为解决沥青老化行为预测研究中室内模拟老化与实际老化偏差大、缺乏易于工程推广的科学评价指标等问题，以在役高速不同老化程度的SBS（苯乙烯-丁二烯-苯乙烯三嵌段共聚物）改性沥青为对象，借助沥青质量激光回弹快速检测系统（ABQT），通过获取最大蠕变变形、弹性恢复率、加载变形速率系数和回弹变形速率系数等指标评价沥青性能变化规律，并与沥青常规老化性能指标相对比，分析这些指标评价沥青自然老化行为的准确性；在此基础上，以这些指标建立沥青老化非线性预测模型，通过获取沥青老化速率值分析自然老化状态下沥青性能衰变规律，并最终提出合理的预防性养护时间。结果表明，最大蠕变变形、加载变形速率系数和回弹变形速率系数等指标可以有效表征沥青老化性能衰变过程，同时基于这些指标建立的沥青老化非线性预测模型适用性强，可以实现沥青性能-老化时间的双向计算；随老化时间的延长，最大蠕变变形、加载变形速率系数和回弹变形速率系数指标所对应的老化速率值均以三次函数模型形式衰变，老化速率拐点分别为6.67、5.56、6.67a，考虑到沥青混合料性能安全，建议以5.56 a作为介入预防性养护措施恢复沥青性能的阈值。

揭示南昌市植被覆盖的演化特征和确定影响其变化趋势的主要气候驱动因子，为其长期稳定、良性发展和积极应对后续的气候变化提供指导。采用2000—2020年南昌市的NDVI月度数据、气温、降水和气压等10个气象驱动因子，利用单样本K-S(Kolmogorov-Smirnov)检验、弗里德曼检验、肯德尔和谐系数检验法等非参数检验法和随机森林分析方法，对不同驱动因子的重要性进行研究。结果表明，1）在2000—2020年南昌市的NDVI呈波动下降趋势，该峰值出现在2000年，谷值则在2010年；2）城市整体植被覆盖呈现四周高中间低的空间分布规律，北部和西部地区的NDVI值相对较高且下降速率缓慢，中部的东湖区和青山湖区等NDVI值相对较低且有明显下降趋势；3）植被覆盖的极大平均值出现在每年8月，受温度的影响最显著，而风向的影响最不显著，降水的影响有一定的时滞性，植被覆盖变化应结合多要素的共同驱动作用来决定。

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

以正交试验设计方法为基础，对张家口市京新高速公路路堑膨胀性泥岩开展试验，重点研究不同上覆荷载下泥岩试样的膨胀率与浸水时间、初始含水率、压实度的规律关系，并采用极差分析法对各种因素进行敏感性分析。研究结果表明，膨胀率随浸水时间的变化曲线呈现出S形走势，大致可划分为初始膨胀阶段、加速膨胀阶段、缓慢膨胀阶段；在相同初始含水率条件下，膨胀率随着压实度的增大而增大；在相同的压实度条件下，膨胀率随初始含水率增大而减小。通过极差分析法可知，试样浸水时间为影响膨胀率的主导因素，含水率和压实度为次要影响因素，且小荷载下含水率对试样膨胀率的影响比较明显，荷载继续增大，压实度超过含水率对试样膨胀率的影响。

