Search Results (808)

Timely detection of pathogen spores is fundamental to ensuring early intervention and reducing the spread of corn diseases, like northern corn leaf blight, corn head smut, and corn rust. Traditional spore detection methods struggle to identify spore-level targets within complex backgrounds. To improve the recognition accuracy of various maize disease spores, this study introduced the YOLOv8s-SPM model by incorporating the space-to-depth and convolution (SPD-Conv) layers, the Partial Self-Attention (PSA) mechanism, and Minimum Point Distance Intersection over Union (MPDIoU) loss function. First, we combined SPD-Conv layers into the Backbone of the YOLOv8s to enhance recognition performance on small targets and low-resolution images. To improve computational efficiency, the PSA mechanism was incorporated within the Neck layer of the network. Finally, MPDIoU loss function was applied to refine the localization performance of bounding boxes. The results revealed that the YOLOv8s-SPM model achieved 98.9% accuracy on the mixed spore dataset. Relative to the baseline YOLOv8s, the YOLOv8s-SPM model yielded a 1.4% gain in accuracy. The improved model significantly improved spore detection accuracy and demonstrated superior performance in recognizing diverse spore types under complex background conditions. It met the demands for high-precision spore detection and filled a gap in intelligent spore recognition for maize, offering an effective starting point and practical path for future research in this field. Full article

Nickel-based superalloys are widely utilized in critical hot-end components, such as aeroengine turbine blades, owing to their exceptional high-temperature strength, creep resistance, and oxidation resistance. During service, these components are frequently subjected to complex localized loading, leading to non-uniform plastic deformation and microstructure evolution within the material. Combining nanoindentation experiments with the crystal plasticity finite element method (CPFEM), this study systematically investigates the effects of loading rate and crystal orientation on the elastoplastic deformation of DD6 alloy under spherical indenter loading. The results indicate that the maximum indentation depth increases and hardness decreases with prolonged loading time, exhibiting a significant strain rate strengthening effect. The CPFEM model incorporating dislocation density effectively simulates the nonlinear characteristics of the nanoindentation process and elucidates the evolution of dislocation density and slip system strength with indentation depth. At low loading rates, both dislocation density and slip system strength increase with loading time. Significant differences in mechanical behavior are observed across different crystal orientations, which correspond to the extent of lattice rotation during texture evolution. For the [111] orientation, crystal rotation is concentrated and highly regular, while the [001] orientation shows uniform texture evolution. This demonstrates that anisotropy governs the deformation mechanism through differential slip system activation and texture evolution. Full article

The Junggar Basin contains a large amount of coal resources and is an important coal production base in China. The coal seam in Zhundong coalfield has a large single-layer thickness and high content of inertinite, but large particle Fe-sulphide minerals are associated with coal seams in some mining areas. A series of economic and environmental problems caused by the combustion of large-grained Fe-sulphide minerals in coal have seriously affected the economic, clean and efficient utilization of coal. In this paper, the ultra-thick coal seam of the Xishanyao formation in the Yihua open-pit mine of the Zhundong coalfield is taken as the research object. Through the analysis of coal quality, X-ray fluorescence spectrometer test of major elements in coal, inductively coupled plasma mass spectrometry test of trace elements, SEM-Raman identification of Fe-sulphide minerals in coal and LA-MC-ICP-MS test of sulfur isotope of marcasite, the coal quality characteristics, main and trace element characteristics, macro and micro occurrence characteristics of Fe-sulphide minerals and sulfur isotope characteristics of marcasite in the ultra-thick coal seam of the Xishanyao formation are tested. On this basis, the occurrence state and genesis of large particle Fe-sulphide minerals in the ultra-thick coal seam of the Xishanyao formation are clarified. The main results and understandings are as follows: (1) the occurrence state of Fe-sulphide minerals in extremely thick coal seams is clarified. The Fe-sulphide minerals in the extremely thick coal seam are mainly marcasite, and concentrated in the YH-2, YH-3, YH-8, YH-9, YH-14, YH-15 and YH-16 horizons. Macroscopically, Fe-sulphide minerals mainly occur in three forms: thin film Fe-sulphide minerals, nodular Fe-sulphide minerals, and disseminated Fe-sulphide minerals. Microscopically, they mainly occur in four forms: flake, block, spearhead, and crack filling. (2) The difference in sulfur isotope of marcasite was discussed, and the formation period of marcasite was preliminarily divided. The overall variation range of the δ³⁴S value of marcasite is wide, and the extreme values are quite different. The polyflake marcasite was formed in the early stage of diagenesis and the δ³⁴S value was negative, while the fissure filling marcasite was formed in the late stage of diagenesis and the δ³⁴S value was positive. (3) The coal quality characteristics of the thick coal seam were analyzed. The organic components in the thick coal seam are mainly inertinite, and the inorganic components are mainly clay minerals and marcasite. (4) The difference between the element content in the thick coal seam of the Zhundong coalfield and the average element content of Chinese coal was compared. The major element oxides in the thick coal seam are mainly CaO and MgO, followed by SiO₂, Al₂O₃, Fe₂O₃ and Na₂O. Li, Ga, Ba, U and Th are enriched in trace elements. (5) The coal-accumulating environment characteristics of the extremely thick coal seam are revealed. The whole thick coal seam is formed in an acidic oxidation environment, and the horizon with Fe-sulphide minerals is in an acidic reduction environment. The acidic reduction environment is conducive to the formation of marcasite and is not conducive to the formation of pyrite. (6) There are many matrix vitrinite, inertinite content, clay content, and terrigenous debris in the extremely thick coal seam. The good supply of peat swamp, suitable reduction environment and pH value, as well as groundwater leaching and infiltration, together cause the occurrence of large-grained Fe-sulphide minerals in the extremely thick coal seam of the Xishanyao formation in the Zhundong coalfield. Full article

