Search Results (306)

Lonicera japonica Thunb. is a traditional Chinese medicinal herb whose floral buds are the primary source of pharmacological compounds that require manual harvesting. As a result, its floral bud duration, determined by the opening time, is a key determinant of both quality and economic value. However, the genetic mechanisms controlling floral bud duration remain poorly understood. In this study, we employed population structure analysis and molecular experiments to identify candidate genes associated with this trait. The improved cultivar Beihua No. 1 (BH1) opens its floral buds significantly later than the landrace Damaohua (DMH). Exogenous application of methyl jasmonate (MeJA) to BH1 indicated that jasmonate acts as a negative regulator of floral bud duration by accelerating floral bud opening. A genome-wide selection scan across 35 germplasms with varying floral bud durations identified the transcription factor LjWRKY50 as the causative gene influencing this trait. The dual-luciferase reporter assay and qRT-PCR experiments showed that LjWRKY50 activates the expression of the jasmonate biosynthesis gene, LjAOS. A functional variant within LjWRKY50 (Chr7:24636061) was further developed into a derived cleaved amplified polymorphic sequence (dCAPS) marker. These findings provide valuable insights into the jasmonate-mediated regulation of floral bud duration, offering genetic and marker resources for molecular breeding in L. japonica. Full article

Background: Skipping breakfast, a prevalent issue among children and adolescents, has been reported to be associated with academic performance and long-term health. However, less attention has been given to the types of breakfast foods consumed. Therefore, our study aims to investigate the association between breakfast variety and dietary knowledge, attitude, and practice (KAP) among preadolescents. Methods: The study included 1449 students in grades 4–6 from Zhongshan city, Guangdong province. Data were collected through face-to-face field investigation using a validated questionnaire. The questionnaire encompassed sociodemographic characteristics, as well as dietary KAP. Results: Among all participants, 1315 reported consuming breakfast daily. Dietary diversity varied significantly: 8.8% consumed only 1 type of food, 52.9% consumed 2–4 types, and 38.3% consumed ≥5 types. Students who consumed a greater variety of breakfast foods exhibited more favorable dietary and lifestyle patterns. Specifically, those who consumed ≥5 types of food showed statistically significant associations with healthier practices, including reduced intake of sugary beverages and night snacks, stronger adherence to dietary guidelines, more positive attitudes toward improving eating habits, longer sleep durations, increased participation in meal preparation, greater dish variety in meals, and higher engagement in daily physical activity. Conclusions: Breakfast variety was associated with KAP, particularly when breakfast types ≥ 5, providing more sufficient and favorable evidence for breakfast consumption. Full article

To explore the dyeing technique and karyotype analysis of winter turnip rape (Brassica rape L.), the root tip of winter turnip rape Longyou 7 was used as the experimental material. Chromosome preparation technology was optimized, and karyotype analysis was carried out by changing the conditions of material collection time, pretreatment, fixation, and dissociation. The results showed that the optimal conditions for the preparation of dyeing winter turnip rape were as follows: the sampling time was 8:00–10:00, the ice–water mixture was pretreated at 4 °C for 20 h, the Carnot’s fixative solution I and 4 °C were fixed for 12 h, and the 1 mol/L HCl solution was bathed in a water bath at 60 °C for 10~15 min. Karyotype analysis showed that the number of chromosomes in winter turnip rape cells was 2n = 20, and the karyotype analysis formula was 2n = 2x = 20 = 16m + 4sm. The karyotype asymmetry coefficient was 58.85%, and the karyotype type belonged to type 2A, which may belong to the primitive type in terms of evolution. The results of this study provide a theoretical basis for further in-depth study of the phylogenetic evolution and genetic trend of Brassica rapa. Full article

Underwater image enhancement (UIE) technology plays a vital role in marine resource exploration, environmental monitoring, and underwater archaeology. However, due to the absorption and scattering of light in underwater environments, images often suffer from blurred details, color distortion, and low contrast, which seriously affect the usability of underwater images. To address the above limitations, a lightweight transformer-based model (LITM) is proposed for improving underwater degraded images. Firstly, our proposed method utilizes a lightweight RGB transformer enhancer (LRTE) that uses efficient channel attention blocks to capture local detail features in the RGB domain. Subsequently, a lightweight HSV transformer encoder (LHTE) is utilized to extract global brightness, color, and saturation from the hue–saturation–value (HSV) domain. Finally, we propose a multi-modal integration block (MMIB) to effectively fuse enhanced information from the RGB and HSV pathways, as well as the input image. Our proposed LITM method significantly outperforms state-of-the-art methods, achieving a peak signal-to-noise ratio (PSNR) of 26.70 and a structural similarity index (SSIM) of 0.9405 on the LSUI dataset. Furthermore, the designed method also exhibits good generality and adaptability on unpaired datasets. Full article

