Search Results (1,174)

Background: Oxidative stress contributes significantly to premature skin aging and inflammatory dermatological conditions. While plant-derived antioxidants have demonstrated considerable promise in topical applications, Bougainvillea glabra Choisy remains underexplored in standardized pharmaceutical dosage form development despite its documented phytochemical richness. Objective: This study aimed to develop, standardize, and characterize topical cold cream formulations incorporating B. glabra ethanolic leaf extract, with HPTLC-based quantification of marker compounds, validated antioxidant assessment, and preliminary dermal safety evaluation. Methods: The ethanolic leaf extract was prepared by maceration and characterized by preliminary phytochemical screening and HPTLC fingerprinting with quantitative densitometric analysis of quercetin and pinitol. Three cold cream formulations were developed at 10% (F1), 20% (F2), and 30% (w/w) (F3) extract loading. Formulations were evaluated for organoleptic properties, pH, homogeneity, spreadability, and viscosity. Antioxidant activity was assessed using a validated methanol extraction procedure followed by DPPH radical scavenging and potassium permanganate reduction assays. Ex vivo skin permeation was evaluated using Franz diffusion cells with freshly excised goat skin. Accelerated stability was conducted at 40 ± 2 °C/75 ± 5% RH for 90 days with HPTLC-based marker retention monitoring. Primary dermal safety was assessed in Wistar albino rats (n = 6) following OECD Test Guideline 404. Results: Quantitative HPTLC confirmed quercetin (4.82 ± 0.14 mg/g dry extract) and pinitol (2.31 ± 0.09 mg/g) as marker compounds, with linearly increasing content across F1–F3. All formulations demonstrated acceptable physicochemical properties (pH 5.7–5.9, viscosity 440,000–460,000 cP, spreadability 11.8 ± 0.3 cm·g/s). F3 exhibited the highest DPPH scavenging activity (56.68 ± 1.05%) with IC₅₀ of 1.3 ± 0.1% w/v, demonstrating a 3.2-fold improvement over F1. Extraction recovery from the cream matrix was 96.4–97.1%, validating the antioxidant data. Ex vivo quercetin permeation through goat skin reached 51.3 ± 2.8 μg/cm² at 24 h for F3, following Higuchi diffusion kinetics (R² > 0.99). No dermal irritation was observed (Primary Irritation Index = 0). Accelerated stability confirmed ≥98.3% retention of both marker compounds and antioxidant activity after 90 days. Conclusions: B. glabra leaf extract was successfully incorporated into a physicochemically stable, non-irritating cold cream with demonstrated dose-dependent antioxidant efficacy and cutaneous delivery capability. The study establishes preliminary dermal safety and in vitro antioxidant efficacy warranting further controlled clinical evaluation. Full article

The purpose of this study was to use data mining and network pharmacology to determine drug patterns for bovine viral diarrhea (BVD). The frequency, properties, tastes, meridian tropism, and functions of prescription data were gathered and examined from four literature databases (2004–2024). Cluster analysis was used to find prescription patterns, and the Apriori algorithm (SPSS Modeler 18) was used to find associations. A total of 391 literature-derived prescription records were included in the analysis, involving 189 distinct herbal medicines and a cumulative herb-use frequency of 2031 occurrences. These herbs primarily enter the liver meridian and were categorized as cold, warm, or neutral. The predominant tastes were bitter, pungent, and sweet. Five frequently recorded herbs and 14 co-occurrence patterns among herbs were extracted. According to traditional Chinese medicine (TCM), spleen–stomach deficiencies and damp-heat pathogens are linked to BVD. These medication patterns were mainly associated with heat-clearing, detoxification, spleen-strengthening, and Qi-regulating strategies in TCVM theory. Targets were screened, PPI networks were constructed, and enrichment studies for core herbs (Baitouweng, Huangbo, Huangqin, Qinpi, and Zhizi) were performed using network pharmacology. The binding affinities between disease targets and active components were further assessed using molecular docking. The findings provide a descriptive summary of medication patterns and generate preliminary hypotheses regarding potential compound–target–pathway associations involved in the symptomatic and supportive use of TCM for BVD. Full article