Image super-resolution (SR) is essential for enhancing image quality in critical applications, such as medical imaging and satellite remote sensing. However, existing methods were often limited in their ability to effectively process and integrate multi-scales information from fine textures to global structures. To address these limitations, this paper proposes DSCNN, a dynamic snake convolution neural network for enhanced image super-resolution. DSCNN optimizes feature extraction and network architecture to enhance both performance and efficiency: To improve feature extraction, the core innovation is a feature extraction and enhancement module with dynamic snake convolution that dynamically adjusts the convolution kernel’s shape and position to better fit the image’s geometric structures, significantly improving feature extraction. To optimize the network’s structure, DSCNN employs an enhanced residual network framework. This framework utilizes parallel convolutional layers and a global feature fusion mechanism to further strengthen feature extraction capability and gradient flow efficiency. Additionally, the network incorporates a SwishReLU-based activation function and a multi-scale convolutional concatenation structure. This multi-scale design effectively captures both local details and global image structure, enhancing SR reconstruction. In summary, the proposed DSCNN outperforms existing methods in both objective metrics and visual perception (e.g., our method achieved optimal PSNR and SSIM results on the Set5 $\times 4$ dataset). Full article

Although the importance of digital transformation in contemporary port development has been widely acknowledged, there is little empirical research on the extent to which it promotes sustainable development by reducing costs and increasing efficiency. This study takes the digital transformation of one of the largest ports in northern China—Tangshan Port—as an example, as the application of digital technologies has greatly improved its operational efficiency. By using cargo throughput and container throughput data from Tangshan Port as the experimental group and from Qinhuangdao Port as the control group, difference-in-differences regression models with monthly data and port fixed effects were adopted to clarify the impact of digital transformation on sustainability for different types of cargo throughput, as well as the differential effects of policy impact on port production efficiency and economic performance in the short and long term, in order to examine the impact of digitalization on port operation performance. Our findings demonstrate that digital transformation has a significant positive impact on both port cargo and container throughput, with the long-term effect surpassing the short-term effect. Additionally, regional economic level positively moderates policy impact. These findings provide critical evidence that ports can balance economic growth and environmental sustainability within sustainable development frameworks. Full article