松材线虫病作为世界检疫性病害严重危害松属植物并威胁森林生态安全，至今没有行之有效的措施对其进行控制和除杀。为满足人们对森林提供多种生态服务的需要，通过对马尾松林进行高强度（采伐强度60%）和低强度（采伐强度15%）2种间伐措施采伐松材线虫病疫木后，采用高通量测序手段对松材线虫病疫区马尾松林土壤的细菌和真菌结构变化进行分析，旨在探究间伐对松材线虫病疫区马尾松林土壤微生物群落结构的变化，分析对土壤微环境的影响。采用高通量测序法对间伐抚育后的马尾松林土壤细菌和真菌群落进行研究，结果表明，3种林分土壤细菌和真菌群落表现出明显的差异，高间伐措施林分组（H-CK）的Shannon多样性指数和Chao1指数最高，H-CK组的土壤微生物群落多样性和丰富度显著高于低间伐措施林分组（L-CK）和对照组（CK）。L-CK组中标志细菌群落为Actinobacteriota（放线菌门），标志真菌群落为Leotiomycetes（锤舌菌纲）；H-CK组中标志细菌群落为Chloroflexi（绿弯菌门），标志真菌群落为Dothideomycetes（座囊菌纲）。结合土壤理化性质分析表明，与对照组相比，高强度间伐H-CK组的土壤有机质含量上升13%，在增加土壤有机质积累的同时，H-CK组的全氮含量达1.12 g/kg，碱解氮的含量为64.15 mg/kg，可显著提高土壤全氮含量和有效氮含量的积累（P<0.05），从而提升林地地力。稳步提升森林质量，逐步将现有发生松材线虫病的马尾松纯林培育成复层异龄针阔混交林，使其抗病能力增加，生态功能增强。

纳米铜锌防腐剂（Micro sized copper zinc compounds,MCZ）改性橡胶木耐腐性能可以达到国家Ⅰ级耐腐标准，但为适应复杂的环境应用，抗流失性和尺寸稳定性还需要进一步提高。使用丙二醇对MCZ进行改性（Propylene glycol-micro sized copper zinc compounds,PG-MCZ），再与单宁-硼酸联合对橡胶木进行协效处理。结果表明，当橡胶木浸渍PG-MCZ后再用质量浓度为1%的单宁-硼酸改性，处理材的抗流失性最佳。此时，橡胶木的抗湿胀性和抗干缩性效果最好，尺寸稳定性最佳，虽然加入丙二醇对橡胶木的防腐性有轻微减弱，但是仍然可以达到国家Ⅰ级耐腐标准，并且提高橡胶木的阻燃抑烟性。研究结果对促进环保木材防腐剂的开发利用具有积极作用。

牵引车在承担林业运载作业时，由于林间路况复杂，因此多以低速运行，有较高的转向需求，且往往带有负载单位，所以更关注其行驶稳定性与转向灵敏性。利用CarSim软件构建牵引车物理模型与负载模型，基于Simulink软件进行牵引车控制策略的设计，结合二自由度动力学模型建立牵引车二自由度模型，以牵引车横摆角速度与质心侧偏角参数结合模糊控制理论对牵引车转向过程进行制动力矩反馈控制，另外根据牵引车四轮转向模式，引入前后轮转角比例控制策略。在CarSim-Simulink联合仿真平台下，结合林业作业复杂路况进行不同控制策略下牵引车低速行驶与转向试验。结果表明，采取的控制策略有效减少复杂路况下牵引车的转向半径，提升转向速度，并将质心侧偏角限制在较小范围，牵引车稳定性较高，且横摆角速度稳态值更接近理想状态，转向更加灵敏。

通过对广州市增城区按照起源和林分类型划分的4种典型林型（天然阔叶混交林、人工阔叶混交林、人工阔叶纯林、人工针阔混交林）群落的物种多样性和土壤理化性质的探究，分析其物种多样性差异，以及物种多样性和土壤环境因子之间的关系，为精准提升天然林质量提供理论基础。采用典型样方调查法，运用统计学分析方法，计算Magalef丰富度指数、Shannon-Wiener指数、Simpson优势度指数，对增城区典型森林群落的物种组成、优势度和乔木、灌草层的物种多样性的差异进行分析，并结合土壤环境因子进行相关性分析。研究区域共调查到植物193种82科153属。4种林型优势种（及其重要值）分别为天然阔叶混交林中的浙江润楠（6.36%）、人工阔叶混交林中的浙江润楠（5.52%）、人工阔叶纯林中的桉树（29.54%）、人工针阔混交林中的鸭脚木（4.73%）。天然和人工阔叶混交林优势种径级呈倒J形分布，人工针阔混交林和阔叶纯林为正态分布。4种林型群落在物种多样性、土壤氮、磷含量方面具有显著差异，其中，人工阔叶纯林乔木层物种多样性最低，灌草层物种多样性最高。研究结果表明，4种林型土壤营养物质含量差异显著，物种多样性和土壤理化性质具有显著相关性，区域内不同起源、不同林分类型的森林需要不同的经营措施。

