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.

In order to explore the effects of different soil and water conservation engineering measures on soil productivity of slope farmland in black soil area， two kinds of soil and water conservation measures （ terrace and ridge ） in Xingmu small watershed of Dongliao County， Liaoyuan City， Jilin Province in northeast black soil area were taken as the research objects， and the slope farmland without measureswas taken as the control （CK1 and CK2）. The differences of soil productivity of different soil and water conservation measures at different slope positions and the dominant factors affecting soil productivity were compared and analyzed. The results showed that： 1） terraces and ridges significantly improved soil quality. Compared with CK1 and CK2 slope farmland without measures， the contents of total nitrogen， total potassium， available phosphorus， organic matter and clay mass fraction in terraces and ridges increased by 40.25% and 16.16%， 9.14% and 5.57%， 33.27% and 24.50%， 30.25% and 7.94%， 8.47% and 5.03%， respectively， and the sand mass fraction decreased by 7.08% and 12.35 %. 2） The middle slope position was the most sensitive to soil and water conservation measures. Compared with CK1 and CK2， the contents of total nitrogen， total potassium， available phosphorus， organic matter and clay mass fraction in the middle slope position increased by 84.58% and 30.15%， 16% and 8.04%， 48.49% and 38.92%， 38.02% and 11.24%， 18.92% and 9.21%， respectively， and the difference was significant （P<0.05）. 3） The soil productivity index ranged from 0.29 to 0.49， and there were differences in soil productivity index under different measures. Compared with CK1， the soil productivity index of terraced fields increased by 24.05%， 65.85% （P<0.05） and 11.81% at the upper， middle and lower slopes， respectively. Compared with CK2， the soil productivity index of ridges increased by 10.00%， 49.95% （P<0.05） and 35.20% at the upper， middle and lower slopes， respectively. 4） The differences of soil water content， total nitrogen， total potassium， available phosphorus content and organic matter mass fraction under different measures gradually decreased with the increase of soil depth. The depth of 0-10cm soil layer was significantly higher than that of other soil layers， while the change trend of soil bulk density was opposite. 5） Multivariate variance and redundancy analysis showed that the type of measures， slope position， soil layer， type×slope position， type×soil layer had significant effects on soil productivity. Soil total nitrogen mass fraction had the greatest impact on soil productivity index， followed by soil bulk density. The implementation of soil and water conservation measures on slope farmland in black soil area increased soil water content， soil nutrients， clay mass fraction， and reduced bulk density and sand mass fraction， indicating that soil and water conservation measures can effectively improve soil productivity， and terrace measures are better than ridges； among different measures， the change of middle slope position is the most significant， which mainly affects the soil productivity by changing the soil total nitrogen mass fraction and bulk density of middle slope position. It is recommended to give priority to the implementation of terrace projects in the middle slope position， and to use total nitrogen and bulk density as key monitoring indicators to maximize the benefits of soil improvement and productivity improvement of soil and water conservation measures.

Tree height is a key parameter for assessing forest carbon storage， and satellite-borne laser radar technology provides an effective means for large-scale monitoring. The new generation of ice， cloud and land elevation satellite-2 （ICESat-2） equipped with the advanced topographic laser altimeter system （ATLAS） generates a lot of noise in the process of receiving signals， and the terrain is a key factor affecting the denoising results. To address this problem， a ground slope adaptive density clustering denoising algorithm is proposed to complete the photon cloud data denoising. Iterative median filtering and dynamic residual threshold method are used to classify photon clouds and then extract tree height. The canopy height model （CHM） obtained from airborne laser radar data is used as verification data. The reliability of extracting tree height from ICESat-2/ATLAS global geolocated photon data （ATL03） is analyzed and evaluated from three aspects： strong and weak beams， slope， and vegetation coverage. The results show that， 1） The recall rate （R）， precision rate （P） and harmonic mean （F） of the proposed denoising algorithm are better than those of the differential progressive gaussian adaptive denoising algorithm （DRAGANN）. 2） The accuracy of extracting tree height from nighttime strong beam data is the best， with a mean absolute error （MAE） of 2.49 m and a root mean square error （RMSE） of 3.03 m. 3） As the slope increases， the accuracy of tree height extraction gradually decreases， and the RMSE increases from 2.25 m to 6.52 m. 4） As the vegetation coverage increases， the accuracy of tree height extraction gradually decreases， and the RMSE increases from 3.06 m to 4.53 m. The results show that it is feasible to extract tree height using ATL03 photon cloud data， which can provide effective data support for studying forest growth conditions in forest areas.