Although exercise is known to exert anti-inflammatory effects in neurodegenerative diseases, its specific impact and underlying mechanisms in Parkinson’s disease (PD) remain poorly understood. This study explores the effects of exercise on microglia-mediated neuroinflammation and apoptosis in a PD model, focusing on the role of irisin signaling in mediating these effects. Using a 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP)-induced PD mouse model, we found that a 10-week treadmill exercise regimen significantly enhanced motor function, reduced dopaminergic neuron loss, attenuated neuronal apoptosis, and alleviated neuroinflammation. Exercise also shifted microglia from a pro-inflammatory to an anti-inflammatory phenotype. Notably, levels of irisin, phosphorylated AMP-activated protein kinase (p-AMPK), and sirtuin 1 (Sirt1), which were decreased in the PD brain, were significantly increased following exercise. These beneficial effects were abolished by blocking the irisin receptor with cyclic arginine–glycine–aspartic acid–tyrosine–lysine (cycloRGDyk). Our results indicate that exercise promotes neuroprotection in PD by modulating microglial activation and the AMPK/Sirt1 pathway through irisin signaling, offering new insights into exercise-based therapeutic approaches for PD. Full article

In recent years, a growing number of real-time semantic segmentation networks have been developed to improve segmentation accuracy. However, these advancements often come at the cost of increased computational complexity, which limits their inference efficiency, particularly in scenarios such as autonomous driving, where strict real-time performance is essential. Achieving an effective balance between speed and accuracy has thus become a central challenge in this field. To address this issue, we present a lightweight semantic segmentation model tailored for the perception requirements of autonomous vehicles. The architecture follows an encoder–decoder paradigm, which not only preserves the capability for deep feature extraction but also facilitates multi-scale information integration. The encoder leverages a high-efficiency backbone, while the decoder introduces a dynamic fusion mechanism designed to enhance information interaction between different feature branches. Recognizing the limitations of convolutional networks in modeling long-range dependencies and capturing global semantic context, the model incorporates an attention-based feature extraction component. This is further augmented by positional encoding, enabling better awareness of spatial structures and local details. The dynamic fusion mechanism employs an adaptive weighting strategy, adjusting the contribution of each feature channel to reduce redundancy and improve representation quality. To validate the effectiveness of the proposed network, experiments were conducted on a single RTX 3090 GPU. The Dynamic Real-time Integrated Vision Encoder–Segmenter Network (DriveSegNet) achieved a mean Intersection over Union (mIoU) of 76.9% and an inference speed of 70.5 FPS on the Cityscapes test dataset, 74.6% mIoU and 139.8 FPS on the CamVid test dataset, and 35.8% mIoU with 108.4 FPS on the ADE20K dataset. The experimental results demonstrate that the proposed method achieves an excellent balance between inference speed, segmentation accuracy, and model size. Full article

Snow serves as a crucial water source for vegetation growth on the Mongolian Plateau, and its temporal and spatial variations exert profound influences on terrestrial vegetation phenology. In recent years, global climate change has led to significant changes in snow and vegetation start of growing season (SOS). Therefore, it is necessary to study the mechanism of snow cover on vegetation growth and changes on the Mongolian Plateau. The study found that the spatial snow cover fraction (SCF) of the Mongolian Plateau ranged from 50% to 60%, and the snow melt date (SMD) ranged from day of the year (DOY) 88 to 220, mainly concentrated on the northwest Mongolian Plateau mountainous areas. Using different SOS methods to calculate the vegetation SOS distribution map. Vegetation SOS occurs earlier in the eastern part compared to the western part of the Mongolian Plateau. In this study, we assessed spatiotemporal distribution characteristics of snow on the Mongolian Plateau over the period from 2001 to 2023. The results showed that the SOS of the Mongolian Plateau was mainly concentrated on DOY 71-186. The Cox survival analysis model system established SCF and SMD on vegetation SOS. The SCF standard coefficient is 0.06, and the SMD standard coefficient is 0.02. The SOS_NDVI coefficient is −0.15, and the SOS_NDGI coefficient is −0.096. The results showed that the vegetation SOS process exhibited differential response characteristics to snow driving factors. These research results also highlight the important role of snow in vegetation phenology and emphasize the importance of incorporating the unique effects of vegetation SOS on the Mongolian Plateau. Full article

Grid-forming (GFM) energy storage can be utilized as a backup power source for the power grid to ensure the security of the power grid. GFM energy storage can also enhance the strength of the power grid and improve its stability. However, the GFM energy storage inherits the characteristics of the synchronous generator. Low-frequency oscillations may occur in GFM energy storage, which affect the stable operation of the power system. This paper proposed an additional damping control strategy for GFM energy storage to address the low-frequency oscillation. Firstly, this paper builds the state-space small-signal mathematical model of the GFM energy storage grid-connected system to analyze the participation factors of the low-frequency oscillation mode and clarify the key control parameters affecting the GFM energy storage grid-connected system the low-frequency oscillation. Then, this paper proposed an additional damping control strategy to increase the damping ratio of the low-frequency oscillation mode and improve the stability of the GFM energy storage grid-connected system. Finally, semi-physical experiments verified the effectiveness of the proposed additional damping control strategy. Full article