为研究大兴安岭地区火烧迹地识别和火后植被恢复状况，基于2006—2020年Landsat TM遥感影像，利用Google Earth Engine编写代码，以2006年大兴安岭地区松岭区那源林场森林火灾为研究背景，以差分归一化燃烧指数（dNBR）为基础数据进行火烧迹地识别，并对火烧烈度进行轻度、中度、重度和极重的等级划分；基于火烧迹地的增强型植被指数（EVI）值，采用一元线性回归分析、用于气候诊断与预测的Mann-Kendall突变检验法和用于做趋势分析的Theil-Sen median趋势分析等方法分析火烧迹地2006—2020年的植被恢复特征，探究大兴安岭地区火烧迹地植被恢复进程。结果表明，1）基于dNBR得到研究区过火面积为2 488.7 hm~2，其中轻度、中度、重度和极重火烧迹地面积占比分别为23.5%、9.6%、35.2%和31.7%，重度和极重过火区分布于火烧迹地西部和东部，过火强度从中部向南部和北部逐渐降低，其EVI值与火烧前相比分别下降了约30%、40%、58%和67%;2）不同烈度林地火烧迹地EVI恢复速率由大到小表现为极重、重度、中度、轻度，植被恢复过程中，迹地EVI值逐渐增加，其中，轻度和中度火烧迹地可在火后6～8 a恢复，而重度火烧迹地的恢复则需14 a;3）火烧迹地恢复过程中，林地EVI突变点较灌草地少，说明森林生态系统较灌草地稳定性强。不同烈度林地火烧迹地的突变情况也存在一定差异，且对照区的突变时间点滞后于火烧迹地。

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

采用微波辅助离子液体提取方法从黄檗树皮中提取3种生物碱（药根碱、巴马汀和小檗碱）。首先基于单因素试验考察离子液体的种类和料液比对提取效率的影响，进一步考察离子液体浓度、提取时间和温度3种因素对提取效率的影响，最终应用Box-Behnken响应面方法优化，得到最佳提取工艺，并与传统提取方法进行比较。研究结果表明，1-丁基-3-甲基咪唑嗅盐（[Bmim][Br]）溶液对生物碱的提取效率最高，最佳提取条件为离子液体浓度为0.31 mol/L，温度为94℃，时间为15 min，在该条件下药根碱得率为0.047%±0.003%，巴马汀得率为0.421%±0.011%，小檗碱得率为1.150%±0.028%。与传统水加热回流法相比，提取效率分别高出9.3%、12.8%和23.6%。该研究表明，利用微波辅助离子液体提取技术可以高效地从黄檗树皮中提取生物碱，为提取黄檗中活性成分提供坚实的理论基础和实证支持。

将局部密度因子引入削度方程，通过地基激光雷达对黑龙江省佳木斯市桦南县孟家岗林场5块落叶松人工林进行扫描，采集的点云数据用于提取落叶松树高、胸径以及不同相对高度处直径，基于Kozak可变指数削度方程构建引入局部密度因子的削度方程，削度方程拟合结果为调整决定系数R_(adj)~2=0.935，均方根差RMSE=0.735 6，平均偏差Bias=0.869 3。研究表明，引入局部密度因子的削度方程具有良好的拟合精度；对比传统Kozak削度方程和引入全局密度因子的削度方程，引入局部密度因子的削度方程拟合效果更好；落叶松的削度方程其局部密度因子的计算范围与树高和胸径呈正相关。试验结果可以为孟家岗林场科学合理的造材提供理论依据，初步揭示了局部密度因子对于落叶松树干干形的影响。