Urban heat mitigation in megacities depends not only on cooling sources, but also on the connectivity through which cooling effects are transmitted across heterogeneous landscapes. However, existing studies have mainly focused on the static patterns of urban cold islands (UCIs), while the connectivity and disturbance response of urban cooling systems remain poorly understood. Taking Landsat-based summer thermal observations in Beijing, this study developed an integrated framework to assess the structure and resilience of the urban cold island network (CIN) by combining thermal source identification, resistance-surface construction, connectivity modeling, and disturbance simulations. Land surface temperature (LST) was extracted from Landsat 8 OLI/TIRS Collection 2 Level-2 surface temperature products acquired in July–August 2022, and cold island core sources (CICS) were subsequently identified by integrating thermal conditions with land-use characteristics. GeoDetector was used to quantify the explanatory power and interaction effects of natural, land-use, and socio-economic factors on LST spatial heterogeneity, serving as an attribution tool for interpreting thermal-environment drivers. These factors were then integrated into a resistance surface for circuit-theory-based connectivity analysis. Under the summer heat-stress scenario, 202 CICS covering 6416.95 km² were identified, mainly concentrated in peripheral mountainous areas. A total of 401 corridors were identified, including 70 primary corridors forming the structural backbone of the CIN. This spatial distribution reveals a mountain–plain cooling structure in Beijing, in which mountainous CICS constitute the regional cooling-supply base, while potential cooling transmission toward the urban core mainly depends on a limited number of backbone corridors. LULC was the dominant driver of LST, and its interactions with PD, NTL, and vegetation-related factors substantially enhanced explanatory power. Compared with random disturbance, targeted node removal led to an earlier and sharper decline in network resilience, with substantial deterioration already evident after approximately 20–30% of critical nodes were removed. These summer-based findings provide spatially explicit evidence for prioritizing cooling corridors, critical nodes, and restoration areas in connectivity-oriented urban heat mitigation and climate-responsive planning, thereby supporting hierarchical maintenance and restoration strategies based on their relative importance within the cooling network. Full article

Serverless computing has emerged as a prominent research focus in cloud computing because it provides infrastructure-transparent development and elastic resource management. However, this computing paradigm still faces the inherent challenge of cold start. Existing approaches have two major limitations: insufficient workload prediction accuracy and inefficient allocation of reusable container replicas to incoming function requests. To address these challenges, we propose a container scheduling approach based on Workload Prediction and Particle Swarm Optimization (PSO), named WPPSO. WPPSO first leverages a code-pre-trained large language model (LLM) to extract intrinsic function features and then uses a spatio-temporal fusion-based temporal neural network (STF-TNN) to predict serverless workloads. It subsequently employs a greedy algorithm to construct a high-quality initial matching state and uses PSO to refine the container scheduling strategy. Finally, WPPSO introduces a hierarchical container recycling mechanism to reduce idle resource waste. Extensive experiments show that WPPSO reduces startup latency by up to 72.2% and memory footprint by 63.4% compared with the native Knative platform. Compared with RainbowCake, WPPSO achieves a 15.6% lower mean startup latency without statistical significance and a statistically significant 31% reduction in idle memory consumption. Full article

