Taking Wuyishan national park as a case, this study developed a community participation in social-ecological systems (CP-SES) framework to identify the key factors and mechanisms influencing community participation. The aim is to promote effective community engagement and achieve harmony between humans and nature.

Based on Ostrom's social-ecological system (SES) framework, this study constructed a tailored CP-SES analytical framework. A questionnaire survey and semi-structured interviews were conducted in the townships within and surrounding Wuyishan national park to collect data. An ordered multinomial logistic regression model was employed to identify significant influencing factors.

Four key factors were identified as significantly impacting community participation in Wuyishan national park: 1) “Community empowerment” within the governance system (OR is 2.325, P<0.05); 2) “Willingness to insure” (OR is 1.430, P<0.05) and “skills training” (OR is 1.850, P<0.05) within the actor system; 3) “Human-wildlife conflict” under interactions and outcomes (OR is 0.641, P<0.01). Community empowerment, willingness to insure, and skills training were found to be primary drivers promoting participation, whereas human-wildlife conflict was a significant impeding factor. The analysis of mechanisms revealed that community empowerment enhances residents' motivation by granting decision-making power and broadening participation channels. Residents' willingness to insure, driven by the benefits of commercial insurance, positively alters their attitudes and strategies towards the national park. Skills training overcomes the limitations of low educational attainment, thereby enhancing livelihood security and participatory impetus. Conversely, human-wildlife conflict undermines livelihoods, leading to negative attitudes towards the park and hindering community participation.

Based on the CP-SES framework adapted from Ostrom's social-ecological systems (SES) framework—comprising subsystems of resource systems, resource units, governance systems, and actors—this study provides a panoramic characterization of the logical interplays among various elements within the action situation of community participation in national parks. Addressing the critical issues diagnosed and the influencing factors identified through this framework, it is imperative to secure community empowerment, strengthen skills training, and refine human-wildlife conflict compensation mechanisms. Such measures are essential to facilitate robust community participation and achieve the sustainable development of both national parks and local communities.

This study proposed a high-precision, high-efficiency method for surface defect detection of particleboard that is suitable for practical industrial scenarios to address the problems of high missed detection rate, slow detection speed, and insufficient ability to identify complex defects associated with traditional detection methods in current particleboard production, thereby achieving a dual optimization of detection accuracy and real-time performance.

Based on RT-DETR-r18, ablation experiments were conducted using the control variable method to verify the effectiveness of the three key modules: ECG-CFNet, FSA, and MPCA. Several mainstream lightweight backbone networks, including Swin Transformer and VanillaNet, were substituted for comparison of their training performance. The improved model was further compared with SSD, Faster-RCNN, YOLO series, and the original RT-DETR to validate its superiority. Key indicators such as mAP@50 and GFLOPs were adopted, combined with performance curves, PR curves, and visualization results, to comprehensively evaluate the detection accuracy, efficiency, and industrial applicability of the model.

Comparison experimental results on standard defect image datasets show that the proposed improved algorithm achieves 91.9% in terms of mean detection accuracy (mAP@50), which is 2.9% higher than the original RT-DETR-r18 model. At the same time, the number of parameters of the model and the computational complexity are both decreased, which suggests that while guaranteeing the improvement of the detection accuracy, the detection efficiency is optimized as well. Specifically, in the detection of typical defect types such as cracks, shavings and black spots, both show higher recognition rates and lower false alarm rates.

The improved RT-DETR algorithm significantly improves the accuracy and real-time performance of particleboard surface defect detection while maintaining low computational resource consumption, and has strong industrial deployability and application prospects.

To precisely obtain the dynamic responses of fruits and flower buds under vibratory excitation and determine their detachment states through coupled modeling of wood viscoelasticity and tree dynamics, so as to provide reference for the design of vibratory harvesting parameters for Camellia oleifera fruits.

Creep data of C. oleifera wood samples were first obtained via compression creep tests, and the generalized Kelvin-Voigt model was employed to fit the creep data to determine the viscoelastic parameters of the wood. Subsequently, finite element simulations of compression creep tests were conducted to verify the predictive accuracy of the identified viscoelastic parameters. On this basis, a finite element model of the C. oleifera tree was further established based on the viscoelastic constitutive model, and both modal analysis and transient dynamic analysis were performed to obtain the modal shapes and natural frequencies of the tree. The inertial force responses of C. oleifera fruits and flower buds under vibratory excitation were then calculated, and finally the detachment rates of the fruits and buds were predicted based on the peak inertial forces.