为了对红松早期生长进行初步评价并选择优良家系，以长白山森林工业集团汪清林业分公司亲和种子园控制授粉获得的117个全同胞家系苗木为研究对象，在亲和种子园91林班4小班营建子代测定林后连续3 a观察其生长情况，并进行综合评价。6～8 a的红松全同胞家系子代苗的苗高、地径在家系间差异达极显著水平，且家系与区组间的交互作用达极显著水平（P<0.01）;6 a平均苗高为39.79 cm，到8 a时年平均增长率为25%,6 a平均地径为11.11 mm，到8 a时平均年增长率为34%，各性状表型变异系数变化范围为15.84%～25.43%；各性状的遗传力较高，有利于家系的进一步选择评价。利用布雷金多性状综合分析法进行优良家系选择，以20%的基准在117个家系中选择24个优势家系。利用生长性状初选出的优良家系，可以为更新种子园世代、促进红松优育扩繁提供基础。

以内蒙古地区牙克石林业管理局的大兴安岭天然白桦林（Betula platyphylla）为研究对象，利用198块样地数据分析天然白桦林林分碳密度与各林分变量之间的关系，建立碳密度预测基础模型，同时将立地条件（草类白桦林、杜鹃-越桔白桦林、榛子白桦林）作为哑变量引入到预测模型中，对不同林型的林分碳密度进行预测，为林业研究中碳密度模型的构建以及森林碳汇工作提供思路和方法。结果表明，天然白桦林林分碳密度基础模型决定系数（R~2）为0.703，均方根误差（RMSE）为8.615 t/hm~2，赤池信息量（Akaike information criterion,AIC）为841.206，贝叶斯信息量（Bayesian Information Criterion,BIC）为851.071。引入立地条件哑变量后，R~2有所增大，最大达到0.818,RMSE均小于等于8.241 t/hm~2，说明模型具有较好的稳定性，预估参数较为精确。哑变量模型的AIC均小于等于541.431,BIC均小于等于550.320。哑变量模型能够反映不同立地条件下碳密度的变化，在模型的拟合和检验方面都显示适合于研究地区林分碳密度的预测，为天然白桦林碳密度估算提供参考。

地上生物量反映植被生长状况和碳储量的大小，该参数的准确性对于碳循环研究以及减缓气候变化至关重要。以芭蕉树为研究对象，提出一种利用深度学习实现芭蕉树冠检测分割和地上生物量估算的新思路。首先，以深度学习算法YOLOv8s-seg为基础框架改进，并应用无人机遥感影像，实现芭蕉树冠检测分割；然后，提取芭蕉树冠覆盖面积，结合实测地上生物量数据进行拟合，使用线性回归、K最近邻算法（K-NearestNeighbor,KNN）、支持向量机、随机森林和XGBoost(eXtreme Gradient Boosting)方法分别建立芭蕉地上生物量估算模型；最后，对模型估测结果进行比较分析确定最优模型。结果表明，改进后的YOLOv8s-seg模型可以快速有效地对芭蕉树冠进行检测分割。通过验证发现，基于XGBoost的地上生物量估算模型拟合效果和预测误差优于其他模型，决定系数（R~2）为0.881 4，均方根误差（RMSE）为231.37 kg，平均绝对误差（MAE）为140.47kg，能够更准确地预测地上生物量，更适于进行芭蕉地上生物量的反演，进一步验证了利用无人机和深度学习方法提取树冠信息实现估算地上生物量的可行性。

为了对林业运材车差速器总成装配密封质量进行事前预测，提高其产品质量及装配合格率，提出一种灰色关联分析算法结合粒子群（PSO）优化BP神经网络的预测模型。将由灰色关联分析算法筛选出影响差速器总成密封质量的关键装配工艺参数作为输入变量，差速器总成泄漏值作为输出变量，创建基于粒子群（PSO）算法优化BP神经网络（PSO-BP）的预测模型，结果表明，由灰色关联分析简化后的PSO-BP预测方法得到的平均相对误差最小为1.18%。在此基础上，应用PyQt5 GUI库开发差速器总成泄漏值预测系统。研究结果可以为差速器总成密封质量预测提供理论依据。