The enhancement of flavonoid content and antioxidant capacity in plants remains a significant area of focus in the investigation of plant-derived functional foods. This study systematically investigated the impact of exogenous zinc sulfate (5 mM ZnSO₄) stress on flavonoid content and antioxidant capacity in finger millet (Eleusine coracana L.) sprouts, along with its underlying molecular mechanisms. The results demonstrated that treatment with 5 mM ZnSO₄ significantly increased the flavonoid content in sprouts, reaching a maximum value of 5.59 μg/sprout on the 6th day of germination. ZnSO₄ stress significantly enhanced the activities of PAL, 4CL, and C4H, while also considerably upregulating the expression levels of flavonoid-biosynthesis-related genes. Physiological indicators revealed that ZnSO₄ stress increased the contents of malondialdehyde, hydrogen peroxide, and superoxide anion in the sprouts, while inhibiting sprout growth. As a stress response, ZnSO₄ stress enhances the antioxidant system by increasing antioxidant capacity (ABTS, DPPH, and FRAP), antioxidant enzyme activity (POD and SOD), and related gene expression (POD, CAT, and APX) in sprouts. This study provides experimental evidence for ZnSO₄ stress to improve flavonoid accumulation and antioxidant capacity in finger millet sprouts and provides important theoretical and practical guidance for the development of high-quality functional foods. Full article

Finger millet (Eleusine coracana L.) is a nutrient-dense cereal with high flavonoid content, yet the mechanisms regulating its secondary metabolite biosynthesis remain underexplored. Various exogenous stimuli can readily activate the enzymatic pathways and gene expression associated with flavonoid biosynthesis in plants, which are regulated by developmental cues. Research has established that methyl jasmonate (MeJA) application enhances secondary metabolite production in plant systems. This investigation examined MeJA’s influence on flavonoid accumulation and physiological responses in finger millet sprouts to elucidate the molecular mechanisms underlying MeJA-mediated flavonoid accumulation. The findings revealed that MeJA treatment significantly suppressed sprout elongation while enhancing the biosynthesis of total flavonoids and phenolic compounds. MeJA treatment triggered oxidative stress responses, with hydrogen peroxide and superoxide anion concentrations increasing 1.84-fold and 1.70-fold compared to control levels at 4 days post-germination. Furthermore, the antioxidant defense mechanisms in finger millet were upregulated following treatment, resulting in significant enhancement of catalase and peroxidase enzymatic activities and corresponding transcript abundance. MeJA application augmented the activities of key phenylpropanoid pathway enzymes—phenylalanine ammonia-lyase (PAL) and cinnamate 4-hydroxylase (C4H)—and upregulated their respective gene expression. At 4 days post-germination, EcPAL and EcC4H transcript levels were elevated 3.67-fold and 2.61-fold, respectively, compared to untreated controls. MeJA treatment significantly induced the expression of downstream structural genes and transcriptional regulators. This study provides a deeper understanding of the mechanism of flavonoid accumulation in foxtail millet induced by MeJA, and lays a foundation for exogenous conditions to promote flavonoid biosynthesis in plants. Full article

Timely and accurate detection of wind turbine blade surface defects is crucial for ensuring operational safety and improving maintenance efficiency with respect to large-scale wind farms. However, existing methods often suffer from poor generalization, background interference, and inadequate real-time performance. To overcome these limitations, we developed an end-to-end defect recognition framework, structured as a three-stage process: blade localization using YOLOv5, robust feature extraction via the large vision model DINOv2, and defect classification using a Stochastic Configuration Network (SCN). Unlike conventional CNN-based approaches, the use of DINOv2 significantly improves the capability for representation under complex textures. The experimental results reveal that the proposed method achieved a classification accuracy of 97.8% and an average inference time of 19.65 ms per image, satisfying real-time requirements. Compared to traditional methods, this framework provides a more scalable, accurate, and efficient solution for the intelligent inspection and maintenance of wind turbine blades. Full article