Coriandrum sativum L. is a widely cultivated aromatic herb exhibiting substantial variation in essential oil quality and yield among different accessions. This study assessed germination performance, essential oil composition, yield, chemotype distribution, and fragrance characteristics in 48 C. sativum accessions collected from 19 countries spanning four Köppen–Geiger climate zones: Tropical/Subtropical, Arid/Semi-arid, Temperate, and Continental/Cold. All accessions were grown under standardized field conditions, and essential oils were extracted from aerial parts using steam distillation followed by direct-GC/MS analysis. Seed germination rates were consistently high (mean: 92.25 ± 5.85%; range: 71–100%) and did not differ significantly by climate zone (Kruskal–Wallis H = 5.500, p = 0.139) or country of origin (H = 21.833, p = 0.240), indicating that post-harvest management, rather than climatic provenance, primarily determines seed viability. Essential oil profiles were dominated by (E)-2-decenal (mean: 44.56%), decanal (11.75%), and 2-dodecenal (13.47%). Principal component analysis (PCA) of 18 compounds detected in at least 19 accessions accounted for 70.16% of total variance across five components, with PC1 reflecting a gradient from long-chain saturated aldehyde accumulation to linalool enrichment. Permutational multivariate analysis of variance (PERMANOVA) demonstrated significant compositional differentiation among climate zones (Pseudo-F = 1.662, p = 0.028), whereas country-level grouping was not significant (p = 0.256). Tropical/subtropical accessions exhibited the highest linalool content (mean: 15.39 ± 8.71%) and essential oil yield (mean: 0.269 ± 0.120% v/w), significantly surpassing arid/semi-arid and temperate zones (p < 0.05). Two chemotypes were identified, (E)-2-decenal (91.7%) and linalool (8.3%), each associated with distinct fragrance profiles (earthy/aldehydic/woody versus herbal/sweet, respectively). These findings demonstrate that climate zone of origin is significantly associated with C. sativum essential oil composition and productivity, with tropical/subtropical accessions providing superior yield and linalool content. Chemotype characterization offers an additional criterion for germplasm selection in targeted industrial applications. Full article

Black cumin (Nigella sativa L.) oil has been traditionally used to manage infectious diseases. The scientific validation of its antibacterial potential remains of significant pharmacological interest. This study evaluated the in vitro antibacterial activity of cold-pressed black cumin oil against selected bacterial strains and compared its efficacy with that of common β-lactam antibiotics, supplemented by mechanistic insight through molecular docking. Pure oil was obtained via cold-press extraction from seeds. Antibacterial activity was evaluated using the disk diffusion and broth microdilution methods against Staphylococcus aureus NCTC10788, Bacillus cereus NCTC7464, Listeria monocytogenes ATCC11994, Escherichia coli NCTC2001, and Salmonella typhimurium NCTC11994. Commercial antibiotic disks containing cloxacillin (5 µg), cefoperazone (75 µg), penicillin (40 µg), and amoxicillin (25 µg) served as a reference. Potential molecular interactions were explored by the density functional theory (DFT) optimization of thymoquinone at the B3LYP/6-31+G(d,p) level, followed by molecular docking against bacterial targets. Inhibition zone diameters ranged from 13.5 ± 0.7 mm to 34 ± 2.1 mm, and minimum inhibitory concentration (MIC) values varied between 6.7 ± 2.3 and 64 ± 0.0 µg/mL depending on the bacterial strain tested. Black cumin oil demonstrated a stronger inhibitory effect on B. cereus and L. monocytogenes than the other bacteria tested, and exhibited a significantly higher inhibitory effect than some of the antibiotics tested (p < 0.05). In contrast, no statistically significant differences were observed among treatments against E. coli (p > 0.05). Overall, Gram-positive bacteria showed greater susceptibility to black cumin oil than Gram-negative bacteria. The computational analyses demonstrated stable binding interactions supporting the experimental results. These integrative in vitro and in silico findings provide mechanistic evidence for the traditional use of black cumin oil in treating infections. The results suggest that black cumin oil could be a promising natural antibacterial candidate; however, further toxicological and pharmacokinetic evaluations are required prior to clinical use. Full article

