command and decision-making processes. The development and application of this system can significantly enhance the operational proficiency and decision-making capabilities of forest firefighting personnel. Moreover， it offers strong technical support for forest fire prevention， control， and disaster management， ultimately helping to reduce the loss of life， property， and forest resources due to forest fires.

Taking the Northeast China as the research object， this paper investigated the degradation degree and spatial distribution of permafrost over the years. Key meteorological elements were collected， and a multiple linear regression model was used to calibrate the ground surface temperature data. Based on the temperature at the top of permafrost （TTOP） model， and using ANUSPILN software for interpolation， the spatial and temporal distribution of permafrost in Northeast China was analyzed. The results showed that the areas of permafrost in the 1970 s， 1980 s， 1990 s， 2000 s， 2010 s were about 3.99×10⁵， 3.41×10⁵， 2.31×10⁵， 1.80×10⁵， 1.59×10⁵ km²， respectively. During the period of 1970 s to 2010 s， the permafrost area in Northeast China decreased significantly by about 2.40×10⁵ km²， with a decrease of 60.08 %. The proportion of permafrost area in Northeast China decreased from 27.66% to 11.04%， while the proportion of seasonally permafrost area increased from 72.34% to 88.96%. The difference between the model results and the actual borehole data was only 0.05 ℃， and the model results using the corrected ground temperature data were higher than the existing research results.

Quantitative assessment of long-term carbon sequestration capacity in the forest ecosystem of Heihe City， Heilongjiang Province， analyzing forest fire disturbances impacts on carbon sink dynamics to inform China’s ‘Dual Carbon’ goals. Based on dynamic monitoring data （2005， 2010， 2015） from 1 649 forest sample plots in Heihe City， combined with the Canadian Carbon Budget Model （CBM-CFS3） the carbon storage and carbon sink capacity of the forest ecosystem across multiple levels （aboveground， belowground， litter， deadwood， and soil carbon pools） during 2005， 2010， 2015 were evaluated on the basis of localized improvement of model parameters， and the impact of fire disturbances was also analyzed. Results indicated that across the measurement year （2005， 2010， 2015）， the total ecosystem carbon density of Heihe's forests increased from 207.15 t C/hm² to 218.63 t C/hm²， with a carbon sink of 531.54 t C. The frequency of forest fires decreased annually， and carbon-containing gas emissions in 2015 dropped by 60.3% compared to 2005. Using 2005 carbon sequestration patterns under fire disturbance as the baseline scenario， low-intensity fire disturbances slightly enhanced the carbon sequestration capacity of the forest ecosystem， while moderate and severe fire disturbances reduced the carbon sequestration rate by 23.9% and 38.0%， respectively. The forest ecosystem played a positive role in carbon sequestration during this period. Strengthening fire monitoring and prevention can effectively enhance carbon sequestration capacity， ensuring the stability and sustainable development of the regional ecological environment.

In forest and grassland fire scenarios， the diversity of open flame forms and the complexity of the environment may lead to false or missed detection. Therefore， an improved YOLOv8n fire detection algorithm （YOLOv8n-CSA） is proposed for forest and grassland fires. CSA （channel-spatial attention） is the channel spatial attention module， and a group shuffle convolution （GSConv） module is introduced to replace the third layer standard convolution module （Conv） in the original YOLOv8n， reducing model computation and improving feature extraction ability. And introducing the Slim Neck structure in the head further reduces the computational complexity of the model. Simultaneously design a channel spatial attention module （CSA） integrated into the Backbone section to enhance the expressive power of the input feature map.This module combines channel attention， channel shuffle， and spatial attention mechanisms to capture global dependencies within feature maps. Based on a forest and grassland fire dataset， and without utilizing pretrained models， the proposed fire detection network achieves a 3.7% increase in precision， a 1.51% improvement in recall， a 3.24% enhancement in mAP50， and a 5.62% reduction in GFLOPs compared to the baseline YOLOv8n model. Experimental results demonstrate that the proposed algorithm not only reduces computational cost but also enhances the detection performance of fire-related features.

There are various types of robot grasping methods. In the wood fork production process， the length of the wood fork bundle is relatively long， with a flat side and small height， making traditional robot hands less effective for grasping. Based on the characteristics of the wood fork bundles， a linear parallel clamping method at the end is more suitable for grasping. To address this issue， this paper proposes a Jansen linkage-based linear parallel clamping robot hand （Jansen fingers）， which aims to achieve linear motion of the end effector along a straight trajectory during clamping. The Jansen fingers utilize the Jansen linkage mechanism to achieve linear motion at the end， while a four-bar linkage mechanism ensures the stability and reliability of the finger's end during the clamping process. Theoretical analysis and simulation results show that the Jansen fingers can achieve stable linear clamping， meeting the requirements of the wood fork production process.

