Search Results (5,734)

Fine-scale crop type information is essential for agricultural monitoring, irrigation management, and food security assessment. This study mapped three major crops—wheat, corn, and sunflower—in the Hetao Irrigation District, China, using multi-temporal Sentinel-2 optical imagery and Sentinel-1 SAR observations at the parcel scale. A multi-source feature set, including spectral bands, vegetation and red-edge indices, moisture-related variables, radar backscatter coefficients, and derived radar features, was constructed from the full growing season. An LSTM network was used to learn temporal representations of crop phenological dynamics, and the resulting embeddings were then combined with traditional machine learning classifiers, including Random Forest (RF), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost), for final classification. The results show that the hybrid framework substantially improves classification performance compared with the corresponding non-LSTM classifiers. Among all tested models, XGBoost + LSTM achieved the best performance, with an overall accuracy of 93.61%, a Kappa coefficient of 91.66%, and a mean IoU of 87.41%. The class-wise F1-scores were 85.61% for wheat, 97.22% for corn, and 87.27% for sunflower. Additional experiments further confirmed the advantages of parcel-based aggregation in improving spatial consistency and reducing mixed-field noise. The proposed framework provides a promising parcel-scale workflow for crop type mapping in fragmented irrigation districts, while its transferability across years and regions still requires further validation. Full article

Emotions play an important role in shaping aggressive behavior, and understanding their underlying psychological mechanisms is particularly relevant among college students. However, existing research has predominantly focused on reactive aggression, while comparatively less attention has been paid to proactive aggression, which is more instrumental in nature and associated with more severe social consequences. In addition, empirical evidence regarding the valence-specific effects of awe remains limited. The present study aimed to examine the differential effects of positive and negative awe on proactive aggression and to explore the role of empathy as a potential mediating mechanism. A total of 110 college students were randomly assigned to one of three conditions: positive awe, negative awe, or neutral emotion. Awe was induced through video clips depicting natural landscapes. Proactive aggression was assessed using a modified bug-killing paradigm, including two behavioral indicators: force intensity and proportion of bugs killed. Empathy was measured using the Interpersonal Reactivity Index. The results revealed a clear differentiation based on the valence of awe. Participants in the positive awe condition exhibited significantly lower levels of proactive aggression than those in the neutral condition across both force intensity (M = 2.86, SD = 0.81 vs. M = 4.17, SD = 0.81) and proportion of bugs killed (M = 0.68, SD = 0.25 vs. M = 0.93, SD = 0.11). In contrast, the inhibitory effects of negative awe were weaker and less consistent. Compared with the neutral condition, negative awe was associated with a lower proportion of bugs killed, although this effect only reached marginal significance (p = 0.06, η² = 0.04), and no significant difference was observed for force intensity. Mediation analyses indicated that empathy partially mediated the association between positive awe and proactive aggression. Empathy accounted for 31% of the total effect in the force intensity pathway (B = −0.02, t = −4.25, p < 0.001, 95% CI [−0.04, −0.01]) and 18% in the proportion-of-bugs-killed pathway (B = −0.003, t = −2.37, p = 0.02, 95% CI [−0.006, −0.001]). Notably, no significant mediating effect of empathy was observed in the negative awe condition, suggesting that the psychological processes linking awe to proactive aggression may differ as a function of emotional valence. Taken together, the present findings suggest that positive awe is reliably associated with lower levels of proactive aggression among college students, and that this association is partially explained by increased empathy. By contrast, the effects of negative awe appear to be fragile and context-dependent, as reflected in their failure to reach statistical significance, indicator-specific manifestation, and the absence of a consistent mediating pathway. These results highlight the importance of distinguishing between positive and negative awe when examining the behavioral consequences of self-transcendent emotions and underscore the need for further research to clarify the conditions under which negative awe may influence aggressive behavior. Full article