The environmental impact of food waste and agro-industrial by-products has promoted the development of circular economy strategies for food applications. In this study, edible films were developed from biopolymers extracted from avocado peel and seeds (hemicellulose, pectin, lignin, and starch), incorporating ethyl lauroyl arginate (LAE^®) as an antifungal agent. The activity of LAE^® was evaluated against Colletotrichum gloeosporioides on inoculated avocados stored at 12 °C and 22 °C. Fruit shelf life was assessed through physiological, physicochemical and sensory parameters during cold storage and subsequent shelf life. Films containing 10% LAE^® exhibited strong antifungal activity, and their efficacy was higher at 12 °C than at 22 °C. Coated fruits exhibited a ripening delay of up to 2 days compared to controls. These findings highlight the potential use of avocado by-product-based LAE^® coatings as a sustainable strategy for preserve postharvest avocado quality. Full article

Potato (Solanum tuberosum L.) is an important staple and food security crop to many communities in the world. However, potato production and quality is greatly constrained by bacterial wilt, a disease caused by a soil-borne pathogen, Ralstonia solanacearum. Ralstonia solanacearum can be managed through clean seed systems and therefore laboratory testing is a pre-requisite for seed certification to confirm the absence of the pathogen in potato seeds before planting. Molecular diagnostics is the gold standard for detection of R. solanacearum in potato seeds. However, the extraction of genomic DNA from R. solanacearum for molecular diagnostics is complex, tedious, lengthy and/or costly procedure. A simple, rapid and reliable DNA extraction protocol is required for use in routine molecular diagnosis of R. solanacearum, a high-risk quarantine pathogen. In this study, we developed a simple and rapid protocol for extracting genomic DNA from symptomatic and asymptomatic potato tubers infected with R. solanacearum and verified its efficiency for the detection and molecular characterization of the pathogen. The protocol was developed from the evaluation of distilled water, Tris-EDTA (TE) and Tris buffer as a base solution for tissue maceration. The DNA quantity and integrity was determined using the NanoDrop 2000C spectrophotometer and agarose gel electrophoresis, respectively. Both hot and cold solutions produced intact high molecular weight genomic DNA of sufficient yield and purity for molecular-based applications. The detection and determination of phylotypes of R. solanacearum, based on conventional and multiplex polymerase chain reaction (PCR), amplified the expected 280 and 372 bp amplicons, respectively, confirming that the quantity and quality of the extracted pathogen genomic DNA was sufficient for molecular diagnostic applications. The sequencing of the amplified products of the endoglucanase gene produced good quality sequences, which confirmed the R. solanacearum isolates to be members of phylotype II sequevar 1. This protocol is a simple, fast and reliable tool for the extraction of sufficient genomic DNA with high quality, directly from R. solancearum-infected potato tubers for PCR and sequencing applications. Its simplicity and throughput make it valuable for use in routine diagnostics and can be adopted by certification programs to ensure distribution of clean potato seeds to farmers. Full article

The present work investigates the chemical composition of black cumin (Nigella sativa L.) cake obtained as a by-product of cold-pressed oil extraction. The aim of the study is to assess its potential for further utilization and secondary applications. By applying a combination of analytical techniques, including chemical analysis, Soxhlet extraction, ICP-OES, ICP-MS, FTIR, and SEM-EDS, the material was characterized as a rich organic matrix with a significant residual fat content (approximately 20%), proteins, and essential mineral elements such as K, Ca, Fe, Cu, Zn, and P, while containing low levels of toxic elements. Since cold pressing preserves residual bioactive compounds, and considering the high content of essential elements, black cumin cake represents a promising ingredient for food supplements. In addition, its porous surface structure observed by SEM-EDS, together with the functional groups identified by FTIR analysis, suggests potential sorption properties. These findings position black cumin cake as a promising resource within the framework of sustainable agro-industrial waste valorization strategies. Full article