In order to solve the problem of poor flower bud growth caused by insufficient light in autumn under facility cultivation conditions， four-year-old blueberry ‘Liberty’ container seedlings were used as materials， and LED lights were used as light sources. Setting different light intensity， light quality （red and blue light ratio） and photoperiod， a three-factor and three-level orthogonal test， and greenhouse natural light was used as a control. The chlorophyll content， gas exchange parameters， and nutrient resorption efficiency of nitrogen （N） and phosphorus （P） in different periods were measured. The weight and morphological indexes （transverse diameter and length） of flower buds were used to study the characteristics of photosynthesis and nutrient resorption of blueberry under different light conditions and their relationship with flower bud quality. The results showed that the photosynthetic capacity of blueberry was different under different light treatments after three weeks of supplementary light. The net photosynthetic rate of blueberry was the highest under low light intensity （300 μmol/（m²·s））， medium or low red and blue light quality ratio （1∶1 or 1∶3）， medium or short photoperiod （7.5 h or 6 h）. The nutrient resorption efficiency of blueberry under different light treatments was different after two weeks of stopping light supplementation. Under the light conditions of medium light intensity （450 μmol/（m²·s））， high red and blue light ratio （3∶1） and short photoperiod （6 h）， the nutrient resorption efficiency of nitrogen （N） and phosphorus （P） was the highest. The quality of flower buds was better under the conditions of high light intensity （600 μmol/（m²·s））， medium or low red and blue ratio （1∶1 or 1∶3）， medium or short photoperiod （7.5 h or 6 h） or medium light intensity （450 μmol/（m²·s））， high red and blue ratio （3∶1） and short photoperiod （6 h）. Different light treatments can affect the quality of flower buds by regulating the photosynthesis and the nutrient resorption efficiency of N and P in late-season blueberries. The effect of nutrient resorption efficiency on flower bud quality was greater than that of net photosynthetic rate， and the effect of N nutrient resorption efficiency on flower bud quality was greater than that of P nutrient resorption efficiency. Based on the effects of photosynthesis and nutrient resorption on flower bud quality， it was concluded that medium light intensity （450 μmol/（m²·s））， low red and blue light ratio （1∶3）， and short photoperiod （6 h） were most beneficial to improve the flower bud quality of blueberries.

Identifying the types and distribution of tree species is the foundation for monitoring tree diversity， and is crucial for forest protection and management and sustainable development of forests. Forest plot-scale hyperspectral images and LiDAR point cloud data scanned by unmanned aerial vehicle （UAV） were used as the data source， and based on the individual tree-scale hyperspectral and point cloud data obtained by the canopy height model， a convolutional neural network （CNN-EGNet） model combined with the attention mechanism of efficient channel attention （ECA） was proposed in this study， aiming at achieve precise tree species identification in mixed coniferous and broadleaf forests in the Maoershan area of Shangzhi City， Then， CNN-EGNet with three traditional CNN models VGG16， VGG19， and GoogLeNet in identification accuracy. Finally， on the basis of the results of the tree species identification， tree species diversity indices （Shannon-Wiener， Simpson， Pielou， Species richness） in the study area were calculated with the 40 m × 40 m window. Results showed that the proposed CNN-EGNet model achieved an overall accuracy （OA） of 89.58% and a Kappa coefficient of 0.8661. Compared with conventional models， the overall accuracy for species identification improved by 9.37%， 5.20%， and 14.58%， and the Kappa coefficients increased by 0.1175， 0.0652， and 0.1896， respectively. The Shannon-Wiener index primarily ranged from 0.8 to 1.4， while the Simpson index predominantly clustered between 0.5 and 0.7. The Pielou index generally fell within the range of 0.7 to 0.95， and the species richness index mostly varied between 3 and 5 species. The tree species diversity indices indicated that the distribution of tree species was uneven， with certain species being dominant while others were relatively scarce. The results of the study can provide technical and data references for the identification of tree species and the protection and management of tree species diversity in the mixed coniferous and broadleaf forests in the Northeast of China， and validate the possibility of identification， monitoring and evaluating the diversity of tree species by combining hyperspectral and LiDAR data from UAVs with convolutional neural networks.

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

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

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

To 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.