1) Compared with conventional binary and ternary models, the generalized viscoelastic models can significantly enhance the goodness-of-fit for creep data within the testing duration, which enables accurate characterization of the short-term creep mechanical behavior of C. oleifera wood (achieving 99.92% predictive accuracy); 2) At specific excitation frequencies, branch amplitudes far exceed that of the trunk; this “branch-dominated resonance” pattern can serve as a basis for excitation frequency selection; 3) Under trunk excitation at the selected frequency of 9.428 Hz (with a 10 mm amplitude and a 400 mm excitation height), the fruit detachment rate reaches 93.94% and the bud detachment rate remains only 6.06%, meeting the requirements for high-efficiency and low-damage harvesting.

This study reveals the “branch-dominant resonance” phenomenon in the C. oleifera tree at specific frequencies, delineates the distinct inertial force responses between fruits and flower buds under vibratory excitation, and establishes detachment criteria based on peak inertial forces. These findings provide a quantifiable parametric design guideline for the efficient and low-damage vibratory harvesting of C. oleifera fruits.

Market demand in the wood furniture supply chain is strongly affected by seasonal fluctuations, unexpected disturbances, and multi-factor coupling, which often limits the accuracy and stability of traditional single forecasting models. To address this issue, an AMBOP-SRC model is proposed for market demand forecasting in the wood furniture supply chain, with the aim of improving forecasting accuracy, robustness, and generalization capability.

First, the wood furniture sales data were cleaned, normalized, and aggregated at the weekly level to construct a demand time series. Then, a SARIMA model was employed to capture the linear trend and seasonal patterns of the demand series, and the initial residuals were extracted. Next, an SVR model optimized by particle swarm optimization (PSO-SVR) was used to fit the nonlinear fluctuations contained in the residual series. On this basis, an adaptive movement block optimization procedure (AMBOP) algorithm was introduced to dynamically optimize the parameters and collaborative forecasting process of the hybrid model, thereby improving search efficiency and prediction stability in a high-dimensional parameter space. Finally, ablation experiments, comparative experiments, and generalization experiments were conducted, and model performance was evaluated using indicators such as RMSE and MAE.

Ablation experiments demonstrate that the absence of any component in the AMBOP-SRC forecasting model leads to a significant decline in predictive performance. The AMBOP method not only optimizes model parameters but also effectively balances the relationship between seasonal fluctuation modeling and emergency response, achieving deep adaptation to the characteristics of the furniture industry. Performance comparison experiments show that the AMBOP-SRC forecasting model significantly outperforms classical forecasting methods in the corresponding field, effectively improving the accuracy and interpretability of wooden furniture supply chain demand forecasting. Generalization experiments indicate that the AMBOP-SRC forecasting model exhibits strong adaptability across different datasets, making it applicable to demand forecasting in key stages of the wooden furniture supply chain.

The AMBOP-SRC forecasting model is highly suitable for wooden furniture supply chain demand forecasting, featuring high prediction accuracy and excellent generalization performance. Its results provide crucial decision-making references for furniture enterprises in optimizing inventory turnover rates and dynamically adjusting production plans, thereby enhancing supply chain resilience.

To explore the regulatory effect of novel miRNA phe-novel-miR276 on lignin synthesis in moso bamboo, so as to provide theoretical reference for material quality improvement and molecular breeding of moso bamboo.

Using bioinformatics methods, we analyzed the novel differentially expressed miRNA (phe-novel-miR276) screened in the preliminary study of moso bamboo. qRT-PCR was applied to detect its expression patterns in different moso bamboo tissues, followed by calculation of relative expression levels and differential analysis. The phe-novel-miR276 was cloned to construct an overexpression plant vector, which was transformed into rice via Agrobacterium mediation for screening positive transgenic lines. Lignin content as well as POD and SOD activities of transgenic and wild-type rice were determined, and intergroup differences of each index were compared and analyzed.

1) Sequence analysis revealed that phe-novel-miR276 shares high homology with the miR398 family, suggesting it is a new member of the miR398 family; 2) phe-novel-miR276 is expressed in the roots, stems, and leaves of P. edulis, with the highest relative expression level in the roots; 3) Phloroglucinol staining showed that the lignin staining depth in the stems and leaves of transgenic lines was lighter; meanwhile, the results of the microdetermination method indicated that the total lignin content in the stems and leaves of transgenic lines was significantly reduced; 4) qRT-PCR detection demonstrated that, compared with wild-type rice, the expression level of Os10g36848-the rice homologous target gene of phe-novel-miR276 (encoding ferulate 5-hydroxylase, F5H)-was significantly downregulated in transgenic lines. Additionally, the POD and SOD activities in transgenic lines were significantly higher than those in wild-type rice, respectively.