Aiming at the problems of insufficient substrate mixing in the process of soilless culture substrate production and inaccurate simulation results due to too large a model and too many particles when using the discrete element method， we established a simulation model of fertilizer and soil particles by setting up a similar theoretical mixing model and utilizing the Hertz-Mindlin （no slip） contact model， and then three variable factors which affected the mixing uniformity and efficiency of the mixer， such as the feeding mode， the blade spacing of the stirred mixer and the stirrer rotational speed， were simulated and analyzed based on discrete element analysis. The results showed that： the similar theoretical mixing model can be used for the scaling of the simulation model to achieve the effect of reducing the amount of calculation and improving the accuracy of the simulation； in the case of using the flat feeding mode， the mixing effect was better because the upper particles would accelerate the mixing of the particles under the action of gravity； the better spacing between the blades in the model was 20 mm with similarity model； the mixing effect and the economy were the best when the blade rotational speed was at 50 r/min； the mixing efficiency and effect were greatly improved compared with manual mixing， and the blind zones can be avoided. The simulation results can provide theoretical reference for the design and optimization of stirred mixer.

To investigate the mechanical characteristics of Caragana korshinskii stem sawing and determine the optimal sawing parameters for supporting the subsequent optimization and improvement of shearing equipment， a self-designed branch sawing test platform was used in this study. Single-factor experiments were conducted to examine the effects of stem diameter， cutting speed， feeding speed， cutting angle， and saw blade tooth count on the peak cutting force and cutting power of the branches. Building upon the results of the single-factor experiments， a Box-Behnken central composite experimental design was employed. Selecting cutting speed， feeding speed， cutting angle， and saw blade tooth count as experimental factors， a multi factor experiment was carried out with peak cutting force and cutting power of branches as target values， and a regression model was established. The experimental results showed that the peak cutting force increased with the increase of stem diameter， while it exhibited an initial increase followed by a decrease with the increase of cutting speed， feeding speed， cutting angle， and saw blade tooth count. Similarly， the cutting power increased with the increase of stem diameter， but initially decreased and then increased with the increase of cutting speed， feeding speed， cutting angle， and saw blade tooth count. Multi-objective optimization analysis of the regression model yielded the optimal combination of parameters： cutting speed of 45.7 m/s， feeding speed of 0.32 m/s， cutting angle of 8.3°， and tooth count of 104. Under these conditions， the peak cutting force was 7.02 N and the cutting power was 181.57 W. The deviation between the predicted value and the actual value of the peak cutting force and cutting power was 1.9% and 2.2%， respectively. The results of this experiment provide valuable reference for the design of efficient and low-energy consumption Caragana korshinskii shearing equipment.

In order to minimize the self-weight of the structure， it is common practice in engineering projects to utilize concrete slabs with reduced cross-sectional dimensions. To enhance the flexural strength and resistance to cracking of concrete， this study selected well-anchored end-hooked steel fibers （HSF）， polypropylene fibers （PPF） that can significantly mitigate plastic shrinkage and reduce the number and width of cracks， fly ash （FA） that can improve the fluidity and long-term strength of concrete， and silica fume （SF） with high volcanic ash activity as materials to improve the mechanical properties of concrete. This research employed the orthogonal test method to examine the influence of these four material factors on the mechanical properties of concrete when they interact simultaneously. The significance order of each factor’s influence and the optiimal dosage combination were determined through range analysis. On this basis， the single-factor test was employed to verify and supplement the findings of the orthogonal test. The results from both tests were matched and the optimal dosage combinations for the four factors was obtained. The findings of the study indicated that the dosage of HSF had a significant impact on the compressive strength， flexural strength， and split tensile strength of the concrete. The single-factor test analysis further identified that the optimummixing of HSF and PPF were 0.36% and 0.15% by volume， respectively. These proportions led to an enhancement in compressive strength by 30.26% and an increase in splitting tensile strength by 12.79%， in comparison to 0% by volume. The mass fraction of optiumu admixture of FA and SF were 5% and 7.5%， respectively. They can imcrease the compressive strength of concrete by 4.17% and 18.19%， respectively， compared with 0% mass fraction. Scanning electron microscopy （SEM） was conducted on the optimal dosage group to investigate the correlation between mechanical properties and hydration products. The results indicated that the hydration products in this group exhibited greater density， thereby facilitating the enhanced bridging role of the PPF.

