Search Results (1,060)

The phaeozem in Northeast China is rich in soil organic carbon (SOC). However, the excessive and inefficient application of chemical fertilizers, particularly nitrogen fertilizers, has primarily led to a decrease in soil pH in this region. Currently, the relationship between soil pH and the stability of soil organic carbon (SOC) remains ambiguous. This study, conducted over 13 years of field experiments, focused on soils exhibiting varying degrees of pH resulting from different nitrogen application rates. The research employed aggregate classification, ¹³C nuclear magnetic resonance spectroscopy, and analysis of microbial community composition to investigate the alterations in the SOC stabilization mechanisms under varying nitrogen application levels. Our results demonstrated that the decline in soil pH led to reductions in macroaggregates (>2 mm) and the soil aggregate destruction rate (PAD) by 4.8–14.6%, and in soil aggregate unstable agglomerate index (E_LT) by 9.7–13.4%. The mean weight diameter (MWD) and geometric mean diameter (GMD) exhibited significant declines (p < 0.05) with decreasing pH levels. According to the ¹³C NMR analysis, the SOC was predominantly composed of O-alkyl carbon and aromatic carbon. At a pH of 5.32, the Alip/Arom values decreased, while the molecular structure of SOC became more complex under different levels of pH. In addition, the increase in [Fe(Al)-OC] (31.4–71.9%) complex indicates a shift in the stability of organic carbon from physical protection to organic mineral binding. Declining soil pH significantly reduced the diversity of soil microbial communities and promoted a shift toward copiotrophic microbial groups. Overall, declining soil pH resulted in a decline in soil aggregate stability and an increase in SOC aromaticity. This drove the shift in the stabilization mechanism of SOC in the black soil ecosystem of meadows in Northeast China from physical protection to chemical stability. Full article

In response to the difficulties in multi-disciplinary coordination, the complexity of schedule management, and the weakness of risk control in the construction of high-voltage substations, and based on the current construction status and historical experience of high-voltage projects in Jilin Province, this paper, from the perspectives of schedule and risk management, proposes a multi-disciplinary coordination and risk control strategy that integrates the work breakdown structure (WBS), design structure matrix (DSM), critical chain project management (CCPM), and the fuzzy analytic hierarchy process (FAHP). First, the task flow is decomposed using WBS, and DSM-based coupling analysis is employed to identify interdependencies among disciplines, thereby optimizing task sequencing and parallel arrangements. Second, an optimized project schedule model is established using CCPM, with aggregated buffers that enhance the reliability and flexibility of schedule management. Finally, a risk register is developed based on field investigations, and a three-dimensional quality–schedule–safety risk assessment model is constructed using FAHP; targeted risk prevention and control measures are then proposed according to the quantitative evaluation results. A 500 kV substation project in Jilin Province is adopted as a case study for application and verification. Compared with traditional serial scheduling, the proposed schedule optimization strategy shortens the overall project duration by 29.1%. Furthermore, targeted management recommendations were proposed based on the risk assessment results of the project. The proposed optimization strategy can provide theoretical support and practical guidance for the construction of high-voltage substations and their associated projects, forming an effective technical solution that is scalable and replicable, and it is of great significance for improving the level of project construction management. Full article

Influenza D virus (IDV), an emerging orthomyxovirus with zoonotic potential, infects diverse hosts, causes respiratory disease, and remains poorly characterized in China despite its global expansion. From October 2023 to January 2025, we collected 563 nasal swabs from cattle across 28 farms in Jilin Province, Northeast China, and identified seven IDV-positive samples (1.2%), recovering two viable isolates (JL/YB2024 and JL/CC2024). Full-genome sequencing revealed complete, stable seven-segment genomes with high nucleotide identity (up to 99.9%) to contemporary Chinese D/Yamagata/2019 strains and no evidence of reassortment. Maximum-likelihood and time-resolved Bayesian phylogenies of 231 global hemagglutinin-esterase-fusion (HEF) sequences placed the Jilin isolates within the East Asian D/Yamagata/2019 clade and traced their most recent common ancestor to approximately 2017 (95% highest posterior density: 2016–2018), suggesting a cross-border introduction likely associated with regional cattle movement. No IDV was detected in parallel surveillance of swine, underscoring cattle as the principal reservoir and amplifying host. Bayesian skyline analysis demonstrated a marked decline in global IDV genetic diversity during 2020–2022, coinciding with livestock-movement restrictions imposed during the COVID-19 pandemic. Collectively, these findings indicate that IDV circulation in China is sporadic and geographically localized, dominated by the D/Yamagata/2019 lineage, and shaped by multiple independent incursions rather than a single emergence. Both the incorporation of IDV diagnostics into routine bovine respiratory disease surveillance and cattle-import quarantine programs, and the adoption of a One Health framework to monitor potential human spillover and future viral evolution, were recommend. Full article