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

In order to explore the impact mechanism of vegetation restoration on soil aggregate structure and organic carbon dynamics in industrial and mining wastelands， Pinus sylvestris var. mongolica plantations with different planting years （18， 20， 23， 32 years） on industrial and mining wastelands in Qingling Forest Farm， Hegang City were studied. By combining field sampling and indoor analysis， the particle size distribution of soil aggregates， organic carbon mass fraction， and main soil chemical factors （pH， total nitrogen， and total phosphorus， etc.） in the 0-20 cm and 20-40 cm soil layers were systematically measured. The research results indicated that： 1） With the increase of vegetation restoration years， the structure of soil aggregates underwent significant changes. Among them， the proportion of large aggregates larger than 2 mm showed a significant increase trend （an increase of 50.76%）， while the proportion of micro aggregates smaller than 0.25 mm decreased significantly （a decrease of 44.24%）. The mean weight diameter （MWD） and geometric mean diameter （GMD） increased by 17.90% and 18.42%， respectively， indicating that the stability of soil aggregates was significantly enhanced with increasing forest age. 2） Through Mantel analysis， it was found that forest age， soil depth， and chemical factors had a significant impact on soil organic carbon （SOC） accumulation （P<0.01）. At the same time， forest age and chemical factors had a significant impact on aggregate stability （P<0.05）. The trend of changes in surface soil （0-20 cm） and deep soil （20-40 cm） was basically consistent， but the response of deep soil to environmental factors was relatively lagging behind. 3） Random forest analysis showed that ammonia nitrogen （ \mathrm{N} {\mathrm{H}}_{\mathrm{4}}^{+}- N）， pH， total phosphorus （TP）， and nitrate nitrogen （ \mathrm{N} {\mathrm{O}}_{\mathrm{3}}^{-}-N） had the highest explanatory power for soil aggregate stability. In summary， long-term vegetation restoration can effectively improve the soil structure of industrial and mining wastelands， promote the accumulation and sequestration of soil organic carbon， and provide new ideas for the restoration and reconstruction of degraded ecosystems.

The excessive consumption of fossil fuels has precipitated a global energy and environmental crisis， propelling the development of green renewable energy materials into a research hotspot. Wood， owing to its multiscale structural designability and renewability， holds significant application potential across various environmental energy harvesting and conversion domains. This paper systematically integrates the functional development and application progress of wood-based energy materials. By examining the mechanisms， structural regulation strategies， and key performance characteristics for harnessing diverse natural energies—including solar thermal， hydropower， mechanical energy， and thermal radiation—it outlines material design approaches for different application scenarios. Through summarizing multi-energy coupling mechanisms， interfacial regulation pathways， and cross-scale structural construction methods， the paper identifies the inherent advantages of wood-based materials in achieving multi-energy conversion. Building upon this foundation， the paper explores the application potential of wood-based energy materials in distributed energy supply， water resource utilization， environmental thermal management， and electrical signal processing. The review concludes by proposing key future development directions for wood-based energy materials， including structural durability， functional integration， and scalable manufacturing， providing guidance for their further application within green energy systems.

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.

To address the challenge of Sentinel-2 data in distinguishing spectrally similar tree species， this study established a multi-dimensional feature fusion classification system based on the google earth engine （GEE） cloud platform， with Tieling City， Liaoning Province as the experimental area. By integrating Sentinel-2 multi-temporal data and employing multi-dimensional feature statistical methods， we extracted spectral and vegetation index features including quantiles， extremes， and standard deviations， combined with topographic， textural， phenological， and harmonic features， forming a total of 120 features across six categories. Multiple feature combination schemes were designed and implemented through a hierarchical classification strategy using the random forest algorithm， ultimately achieving fine classification of seven dominant tree species： Pinus tabuliformis， Pinus sylvestris var. mongolica， Larix gmelinii， Populus， fruit trees， Quercus mongolica， and Robinia pseudoacacia. The results demonstrated that multi-dimensional temporal statistical features effectively captured subtle interspecies differences. Variations in water content between Pinus sylvestris var. mongolica and Pinus tabuliformis were successfully characterized through multiple vegetation indices. Topographic and textural features played decisive roles in distinguishing deciduous species. The classification overall accuracy reached 94.7% for evergreen species and 88.1% for deciduous species， with all six feature combination schemes achieving overall accuracy exceeding 77.9%. This study confirms that the integration of multi-dimensional feature statistical methods with the GEE platform fully exploits the multi-band advantages of Sentinel-2 data， significantly enhancing large-scale tree species classification capabilities through temporal feature analysis. It provides a cost-effective solution for dynamic monitoring of forest resources at large-scale， with the cloud-based processing framework demonstrating potential for application expansion to broader geographical regions.

