Search Results (317)

Sustainable highway infrastructure maintenance critically depends on precise lane line detection, yet conventional inspection approaches remain resource-depleting, carbon-intensive, and hazardous to personnel. To mitigate these constraints and address the low accuracy and high parameterization of existing models, this study utilizes unmanned aerial vehicle (UAV) imagery and proposes a UAV-based highway lane line segmentation method using an improved DeepLabV3+ model that resolves multi-scale lane line segmentation challenges in UAV imagery. MobileNetV2 is used as the backbone network to significantly reduce the number of model parameters. The Squeeze-and-Excitation (SE) attention mechanism is integrated to enhance feature extraction capabilities, particularly at lane line edges. A Feature Pyramid Network (FPN) is incorporated to improve multi-scale lane line feature extraction. We introduce a novel Waterfall Atrous Spatial Pyramid Pooling (WASPP) module, utilizing cascaded atrous convolutions with strategic dilation rate adjustments to progressively expand the receptive field and aggregate contextual information across scales. The improved model outperforms the original DeepLabV3+ by 5.04% mIoU (85.30% vs. 80.26%) and 3.35% F1-Score (91.74% vs. 88.39%) while cutting parameters by 85% (8.03 M vs. 54.8 M) and reducing training time by 2 h 50 min, thereby enhancing the model’s accuracy in lane line segmentation, reducing the number of parameters, and lowering the carbon footprint. Full article

Appropriate carriers or templates are crucial for maintaining the stability, biological activity, and bioavailability of selenium nanoparticles (SeNPs). Selecting suitable templates remains challenging for fully utilizing SeNPs functionalities and developing applicable products. Exosome-like nanoparticles (ELNs) have gained importance in drug delivery systems, yet research on selenium products prepared using exosomes remains limited. To address this gap, we utilized Cyperus bean ELNs to deliver SeNPs, investigated three preparation methods for SeNPs-ELNs, identified the optimal approach, and performed characterization studies. Notably, all three methods successfully loaded SeNPs. Ultrasonic cell fragmentation is the optimal approach, achieving significant increases in selenium loading (5.59 ± 0.167 ng/μg), enlargement of particle size (431.17 ± 10.78 nm), and reduced absolute zeta potential (−4.1 ± 0.43 mV). Moreover, both exosome formulations demonstrated enhanced stability against aggregation during storage at 4 °C, while their stability varied with pH conditions. In vitro digestibility tests showed greater stability of SeNP-ELNs in digestive fluids compared to ELNs alone. Additionally, neither ELNs nor SeNP-ELNs exhibited cytotoxicity toward LO₂ cells, and the relative erythrocyte hemolysis remained below 5% at protein concentrations of 2.5, 7.5, 15, 30, and 60 μg/mL. Overall, ultrasonic cell fragmentation effectively loaded plant-derived exosomes with nano-selenium at high capacity, presenting new opportunities for their use as functional components in food and pharmaceutical applications. Full article

Flexible energy sources such as electric vehicles and the battery energy storage systems of intelligent distribution systems can provide system-wide auxiliary services such as frequency regulation for power systems. This paper proposes an optimal method for operating the local energy community that is based on two-stage scheduling. Firstly, the basic concepts of the local energy community and flexible service are introduced in detail. Taking LEC as the reserve unit of artificial frequency recovery, an energy information interaction model among LEC, balance service providers, and the power grid is established. Then, a two-stage scheduling framework is proposed to ensure the rationality and economy of community energy scheduling. In the first stage, day-ahead scheduling uses the energy community management center to predict the up/down flexibility capacity that LEC can provide by adjusting the BESS control parameters. In the second stage, real-time scheduling aims at maximizing community profits and scheduling LEC based on the allocation and activation of standby flexibility determined in real time. Finally, the correctness of the two-stage scheduling framework is verified through a case study. The results show that the control parameters used in the day-ahead stage can significantly affect the real-time profitability of LEC, and that LEC benefits more in the case of low BESS utilization than in the case of high BESS utilization and non-participation in frequency recovery reserve. Full article