To address issues such as significant scale differences, complex pose variations, strong background interference, and similar category characteristics of pests in the images obtained from field traps, this study proposes a pest recognition method based on a two-stage “segmentation–detection” approach to improve the accuracy of field pest situation monitoring. In the first stage, an improved segmentation model, BAE-UNet (Background-Aware and Edge-Enhanced U-Net), is adopted. Based on the classic U-Net framework, a Background-Aware Contextual Module (BACM), a Spatial-Channel Refinement and Attention Module (SCRA), and a Multi-Scale Edge-Aware Spatial Attention Module (MESA) are introduced. These modules respectively optimize multi-scale feature extraction, background suppression, and boundary refinement, effectively removing complex background information and accurately extracting pest body regions. In the second stage, the segmented pest body images are input into the YOLOv8 model to achieve precise pest detection and classification. Experimental results show that BAE-UNet performs excellently in the segmentation task, achieving an mIoU of 0.930, a Dice coefficient of 0.951, and a Boundary F1 of 0.943, significantly outperforming both the baseline U-Net and mainstream models such as DeepLabV3+. After segmentation preprocessing, the detection performance of YOLOv8 is also significantly improved. The precision, recall, mAP50, and mAP50–95 increase from 0.748, 0.796, 0.818, and 0.525 to 0.958, 0.971, 0.977, and 0.882, respectively. The results verify that the proposed two-stage recognition method can effectively suppress background interference, enhance the stability and generalization ability of the model in complex natural scenes, and provide an efficient and feasible technical approach for intelligent pest trap image recognition and pest situation monitoring. Full article

This study addresses the practical demand for facial recognition of pigs in the food safety and insurance industries, tackling the challenge of low recognition accuracy caused by complex farming environments, occlusions, and similar textures. To this end, we propose an enhanced model, DGS-YOLO, based on YOLOv11n, designed to achieve precise facial recognition of group-raised young pigs. The core improvements of the model include the following: (1) replacing standard convolutions with dynamic convolutions (DMConv) to enhance the network’s adaptive extraction capability for critical detail features; (2) designing a C3k2_GBC module with a bottleneck structure to replace the C3k2 neck, enabling more efficient capture of multi-scale contextual information; (3) introducing the SimAM parameter-free attention mechanism to optimize feature focusing; (4) employing the Shape-IoU loss function to mitigate the impact of bounding box geometry on regression accuracy. Experiments on self-built datasets demonstrate that DGS-YOLO achieves 4%, 2.1%, and 2.3% improvements in accuracy, recall, and mAP50, respectively, compared to the baseline model YOLOv11n. Furthermore, its overall performance surpasses that of Faster R-CNN and SSD in comprehensive evaluation metrics. Especially in limited sample scenarios, the model demonstrates strong generalization ability, with accuracy and mAP50 further increased by 20.1% and 10.3%. This study provides a highly accurate and robust solution for animal facial recognition in complex scenarios. Full article