Using rubberwood as raw material， orthogonal experimental design was used to explore the influence of silver-loaded nano titanium dioxide impregnated heat treatment on the mold and corrosion resistance of rubberwood， and compared the mold and corrosion resistance， dry shrinkage and wet swelling properties of the control timber， heat-treated timber， silver-loaded nano titanium dioxide impregnated timber and silver-loaded nano titanium dioxide impregnated heat-treated timber， and the influence mechanism of different modification treatments on the physicochemical properties of the timber was revealed through the means of electron microscopy， XRD（X-ray diffraction）. The results showed that the optimal process of silver-loaded nano titanium dioxide impregnated heat treatment was 160 ℃， 2.5 h， 1.5% silver loading， 1.5 mg/mL modifier concentration， 60 min vacuum impregnation time， at this time， the modified timber had the greatest efficacy in the prevention of Aspergillus niger and Penicillium citrinum， both of them were 100%， and the modified timber had the smallest rate of quality loss under the influence of white-rot fungus and brown-rot fungus， which were 2.36% and 1.9%， respectively. Compared with the control timber， heat-treated timber and silver-loaded nano titanium dioxide impregnated timber， silver-loaded nano titanium dioxide impregnated heat-treated timber had the lowest wet swelling and dry shrinkage， the radial， tangential and volumetric water-absorbing wet swelling rate was reduced by 31.34%-36.36% compared with that of the control timber， the radial， tangential and volumetric moisture-absorbing wet swelling rate was reduced by 41.06%-61.7% compared with that of the control timber， the radial， tangential and volumetric air-drying dry shrinkage rate was reduced by up to 23.28%-32.24% compared with that of the control timber. The silver-loaded nano titanium dioxide impregnated heat treatment can realize the uniform loading of silver-loaded nano titanium dioxide particles in wood， which can comprehensively improve the mold and corrosion resistance and dimensional stability of wood， and realize the excellent wood modification effect.

Hemicellulose acid hydrolysates contain diverse sugars， yet existing separation and purification processes remain complex. This study presents a simplified and efficient method for isolating and extracting crystalline xylose from hemicellulose hydrolysates. By leveraging the preferential selectivity of phenylboronic acid toward xylose， a xylose-boronate ester XDE intermediate was formed. Subsequent transesterification with propylene glycol in ethyl acetate enabled the precipitation of high-purity crystalline xylose. The process achieved an average xylose yield of 75% and purity up to 99.8% over five operational cycles. Notably， this green solvent-based tandem system operated at ambient temperature， eliminating industrial-scale procedures including decolorization， desalination， and recrystallization. The developed methodology demonstrates efficient xylose extraction with minimized energy consumption and environmental impact.

To solve the problems of low efficiency， high labor intensity， and poor working environment in plywood production， a surface defect repair equipment for plywood based on visual detection and automation control technology to improve the surface quality and production efficiency of plywood was designed. The equipment mainly included a visual detection and intelligent control system， a positioning system， an extrusion system， and a repairing mechanism. The visual detection and intelligent control system identified the size and position of defects through visual detection technology and generated corresponding G-code instructions （the most widely used CNC programming language for computer numerical control machines）， which were transmitted to the positioning system and extrusion system. The positioning system used the CoreXY mechanism （dual motor drive （Motors M1 and M2）， timing belt drive， X-shaped structure）， and the system received the instructions and controls the repairing mechanism to determine the defect repair point through the coordination of M1， M2， and Z motors. The putty extrusion machine of the extrusion system was used to realize stable extrusion and even coverage of the putty at the defect site. The test showed that the detection accuracy of the equipment for holes， cracks and other defects reached 97.1% and 70.6% respectively， which can effectively cover the surface defects of the plywood and avoid large areas of coating. The repaired plywood is superior to manual repair in terms of putty residue， usage and repair time， providing an effective solution for the automatic repair of the plywood processing industry.

The macro structure， micro structure and cell morphology of the Dalbergia cultrata wood were measured and analyzed by using zoom stereromicroscope， digital microscope and image measuring analysis system， and the wood density was determined at the same time. The results showed that the wood color of the D. cultrata wood was purple red； the growth rings were obvious； the wood was scattered with holes， and the combination type of the wood vessels was mainly single vessels， a few were radial multiple vessels， and the shape of the vessel cells was mainly drum-shaped； the wood fiber cells were superimposed， with small cavity and thick， and the cell length-width ratio and wall cavity ratio were 48.84 and 0.81， respectively. The xylem rays were fine， imposed， and homomorphic 2-3 columns， with a height of 5-9 cells and a length-width ratio of 5.17. The axial parenchyma was superimposed， abundant， wing-shaped or concentric paratracheal belt-shaped； the cell length-width ratio was 6.36. Crystalline cells were mostly divided into compartments containing crystals， with as many as 17 crystalline particles.The wave pattern was slightly visible under the eye； the wood grain was straight and the structure was fine. The wood fiber and crystal cells accounted for the largest and smallest proportion of the wood tissue percentages， respectively； the wood tissue percentages could deeply understand the growth of trees and the properties of wood. The wood density was large， and the density， air-dried density and oven-dried density of the wood were 0.85， 1.04， 0.94 g/cm³， respectively. The above analysis perfected the theory of wood anatomical structure and provided theoretical basis for wood identification and appraisal.