Under the dual-carbon goals, with the rapid increase in the proportion of fluctuating power sources such as wind and solar energy, the regulatory capacity of traditional thermal power generation can no longer meet the demand for intra-day fluctuations. There is an urgent need to tap into the potential of flexible load-side regulatory resources. To this end, this paper proposes a low-carbon economic optimal dispatching strategy for virtual power plants (VPPs), considering the aggregation of diverse flexible and adjustable resources with the integration of wind and solar power. Firstly, the method establishes mathematical models by analyzing the dynamic response characteristics and flexibility regulation boundaries of adjustable resources such as photovoltaic (PV) systems, wind power, energy storage, charging piles, interruptible loads, and air conditioners. Subsequently, considering the aforementioned diverse adjustable resources and aggregating them into a VPP, a low-carbon economic optimal dispatching model for the VPP is constructed with the objective of minimizing the total system operating costs and carbon costs. To address the issue of slow convergence rates in solving high-dimensional state variable optimization problems with the traditional plant growth simulation algorithm, this paper proposes an improved plant growth simulation algorithm through elite selection strategies for growth points and multi-base point parallel optimization strategies. The improved algorithm is then utilized to solve the proposed low-carbon economic optimal dispatching model for the VPP, aggregating diverse adjustable resources. Simulations conducted on an actual VPP platform demonstrate that the proposed method can effectively coordinate diverse load-side adjustable resources and achieve economically low-carbon dispatching, providing theoretical support for the optimal aggregation of diverse flexible resources in new power systems. Full article

Crop disease identification is a pivotal research area in smart agriculture, forming the foundation for disease mapping and targeted prevention strategies. Among the most prevalent global wheat diseases, powdery mildew—caused by fungal infection—poses a significant threat to crop yield and quality, making early and accurate detection crucial for effective management. In this study, we present QY-SE-MResNet34, a deep learning-based classification model that builds upon ResNet34 to perform multi-class classification of wheat leaf images and assess powdery mildew severity at the single-leaf level. The proposed methodology begins with dataset construction following the GBT 17980.22-2000 national standard for powdery mildew severity grading, resulting in a curated collection of 4248 wheat leaf images at the grain-filling stage across six severity levels. To enhance model performance, we integrated transfer learning with ResNet34, leveraging pretrained weights to improve feature extraction and accelerate convergence. Further refinements included embedding a Squeeze-and-Excitation (SE) block to strengthen feature representation while maintaining computational efficiency. The model architecture was also optimized by modifying the first convolutional layer (conv1)—replacing the original 7 × 7 kernel with a 3 × 3 kernel, adjusting the stride to 1, and setting padding to 1—to better capture fine-grained leaf textures and edge features. Subsequently, the optimal training strategy was determined through hyperparameter tuning experiments, and GrabCut-based background processing along with data augmentation were introduced to enhance model robustness. In addition, interpretability techniques such as channel masking and Grad-CAM were employed to visualize the model’s decision-making process. Experimental validation demonstrated that QY-SE-MResNet34 achieved an 89% classification accuracy, outperforming established models such as ResNet50, VGG16, and MobileNetV2 and surpassing the original ResNet34 by 11%. This study delivers a high-performance solution for single-leaf wheat powdery mildew severity assessment, offering practical value for intelligent disease monitoring and early warning systems in precision agriculture. Full article

The mechanisms underlying formation energy depletion after initial fracturing and post-refracturing production decline in shale oil horizontal wells remain poorly understood. This study proposes a novel numerical simulation framework for refracturing processes based on a three-dimensional fully coupled hydromechanical model. By dynamically reconfiguring the in situ stress field through integration of production data from initial fracturing stages, our approach enables precise control over fracture propagation trajectories and intensities, thereby enhancing reservoir stimulation volume (RSV) and residual oil recovery. The implementation of fully coupled hydromechanical simulation reveals two critical findings: (1) the 70 m fracture half-length generated during initial fracturing fails to access residual oil-rich zones due to insufficient fracture network complexity; (2) a 3–5° stress reorientation combined with reservoir repressurization before refracturing significantly improves fracture network interconnectivity. Field validation demonstrates that refracturing extends fracture half-lengths to 97–154 m (38–120% increase) and amplifies RSV by 125% compared to initial operations. The developed seepage–stress coupling methodology establishes a theoretical foundation for optimizing repeated fracturing designs in unconventional reservoirs, providing critical insights into residual oil mobilization through engineered stress field manipulation. Full article