Safe overwintering is a challenging issue in rearing management that is inevitably faced by beekeepers in high-latitude regions. Under the combined influence of multiple factors, the overwintering loss rate of Western honey bees has risen continuously, and investigating the molecular mechanisms related to safe overwintering has become key. The Hunchun bee, an Apis mellifera ecotype in Jilin Province, China, exhibits strong overwintering ability during an overwintering period of more than five months. To investigate the molecular mechanisms of its cold resistance, we conducted a comparative transcriptomic analysis between the summer breeding period (July) and different overwintering intervals (November, December, January, and February), and then systematically identified key genes and signaling pathways related to cold resistance. The results showed that the highest number of differentially expressed genes (DEGs) was found between December and July. Compared with July, the upregulated genes in Hunchun bee in December were significantly enriched in several pathways, such as ion transport and neuroactive ligand–receptor interactions, and the downregulated genes were significantly enriched in pathways related to fatty acid metabolism, glutathione metabolism, and the peroxisome. Notably, a total of 378 shared DEGs were obtained from the four comparison groups, and several candidate cold-resistant gene families, such as AFPs, HSPs, C₂H₂-ZFPs, STKs, and LRRCs, were identified among the shared DEGs of the winter season. Additionally, 749 shared DEGs related to protein modification and metabolic process regulation were identified between the four successive overwintering intervals. Four shared genes, including sensory neuron membrane protein 1 (SNMP1), were revealed by pairwise comparison of the four intervals. The above results collectively indicate that the Hunchun bee attenuates winter-induced stress responses during the overwintering process by maintaining osmotic pressure balance, reducing fatty acid metabolism, increasing antioxidant capacity, and synthesizing cold-resistant macromolecular proteins. It was also found that chemical signal perception may serve a role in maintaining the stability of the overwintering bee colony. The key genes and pathways related to cold resistance identified in this study not only provide a basis for explaining the overwintering molecular mechanism for Apis mellifera of Hunchun bee but also offer key data to improve overwintering management strategies for Western honey bees. Full article

With the expansion of urban scale, forests and water areas have suffered a reduction. This reduction has resulted in insufficient carbon sequestration capacity. Strengthening environmental protection, especially enhancing the function of carbon sinks, is of great significance to the ecologically friendly development of the region. This study aims to clarify the distribution of regional ecological vulnerability and carbon storage capacity, and proposes a scientifically optimized ecological functional zoning plan. Specifically, we conducted a comprehensive assessment of land use and zoning in Huinan County by integrating ecological sensitivity with the InVEST model. First, based on the DPSIRM model, we evaluated the weights of ecological sensitivity influencing factors by combining the Analytic Hierarchy Process (AHP) and Entropy Weight Method (EWM). Using ArcGIS, we overlaid these factors with their respective weights to obtain the distribution of overall ecological sensitivity. Referencing relevant literature, we classified Huinan County’s ecological sensitivity into five categories. These categories include insensitive areas, low-sensitivity areas, medium-sensitivity areas, high-sensitivity areas, and extremely sensitive areas. Second, the carbon sequestration capacity of this region was visualized using the InVEST model to analyze Huinan County’s carbon storage potential. Finally, using the ArcGIS spatial overlay, we combined sensitivity levels with carbon storage zones. Based on varying degrees of ecological sensitivity and carbon storage distribution, we established five ecological conservation zones. These five ecological protection zones were: ecological buffer zone, restoration zone, stabilization zone, potential zone, and fragility zone. We implemented differentiated measures tailored to distinct regions, thereby advancing ecological restoration and sustainable development. This study provides a policy basis for ecological restoration in Huinan County and offers a replicable framework for ecological conservation in urbanized areas. Consequently, it holds practical significance for enhancing landscape multifunctionality and resilience. Full article

With increasing emphasis on ecological conservation and food security, cultivated land issues have become more prominent. This study focuses on Jilin Province and uses nine prefecture-level administrative units and prefectures as the basic analytical units. Using continuous data for 2000–2023, this study analyzes the spatiotemporal evolution of cultivated land use efficiency (CLUE). By 2023, most regions had achieved ecological safety (ES), examined through their coupling and coordination. The Super-Efficiency SBM-DEA model and the Malmquist–Luenberger (ML) index were used to evaluate the static and dynamic changes in CLUE. A DPSIR–PLS-SEM integrated framework was applied to identify causal mechanisms influencing ES, while the TOPSIS method was employed to assess overall evolutionary trends. In addition, the coupling coordination degree (CCD) model combined with kernel density estimation (KDE) was used to characterize the interaction between CLUE and ES and their spatial evolution. Results indicated the following: (1) From 2000 to 2023, overall CLUE in Jilin Province showed an upward trend with fluctuations, while regional disparities narrowed and spatial distribution became more balanced. (2) The composite ES index increased from 0.3009 to 0.7900, accompanied by a marked expansion of areas classified as secure. (3) The CCD improved from a basic level to a high-quality coordination level, indicating enhanced synergistic development. Higher coordination was observed in central and eastern regions, whereas western and peripheral areas lagged. This study integrates multi-dimensional modeling approaches to systematically assess the coupled dynamics on cultivated land use efficiency and ecological safety, providing insights for land management and policy formulation. Full article