This study explored the flavor profiling of baked potatoes, with a focus on how maturity affects the volatile flavor. By using HS-SPME-GC-MS, sensory evaluation, multivariate statistical analysis and computational modeling, a total of 99 volatile compounds were finally identified. Multivariate statistical analysis yielded 36 different important compounds (VIP > 1, p < 0.05). Subsequently, combined with relative odor activity value (ROAV), four key compounds including 2-ethyl-3,5-dimethylpyrazine, 2,6-diethylpyrazine, ethyl acetate and benzeneacetaldehyde were identified as potential indicators of baked potatoes with different maturities. Further, molecular docking analysis revealed the interactions between key pyrazine compounds and human olfactory receptors OR5K1 through hydrogen bonds and other interactions. These findings provide new insights into the relationship between potato maturity and flavor differences, and also lays a foundation for in-depth exploration into flavor identification and perception. Full article

This study was conducted to evaluate the effect of varying dietary energy levels on milk production, feed intake, nutrient digestion and metabolism, and antioxidation function of lactating donkeys, and integrating 16S rRNA gene sequencing, metabolomics, and proteomics to comprehensively reveal the underlying regulatory networks. A single-factor, completely randomized design was used in this study. Twenty-four Dezhou donkeys with similar milk yield (3.25 ± 0.46 kg/d), lactation days (29 ± 4.34 d), parities (4.17 ± 1.17), and body weight (256 ± 34 kg) were randomly divided into three dietary treatments (n = 8), and either a fed high-energy diet (DE = 13.1 MJ/kg, HED), medium-energy diet (DE = 12.4 MJ/kg, MED), and low-energy diet (DE = 11.7 MJ/kg, LED). The experiment period included 2 weeks for adaptation and 8 weeks for data and sample collection. Orthogonal polynomial contrasts were used to evaluate the linear and quadratic effects of increasing dietary energy. There were no significant interaction effects between dietary energy level and lactation week on any milk production and quality variables (p > 0.05). Increasing dietary energy level increased DMI, milk production, milk production efficiency, and milk components (linear and quadratic; p < 0.05). Increasing dietary energy improved the digestibility of DM and neutral detergent fiber (linear; p < 0.05), and crude protein digestibility, energy digestibility and metabolism, and nitrogen metabolism (quadratic; p < 0.05). However, it decreased BHBA and NEFA concentrations (linear; p < 0.05). Furthermore, increasing dietary energy first increased then decreased the activities of GSH-PX, SOD, and T-AOC (linear and quadratic; p < 0.05), while increasing the MDA content (linear; p < 0.05). Compared with HED and MED, LED increased the relative abundance of the genera unclassified_f_Syntrophomonadaceae, Christensenellaceae_R-7_group and Treponema_2. Compared with HED, MED increased the relative abundance of the genera Ruminiclostridium_5, Ruminiclostridium_1, Family_XIII_UCG-001, unclassified_o__Clostridiales and norank_f__PL-11B10. Thyroid hormone synthesis, tyrosine metabolism, and glutathione metabolism pathways are critical metabolic routes; these pathways can enhance energy metabolism and antioxidant function, thereby improving the milk production performance of lactating donkeys. In conclusion, the digestible energy of 12.40 MJ/kg was optimal for the milk performance of lactating donkeys, whereas excessively high dietary energy (13.1 MJ/kg) may reduce milk performance. Full article