In P. edulis, phe-novel-miR276 exerts its regulatory role by downregulating the expression of lignin biosynthesis-related target genes, and indirectly affects the activity of the antioxidant enzyme system in plants.

To reveal the genetic basis of important economic traits in Dendrocalamus latiflorus, elucidate the patterns of genetic variation, and to accelerate the process of genetic improvement and precision breeding.

Eight geographically diverse D. latiflorus provenances were evaluated, with provenance codes LZ-1, LZ-2, LZ-3, LZ-4, BS, YD, JY, and FJ. Seedling stage phenotypic variation was analyzed by assessing above- and below-ground morphological traits and biomass. Genetic parameters were estimated. Superior provenances were identified using membership function for comprehensive evaluation.

11 phenotypic traits showed significant (P<0.05) or extremely significant (P<0.01) differences among different provenances of Ma bamboo (Dendrocalamus latiflorus). Among them, provenance YD had the highest number of shoots (8.83); provenance LZ-4 had the highest values for leaf expansion number, average root length, leaf area, ground diameter, and plant height (25.63, 9.76 cm, 9 566.77 mm², 19.70 mm, 2.96 cm, respectively); provenance LZ-3 had the highest number of roots, average root diameter, root biomass, and culm number (23.19, 1.22 mm, 1.20 g, 1.64, respectively); provenance FJ had the largest culm biomass (82.93±24.46 g). The genetic coefficient of variation for each trait ranged from 0.34% to 36.92%, and heritability (H²) exceeded 0.50 for all traits; heritability was extremely high for plant height (0.96), culm number (0.95), leaf area (0.94), ground diameter (0.92), and number of shoots (0.89). Correlation analysis showed that the number of bud emergence was significantly (P<0.05) or extremely significantly (P<0.01) positively correlated with leaf expansion number, mean root length, mean root diameter, bud biomass, and leaf biomass. Leaf area was extremely significantly (P<0.01) positively correlated with bud biomass, leaf biomass, root biomass, total biomass, and culm number. Regarding phenotypic genetic gain for each trait, provenance LZ-4 was the most prominent, with positive genetic gains for multiple traits, including number of shoots (2.53%), leaf expansion number (7.03%), average root length (1.84%), bud biomass (2.56%), leaf biomass (1.24%), and ground diameter (2.23%). Based on principal component analysis, four principal components were extracted, with a cumulative contribution rate of 79.034%, covering key phenotypic characteristics such as growth activity, root development, and biomass accumulation of Ma bamboo. The comprehensive evaluation results showed that provenance LZ-4 had the highest comprehensive score (1.179).

The eight provenances of Ma bamboo showed differences in seedling traits, among which provenance LZ-4 performed best, followed by JY, BS and LZ-2.

A diameter distribution model was constructed to fit the diameter class structure of a mixed forest of spruce and fir trees.

Taking a 1 hm² coniferous and broad-leaved mixed plots in the Jingouling forest farm of Jilin region as the research object, a finite mixed Weibull model was used for fitting the diameter distribution of each plots, and the fitting results were compared with the Gamma model and Weibull model. The results indicated that for most of the mixed forests of spruce and fir in the region, they belong to a unimodal distribution, and from the perspective of shape parameters, they belong to a positively skewed mountainous distribution.

The RMSE (root mean square error) and MAE (mean absolute error) of the finite mixed Weibull model are significantly smaller than those of the Weibull model and Gamma model, indicating a clear advantage; The RMSE error of the finite mixed Weibull distribution decreased by 29.38% and 31.73% respectively compared to the Weibull distribution and Gamma distribution, and the MAE decreased by 28.27% and 36.23% respectively. Regardless of unimodal or multimodal distribution, the finite mixed Weibull model is superior to the Weibull model. From the composition of tree species in various plots, the advantage of the limited mixed Weibull model is greater in plots with a higher proportion of coniferous trees.

It indicates that more finite mixed Weibull models can be used to replace the Weibull model when fitting the diameter distribution of spruce and fir mixed forests.

This study aimed to systematically analyze the phenotypic traits of Xanthoceras sorbifolium seeds from different provenances, to construct a scientific germplasm evaluation system, and to provide a theoretical basis for genetic improvement, germplasm innovation, and industrial development of Xanthoceras sorbifolium.