In the breeding scene, limited by the small number of samples and environmental interference such as illumination occlusion, sika deer behavior recognition still faces challenges such as insufficient feature representation and weak cross-scale modeling ability. To this end, this study builds a lightweight improved model SDB-YOLO based on YOLOv11n. Firstly, the FPSC module is proposed to enhance the correlation between multi-scale features through the shared convolution mechanism, so as to significantly improve the quality of feature fusion under the condition of small samples. Secondly, the Ghost feature generation and dynamic convolution strategy are introduced into the C3k2 module to construct the C3_GDConv structure, so as to strengthen the fine-grained behavior pattern modeling ability and reduce redundant calculations. In addition, the CBAM attention mechanism is added to the neck of the network to further improve the ability of key information extraction and enhance the discrimination of feature expression. Finally, the EfficientHead was used to replace the original detection head to obtain a more robust training process and higher detection accuracy in small-sample scenarios. Experimental results show that SDB-YOLO achieves 90.2% detection accuracy with only 4.3 GFLOPs of calculation, which achieves significant performance improvement compared with YOLOv11n, and verifies the effectiveness and lightweight advantages of the proposed method in small-sample special animal behavior recognition tasks. Full article

Nitrogen (N) is the most critical element influencing plant growth and development. Different plant species exhibit varying preferences for different N forms. In order to identify an appropriate nutrient solution N formula for optimizing blueberry substrate cultivation, we investigated the effects of seven different NH₄⁺-N/NO₃⁻-N ratios on the growth characteristics, photosynthetic physiology, mineral element content, enzymes related to N metabolism, and fruit quality, with ‘F32’ used as the experimental material and water served as controls. The results demonstrated that both the aboveground and belowground parts of blueberry plants exhibited enhanced growth when NH₄⁺-N was used as the primary N source in the nutrient solution, compared to single NH₄⁺-N or a high NO₃⁻-N ratio. The most significant growth promotion occurred when the NH₄⁺-N to NO₃⁻-N ratio was 7:3. When NH₄⁺-N and NO₃⁻-N are concurrently supplied in the nutrient solution, the processes of NO₃⁻ reduction, the GS-GOGAT cycle, and NH₄⁺ assimilation are significantly enhanced during nitrogen metabolism. Thereby, providing a theoretical foundation for optimizing nutrient solution management in substrate-cultivated blueberry. Full article

Frequent geological hazards such as landslides and rockfalls, intensified by human activities and extreme rainfall, highlight the urgent need for rapid, accurate, and interpretable susceptibility assessment. However, existing methods often struggle with insufficient characterization of spatial heterogeneity, fragmented spatial structures, and limited mechanistic interpretability. To overcome these challenges, this study proposes an intelligent landslide susceptibility assessment framework based on the Swin-UNet architecture, which combines the window-based self-attention mechanism of the Swin Transformer with the encoder–decoder structure of U-Net. Eleven conditioning factors derived from remote sensing data were used to characterize the influencing conditions. Comprehensive experiments conducted in Changbai County, Jilin Province, China, demonstrate that the proposed Swin-UNet framework outperforms traditional models, including the information value method and the standard U-Net. It achieves a maximum overall accuracy of 99.87% and consistently yields higher AUROC, AUPRC, F1-score, and IoU metrics. The generated susceptibility maps exhibit enhanced spatial continuity, improved geomorphological coherence, and greater interpretability of contributing factors. These results confirm the robustness and generalizability of the proposed framework and highlight its potential as a powerful and interpretable tool for large-scale geological hazard assessment, providing a solid technical foundation for refined disaster prevention and mitigation strategies. Full article