Mongolians have high overweight prevalence linked to their nomadic lifestyle and diet, but gut microbiota studies in this population are scarce. This study used fecal metagenomic and serum metabolomic analyses of 96 Mongolian participants (normal-weight n = 55, overweight n = 41) to characterize gut microbiome alterations and identify weight-related biomarkers. The analyses revealed that Parabacteroides distasonis, Barnesiella intestinihominis, and Alistipes onderdonkii were significantly reduced in overweight individuals (p < 0.05). Concurrently, the metabolites such as beta-cryptoxanthin, p-cresol, and ribothymidine were significantly down-regulated in the overweight group (p < 0.05). Random forest models from the three datasets showed a strong diagnostic ability for microbial families (AUC > 0.70). A subsequent integrated multi-kingdom classifier that combined microbiota and metabolite data achieved the highest performance (AUC = 0.818). Key features with high predictive contributions were identified, including Lactobacillus crispatus, Alistipes onderdonkii, and Parabacteroides distasonis, and metabolites, such as beta-cryptoxanthin, p-cresol, and picolinic acid. These results show the random forest model has high predictive value for distinguishing normal weight and overweight individuals. In summary, this study identified specific gut microbiota and serum metabolomic profiles linked to overweight in Mongolians. Multi-omics integration established a diagnostic biomarker model, laying a theoretical basis for microbiome-targeted weight management interventions. Full article

Lodging is a major constraint limiting grain yield in dry direct seeding rice (DDSR), yet the key traits and phenotypic relationships governing lodging resistance in japonica varieties adapted to this system remain poorly understood. This study evaluated 79 japonica accessions over two years in Shenyang, Northeast China, to dissect phenotypic variation in lodging index and identify ideotypes for breeding. Based on hierarchical clustering, varieties were classified into strong lodging resistance (SLR), medium lodging resistance (MLR), and weak lodging resistance (WLR) types, with SLR varieties achieving lodging indices 27.4–31.8% lower than those of MLR and 63.2–83.8% lower than those of WLR varieties. SLR varieties reduced lodging risk by coordinately balancing whole-plant bending moment and stem breaking resistance: plant height and center-of-gravity height were 5.2–10.7% lower, while basal internode bending stress was 27.9–81.9% higher than in other types. Structural equation modeling identified culm dry weight, inner diameter, and culm phenotype index as primary determinants of lodging variation. Notably, despite 11.0–13.7% fewer spikelets per panicle, SLR varieties maintained grain yields comparable to those of WLR varieties through compensatory increases in grain-filling rate (6.7–7.3%) and 1000-grain weight (8.1–8.7%). These findings demonstrate that optimizing basal internode structure and enhancing culm tissue density can simultaneously improve lodging resistance and preserve yield potential, providing a practical framework for breeding lodging-resistant, high-yielding japonica varieties for DDSR systems in Northeast China. Full article

The combined arch theory provides an effective means for designing support parameters in roadways within loose and fractured surrounding rock. A clear understanding of the internal stress evolution during the load-bearing process of the combined arch is of guiding significance for optimizing roadway support. Taking the 11308 return airway of a mine in Inner Mongolia as the engineering background, this study adopts a combined research approach of theoretical calculation, numerical simulation and laboratory testing. It systematically investigates the internal stress evolution of the anchored combined arch load-bearing structure in roadways with loose and fractured surrounding rock. The load-bearing capacity and failure characteristics of the anchored combined arch under different roof support schemes are explored and analyzed. An optimized support scheme for the loose and fractured roof is proposed and applied in the field, and the monitoring results verify its effectiveness. The results indicate that bolt density is a key factor affecting the load-bearing performance of the combined arch. As bolt spacing decreases, the vertical stress concentration in the anchored structure increases, and its deformation resistance is enhanced. During the stage from load-bearing to failure of the combined arch, the changes in vertical and horizontal stresses within the arch become more stable, and the load-bearing capacity is significantly improved. Comparison between the model test results and theoretical calculations shows good agreement, verifying the rationality of the theoretical calculations. Pressure sensors were pre-installed in the laboratory model to monitor the vertical stress changes in the anchored structure throughout the loading process, and numerical simulations confirmed the stress concentration effect of the combined arch. It was also found that the instability of the anchored structure is controlled by the shear plane at the arch feet. Finally, the bolt spacing in the 11308 return airway of the Inner Mongolia mine was optimized to 0.7 m, and field monitoring was introduced. The maximum roof surface settlement displacement was 15 mm, and the maximum roof separation was 3 mm, confirming that these parameters can meet the roadway stability requirements. Full article