Petroleum hydrocarbon contamination of soils remains a persistent environmental problem in Arctic and sub-Arctic regions, where oil extraction, pipeline transportation, fuel storage, industrial legacy sites, and diesel-dependent infrastructure coexist with fragile cold-climate ecosystems. Remediation in these regions is constrained by low temperatures, short thaw seasons, permafrost, waterlogged active layers, slow vegetation recovery, limited infrastructure, and high mobilization costs, which limit the direct transferability of conventional temperate-zone technologies. This study presents a structured narrative review of international and Russian evidence on petroleum-contaminated soil management in cold regions, focusing on monitoring as a basis for remediation decision-making. Peer-reviewed studies, technical guidance documents, regulatory frameworks, and regional case studies were analyzed across key domains, including environmental constraints, hydrocarbon behavior, monitoring methodologies, and remediation technologies. Particular attention is given to chemical analysis, hydrocarbon fractionation, bioavailability-oriented methods, ecotoxicological bioassays, and microbial indicators as tools linking contamination assessment with remediation strategy selection. Reliance on total petroleum hydrocarbon (TPH) concentration as a primary endpoint is shown to be insufficient, especially in cold-region soils where strong sorption and limited mass transfer decouple concentration from biological exposure. Multi-endpoint monitoring systems provide a more reliable basis for assessing contaminant risk, treatment effectiveness, and soil recovery. For the Russian Arctic, the integration of national recultivation frameworks with risk-based assessment and ecotoxicological monitoring is identified as a key pathway for improving remediation outcomes. A decision-oriented framework is proposed that links environmental conditions, contaminant properties, and monitoring data to support the selection and optimization of remediation strategies. This study supports a transition from concentration-based cleanup toward risk-informed and ecosystem-oriented management of petroleum-contaminated soils in Arctic and sub-Arctic environments. Full article

Against the backdrop of global warming and the ‘warming and wetting’ trend in north-western China, changes in seasonal snowpack and glacial ice in high-altitude cold regions directly impact water security in inland river basins. At present, there is a paucity of systematic research concerning the long-term evolution of snow and ice cover, multi-scale climate responses and future trends in the source region of the Keriya River on the northern slope of the Kunlun Mountains. To address this, this study utilised Landsat remote sensing imagery and meteorological station data from 2005 to 2024. Employing a multi-model fusion framework that integrates various machine learning and time-series models—including random forests, gradient boosting trees and ARIMA—the research incorporated trend factors, climate cycle identification and probabilistic modelling of extreme events to systematically analyse the spatiotemporal variability of snow/ice coverage and its multiscale coupling relationships with air temperature and precipitation. Given the inherent limitations of optical remote sensing methods in distinguishing between seasonal snow and glacial ice, this study defines the extracted coverage type as snow/ice coverage. Given the inherent limitations of optical remote sensing methods in distinguishing between seasonal snow and glacial ice, this study defines the extracted coverage type as snow/ice coverage. The results indicate that: (1) the annual average snow/ice cover percentage in the study area shows a non-significant decreasing trend (−0.69%/year, p > 0.1); within the year, it exhibits a pattern of accumulation in winter and melting in summer, with a peak in January (average 63.2%) and a trough in August (average 11.6%); (2) snow/ice cover percentage increases significantly with altitude; the annual average SICP in the <2000 m elevation zone is 5.2%; in the 2000–3000 m and 3000–4000 m altitude ranges, this rises to 5.7% and 8.3%, respectively, representing the primary seasonal snow/ice distribution zones; in areas above 6000 m, the annual average reaches 70.3%, constituting a zone of perennial stable snow/ice cover; (3) the relationship between snow/ice and temperature and precipitation exhibits significant time-scale dependence: correlations are weak on an annual scale (temperature R = −0.25, precipitation R = −0.14), but significantly strengthen on a monthly scale and exhibit seasonal differentiation; during the melting season, temperature exerts a dominant negative influence (August R = −0.35), whilst during the accumulation season, solid precipitation provides a positive supplement (February R = 0.34), with the strongest correlation with temperature occurring in September (R = −0.50); (4) it is projected that between 2025 and 2044, snow and ice cover will follow a fluctuating downward trend (averaging an annual decrease of roughly −0.12%), falling to approximately 29% by 2044; at the same time, temperatures are expected to continue rising (+0.035 °C per year), whilst precipitation will increase slightly (+0.4% per year). The results of this study provide a sound scientific basis for formulating sustainable water resource management strategies for the northern flank of the Kunlun Mountains and optimising measures to regulate snowmelt runoff. They are of great importance for safeguarding the stability of the oasis ecological systems in the Keriya River basin and ensuring the sustainable development and utilisation of water resources. Full article