Data-driven distributed hydrological models utilizing atmospheric assimilation are crucial for simulating hydrological processes, particularly in regions lacking historical observational data, and for managing and developing local water resources due to the impacts of climate change and human activities. The southern part of Yunnan is located at the southwestern border of China, and the small number of observation stations poses a major obstacle to local water-resource management and hydrological research. This paper carries out an evaluation of the accuracy of the China Atmospheric-Assimilation Dataset (CMADS) in southern Yunnan and uses CMADS data and measured data to drive the BTOPMC model to investigate hydrological processes in the Jinping River basin, a representative local sub-basin. The study shows that the probability density function statistic (SS) between CMADS data and the measured precipitation data is 0.941, and their probability density curves of precipitation are basically the same. The relative error of daily precipitation is −19%, with 90% of the daily precipitation error concentrated within ±10 mm/day, which increases as daily precipitation increases. This paper examines three precipitation scenarios to drive the hydrological model, resulting in Nash–Sutcliffe efficiency (NSE) coefficients of 66.8%, 81.0%, and 83.9% for calibration, and 54.5%, 70.2%, and 74.5% for validation. These results indicate that CMADS data possesses a certain degree of applicable accuracy in southern Yunnan. Furthermore, the CMADS-driven BTOPMC model is suitable for simulating hydrological processes and conducting water-resource research in the region. The integration of CMADS data with actual measurement data can enhance the accuracy of hydrological simulations. Overall, the CMADS data have good applicability in southern Yunnan, and the CMADS-driven BTOPMC model can be used for hydrological modeling studies and water-resource management applications in southern Yunnan. Full article

Rice, a vital crop, faces significant threats from the brown planthopper (BPH), which impacts plant growth and yield. Pyramiding the BPH resistance genes BPH14 and BPH15 provides rice crops with reliable and lasting protection against BPH. Nonetheless, current research lacks clarity on the molecular processes responsible for BPH14/BPH15-mediated resistance to BPH. In this study, utilizing high-throughput metabolomics and integrating transcriptomic data, we investigated the metabolic adaptations of the BPH14/BPH15 pyramiding line (B1415) and its recurrent parent (RP) during early and late infestation stages. The analysis identified 1007 metabolites, mainly consisting of lipids and lipid-like molecules, together with phenylpropanoid and polyketide classes. Differentially accumulated metabolites (DAMs) displayed different patterns in B1415 and RP, particularly in flavonoid and phenylpropanoid biosynthesis pathways, which were more pronounced in the resistant B1415. Furthermore, ferulic acid (FA) was found to negatively regulate BPH resistance. These findings elucidate critical metabolic pathways involved in rice defense mechanisms and underscore the potential of B1415’s enhanced metabolic responses in conferring durable resistance against BPH. Full article

Prefabricated buildings are widely utilized due to their effectiveness in reducing carbon emissions. The construction stage has a significantly higher carbon emission rate than the other stages of their life cycle, but this is difficult to accurately quantify and predict due to the high variability. This study clarifies the system boundary of carbon emissions and the parameters of influence in carbon emissions predictions. The carbon emission quantification model was improved by using the process analysis method and the carbon emission factor method, and a modular calculation formula was proposed. Based on the machine learning algorithm, a carbon emissions prediction model for prefabricated buildings’ construction stage was established and hyperparameter optimization was conducted. A sample database for predicting prefabricated buildings’ carbon emissions during the construction stage was established using a modular quantification method, and the thin plate spline interpolation algorithm was introduced to expand this. The prediction results of carbon emission prediction models using four algorithms, SVR, BPNN, ELM, and RF, were compared and analyzed by RMSE and R². The results show that the model based on BPNN has the highest prediction accuracy when determining the carbon emissions of prefabricated building during the construction stage, and this method can provide a more accurate reference for subsequent quantitative research on carbon emissions from prefabricated buildings. Full article

The internal response mechanism of vegetation change in fragile high-altitude ecosystems is pivotal for ecological stability. This study focuses on the Lhasa River Basin (LRB) on the Tibetan Plateau (TP), a typical high-altitude fragile ecosystem where vegetation dynamics are highly sensitive to climate change and human activities. Utilizing MODIS surface reflectance data (MOD09Q1), a general regression neural network (GRNN) was applied to create a 250 m resolution fractional vegetation cover (FVC) dataset from 2001 to 2022, whose accuracy was verified with field survey data. Through methods like the Theil–Sen Median trend analysis, Mann–Kendall significance test, Hurst exponent, and geographical detector, the collaborative mechanism of 14 driving factors was systematically explored. Key conclusions are as follows: (1) The FVC in the LRB evolved in stages, first decreasing and then increasing, with 46.71% of the basin area expected to show an improvement trend in the future. (2) Among natural factors, elevation (q = 0.480), annual mean potential evapotranspiration (q = 0.362), and annual mean temperature (q = 0.361) are the main determinants of FVC spatiotemporal variation. (3) In terms of human activities, land use type has the highest explanatory power (q = 0.365) for FVC. (4) The interaction of two factors on FVC is stronger than that of a single factor, with the elevation–land use interaction being the most significant (q = 0.558). These results deepen our understanding of the interactions among vegetation, climate, and humans in fragile high-altitude ecosystems and provide a scientific basis for formulating zoned restoration strategies on the TP. Full article