Galeruca daurica (Joannis) (Coleoptera: Chrysomelidae) is an oligophagous pest in which both adults and larvae prefer to feed on Allium forage grasses of the Liliaceae family. In this study, we identified gustatory receptor (GR) genes based on the transcriptome data of G. daurica; analyzed the expression profiles of these GR genes across different larval instars and various tissues of male and female adults using quantitative real-time PCR (qRT-PCR); detected the electrophysiological responses of the mouthparts of male and female G. daurica adults to flavonoids and carbohydrates using single sensillum recording (SSR); and recorded the changes in food consumption of G. daurica adults after feeding on six host plant-derived metabolites. A total of 26 GR genes were identified from the transcriptome data of adult and larval of G. daurica. Phylogenetic analysis was performed to screen candidate functional gustatory receptor genes, including four sugar receptors (GdauGR7, GdauGR10, GdauGR14 and GdauGR28), seven bitter receptors (GdauGR11, GdauGR16~17, GdauGR22, GdauGR25~26 and GdauGR30), and two CO₂ receptors (GdauGR15 and GdauGR20). Larval expression profiling of GdauGRs in G. daurica revealed that the relative expression levels of 17 genes exhibited dynamic changes during larval growth and development. GdauGRs were expressed to varying degrees in the antennae, mouthparts, brain, gut, and forelegs of adult G. daurica, with sex-specific differences. Notably, the expression levels of GdauGR4, GdauGR9 and GdauGR16 in the gut were extremely significantly higher than those in other tissues. In the SSR test, the six tested flavonoids and one carbohydrate were able to induce robust electrophysiological responses in the gustatory sensilla on the antennae and mouthparts of adult G. daurica at specific concentrations. In addition, the supplementation of several host-derived metabolites altered the food consumption of adult G. daurica. These findings lay a solid foundation for elucidating the molecular mechanisms underlying gustatory recognition and host adaptation in G. daurica. Full article

This study estimates China’s methane (CH₄) emissions from 43 specific emission sources in 2020 and projects future trends through 2050 under two scenarios: Current Legislation (CLE) and Maximum Technically Feasible Reduction (MFR). The analysis utilises the Greenhouse gas and Air pollution Interactions and Synergies (GAINS) model methane framework, incorporating updated province-level activity data to capture the pronounced regional heterogeneity inherent in emission profiles and mitigation capacities. The results reveal a national CH₄ budget of 1114 MtCO₂e in 2020, with the energy sector (59%) and agriculture (28%) emerging as the primary contributors. A substantial technical mitigation potential is identified; by 2050, emissions could be curtailed by up to 48% relative to the CLE scenario, representing a 46% reduction from 2020 levels. The energy and waste sectors emerge as the primary contributors to this potential. Specifically, coal mining CH₄ abatement constitutes 58% of the energy sector’s total reduction potential, while enhanced solid waste management accounts for 97% of the mitigation within the waste sector. Key measures include ventilation air methane (VAM) oxidation and pre-mining degasification, as well as anaerobic digestion and recovery and utilization for energy use. Owing to regional disparities in hydrothermal conditions (representing the combined influence of temperature and moisture), demographic status, economic development, the most effective mitigation strategies vary across provinces. For example, pre-mining degasification and VAM oxidation are most impactful in major coal-producing regions such as Shanxi, Inner Mongolia, and Shaanxi. In contrast, anaerobic digestion, recovery and utilization, and waste incineration play a dominant role in more economically developed and densely populated provinces such as Jiangsu, Shandong and Zhejiang. By delineating region-specific technological priorities, this study quantifies the maximum technical mitigation potential for China and offers guidance for other nations facing similar mitigation challenges. Full article