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.

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.

The amplification circuit represents a pivotal component within the domain of forestry intelligent equipment， with its functionality exerting a direct influence on the efficacy with which weak signals are monitored within forestry monitoring applications. High-performance discrete amplification circuits are complex in structure， and the traditional manual selection of discrete component parameters in analogue circuits is inefficient and difficult to meet the requirements of low noise and high stability in fields such as forest fire monitoring and wood defect detection. The purpose of this paper is to propose an automatic parameter optimization method for discrete components in circuits based on the nondominated sorting genetic algorithm Ⅱ （NSGA-Ⅱ）. Firstly， a parameter optimization model of the amplifier circuit is established， and design indicators are proposed in accordance with the requirements. Next， NSGA-Ⅱ is used to solve the circuit parameters. Finally， the optimization results are verified by comparing NSGA-Ⅱ with manual methods， particle swarm algorithms， vortex search algorithms and genetic algorithms through simulation and physical testing of circuit boards. The experimental findings demonstrate that the NSGA-Ⅱ circuit parameter optimization method proposed in this study exhibits a substantial superiority over manual approaches in terms of circuit performance. In comparison with classical single-objective optimization， it also possesses advantages in terms of convergence speed and optimization stability. This method provides an efficient solution for the design of high-precision amplification circuits for forestry sensors， and it can be expanded to encompass the design optimization of other forestry equipment circuits in the future.

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.

To enhance the weathering resistance of wood， anatase titanium dioxide was encapsulated with silica to form silica-coated anatase-type titanium dioxide （A-TiO₂@SiO₂）， which was then modified with a silane coupling agent to produce the modifier （KH560-TiO₂@SiO₂）. KH560 referred to γ-glycidoxypropyltrimethoxysilane. Fast-growing poplar wood was treated with this modifier via vacuum impregnation， and the effects of KH560-TiO₂@SiO₂ on the ultraviolet resistance of poplar wood were systematically evaluated. The results indicated that Ti and Si elements were introduced into the surface of the modified wood， with a significant amount of particulate matter being loaded. The treated wood exhibited a higher contact angle compared to the untreated wood， which helped to reduce the degradation caused by water in outdoor applications. The ability of the modified wood to excite hydroxyl radicals， superoxide anions， and phenoxyl radicals was reduced， while its absorbance in the UV region increased. Untreated poplar wood experienced rapid lignin degradation after UV aging， leading to photodegradation and structural deterioration. The photocatalytic properties of anatase titanium dioxide （A-TiO₂） promoted the degradation of cellulose in poplar wood. However， the KH560-TiO₂@SiO₂ modification delayed lignin degradation while maintaining the stability of cellulose. In summary， the encapsulation treatment reduced the photocatalytic activity of A-TiO₂， and the modifier acted as a UV shield while decreasing the wettability of the wood， thereby enhancing the weathering resistance of poplar wood. Fast-growing poplar wood treated with KH560-TiO₂@SiO₂ has the potential for outdoor applications.

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.

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.

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.

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.

As the global climate warms， the frequency of extreme events increases， resulting in the decline and even death of some of the world's forests， and in some places the impact is more pronounced on large trees. The climate in Northeast China is gradually becoming warmer and drier， and Pinus koraiensis， as the main precious tree species in Northeast China， has already experienced a decline in the study area. However， the differences in the effects of temperature rise and extreme events on the growth of Pinus koraiensis of different diameter classes have not been studied in detail. The response of different diameter classes of Pinus koraiensis to climate change and the adaptability characteristics （resistance， resilience， restoring elasticity and relative resilience to extreme drought were analyzed and compared by dendrochronology in the natural forest area of southern Xiaoxing'anling. The results showed as follows： 1） There was a negative correlation between large diameter and the maximum temperature at the beginning of the growing season， and a positive correlation between large diameter and the precipitation in June of the same year； the minor grade was negatively correlated with the precipitation at the end of the growing season in the current year and the previous year. 2） The growth trend of large diameter class and small diameter class was basically the same， in which the response stability of large diameter class to the maximum temperature of the growing season was lower than that of small diameter class， and the growth of small diameter class was mainly affected by the decrease of precipitation. 3） With the warming of climate， the resistance and relative resilience of different diameter classes to extreme drought showed a downward trend， and the adaptability of large diameter class to drought was slightly lower than that of small diameter class， but the difference was not obvious. The difference of response to climate and the stability of response of different diameter classes of Pinus koraiensis mainly appeared in the early growing season of rapid growth， reflecting the different demand of different diameter classes for hydrothermal conditions. With the warming of climate， the adaptability of radial growth of different diameter classes of Pinus koraiensis to extreme drought events decreased， and it could not recover to the pre-drought level in the short term. It is predicted that the future climate will continue to rise， and the adaptability of large diameter class Pinus koraiensis may weaken. Further analysis should be carried out based on the frequency and time of drought， and the research scope should be expanded to deal with the adverse effects of warming on Pinus koraiensis forest， which will play an important role in forest management.