Consumption of food contaminated with Shiga toxin-producing Escherichia coli (STEC) causes thousands of illnesses in the United States annually. Long-read sequencing could reduce the time needed to test food for STEC, but sequencing is not quantitative and cannot differentiate between live and dead bacteria. Therefore, this study investigated combining serial plating with long-read sequencing to quantify only live STEC in a sample. Ground beef was inoculated with STEC and homogenized with a stomacher. The liquid was filtered to remove eukaryotic cells and then centrifuged to pellet the bacteria. Serial dilutions of the pellet were plated on selective agar, and single colonies were subsequently sequenced. Initial experiments revealed that processing samples at room temperature led to a 1 log increase in STEC levels from the initial inoculated concentration, confounding accurate enumeration. In subsequent experiments, samples and reagents were kept cold, and the amount of STEC recovered did not significantly differ from the amount inoculated. The DNA extracted from a single colony provided enough data to identify all virulence genes of interest multiple times. The amount of STEC in a sample could be quantified down to 1 cfu g⁻¹. Quantification of STEC in food samples using this method would improve risk assessment and guide mitigation efforts in industry. Full article

The aim of this study was to valorize by-products of blue poppy (Papaver somniferum), a widely used ingredient in the food industry. This study focused on the isolation of bioactive compounds from leaves, stems, roots, capsules and cold-pressed cake. All samples were subjected to conventional solid–liquid extraction (SLE) using ethanol–water solutions of varying concentrations (0, 20, 40, 60, 80 and 96%) as the extraction solvent. The obtained extracts were analyzed for total phenolic content (TP), hydroxycinnamic acids (HCA), flavonols (FL), total flavonoids (TF), condensed tannins (CT) and antioxidant activity. Furthermore, the extracts were subjected to untargeted LC-MS analysis for qualitative characterization. Ethanol concentration significantly influenced the extraction efficiency of bioactive compounds, with the optimal solvent varying depending on the plant part and the specific class of compounds analyzed. Based on TP and TF content, capsule extracts exhibited the highest polyphenol levels. HCAs were detected in extracts from leaves, capsules, and cold-pressed cake. In total, 62 compounds were identified, belonging to various biochemical classes, including organic acids, hydroxycinnamic acids, alkaloids, flavonoids, and fatty acids. Overall, the results indicate that poppy by-products are a valuable source of bioactive components, with promising applications across different industrial sectors. Full article

Early-spring ephemeral plants act as pioneer species on stabilized dunes in cold arid deserts; they are capable of rapid growth under extreme drought and low-temperature conditions while sustaining dune ecosystem functions. These species are highly sensitive to climate change, yet their spatiotemporal dynamics and the mechanisms by which climatic factors regulate their growth remain poorly understood. In this study, we investigated the Gurbantunggut Desert, China, using long-term NDVI time series to extract phenological traits associated with their life cycle and developed a remote-sensing-based analytical framework to quantify the distribution patterns of early-spring ephemeral plants and their environmental drivers. We combined random forest (RF), structural equation modeling (SEM), and convolutional neural networks (CNN) to assess the relative importance and pathways of key climatic drivers and to predict future distribution changes. Our results indicate that: (1) the life cycle extraction method achieved a classification accuracy exceeding 80%, and from 2001 to 2022, the overall distribution of early-spring ephemeral plants exhibited an increasing trend; (2) snowend, snowday, and precipitation during the driest quarter were the primary drivers of ephemeral plant distribution, collectively explaining over 60% of the observed variation, and structural equation modeling further revealed that snow and precipitation had significant positive effects on their distribution; and (3) under future climate scenarios, Medium-NDVI areas are projected to expand northward and westward, with the potential emergence of new suitable habitats in northern localities by mid-century. Climate warming may facilitate the dispersal and latitudinal migration of early-spring ephemeral plants. Based on these findings, biodiversity conservation efforts should prioritize ecologically sensitive transitional zones and promote species migration and establishment under climate change through the construction of ecological corridors. Full article