The increasing penetration of renewable energy has led to the large-scale integration of power electronic devices into the power grid. In weakly connected grids, such devices are connected to the grid via voltage source converters (VSCs) using grid-forming (GFM) control strategies. Ideally, the point of common coupling (PCC) with the grid is treated as a purely inductive circuit. However, in weak grids, the resistance-to-inductance ratio (R/X) cannot be ignored, which leads to the power coupling problem between active power (P) and reactive power (Q). This phenomenon impedes the precise control of P and Q, potentially resulting in steady-state power deviations and even system instability. Traditional power-decoupling methods based on virtual inductance (VI) have inherent limitations and fail to achieve complete decoupling between P and Q. To address this issue, this paper first analyzes the influencing factors of power coupling through an established power coupling model. Comparisons between the output voltage and the degree of power coupling demonstrate that power decoupling can be achieved by compensating the output voltage. Consequently, an improved power-decoupling strategy based on apparent power feedforward (APPFF) is proposed. The proposed APPFF method realizes complete P-Q decoupling, with a steady-state reactive power error of less than 1% of the rated value. Compared with the PI-decoupling method, the reactive power overshoot is reduced by about 24%, and no additional active power overshoot is introduced. Compared with the conventional virtual inductance method that only reduces coupling by up to 35%, APPFF eliminates the power coupling fundamentally while retaining the reactive power–voltage droop characteristics and fast dynamic response. By directly compensating the reference voltage to the ideal value using apparent power as the feedforward variable, the proposed method is essentially different from the existing voltage/angle compensation schemes. The feasibility and effectiveness of the proposed decoupling method are verified under various working conditions, such as different R/X ratios, line resistances and power references, through both Simulink simulations and experimental results. Full article

Remote sensing offers distinct advantages for lithological mapping, but its ability to detect underlying bedrock is limited in covered areas, whereas geochemical data are constrained by sparse sampling and low spatial resolution. To address these challenges, this study proposes a texture-guided adaptive data fusion framework combined with a Multi-scale Convolutional Neural Network (MCNN) for lithological mapping, using the Guyang area in Inner Mongolia as a case study. First, the non-linear relationships between geochemical components and remote sensing spatial textures are modeled to achieve complementary integration of heterogeneous multi-source data. Second, an MCNN model is constructed to extract multi-scale geological features, enabling improved discrimination of lithological units and more effective inference of concealed bedrock beneath Quaternary cover. Experimental results show that the proposed method overcomes the limitations of single data sources and achieves an overall accuracy (OA) of 0.95 on the fused dataset. Ablation experiments further demonstrate that the texture-guided fusion strategy significantly improves lithological identification performance. This study provides an effective framework for intelligent geological mapping and confirms the feasibility of inferring underlying bedrock in covered areas using multi-source surface information. Full article

