Search Results (786)

This study investigates premature fatigue failures in three EA1N steel axles from permanent magnet direct-drive locomotives during wheel-seat bending tests. Complete fracture occurred in one axle at 3 million cycles, and in the other two axles, cracks appeared and were observed through magnetic particle detection at 3.5 million and 1.6 million cycles, respectively. A comprehensive failure analysis was conducted through metallurgical examination, fractography, mechanical testing, residual stress measurement, and finite element analysis. The fractographic results revealed fractures consistently initiated at the wheel-seat to axle-body transition arc, exhibiting characteristic ratchet marks and beach patterns. The premature fracture mechanism was identified as a high-stress fatigue fracture. The residual stress measurements showed detrimental tensile stresses at the surface. Coupled with the operating stress, the stress on the axle exceeds fatigue strength, which accelerates the initiation and propagation of fatigue cracks. Based on these observations, the failure mechanism was identified, and preventive methods were proposed to reduce the risk of recurrence of the in-service axles. Full article

Microplastics (MPs) can affect fish health by inducing oxidative stress, but their impact on structural coloration remains poorly understood. This study investigated the effects of environmentally relevant concentrations (16 and 160 μg/L) of MPs and nanoplastics (NPs) exposure on growth, oxidative stress and structural coloration in blue strain guppy fish (Poecilia reticulata). Results showed exposure to 160 μg/L MPs significantly reduced specific growth rate of fish compared to controls. Plastic accumulation followed a dose-dependent pattern, especially within gut concentrations. Oxidative stress responses differed between MPs and NPs: 160 μg/L MPs decreased SOD activity in skin and reduced GSH levels, while 160 μg/L NPs increased MDA levels in gut tissues, indicating severe lipid peroxidation. Structural coloration analysis revealed exposure to 160 μg/L MPs decreased lightness and increased yellowness, demonstrating reduced blue coloration. This was accompanied by an increase in skin uric acid content, suggesting that guanine conversion might occur to combat oxidative stress. These findings demonstrate that MPs, particularly at high concentrations, impair growth and induce oxidative stress in guppies. To counteract stress, guanine in iridophores may be converted into uric acid, leading to a decline in structural coloration. This study is the first to reveal that MPs disrupt structural coloration of fish, providing new insights into the ecological risks of plastic pollution on aquatic organisms. Full article

This paper presents a flight control framework based on neural network Model Predictive Control (NN-MPC) to tackle the challenges of acceleration command tracking for supersonic vehicles (SVs) in complex flight environments, addressing the shortcomings of traditional methods in managing nonlinearity, random disturbances, and real-time performance requirements. Initially, a dynamic model is developed through a comprehensive analysis of the vehicle’s dynamic characteristics, incorporating strong cross-coupling effects and disturbance influences. Subsequently, a predictive mechanism is employed to forecast future states and generate virtual control commands, effectively resolving the issue of sluggish responses under rapidly changing commands. Furthermore, the approximation capability of neural networks is leveraged to optimize the control strategy in real time, ensuring that rudder deflection commands adapt to disturbance variations, thus overcoming the robustness limitations inherent in fixed-parameter control approaches. Within the proposed framework, the ultimate uniform bounded stability of the control system is rigorously established using the Lyapunov method. Simulation results demonstrate that the method exhibits exceptional performance under conditions of system state uncertainty and unknown external disturbances, confirming its effectiveness and reliability. Full article

The wave spectrum, as a key statistical feature describing wave energy distribution, is crucial for understanding wave propagation mechanisms and supporting ocean engineering applications. This study, based on ERA5 reanalysis spectrum data, proposes a model combining CNN and xLSTM for rapid gridded wave spectrum prediction over the Bohai and Yellow Seas domain. It uses 2D gridded spectrum data rather than a spectrum at specific points as input and analyzes the impact of various input factors at different time lags on wave development. The results show that incorporating water depth and mean sea level pressure significantly reduces errors. The model performs well across seasons with the seasonal spatial average root mean square error (SARMSE) of spectral energy remaining below 0.040 m²·s and RMSEs for significant wave height (SWH) and mean wave period (MWP) of 0.138 m and 1.331 s, respectively. At individual points, the spectral density bias is near zero, correlation coefficients range from 0.95 to 0.98, and the peak frequency RMSE is between 0.03 and 0.04 Hz. During a typical cold wave event, the model accurately reproduces the energy evolution and peak frequency shift. Buoy observations confirm that the model effectively tracks significant wave height trends under varying conditions. Moreover, applying a frequency-weighted loss function enhances the model’s ability to capture high-frequency spectral components, further improving prediction accuracy. Overall, the proposed method shows strong performance in spectrum prediction and provides a valuable approach for regional wave spectrum modeling. Full article