为准确获取森林冠层高度信息，精确估计森林地上生物量及评估森林碳汇能力，基于长时序的地面实测、多源遥感数据与数字高程模型构建30个长时序的特征变量，结合机器学习算法（Machine Learning,ML）对浙江省丽水市的森林冠层高度进行反演。研究表明，地形因素对森林冠层高度的反演呈“不重要性”，而与红绿波段相关的植被因子、森林冠层高度强相关；加入长时序的特征因子可有助于提升ML算法对森林冠层高度反演精度，卷积神经网络（Convolutional Neural Network,CNN）提升的性能尤为显著，实现最佳0.39的决定系数（R²）提升与4.15的均方根误差(R_MSE，式中记为R_MSE)下降；随机森林在4种ML算法中的反演精度最高(R²=0.79,R_MSE=1.65)，大于支持向量机(R²=0.65,R_MSE=1.97)、极端梯度上升法(R²=0.76,R_MSE=1.81)与卷积神经网络(R²=0.71,R_MSE=1.83)。

白桦汁液采集时间大多在早春阶段，容易采集且产量高，为探讨温度对白桦木质部汁液变化的影响及不同发育时期汁液组分质量浓度的变化，以黑龙江省东北林业大学帽儿山实验林场白桦天然次生林为研究对象，通过收集2022年和2023年的白桦汁液，统计春季汁液流量。对2023年白桦不同发育时期汁液中的碳水化合物、氨基酸、总三萜、可溶性蛋白、pH以及钾、钙、钠、镁离子质量浓度进行分析，解析汁液组分变异规律。通过连续2 a取汁的白桦与当年取汁的白桦汁液组分质量浓度对比分析，探索连续取汁是否对白桦生长发育产生影响。结果表明，白桦汁液在4—6月中产量整体呈现先增后降的趋势，高峰期集中在4—5月份，且温度的升高会促进产汁。白桦木质部汁液的总三萜、碳水化合物、氨基酸、蛋白质组分质量浓度在整个物候发育时期均呈现先升后降再升的趋势，均在旺盛生长期时达到最低值。随物候发育白桦汁液pH整体呈下降趋势，在完全展叶前不断酸化。离子质量浓度整体呈先上升后下降的趋势。持续取汁不会对白桦汁液中总三萜、氨基酸、蛋白质质量浓度造成显著影响，会使碳水化合物质量浓度及pH降低。研究结果可为黑龙江省东部山地白桦汁液的科学采集提供指导依据。

沥青胶砂-集料界面相的低温黏结性能对寒区沥青混合料的低温抗裂性、水稳性等性能有重要影响。基于拉拔试验并采用ImageJ软件对破坏面进行图像分析，通过黏结强度和黏结失效比2个指标，分析不同低温（-10、-20、-30℃）和有水条件下沥青、沥青胶浆、沥青砂浆与石灰岩集料组成的界面相黏结性能。研究结果表明，沥青-集料界面相在-20℃黏结强度最大，黏结失效比随着温度的降低在增大；水的介入会使界面相低温黏结强度的衰减率达到49%，界面连续脱落，黏结失效比增大44%；合适的粉胶比能提高沥青胶浆的低温黏结强度，粉胶比为1.2的沥青胶浆黏结强度为沥青基质的1.44倍，并通过弯曲蠕变劲度试验验证与沥青胶浆低温黏结强度结果的一致性；矿粉和细集料的加入改变了界面的接触形式，破坏位置和黏结失效比也发生了明显变化；沥青胶浆的破坏位置发生于胶浆内部，沥青砂浆的破坏位置多发生于界面处，其黏结失效比远大于沥青胶浆。