Let $\epsilon >0$ and $\mathcal{TB}(\mathcal{X}\times \mathcal{X})$ be the Banach algebra of all $2\times 2$ bounded upper triangular operator matrices on a separable Hilbert space $\mathcal{X}\times \mathcal{X}$. In this paper, we first establish the spectrum equalities for special cases of upper triangular operator matrices—diagonal block operator matrix $M_0=\left({\displaystyle \genfrac{}{}{0pt}{}{A0}{0B}}\right)$. We obtain that ${\widehat{\Sigma }}_{bi,\epsilon }\left(M_0\right)={\Sigma }_{bi,\epsilon }\left(A\right)\cup {\Sigma }_{bi,\epsilon }\left(B\right)$, $i\in \{1,2,4\}$, where ${\Sigma }_{bi,\epsilon }(·)$ and ${\widehat{\Sigma }}_{bi,\epsilon }(·)$ denote the noncommutative pseudo-upper (resp. lower) semi-Browder essential spectrum, noncommutative pseudo-Browder essential spectrum, sub-noncommutative pseudo-upper (resp. lower) semi-Browder essential spectrum, and sub-noncommutative pseudo-Browder essential spectrum. Secondly, based on Cao and Bai’s works, we study the perturbation of the sub-noncommutative pseudo-Browder essential spectrum ${\widehat{\Sigma }}_{b4,\epsilon }(·)$ of a 2 × 2 bounded upper triangular operator matrix $M_C=\left({\displaystyle \genfrac{}{}{0pt}{}{AC}{0B}}\right)$ on a separable Hilbert space. We obtain that ${\displaystyle \bigcap _{C\in \mathcal{B}\left(\mathcal{X}\right)}{\widehat{\Sigma }}_{b4,\epsilon }\left(M_C\right)={\Sigma }_{b1,\epsilon }\left(A\right)\cup {\Sigma }_{b2,\epsilon }\left(B\right)\cup \Delta }$, where $\Delta =\{\lambda \in \mathbb{C}:$ there exist $P_i\in \mathcal{B}\left(\mathcal{X}\right)$ with $\parallel P_i\parallel <\epsilon ,i\in \{1,2\},$ such that $\alpha (A+P_1-\lambda I)+\alpha (B+P_2-\lambda I)\ne \beta (A+P_1-\lambda I)+\beta (B+P_2-\lambda I)\}$. Finally, we obtain ${\Sigma }_{bi,\epsilon }\left(A\right)\cup {\Sigma }_{bi,\epsilon }\left(B\right)={\widehat{\Sigma }}_{bi,\epsilon }\left(M_C\right)\cup W,i\in \{1,2,4\}$, where W is the union of certain holes in $({\Sigma }_{bi,\epsilon }\left(A\right)\cup {\Sigma }_{bi,\epsilon }\left(B\right))\backslash {\widehat{\Sigma }}_{bi,\epsilon }\left(M_C\right)$. Full article

To explicitly illustrate the relationship between heliostat field optimization and power generation, a coupled model was established in Simulink. By optimizing the geometric layout of the heliostat field, the solar heat collection efficiency can be significantly improved, thereby increasing the thermal input to the system. The optimized heliostat field design can convert solar energy into thermal energy more efficiently and transfer it to the steam generator through the molten salt loop, thereby driving power generation in the Rankine cycle. In this process, the Rankine cycle is responsible for converting the thermal energy supplied by the molten salt loop into mechanical work and ultimately into electrical power output. At the same time, real meteorological data from a commercial heliostat field were introduced, and annual power generation simulations demonstrated that the integrated modeling of the heliostat field, thermal storage, and power block based on actual meteorological boundary conditions and system parameters can effectively reflect the power generation performance of a commercial tower solar thermal power plant. Meanwhile, research on heliostat field optimization should further evolve from identifying general patterns toward parameter design and overall system performance improvement. For molten-salt tower solar thermal power plants, key design variables such as receiver tower height, receiver dimensions, heliostat dimensions, and heliostat field spacing parameters affect not only the annual average optical efficiency of the heliostat field and the thermal power output of the receiver, but also the annual power generation of the entire plant. By integrating SOLARPILOT 1.5.2 and SAM 2025.4.16, the design variables were systematically analyzed to investigate their effects on the annual average optical efficiency of the heliostat field, the number of heliostats, the receiver output power, and the annual power generation, and the reasonable value ranges of the heliostat field parameters were determined accordingly. The established Rankine cycle power block model was then coupled with the parameter optimization results to carry out a secondary optimization of the initial heliostat field. Through the above study, the aim is to realize a shift from single-objective geometric optimization of the heliostat field to comprehensive optimization oriented toward annual plant power generation performance and scenario adaptability, thereby providing a basis for scheme design and parameter selection of molten-salt tower solar thermal power plants. For external validation, the annual generation predicted for the Delingha 50 MW commercial plant was 142.15 GWh, corresponding to a relative deviation of 2.64% from the published design value of 146 GWh. This indicates that the coupled framework can reasonably capture the integrated response of the heliostat field, thermal storage system, and power block at the plant level. The model is therefore suitable for generation-oriented parameter screening and preliminary design of tower molten-salt CSP plants, while detailed component-level transient design still requires higher-fidelity engineering models. Full article