Fruit yield and quality improvement are challenges for researchers and farmers. This study reveals that the main fruit traits of blueberry (Vaccinium ashei ‘Bluegem’) were significantly improved after hydrogen (H₂)-based irrigation, assessed by the increased single fruit weight (14.59 ± 6.66%) and fruit equatorial diameter (4.19 ± 2.39%), decreased titratable acidity, increased solid–acid and sugar–acid ratios. The enhancement of fruit quality was confirmed by the increased total volatiles, vitamin C contents, and antioxidant capacity. Using weighted protein co-expression network analysis (WPCNA), proteomic interrogation revealed that serine carboxypeptidase-like proteins I/II (SCPLI/II), ADP ribosylation factor 1/2 (ARF1/2), and UDP-glucosyltransferase 85A (UGT85A) might be functionally associated with the increased fruit weight and size driven by H₂. Reduced organic acid accumulation was caused by the regulation of the specific enzymes involved in sucrose metabolism (e.g., α-amylase, endoglucanase, β-glucosidase, etc.). H₂ regulation of fatty acid degradation (e.g., acyl CoA oxidase 1 (ACX1), acetyl CoA acyltransferase 1 (ACAA1), etc.) and phenylpropanoid metabolism were used to explain the improved fruit aroma and anthocyanin accumulation. Meanwhile, the upregulated heat shock protein 20/70 matched with the enhanced antioxidant activity. Together, this study provides a novel approach for yield and quality improvement in horticultural crops. Full article

Information regarding the impact of polymetallic exposure on metabolic syndrome (MetS) among residents living near abandoned Pb-Zn mines is limited. Our objective was to investigate the impact of co-exposure to metal mixtures on the prevalence of MetS among residents. ICP-MS was used to measure the levels of 24 metals in the urine of 1744 participants, including 723 participants living near abandoned Pb-Zn mines, labeled as exposed area, and 1021 participants from other towns, labeled as reference area in the same city. Multivariable generalized linear regression, adaptive LASSO penalized regression, and BKMR were used to assess the associations between metals and MetS. The levels of eleven metals were higher, while those of nine metals were lower in the exposed area than those in the reference area. Mg, Cd, Ti, TI, Zn, Rb, and Pb were selected as important MetS predictors using LASSO regression. In exposed area, urinary Zn and TI were positively associated with MetS, whereas Mg was negatively associated with MetS. In the reference area, urinary Zn was positively associated with MetS, whereas Mg and Ti were negatively associated with MetS. The BKMR model indicates a statistically significant positive overall effect of the seven metal mixtures on MetS in the exposed area. Polymetallic exposure was positively associated with MetS risk in the abandoned Pb-Zn mining areas, suggesting that excessive Zn and TI may be associated with a higher MetS risk among residents living near abandoned Pb-Zn mines. Full article

In biomass estimation based on size-related features, regression models are commonly used to predict fish mass. However, in real-world scenarios, fish are often partially occluded by others, resulting in missing or corrupted features. To address this issue, we propose a robust framework that integrates feature imputation with regression. Missing features are first reconstructed through imputation, followed by regression for biomass prediction. We evaluated various imputation and regression methods and found that the autoencoder achieved the best performance in imputation. Among regression models, SVR, Extra Trees, and MLP performed best in their respective categories. These three models, combined with the autoencoder, were selected to construct the final framework. Experimental results demonstrate that the proposed framework significantly improves performance. For instance, the RMSE of SVR, Extra Trees, and MLP decreased from 21.10 g, 2.49 g, and 18.40 g to 6.53 g, 1.95 g, and 5.09 g, respectively. Full article

With the rise of digital twin technology, the application of digital twin technology to industrial automation provides a new direction for the digital transformation of the global smart manufacturing industry. In order to further improve production efficiency, as well as realize enterprise digital empowerment, this paper takes a welding robot arm as the research object and constructs a welding robot arm digital twin system. Using three-dimensional modeling technology and model rendering, the welding robot arm digital twin simulation environment was built. Parent–child hierarchy and particle effects were used to truly restore the movement characteristics of the robot arm and the welding effect, with the help of TCP communication and Bluetooth communication to realize data transmission between the virtual segment and the physical end. A variety of UI components were used to design the human–machine interaction interface of the digital twin system, ultimately realizing the data-driven digital twin system. Finally, according to the digital twin maturity model constructed by Prof. Tao Fei’s team, the system was scored using five dimensions and 19 evaluation factors. After testing the system, we found that the combination of digital twin technology and automation is feasible and achieves the expected results. Full article