Grassland fire is one of the major disasters threatening regional ecological security. Its occurrence, development, and spread are closely related to the spatial distribution and coverage of surface vegetation and snow cover across grassland areas. As the primary combustible fuel source, higher vegetation coverage increases fuel load and continuity, thereby directly determining grassland fire danger levels and accelerating fire spread velocity. In contrast, snow cover imposes an indirect regulatory effect on the spatiotemporal pattern of fire danger factors: it lowers surface temperature, raises near-surface humidity, and restricts the germination and growth of herbaceous vegetation in cold seasons, which effectively reduces available combustible materials and weakens regional fire hazard conditions. Therefore, accurately obtaining the coverage status of vegetation (direct combustible fuel factor) and snow cover (indirect fire-regulating factor) in complex grassland scenarios is the essential premise for reliable grassland fire danger monitoring, early warning, disaster prevention and control, and regional ecological management. Aiming at the practical problems in complex grassland scenarios (such as undulating terrain, uneven vegetation growth, large differences in snow depth, and complex lighting conditions), including difficulty in extracting vegetation and snow-covered areas, blurred and confusing boundaries, and low accuracy in coverage calculation, which seriously restrict the technical bottleneck of precise monitoring of grassland fire danger factors, this study takes near-ground images collected by grassland fire danger factor monitoring stations as the core data source, and proposes an improved UNet image segmentation model combined with image segmentation technology and deep learning methods to realize precise extraction of vegetation and snow-covered areas and efficient calculation of coverage in complex scenarios. To improve the model’s feature extraction ability, boundary localization accuracy, and reduce model parameters and computational overhead, the CBAM-ASPP (Convolutional Block Attention Module—Atrous Spatial Pyramid Pooling) module is integrated at the end of the encoding path. The attention mechanism is used to enhance the weight of key features, and the multi-scale receptive field of atrous spatial pyramid pooling is utilized to strengthen the model’s ability to fuse features of vegetation and snow areas of different scales. The residual attention mechanism is introduced in the upsampling stage to effectively alleviate the gradient disappearance problem, improve the model’s ability to accurately locate the boundaries of vegetation and snow areas, and reduce segmentation errors. In the training process, a dynamically weighted hybrid loss function is adopted to dynamically adjust the weights according to the segmentation difficulty of different types of samples during training, optimize the model training effect, and improve the segmentation accuracy and generalization ability. Experiments were conducted using near-ground images of typical complex grassland scenarios as the dataset, and the performance of the proposed model was verified through comparative experiments. The results show that in the vegetation segmentation task, the mean Intersection over Union (mIoU) of the model reaches 84.70%, and the accuracy rate is 91.28%, which are 1.48 and 1.58 percentage points higher than those of the standard UNet model, respectively. In the snow segmentation task, the mIoU of the model reaches 92.74%, and the accuracy rate is 94.19%, which are 2.39 and 2.36 percentage points higher than those of the standard UNet model, respectively. At the same time, the number of parameters of the model is reduced by 12.85% compared with the standard UNet. Also, its comprehensive performance is significantly better than that of mainstream image segmentation models such as FCN, SegNet, and DeepLabv3+. Based on the standardized time-series data retrieved by the optimized segmentation model, this study further constructs a Grassland Fire Risk Index (GFRI) using the Analytic Hierarchy Process (AHP). Pearson correlation verification confirms that the GFRI has an extremely significant positive correlation with historical fire frequency, accurately capturing the seasonal dynamic rhythm of regional grassland fire occurrence. This integrated framework of intelligent segmentation and fire risk quantification provides a reliable technical solution for grassland fire factor monitoring, dynamic fire risk assessment, early warning systems, and refined regional ecological management. Full article