Mongolian horses are an indigenous Chinese breed known for their endurance capacity, yet quantitative descriptions of their gait-related kinematic characteristics remain limited. This pilot exploratory study aimed to describe the kinematics of Mongolian horses during walk, slow trot, and fast trot, and to examine whether selected variables differed between race-result groups in a 12 km endurance race. Forty-six horses were classified into an excellent group and an ordinary group based on the result of a single race. Kinematic data were collected using optical motion capture and three-dimensional skeletal modelling. Separate gait-specific linear mixed-effects models were fitted, with horse identity as a random effect and group and speed as fixed effects. The results showed gait-dependent between-group differences. During walk, the excellent group had significantly greater range of motion of the tarsal, hip, and elbow joints, as well as a greater maximum forelimb retraction angle (all p < 0.001). During slow trot, the excellent group showed significantly greater stride length (p = 0.009), elbow joint range of motion (p < 0.001), minimum hindlimb forward extension angle (p = 0.033), and minimum forelimb forward extension angle (p = 0.004). During fast trot, the between-group differences were most pronounced, with significantly greater stride length (p < 0.001) and range of motion of the tarsal joint (p < 0.001), hip joint (p = 0.015), and elbow joint (p = 0.014), together with greater maximum hindlimb retraction angle (p = 0.001) and minimum forelimb forward extension angle (p = 0.026). Overall, these findings provide preliminary evidence that gait-related kinematic differences may exist between race-result groups in Mongolian horses. However, because this was an exploratory study based on a single race, the findings should be interpreted cautiously and require validation in larger and more diverse cohorts. Full article

Hordeum brevisubulatum ‘Mengnong No. 2’ is a new forage variety developed using traditional group selection breeding techniques. It features notable advantages in plant height, tillering capacity, and overall biomass yield. However, key molecular breeding techniques such as molecular marker identification and genetic manipulation have yet to be established for this variety, limiting improvements in important traits. Consequently, we assessed the biomass of ‘Mengnong No. 2’ against ‘Mengnong No. 1’, the most widely cultivated variety in the central and western regions of Inner Mongolia, China. We report that fresh forage, dry forage, and seed yields of ‘Mengnong No. 2’ increased by 20.6%, 31.78%, and 34.35%, respectively, compared with the control variety, indicating broad prospects for its application and promotion. To enable rapid identification of ‘Mengnong No. 2’ during its promotion and to prevent production losses caused by variety admixture, we used three screened SSR primer pairs (GST25, GST37, GST127) to construct a DNA fingerprint for five H. brevisubulatum varieties, including ‘Mengnong No. 2’. With the percentage of polymorphic bands exceeding 95%, these profiles enabled precise identification of the ‘Mengnong No. 2’ variety. Furthermore, callus regeneration in H. brevisubulatum represents a bottleneck for directed molecular breeding techniques such as genetic transformation and gene editing. Accordingly, we selected the inflorescences of ‘Mengnong No. 2’ as explants and investigated the callus induction and regeneration capacity of inflorescences at different developmental stages. We found that explants at the spikelet primordia differentiation stage exhibited the highest callus induction and regeneration efficiencies, reaching 62.7% and 72.8%, respectively. The resulting embryogenic callus lines can serve as recipients for Agrobacterium-mediated transformation or gene gun bombardment, facilitating the development of subsequent high-efficiency genetic transformation and gene-editing systems. The SSR-based variety identification system and the highly efficient regeneration technology using inflorescence-derived callus established in this study lay a solid foundation for the development of a molecular breeding system for ‘Mengnong No. 2’. Full article