Pulverized coal mass concentration in the primary air pipe is one of the essential parameters for promoting furnace combustion efficiency. However, attaining accurate, real-time, and online detection for pulverized coal mass concentration remains challenging due to factors such as large pipe diameter and high flow rate. This study introduces a quasi-open microwave resonant cavity sensor. The principle and model were analyzed using the perturbation method, and the design and optimization were conducted with the simulation. A prototype and its test system were constructed, and the test results demonstrated good agreement between the simulations and experiments. The simulation revealed that the resonant frequency decreased monotonically from 861 to 644 MHz as mass concentration increased within 20%~80%, resulting in a change of about 3.62 MHz/1% under static mixture. The resonant frequency showed a drop from 21 MHz to 9 MHz with an increase in mass concentration under pulverized coal flow. Prediction models were developed and validated, showing the absolute values of the relative errors to be within 4% under operational scenarios. Additionally, the impact of the sensor on pulverized coal flow was evaluated, and it was found that the sensor structure had minimal impact on the flow in terms of velocity and the distribution of continuous flow. Finally, the long-term stability was assessed by examining the wear of the antennas and barriers. With inner barriers experiencing up to 2/3d wear, the resonant frequency drift ratio remained below 1.5%, corresponding to a mass concentration deviation of less than 3.2%. Full article

A pure pursuit method based on an improved sparrow search algorithm is proposed to address low path-tracking accuracy of intelligent agricultural machinery in complex farmland environments. Firstly, we construct a function relating speed to look-ahead distance and develop a fitness function based on the prototype’s speed and pose deviation. Subsequently, an improved sparrow search algorithm (ISSA) is employed to adjust the pure pursuit model’s speed and look-ahead distance dynamically. Finally, improvements are made to the initialization of the original algorithm and the position update method between different populations. Simulation results indicate that the improved sparrow search algorithm exhibits faster convergence speed and better capability to escape local extrema. The real vehicle test results show that the proposed algorithm achieves an average lateral deviation of approximately 3 cm, an average heading deviation below 5°, an average stabilization distance under 5 m, and an average navigation time of around 46 s during path tracking. These results represent reductions of 51.25%, 30.62%, 49.41%, and 10.67%, respectively, compared to the traditional pure pursuit model. Compared to the pure pursuit model that only dynamically adjusts the look-ahead distance, the proposed algorithm shows reductions of 34.11%, 24.96%, 32.13%, and 11.23%, respectively. These metrics demonstrate significant improvements in path-tracking accuracy, pose correction speed, and path-tracking efficiency, indicating that the proposed algorithm can serve as a valuable reference for path-tracking research in complex agricultural environments. Full article

This study develops a novel physics-informed neural network (PINN) framework for predicting steady-state dynamic responses of infinite Euler–Bernoulli (E–B) beams on foundations under moving loads. By combining localized PINN modeling with transfer learning techniques, our approach achieves high-fidelity predictions across broad parameter ranges while significantly reducing data requirements. Numerical results show that the method maintains accuracy with less than half the training data of conventional PINN models (15 target domains) and remains effective with just four domains for approximate solutions. Key findings demonstrate that optimal spatial distribution—rather than quantity—of target domains ensures robustness against noise and parameter variations. The framework advances data-efficient surrogate modeling, enabling reliable predictions in data-scarce scenarios with applications to complex engineering systems where experimental data are limited. Full article

The module division scheme of commercial aircraft and other complex system products has a significant impact on the functionality, performance, and cost of the aircraft. To obtain scientifically rational modular division solutions for commercial aircraft, this study establishes an Analytic Hierarchy Process–Gray Fuzzy Comprehensive Evaluation (AHP-GFCE) model by integrating hierarchical analysis method and gray fuzzy evaluation theory. This model develops a comprehensive evaluation methodology for aircraft modular division schemes. The proposed method was applied to evaluate the structural modular division scheme of the nose structure section of a certain type of aircraft. Results demonstrate that the AHP-GFCE model successfully identified the optimal nose structure modular division scheme. Compared with traditional installation processes, this optimal solution achieves a 40% improvement in overall assembly efficiency and a 25% reduction in total production cycle duration while better aligning with the engineering and manufacturing requirements of nose structure fabrication, thus revealing the superiority of the AHP-GFCE model in modular division evaluation. This research provides novel insights for modular division schemes of complex system products like commercial aircraft, and the methodology can be extended to modular maintenance domains of sophisticated products such as aero-engines. Although there remains room for model refinement, the findings carry significant theoretical and practical implications for modular division of complex system products. Full article