为降低蒙古栎（Quercus mongolica）组培过程褐化现象，阐明褐化发生是否与抗氧化酶系统及酚类物质产生有关。以蒙古栎成熟合子胚为材料，利用组织培养技术优化蒙古栎再生植株体系获得再生植株，探究聚乙烯吡咯烷酮（PVP）抑制褐化处理对蒙古栎合子胚萌发的生理生化指标的影响。结果表明，PVP 40(40代表PVP平均分子量范围)抑制褐化效果最佳，添加0.2 g/L PVP 40不定芽诱导率最高，为71.17%，其芽增殖系数为3.90，芽生根率为40.37%，移栽存活率为73%;PVP降低了蒙古栎外植体抗氧化酶、多酚氧化酶活性及总酚酶活力，有效抑制褐化现象。PVP有效降低蒙古栎组培褐化效果抑制褐化不再加剧，优化蒙古栎器官再生植株体系，分析PVP抑制褐化处理对蒙古栎合子胚萌发的内在生理机制，为栎属其他树种抑制褐化提供参考。

新疆是中国重要的林果产业基地，特色林果业是区域经济发展的重要组成部分。为预防果树病害制约林果业发展，设计一款归一化注意力（normalization-based attention module,NAM）轻量级深度卷积神经网络（MobileNet-V2）果树叶片分类及病害识别模型。其中融入轻量型的归一化注意力机制，提高模型对特征信息的敏感度，使模型关注显著性特征。同时，将L1正则化（L1 regularization或losso）添加到损失函数中，对权重进行稀疏性惩罚，抑制非显著性权重。试验结果表明，在叶片分类中，模型对自构建植物叶片病害识别数据集（Plant Village）、混合数据集的分类结果均表现良好，准确率分别达到97.05%、98.73%、94.91%，具有较好的泛化能力。在病害识别中，MobileNet-V2 NAM模型实现94.55%的识别准确率，高于深度卷积神经网络（AlexNet）、视觉几何群网络（VGG16）经典卷积神经网络（Convolutional Neural Networks,CNN）模型，且模型参数量只有3.56 M。MobileNet-V2 NAM在具有良好准确率同时保持了较低的模型参数量，为深度学习模型嵌入到移动设备提供技术支持。

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

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

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

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

探讨不同林龄苹果林地枯落物和土壤水源涵养能力的变异特征，明确苹果林地的水分特征，对建立合理的果园管理制度等具有重要意义。以运城市临猗县不同林龄（4、6、15、30 a）苹果林地为研究对象，通过野外调查和室内试验，对苹果林地枯落物和土壤水源涵养能力进行定量描述和差异性研究。结果表明，4种林龄苹果林地枯落物层总蓄积量为0.82～2.09 t/hm²，最大持水量介于4.91～13.09 t/hm²，有效拦蓄量在3.36～9.38 t/hm²，随着林龄的增长，以上参数量均表现为先增大再减小的趋势，以15 a为最大。枯落物层持水量、吸水速率与浸水时间分别存在良好的对数和幂函数关系。在0～60 cm范围内，随着林龄的增长，苹果林地土壤容重逐渐减小，总孔隙度和最大持水量逐渐增加，以30 a为最大。随着土层加深，不同林龄苹果林地土壤容重呈现先增大再减小的趋势；除林龄4 a外，其余林龄土壤总孔隙度逐渐减小，但各土层之间差异性不显著。30 a苹果林地土壤最大持水量随土层加深逐渐降低，其余林龄呈现先减小再增加的趋势，均以0～10 cm土层持水量最大，20～40 cm土层最低。坐标综合评定法得出林龄在15 a时枯落物层和土壤层（0～60 cm）总水源涵养功能最强，30 a林龄次之，4 a林龄最弱。