The conservation and breeding of the western honey bee (Apis mellifera) is central dependent on accurate subspecies assignment, but the most commonly used methods are labor-intensive classical morphometrics and costly molecular assays. We developed an XGBoost-based classification framework using a compact set of routinely measurable characters. A curated dataset of labeled workers was measured under harmonized protocols; features were screened according to embedded importance, and model performance was assessed using five-fold cross-validation, outperforming standard machine learning baselines. The resulting model using only the top 10 characters—primarily forewing venation angles and abdominal plate metrics—achieved high performance (accuracy = 0.98; F1 = 0.99) and an area under the receiver operating characteristic curve (AUC) of 0.99 (95% CI = 0.995–0.999). SHAP analyses confirmed the discriminatory contributions of these features, while error inspection suggested that misclassifications were concentrated in morphologically overlapping lineages. The model’s performance supports its use as a rapid triage tool alongside genetic testing, providing a scalable and interpretable tool for researchers to create and deploy custom morphometric models, demonstrated here for A. mellifera but portable to other insect taxa. Full article

Climate change induces widespread effects on crop production, influencing multiple developmental stages and associated agronomic outcomes. Using long-term meteorological data from Jilin Province, Northeast China, this study examined temporal and spatial variations in climatic conditions through trend analysis, Mann–Kendall tests, and inverse distance weighting interpolation. A fuzzy comprehensive evaluation model was applied to classify maize cultivation suitability into four levels across major production areas, with Level I representing the most suitable regions, Level II highly suitable regions, Level III moderately suitable regions, and Level IV low suitable regions. Changes in suitable areas were analyzed before and after abrupt climatic shifts. From 1976 to 2020, Jilin Province experienced a significant rise in annual mean temperature, a marked decline in sunshine duration, and a slight increase in precipitation. The area of Level I suitability remained stable, while Level II expanded to approximately 1.3 times its original area. Conversely, Level III and IV areas decreased by 4.59% and 28.77%, respectively, compared with the pre-transition period. Spatially, the most suitable maize cultivation areas shifted from central to northern and eastern Jilin due to climatic warming. Although rising temperatures enhanced thermal conditions for maize production, reduced sunshine and variable precipitation constrained further expansion. These findings provide a scientific basis for optimizing maize variety selection and cropping structure in high-latitude regions, supporting yield improvement and sustainable development of the maize industry under a changing climate. Full article

Rice blast, caused by Magnaporthe oryzae, is a devastating global disease. Its control through the deployment of host resistance genes relies on a detailed knowledge of the pathogen’s race structure and the corresponding avirulence (Avr) genes. To guide effective rice breeding for blast resistance, this study investigated the population dynamics of M. oryzae in Jilin Province from 2022 to 2024. The distribution frequencies of seven Avr genes were detected using PCR and Avr gene-specific primers, and the physiological race structure of 193 isolates was characterized using a set of Chinese differential cultivars, which contains seven cultivars. The results revealed a high prevalence and stability of specific Avr genes, with Avr-Pi9, Avr-Pias, Avr-Piz-t, and Avr-Pib all exhibiting detection frequencies exceeding 80%. In particular, Avr-Pib showed a high frequency (80.83%) and a very low disease incidence (0.64%) on the differential variety Sifeng 43 (which carries Pib), confirming its low mutation rate and the ongoing effectiveness of the corresponding resistance gene. Conversely, the significant decline in Avr-co39 suggests that its corresponding resistance gene should be avoided. Race diversity increased over the three-year period, characterized by a shift toward a more complex structure dominated by ZG1, ZA17, ZA43, and ZB31. Based on the gene-for-gene interactions and pathogen population structure, we recommend a breeding strategy that prioritizes the incorporation of the highly effective Pib, Pi54, and Pik genes, utilizing resistant donors like Sifeng 43. These results can help inform the design of sustainable management strategies adapted to the changing pathogen population. Full article