Soil nitrogen （N） and phosphorus （P） are critical for tree growth. Investigating the variation characteristics of N and P fractions across soil profiles and their primary influencing factors following the transformation from pure plantations to multi-layered， uneven-aged plantations provides insights into the mechanisms by which structural regulation of pure forest stands impacts soil quality. This study focused on pure Cunninghamia lanceolata plantations and multi-layered uneven-aged mixed C. lanceolata and Phoebe bournei plantations （hereafter referred to as mixed C. lanceolata and P. bournei plantations）. We examined the variations in N and P fractions， other soil properties， and root traits across different soil layers （0-10 cm， >10-30 cm， >30-50 cm） in these two stands， as well as their interrelationships. The results revealed that after the transition from pure C. lanceolata plantations to mixed C. lanceolata and P. bournei plantations， the contents of most N and P fractions in the 0-10 cm soil layer significantly increased. Specifically， labile， moderately labile， and stable P fractions， along with total P contents， increased by 38.2%， 31.6%， 15.4%， and 25.1%， respectively， compared to the pure C. lanceolata plantations， and inorganic， organic， and total N contents increased by 42.1%， 35.8%， and 35.9%， respectively. In the >10-30 cm soil layer， the contents of moderately labile organic P， inorganic N， acid-hydrolysable organic N， total N， and total P were significantly higher in the mixed C. lanceolata and P. bournei plantations than pure C. lanceolata plantations. However， no significant differences in N and P fractions contents were observed between the two stands in the >30-50 cm soil layer. The contents of N and P fractions of the two stands decreased with increasing soil depth. Furthermore， the transformation of stand structure affects root distribution and traits. The mixed C. lanceolata and P. bournei plantations exhibited higher root biomass， root length density， and specific root length compared to pure C. lanceolata plantations. The root biomass and root length density at 0-10 cm increased by 124.66% and 269.23%， respectively， compared to the pure C. lanceolata plantations. In pure C. lanceolata plantations， root biomass and root length density initially increased and then decreased with soil depth. In contrast， due to the surface aggregation of P. bournei roots in the mixed C. lanceolata and P. bournei plantation， the root biomass and root length density of C. lanceolata gradually increased with soil depth. The variations in soil nitrogen and phosphorus components across different stand types and soil depths were primarily associated with root distribution， soil organic carbon， and soil microbial characteristics. The findings highlight that structural optimization of C. lanceolata plantations in subtropical regions significantly influences soil fertility.

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.

Land is an indispensable part of human life. The analysis of land use status is helpful to deeply understand the relationship between environmental conditions and economic development， and to achieve a more reasonable land use model. Predicting future land use will help improve the sustainable management of land resources and provide a scientific basis for assessing carbon potential. Taking Heilongjiang Province as the research area， the current situation of land use in Heilongjiang Province from 2000 to 2020 was analyzed， and the patch-generating land use simulation （PLUS） model coupled with the long short-term memory （LSTM） model was adopted to simulate and predict the land use situation in Heilongjiang Province in 2030. The results showed that： 1） The Kappa coefficient for verifying the PLUS-LSTM model was 0.878. The relative simulation errors of the six land types （cultivated land， forest land， grassland， water area， construction land， and unused land） were all less than 15%. Compared with the traditional model， it had higher accuracy and can be used to simulate the land use situation in Heilongjiang Province in 2030. 2） Compared with 2020， the area of forest land， grassland， water area， and construction land in Heilongjiang Province would increase in 2030. Among them， the change rate of construction land was the highest， 8.57%； the area of forest land increased by 2 584.26 km²， mainly in the central region； the expansion of grassland was mainly in the southwest. The area of cultivated land and unused land decreased， and the unused land changed the most， with a change rate of 29.68%.

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.