目前，虽然已经有一些基于图像处理技术的竹片缺陷检测方案，但这些方案检测存在种类较少、实用性较差且难以部署在机器上等缺陷，为此，提出一种改进的竹片缺陷检测模型。该模型为改进的可变形-端到端目标检测（DeformableDETR）模型，首先将骨干网络替换成由DCNv3卷积为核心而堆叠设计的InternImage，该网络在保留卷积神经网络（CNN）先验特性的情况下还能捕捉到长距离依赖，使提取到的特征空间语义更丰富；然后在特征提取后新增一个采样模块，该采样模块将图像特征抽象为精细的前景特征和少量粗糙的背景特征，不仅能去除冗余的背景特征信息，还能提取高语义前景信息；最后引入一种新颖的协作混合分配训练策略，该策略通过训练由一对多标签分配监督的多个并行辅助头，提高编码器在端到端检测器中的学习能力。此外，使用数据增强来扩展数据集，并使用迁移学习，以增强竹片缺陷的检测。试验结果表明，该改进方法可以提高模型的缺陷特征提取与解析的能力，并在测试数据集上取得了85.7%mAP50（全类平均精确度），单张图片推理时间为0.28 s，检测精度优于其他主流目标检测模型，为竹片缺陷检测提供新的方法。

隧道结构在施工及运营期间，受诸多复杂因素的共同作用，其受力状态与变形特性会随时间逐渐演变。为此，提出一种融合注意力机制的深度学习概率模型，旨在精准预测与评估隧道衬砌结构中关键不利位置的安全状态。首先，采用斯皮尔曼秩相关系数进行数据预处理，筛选出与衬砌结构最不利位置高度相关的土压力数据和混凝土应变数据作为输入特征；随后，设计多层卷积神经网络（CNN）进行多源数据特征提取，并构建特征共享层以融合不同位置的数据信息；接着，将提取的特征送入长短期记忆网络（LSTM）进行时间序列分析预测，并引入自注意力（Attention）机制对特征进行加权优化，从而进一步提高预测的精确度；最后，建立高斯概率回归模型，以解决结构响应预测误差所引起的安全系数计算不确定性量化和评价问题。使用实际隧道工程项目的数据，对不利位置的响应预测结果显示，该模型能够全面考虑多源测点数据在时间和空间上的相关性。在训练集、验证集和预测集上，混凝土应变的预测平均误差分别为0.89、1.02、1.24με，未出现过拟合现象，从而验证所提方法在处理复杂非线性问题的良好泛化能力。此外，采用高斯概率区间预测方法，结合预测得到的安全系数进行了90%置信水平的区间估计，结果显示，二次衬砌3个不利位置的安全系数均位于该置信区间内，从而进一步验证所提模型在隧道衬砌结构安全性评估中的可靠性和实用性。

木材及其制品被广泛应用于人们日常生活，包括家具制造、室内装饰和建筑等领域。木材工业的年产值总量已经超过3万亿元，是重要的国民经济基础产业。然而，木业发展过程中仍存在产品附加值低、企业创新能力弱和生产供应链分散等问题，向新出发、向绿而行，孵化功能木质制造新技术、新材料，培育木业新质生产力是助推行业发展、蓄能助力低碳转型的重要方向。为此，聚焦木材科学前沿发展方向，针对木业新质生产力重要论述，阐明科技创新对木业发展的重要意义，提出木材科学未来发展向极微观深入、向极宏观拓展、向极端条件迈进、向极综合交叉发力4个发展方向，并综述了现阶段具代表性研究成果，梳理代表性木质新产品和功能新材料的发展现状和发展趋势，为进一步提出我国木业新质生产力发展路径奠定基础。

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

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

为开发刨花板用无醛胶黏剂、满足广阔的市场需求，针对传统淀粉胶黏剂存在固含量低、黏度高和耐水胶接性能差等应用问题，采用新型无醛淀粉胶黏剂（DSA）制备刨花板，探讨其热压工艺、施胶量以及DSA和异氰酸酯（pMDI）胶黏剂复合使用对制造刨花板性能的影响。结果表明，以DSA制得刨花板的性能略低于以pMDI制得刨花板的性能，但DSA与pMDI复合使用时表现出节胶增效的协同作用，而且使用DSA可替代约20%的pMDI胶黏剂，同时热压因子可降低13.3%，有助于提升ENF级刨花板的生产效率。DSA胶黏剂单独使用或与pMDI联用时，能够有效发挥黏结、填充、扩链、固化的作用，有助于解决传统淀粉胶黏剂工艺性能不佳、耐水胶接性能差等关键问题，有利于推动生物质绿色胶黏剂在人造板中的规模化应用。