Three new species of Collybia in China, Collybia clavipes, C. carnea and C. violea, are originally reported and described based on morphological characteristics and molecular data. This study provides detailed morphological descriptions of these three new species of Collybia, which can be accurately distinguished from other species within the genus Collybia. Phylogenetic relationships of Clitocybaceae were analyzed using a four-loci combined dataset (ITS-nrLSU-rpb2-tef1-α), and the results show that the three newly discovered species of Collybia form three distinct lineages, respectively. Based on the combination of morphological and molecular methods, these three newly collected species of Collybia are confirmed as new to science. A theoretical basis is provided for the species diversity of Collybia. Full article

Haploid breeding technology has advantages in terms of saving time and reducing labor intensity and costs. However, the low induction rate limits the application of this technology. Previous researchers found that heat shock can increase the rate of Embryo-like structures (ELSs) induction. However, molecular mechanisms underlying heat-shocked haploid induction remain poorly understood. In the current study, unfertilized ovules of watermelon were subjected to heat shock for 0–5 days and conducted transcriptomics sequencing and DIA-based proteomics sequencing. Results indicated that, in contrast to the non-heat-shock condition, the expression level of protein phosphatase 2C (PP2C), a negative regulator in abscisic acid (ABA) signal transduction pathway, was repressed, and the expression level of Sucrose-non-fermenting 1-related protein kinases (SnRK2) was activated. The activated SnRK2s are enabled to promote the accumulation of storage substances in ovules. Through analysis, the expression of many genes involved in the biosynthesis of unsaturated fatty acids and amino acids has indeed been upregulated. In conclusion, our findings demonstrate that heat shock promotes the accumulation of storage substances in unfertilized ovules by activating the signal transduction process of ABA, which correspondingly increases ELSs induction rate. Full article

The balanced application of organic and chemical fertilizers is essential for maintaining soil fertility and crop productivity. To optimize nitrogen (N) balance and maize yield through integrated pig slurry and straw mulching management, a split-plot field experiment was conducted in Northeast China. The study included two straw treatments (straw mulching, S; no straw, NS) and three substitution levels of pig slurry for chemical fertilizer (0%, 20%, and 40%; denoted as M0, M20, and M40). Parameters evaluated included N balance, maize biomass, soil available N, and the mineral N to TN ratio (mineral-N/TN), measured across 0–100 cm at key maize growth stages. Results showed that pig slurry substitution significantly increased soil DON, mineral N, and mineral-N/TN in the topsoil (0–20 cm) at the maize seeding stage and decreased mineral-N/TN at the maize milk (10–40 cm) and maturity (80–100 cm) stages. Meanwhile, straw mulching reduced NH₄⁺-N accumulation in the 0–10 cm of topsoil at the seeding stage, decreased NO₃⁻-N in the 0–40 cm soil layer from the jointing to maturity stages, and lowered the mineral-N/TN ratio in the topsoil, thereby mitigating the risk of N leaching. Notably, the combination of pig slurry substitution and straw mulching slightly increased DON and NO₃⁻-N in the topsoil while significantly reducing the mineral-N/TN in the deep soil layer at the seeding and milk stages. Pig slurry substitution significantly improved maize yield, N uptake, and N use efficiency (NUE). The highest maize yield (14,628 kg ha⁻¹) was observed in the S-M20 treatment, representing a 19% increase compared to NS-M0. N balance analysis indicated that pig slurry substitution alone increased maize yield and N uptake but depleted soil N, whereas straw mulching maintained N surplus. The findings highlight that combining pig slurry with straw mulching optimizes soil N availability and improves sustainable N management and crop productivity in agroecosystems. Full article

In this paper, a wideband OAM antenna array for wireless communication is proposed, which has a wide impedance bandwidth and can cover the S-C band with a relative bandwidth of 61.58%. The measured gain can reach 7.81 dBi and the radiation efficiency can reach 74.7%. Compared with similar antennas, the antenna array has a metal back cavity as the supporting structure, which further improves the structural stability of the array. The array adopts Z-shaped parasitic radiation units, a ring-shaped stepped metal reflection back cavity, and other structures. These can be verified to improve the performance of the array after design analysis and testing. In addition, the performance enhancement of a conventional Wilkinson divider by adding the S-shaped parasitic radiation patch is analysed by parameter scanning. The array is robust, simple to process, and easy to integrate. It can maximise its value in the crowded retrofit space of wireless antennas. Full article