As an important carrier of the national new energy strategy， the leakage of gas pressure pipelines in forest areas not only causes direct economic losses， but also may lead to secondary disasters such as soil pollution， vegetation destruction and even forest fires due to the sensitivity of forest ecosystems. The current ultrasonic sound source-based localization methods suffer from challenges such as high sidelobe artifact interference and insufficient localization accuracy caused by wide main lobe beamwidth in multi-leakage source scenarios. Moreover， the complex terrain， dense vegetation coverage， and environmental noise in forest areas further compromise the applicability of conventional detection techniques. In this paper， an adaptive inverse convolution beamforming algorithm is proposed to achieve high-precision leakage localization by optimising the weight matrix and the inverse convolution iteration strategy. Firstly， the initial weight matrix is constructed based on the minimum variance distortionless response （MVDR） criterion， and the weights are adjusted with adaptive iteration to enhance the focusing ability of the target signal while suppressing the interference of the sidelobes. Secondly， the main lobe width is compressed through Gauss-Seidel deconvolutional iteration， thereby enhancing resolution. To validate the algorithm's performance， this study establishes a pressure pipeline model with a diameter of 150 mm and operating pressure of 0.8 MPa to simulate ultrasonic signals from 0.5 mm and 0.7 mm leakage orifices， while constructing an experimental system for comparative analysis. Results demonstrate that compared with conventional deconvolution beamforming， the proposed algorithm reduces localization errors by 0.06 m for Source 1 （0.7 mm orifice） and 0.05 m for Source 2 （0.5 mm orifice） under signal-to-noise ratio （SNR） conditions ranging from -10 dB to 20 dB， while effectively eliminating artifact interference. The experiments further validate the method's robustness and computational efficiency advantages under low SNR conditions （-10 dB to 20 dB）. This study provides a high-precision solution for non-contact detection of minor pressure pipeline leaks in forest environments， characterized by strong anti-interference capability and superior environmental adaptability. The findings hold significant implications for ensuring energy transportation safety and ecological conservation.

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.

Traditional methods for harvesting pinecone species face challenges such as low efficiency， high risks， and uncontrollable costs. To address real-time recognition and localization in automated pinecone harvesting， we proposed an improved YOLOv5s-7.0 （you only look once） object detection model and construct a binocular depth camera-based detection and localization network. To improve the accuracy and efficiency of object detection， the YOLOv5s model was improved by embedding partial convolutions （PConv） into the neck module's multi-branch stacked structure to enhance sparse feature processing capability， improve robustness， and reduce feature redundancy in complex scenarios of pinecones. Additionally， the simple attention mechanism （SimAM） was integrated at deep backbone layers and backbone-neck connections to optimize the model’s feature extraction ability and information transmission efficiency in complex backgrounds without significant parameter increases. To meet the requirements of efficient detection and localization， a target detection and real-time localization code was developed using binocular vision principles and the improved YOLOv5s model， and a pinecone detection and localization system was constructed through depth matching. Based on the constructed dataset of Pinus sylvestris var. mongolica cones from the Greater Khingan Mountains and Pinus koraiensis cones from the Lesser Khingan Mountains， the improved YOLOv5s model achieved a precision of 96.8%， a recall of 94.0%， and an average precision （AP） of 96.3% in target detection tasks. The proposed pinecone detection and localization system demonstrated mean absolute errors of 0.644 cm， 0.620 cm， and 0.740 cm along the x-， y-， and z-axes， respectively. Under front， side， and backlighting conditions， the localization success rate reached 93.3%， while in low-light environments， it maintained a success rate of 83.3%. Other performance indicators， including field of view， meet the operational requirements for pinecone harvesting. The proposed pinecone detection and localization system provides a reliable solution for real-time target detection and localization problems in mechanized pinecone harvesting.

Used 15-year-old Pinus koraiensis on three slope aspects （sunny slope， semi-sunny slope， and shady slope） in Wangjiagou Management Area， Maoshan Experimental Forest Farm of Northeast Forestry University， as materials to investigate the responses of the functional traits and photosynthetic characteristics of Pinus koraiensis needles to the differences in environmental factors on different slope aspects. The results showed that the soil moisture content of the shady slope was 5% higher than that of the sunny slope， and the photosynthetically active radiation of the sunny slope was 3 times that of the semi-sunny slope and 10 times that of the shady slope. The specific leaf area of Pinus koraiensis needles on shady slopes was about 1.29 times that of sunny slopes. Pinus koraiensis growing on the shady slope obtained more light energy by expanding the specific leaf area. The stomatal density， water content and NSC content of the coniferous leaves on the sunny slope were significantly higher than those on the semi-sunny slope and the shady slope. The photosynthetic capacity of the one-year coniferous leaves was higher than that of the two-year coniferous leaves， and the photosynthetic characteristics on the sunny slope were higher than those on the semi-sunny slope and the shady slope. The environmental conditions on the sunny slope were more conducive to the growth and development of Pinus koraiensis. The main environmental factors affecting the photosynthetic characteristics of Pinus koraiensis needles were land surface temperature and photosynthetically active radiation， and the main environmental factors affecting the functional traits of Pinus koraiensis needles were photosynthetically active radiation. Pinus koraiensis of different slopes adapted to environmental changes by adjusting their own needle morphology， photosynthetic characteristics and nutrient distribution to form a unique adaptation strategy. The environmental factors that affect the growth and development of Pinus koraiensis trees are not single， but the result of the coupling of multiple environmental factors.