将单板层积材（laminated veneer lumber,LVL）和冷弯薄壁型钢通过结构胶复合构成工字形截面的钢-木组合梁，以梁的LVL板厚度、截面高度、薄壁型钢厚及翼缘LVL宽度等尺寸为参数，对9根钢-LVL组合工字形梁进行受弯性能试验，观察试件的破坏形态、挠度发展、应变分布和承载能力，对影响其受弯承载力的因素进行分析，并提出考虑滑移效应的跨中挠度计算公式。研究结果表明，钢-LVL组合梁具有良好的整体工作性能，破坏前变形能力较强，其受弯时最典型的破坏模式为下翼缘LVL被拉断，材料性能得到充分发挥，表现出较好的延性。增大截面高宽比、腹板高厚比，减小翼缘宽厚比可以提高组合梁的抗弯承载能力。LVL和型钢之间的滑移会降低组合梁的刚度，与未考虑滑移的结果相比，考虑界面滑移效应的组合梁跨中挠度理论计算结果更接近梁的真实变形，计算精度提高2.38%。

随着中宣部2021年《关于推动学术期刊繁荣发展的意见》的印发，国家对于期刊的发展越来越重视，学术期刊的高质量发展模式成为当前出版行业研究热点。从期刊的可持续发展来说，人才至关重要。期刊的人才包括编辑、作者和编委（青年编委）。其中，青年编委在期刊的发展过程中发挥着越来越大的作用。为此，结合《森林工程》启用青年编委十多年的工作经验，着眼于青年编委作为同行评议专家对期刊发展的作用，深入探讨青年编委在农林类科技期刊办刊中的参与性与重要性，为学术期刊高质量、可持续发展提供参考与借鉴。

为探究基于主成分分析（Principal Component Analysis,PCA）下贝叶斯优化（Bayesian Optimization,BO）卷积神经网络（Convolutional Neural Network,CNN）算法（PCA-BO-CNN）模型对人工林树种识别的方法，以提高遥感技术在人工林树种识别中的准确率和鲁棒性。以塞罕坝机械林场为研究区域，利用Sentinel-1遥感数据、Sentinel-2遥感数据、数字高程模型（digital Elevation Model,DEM）数据及森林资源二类调查数据和PCA-BO-CNN算法模型结合，并与其他不同算法模型对比分析，以提高人工林树种识别的准确性。结果表明，1）相比PCA算法处理前，PCA算法处理后多源数据特征的PCA1—PCA39共计39个特征的标准差和特征间的区分性明显提升。因此，PCA算法处理有利于提升对华北落叶松、白桦、樟子松、蒙古栎和云杉主要优势树种及非林地的识别精度；2）在PCA算法处理前，BO-随机森林（random forest,RF）算法模型对主要优势树种及非林地识别的总体准确度（OA）和Kappa系数精度，分别为81.87%,0.754 5。在PCA算法处理后，PCA-BO-CNN算法模型对主要优势树种及非林地识别的OA和Kappa系数精度相对提高，分别为83.10%,0.770 3;3）相比PCA算法处理前的BO-RF算法模型，PCA算法处理后的PCA-BO-CNN算法模型对塞罕坝林场主要优势树种及非林地识别的调和平均数（F1）、OA和Kappa系数的整体精度相对较高。具体，相比BO-RF算法模型PCA-BO-CNN算法模型的OA提升了1.24%，且相比PCA算法处理前PCA-BO-CNN算法模型OA提升了3.71%。与其他算法模型相比，基于PCA-BO-CNN算法模型的人工林树种识别方法具有很强的准确性和鲁棒性，为掌握塞罕坝林场人工林的树种分布，进而了解森林碳储量、森林对气候变化的响应、制定碳减排政策以及推动森林可持续发展提供重要的理论依据。

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

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

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

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

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