To solve the problem of poor low-temperature cracking resistance for traditional high modulus asphalt， desulfurization rubber powder （DRP） was used as the main modifier， and composite modification technology was used to to prepare the desulfurization rubber powder-polyphosphate （DRP-PPA） and desulfurization rubber powder-rock asphalt （DRP-ROCK） composite modified high modulus asphalt. Firstly， the viscoelastic mechanical properties of the composite modified high modulus asphalt were tested by traditional physical properties tests’ methods and rheological properties test methods； meanwhile， the modification mechanism and thermal stability were explored by Fourier transform infrared spectroscopy （FTIR） and differential scanning calorimeter （DSC） tests. On this basis， high modulus asphalt mixture specimens were prepared， and their road performance were evaluated through domestic high-temperature rutting test， low-temperature small beam bending test， and four points bending fatigue test， and compared with the technical performance of traditional PR and HM high modulus mixture. Tests results showed that， the two types of composite modified high modulus asphalt binders had excellent high-temperature properties， which can meet the performance requirements of the traditional high modulus asphalt binders. Furthermore， low-temperature performance and fatigue resistance of the composite modified high modulus asphalt were superior to the traditional high modulus asphalt， among which the road properties of DRP-PPA high modulus asphalt were the best. FTIR test results showed that， the modification process of the above modifiers on asphalt was mainly based on physical modification， supplemented by chemical modification. And DSC test results revealed that addition of PPA and rock asphalt both significantly improved the thermal stability of modified asphalt. The performance test results of the asphalt mixture indicated that the low-temperature performance and fatigue resistance of the composite modified high modulus asphalt mixture were better than those of PR and HM high modulus asphalt mixture. And their high-temperature rutting resistance was slightly lower than those of traditional high modulus asphalt mixture， but it still met the relevant technical standards.

In order to study the flexural behavior of steel-concrete composite beams （PSCB） connected by Perfobond Leiste （PBL）， in this paper， the PSCB was designed and the flexural loading test was carried out to analyze its failure mode， deflection， strain and bearing capacity. Based on the simplified plastic theory， a formula for calculating the flexural capacity of steel-concrete composite beams considering the influence of PBL connectors was established. The results showed that the failure mode of PSCB was bending damage， and its strain pattern along the beam height direction was basically consistent with the flat section assumption before 0.79P _u. The formula for calculating the flexural capacity of steel-concrete composite beams with PBL connection was derived by simplified plastic theory. The calculated values of the formula were basically consistent with the experimental values. When calculating the flexural capacity of PSCB， the role of perforated steel plates in PBL connectors cannot be ignored.

To address the issue that drivers cannot perceive road sense through the steering wheel in a steer-by-wire （SBW） system， a road sense simulation motor is employed to provide feedback on road conditions， enabling the driver to perceive road sense and effectively control the vehicle. This study establishes a dynamic model of the SBW system， utilizing the magic formula tire model to describe lateral force， calculate individual wheel slip angles， and determine the self-aligning torque of the wheels. Assist torque， limit torque， friction torque， and damping torque， are designed to obtain the road sense feedback torque of the SBW system. A super-twisting algorithm （STA） approach is implemented to track the current corresponding to the feedback torque， simulation and experimental tests are conducted to analyze experimental results. The findings indicate that the self-aligning torque calculated using the magic formula tire model is highly accurate. The designed road sense simulation control algorithm meets the requirements of light steering at low speeds and clear， stable road sense at high speeds. Moreover， the robustness of the STA surpasses that of the proportional integration differentiation （PID） control. Compared with conventional sliding mode control， the proposed method effectively eliminates chattering effects.

To analyze the degradation characteristics of permafrost surrounding the transmission tower foundations of the pump station line A 12^#， 14 ^#and line B 12^#， 14^# powering the Jagdaqi oil pump station of the China-Russia crude oil pipeline， electrical resistivity tomography （ERT） was conducted to investigate the distribution range of unfrozen area in different seasons and evaluate the protection effectiveness of furnace ash backfill measure on the permafrost. The results demonstrated that resistivity difference can effectively characterize the spatial distribution of permafrost around the tower foundations， as well as its degradation processes and the formation mechanisms of talik. Tower foundation construction caused vertical seepage-induced thermal erosion through accumulated water. The thawed permafrost and weathered layers provided hydraulic channels for water seepage that led to pore water enrichment in inter-tower subsoils and low-resistivity characteristics in the underlying bedrock， forming interconnected talik （maximum thaw depth >28 m）. Compared with line A 14^# （with a thaw depth of 16 m）， furnace ash backfill at line B 14^# reduced the thaw depth by 1.5 m and suppressed lateral talik by approximately 60%， effectively slowing the talik development around foundations. These findings provide critical foundational data for developing engineering disturbance mitigation strategies and permafrost-related hazard prevention approaches in permafrost.