To identify superior poplar varieties adapted to different soil moisture conditions， this study evaluated nine major cultivated poplar varieties in Heilongjiang Province—namely， Populus cathayana （JDQY）， Populus deltiodes×P.cathayana ZHF2， （Populus×euramericana）×（P.simonii×P.nigra）（2111）， Populus euramericana ‘N3016’×P.ussuriensis ‘HQ-1’ （HQY）， P.alba L×P.berolinensis Dippel （YZY）， 1019， Populus ‘Heifang-3’ （QSY）， 406， and Populus deltoides×Populus simonii ‘LongFeng-2’ （LF2）. By applying two soil moisture gradients （mild drought， HL is 14%- 18%； moderate drought， HM is 6%-10%）， we systematically measured 14 morphological and physiological-biochemical indicators， including apparent morphology， leaf water content， relative chlorophyll content， ion metabolism， and antioxidant enzyme activities. Based on principal component analysis and membership function evaluation， the drought resistance rankings were as follows： （1） under mild drought， JDQY， HQY， QSY， YZY， LF2， 2111， 406， ZHF2， 1019； （2） under moderate drought， JDQY， HQY， 2111， YZY， LF2， QSY， ZHF2， 406， 1019. Research indicated that， JDQY maintained osmotic balance by by significantly accumulating K⁺ and Ca²⁺， while HQY responded rapidly to oxidative stress via elevated superoxide dismutase （SOD） activity， both exhibited broad-spectrum drought resistance under two types of stress. This study reveals inter-varietal differences in drought resistance and underlying physiological mechanisms among the nine poplar varieties， identifying JDQY and HQY as drought-tolerant candidates. The findings provide a theoretical basis for stress-resilient afforestation and precision tree species selection in cold， drought-prone regions.

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 address the issues of low efficiency， high labor cost， and safety risks associated with manual harvesting of deep-striped walnuts in Yunnan， and to provide a theoretical basis for the design of deep-striped walnuts branch-shaking harvesting equipment， a study on the vibration parameters for deep-striped walnut harvesting was conducted. A dynamic model for vibration-based harvesting of deep-striped walnuts was established， and the primary factors affecting harvesting performance were analyzed. Through static and dynamic detachment force tests， the operational parameter range for deep-striped walnuts vibration harvesting was determined. A multi-factor horizontal vibration experiment was designed to identify the optimal parameter combination for the branch-shaking harvesting device.The results showed that the primary factors affecting deep-striped walnut detachment， in descending order of influence， were excitation position， vibration frequency， and amplitude. Measurements indicated that the axial detachment force ranged from 18.2 to 24.8 N， the bending force from 4.0 to 61.4 N， and the inertial force from 5.1 to 42.3 N. Analysis revealed that bending fracture was the main detachment mode. Optimization analysis of the regression model yielded the optimal parameter combination： vibration frequency of 7 Hz， amplitude of 92 mm， and excitation position at 0.6 l （where l was the total length of the lateral branch）， under which the harvesting efficiency of deep-striped walnuts reached 95.7%.

In recent years， biomass pyrolysis equipment has emerged as a focal point of research in the global energy sector. To tackle challenges such as uneven material heating and accumulation at the tail section commonly observed in fixed-bed pyrolysis systems， this study focuses on optimizing and analyzing the performance of spiral combined flights， a key component in fixed-bed reactors. A novel variable-pitch combined flights structure was designed， and its critical parameters were systematically determined. Using Altair EDEM simulations， the effects of rotational speed and equipment inclination angle on the discharge rate were evaluated， and the simulation outcomes were validated through experiments. Simulation results demonstrated that the variable-pitch combined flights structure effectively lifted materials and redirected tail-end accumulation towards the discharge outlet， enabling uniform heating and resolving the issue of particle buildup at the tail. Meanwhile， the inclination angle exerted a significantly stronger influence on the discharge rate， with a between-group to within-group mean square ratio of 240.00， far surpassing the ratio of 25.60 observed for rotational speed. Experimental results aligned closely with the simulations， yielding a correlation coefficient of 0.998 7.