Chinese tongue sole (Cynoglossus semilaevis) is an important marine fish in China. It has sexual dimorphism. The weight and growth rate of female fish are much greater than those of male fish. However, high temperatures can induce sex reversal in genetic female fish (ZW) to phenotypic male fish; thus, identifying the genetic elements involved in temperature perception will provide the molecular basis for sex control. The heat shock transcription factor (hsf) is known as an important component of temperature sensing and mediates the heat shock response in fish such as Danio rerio; however, its function in C. semilaevis is unclear. In this study, five hsf genes (hsf1, hsf2, hsf4, hsf5a, and hsf5b) were identified in tongue sole and found to be expressed in the gonads at different developmental stages, peaking from 7M to 1Y. Gonadal in situ hybridization revealed that hsf gene signals were mainly localized in germ cells, e.g., sperm in the testis and all-stage oocytes in the ovary. Upon high-temperature stimulation, the expression of the hsf gene in the gonads increased gradually with increasing stimulation time, but different hsf genes presented different response patterns. After the RNA interference of hsf in the testis and ovarian cell lines, a series of sex-related genes, such as foxl2 and dmrt1, significantly changed. In vivo RNA interference had an effect on the female gonads and mainly affected neurl3 expression. On the basis of these data, we speculate that hsf responds to temperature stimulation and plays an important role in sex differentiation. This study helps elucidate the relationship between temperature sensing and sex differentiation in C. semilaevis. Full article

Ground penetrating radar (GPR) is an effective and efficient nondestructive testing technology for subsurface investigations. Deep learning-based methods have been successfully used in automatic underground target recognition. However, these methods are mostly based on supervised learning, requiring large amounts of labeled training data to guarantee high accuracy and generalization ability, which is a challenge in GPR fields due to time-consuming and labor-intensive data annotation work. To alleviate the demand for abundant labeled data, a semi-supervised deep learning method named attention–temporal ensembling (Attention-TE) is proposed for underground target recognition using GPR B-scan images. This method integrates a semi-supervised temporal ensembling architecture with a triplet attention module to enhance the classification performance. Experimental results of laboratory and field data demonstrate that the proposed method can automatically recognize underground targets with an average accuracy of above 90% using less than 30% of labeled data in the training dataset. Ablation experimental results verify the efficiency of the triplet attention module. Moreover, comparative experimental results validate that the proposed Attention-TE algorithm outperforms the supervised method based on transfer learning and four semi-supervised state-of-the-art methods. Full article

Cold stress critically restricts maize seedling growth in Northeast China, yet the mechanism by which cold priming (CP) enhances cold tolerance through proline–nitrogen metabolic networks remains unclear. This study systematically investigated CP’s synergistic regulation in cold-tolerant (Heyu27) and cold-sensitive (Dunyu213) maize using a two-phase temperature regime (priming induction/stress response) with physiological and multivariate analyses. CP alleviated cold-induced photosynthetic inhibition while maintaining a higher chlorophyll and photosynthetic rate, though biomass responses showed varietal specificity, with Heyu27 minimizing growth loss through optimized carbon–nitrogen allocation. Antioxidant enzymes such as superoxide dismutase (SOD), peroxidase (POD), and catalase (CAT) were pre-activated during early stress, effectively scavenging reactive oxygen species (ROS) and reducing malondialdehyde (MDA) accumulation, with Heyu27 showing superior redox homeostasis. CP enhanced proline accumulation via bidirectional enzyme regulation (upregulating ∆¹-pyrroline-5-carboxylate synthase/reductase [P5CS/P5CR], inhibiting proline dehydrogenase [ProDH]) and reprogrammed nitrogen metabolism through glutamate dehydrogenase/isocitrate dehydrogenase (GDH/ICDH)-mediated ammonium conversion to glutamate, alleviating nitrogen dysregulation while supplying proline precursors. Principal component analysis revealed divergent strategies: Heyu27 prioritized proline–antioxidant synergy, whereas Dunyu213 emphasized photosynthetic adjustments. These findings demonstrate that CP establishes “metabolic memory” through optimized proline–nitrogen coordination, synergistically enhancing osmoregulation, reactive oxygen species (ROS) scavenging, and nitrogen utilization. This study elucidates C₄-specific cold adaptation mechanisms, advancing cold-resistant breeding and stress-resilient agronomy. Full article