Stroke poses a serious threat to human health, while there are very few drugs that can directly alleviate ischemia/reperfusion injury and improve the prognosis. Studies have shown that small-molecule activators of aldehyde dehydrogenase 2 (ALDH2) have the potential to become novel therapeutic drugs for ischemic stroke. In this study, through the systematic structural optimization of novel N-benzylaniline-based ALDH2 activators obtained from our previous virtual screening, ALDH2 activators with improved water solubility and activity were obtained. Among them, compound D10 exhibits the best activity, with a maximum activation fold reaching 114% relative to Alda-1. And the water solubility of its hydrochloride salt D27 was increased by more than 200-fold. The intravenous injection of this compound can significantly reduce the infarct area in the rat model of cerebral infarction compared with the model group. This study lays a good foundation for the future research on ALDH2 activators used in the treatment of stroke. Full article

In this study, we integrated HSQC-based DeepSAT with UPLC-MS/MS to guide the isolation of omega-3 polyunsaturated fatty acid derivatives (PUFAs) from marine resources. Through this approach, four new (1–4) and nine known (5–13) PUFA analogues were obtained from large-scale cultures of the marine dinoflagellate Prorocentrum lima, with lipidomic profiling identifying FA18:5 (5), FA18:4 (7), FA22:6 (8), and FA22:6 methyl ester (11) as major constituents of the algal oil extract. Structural elucidation was achieved through integrated spectroscopic analyses of IR, 1D and 2D NMR, and HR-ESI-MS data. Given the pivotal role of microglia in Alzheimer’s disease (AD) pathogenesis, we further evaluated the neuroprotective potential of these PUFAs by assessing their regulatory effects on critical microglial functions in human microglia clone 3 (HMC3) cells, including chemotactic migration and amyloid-β42 (Aβ42) phagocytic clearance. Pharmacological evaluation demonstrated that FA20:5 butanediol ester (1), FA18:5 (5), FA18:4 (7), FA22:6 (8), and (Z)-10-nonadecenoic acid (13) significantly enhanced HMC3 migration in a wound-healing assay. Notably, FA18:4 (7) also significantly promoted Aβ42 phagocytosis by HMC3 microglia while maintaining cellular viability and avoiding pro-inflammatory activation at 20 μM. Collectively, our study suggests that FA18:4 (7) modulates microglial function in vitro, indicating its potential to exert neuroprotective effects. Full article

In this study, we investigated the electrochemical corrosion behavior and mechanisms of FeCrNi/WC alloys with varying contents of CTC-S (spherical WC) and CTC-A (angular WC) in a 3.5 wt.% NaCl solution, addressing the corrosion resistance requirements for stainless steel composites in marine environments. The electrochemical test results demonstrate that the corrosion resistance of the alloy initially increases with the CTC-A content, followed by a decrease, which is associated with the formation, stability, and rupture of the passivated film. Nyquist and Bode diagrams for electrochemical impedance spectroscopy confirm that the charge transfer resistance of the passivated film is the primary determinant of the composite’s corrosion performance. A modest increase in CTC-A contributes to the formation of a more heterogeneous second phase, providing a physical barrier and enhancing solid solution strengthening, and thus delaying the cracking and corrosion processes of the passivation film. However, excessive CTC-A content leads to significant dissolution of the alloy’s reinforcement phase and promotes decarburization, resulting in the formation of corrosion pits, craters, and cracks that compromise the passivation film and expose fresh alloy surfaces to further corrosion. When the CTC-A content is 10% and the CTC-S content is 30%, this combination results in minimal degradation in the corrosion performance (0.213 μA·cm²) while balancing the hardness and toughness of the alloy. Additionally, electrochemical evaluations reveal that incorporating angular CTC-A particles at 10 vol% effectively delays the breakdown of the passivation film by mitigating the interfacial galvanic coupling through enhancing the mechanical interlocking at the WC/FeCrNi interface. The CTC-A/CTC-S hybrid system exhibits a remarkable 62% reduction in the pitting propagation rate compared to composites reinforced solely with spherical WC, which is attributed to the preferential dissolution of angular WC protrusions that sacrificially suppress crack initiation at the phase boundaries. Full article