Seeds from 12 provenances (including Gansu, Shanxi, Shandong, etc.) were used as experimental materials. Ten traits, including longitudinal diameter, transverse diameter, and single seed weight, were measured. Multivariate analyses, including one-way ANOVA, Pearson correlation analysis, cluster analysis, and principal component analysis (PCA), were combined to reveal differences in phenotypic traits, internal correlations, and comprehensive performance among provenances.

Significant differences in seed phenotypic traits were observed among provenances (P<0.05). Single seed weight showed coefficient of variation, while seed shape index exhibited the lowest. 100-seed weight was significantly and positively correlated with transverse diameter, longitudinal diameter, kernel weight, and single seed weight (P<0.05), whereas seed volume was significantly negatively correlated with kernel rate (P<0.05). Cluster analysis divided the provenances into three groups: Group I (provenances 2, 3, 9, 11, 12), termed the “small-seed low-weight type”; Group II (provenances 1, 7, 8, 10), the “medium-seed medium-weight type”; and Group III (provenances 4, 5, 6), the “large-seed high-weight type”. PCA indicated that the first three principal components explained 74.05% of the cumulative variance, with provenance 4 achieving the highest comprehensive score (164.89 points), demonstrating the most superior germplasm characteristics.

The established “morphological trait analysis—statistical model construction—germplasm classification” three-level evaluation system provides a new framework for scientific evaluation and genetic improvement of Xanthoceras sorbifolium germplasm. Due to its outstanding comprehensive performance, Provenance 4 is recommended as an excellent parental material for stress-resistant breeding.

This study focused on the soil nutrient characteristics of the mixed forest of Cunninghamia lanceolata/Sassafras tzumu with Chimonobambusa quadrangularis planted under the canopy in the state-owned forest farm of Junlian County, Sichuan Province. By comparing the soil nutrient features of the mixed forests of Cunninghamia lanceolata + Chimonobambusa quadrangularis and Sassafras tzumu+Chimonobambusa quadrangularis with those of the pure forests of Cunninghamia lanceolata, Sassafras tzumu and Chimonobambusa quadrangularis, the study aimed to explore the effect of forest and bamboo compound management on soil nutrients, and to provide a scientific basis for improving soil quality and efficiency in the hilly areas of southern Sichuan.

By analyzing and determining the soil chemical properties of the compound forest stands of Cunninghamia lanceolata + Chimonobambusa quadrangularis, Sassafras tzumu + Chimonobambusa quadrangularis, the pure forest stands of Cunninghamia lanceolata, Sassafras tzumu and Chimonobambusa quadrangularis, using the Nemerow method for comprehensive evaluation of soil, the influence of mixed management on forest soil was explored.

1) The mixed management significantly increased soil nutrient content. In terms of basic nutrients, the soil organic matter content of the mixed forests of Cunninghamia lanceolata+Chimonobambusa quadrangularis and Sassafras tzumu+Chimonobambusa quadrangularis was 77.20±3.07 g/kg and 82.57±16.43 g/kg, respectively, which was 43.1% and 14.4% higher than that of the pure forests of Cunninghamia lanceolata (53.97±6.08 g/kg) and Sassafras tzumu (72.17±7.40 g/kg); 2) In terms of available nutrients, the contents of water-soluble nitrogen, available phosphorus and available potassium in the mixed forest of Sassafras tzumu+Chimonobambusa quadrangularis were 268.00 ± 43.21 mg/kg, 3.42 ± 1.16 mg/kg and 145.67 ± 20.67 mg/kg, respectively, which were 16.0%, 10.7% and 20.7% higher than those of the pure forest of Sassafras tzumu; 3) The comprehensive evaluation of soil showed that the Nemerow comprehensive index of the mixed forest of Sassafras tzumu+Chimonobambusa quadrangularis was 1.46, and the forest soil nutrient grade was improved from medium to good.

The mixed management of forest and bamboo effectively improved the soil nutrient status through the ecological complementary effect among speciess compared with pure forest management. Among them, the compound forest of Sassafras tzumu+Chimonobambusa quadrangularis performed best in increasing soil organic matter content, promoting the accumulation of nitrogen, phosphorus and potassium nutrients and improving the proportion of available nutrients. The research results not only enriched the soil ecological theory of forest-bamboo mixed management, but also provided practical references for the ecological restoration and sustainable management of degraded forest land in the hilly areas of southern Sichuan.