In complex dynamic environments, robotic arms face multiple challenges such as real-time environmental changes, high-dimensional state spaces, and strong uncertainties. Trajectory planning tasks based on deep reinforcement learning (DRL) suffer from difficulties in acquiring human expert strategies, low experience utilization (leading to slow convergence), and unreasonable reward function design. To address these issues, this paper designs a neural network-based expert-guided triple experience replay mechanism (NETM) and proposes an improved reward function adapted to dynamic environments. This replay mechanism integrates imitation learning’s fast data fitting with DRL’s self-optimization to expand limited expert demonstrations and algorithm-generated successes into optimized expert experiences. Experimental results show the expanded expert experience accelerates convergence: in dynamic scenarios, NETM boosts accuracy by over 30% and safe rate by 2.28% compared to baseline algorithms. Full article

Corn smut, caused by Ustilago maydis, significantly threatens maize production. This study evaluated 199 maize inbred lines at the seedling stage under greenhouse conditions for resistance to U. maydis, identifying 39 highly resistant lines. A genome-wide association study (GWAS) using the mrMLM model detected 19 significant single-nucleotide polymorphism (SNP) loci. Based on a linkage disequilibrium (LD) decay distance of 260 kb, 226 candidate genes were identified. Utilizing the significant loci chr1_244281660 and chr5_220156746, two kompetitive allele-specific PCR (KASP) markers were successfully developed. A PCR-based sequence-specific oligonucleotide probe hybridization technique applied to the 199 experimental lines and 60 validation lines confirmed polymorphism for both markers, with selection efficiencies of 48.12% and 43.33%, respectively. The tested materials were derived from foundational inbred lines of domestic and foreign origin. Analysis of 39 highly resistant lines showed that the advantageous alleles carrying thymine/cytosine (T/C) predominated at frequencies of 94.87% and 53.84%, respectively. The genotype TTCC conferred high resistance, while CCTT was highly susceptible. The resistance exhibited high heritability and significant gene-by-environment interaction. This work systematically dissects the genetic basis of common smut resistance in maize, identifies favorable alleles, and provides a novel KASP marker-based strategy for developing disease-resistant germplasm. Full article

Background/Objectives: All current FDA-approved antibody–drug conjugates (ADCs) are single-target and single-payload molecules that have limited efficacy in patients due to drug resistance. Therefore, our goal was to generate a novel ADC that was less susceptible to single points of resistance to reduce the likelihood of patient relapse. Methods: We developed a dual-targeting, dual-payload ADC by conjugating a bispecific EGFR x cMET antibody to two payloads (MMAF and SN38) that had separate mechanisms of action using a novel tri-functional linker. This dual-payload ADC was tested for potency and efficacy in dividing and nondividing in vitro cell models using multiple tumor cell types. Efficacy of the dual-payload ADC was confirmed using in vivo models. Results: Our ADC with dual MMAF and SN38 payloads was more efficacious in inhibiting cell proliferation than single-payload ADCs across multiple cancer cell lines. In addition, the dual-payload molecule inhibited nondividing cells, which were more resistant to traditional ADC payloads. The dual-payload ADC also exhibited more potent tumor growth inhibition in vivo compared to that of single-payload ADCs. Conclusions: Overall, the bispecific antibody conjugated with both the MMAF and SN38 payloads inhibited tumor growth more strongly than ADCs conjugated with MMAF or SN38 alone. Developing dual-payload ADCs could limit the impact of acquired resistance in patients as well as lower the effective dose of each payload. Full article

Carbon fiber-reinforced polymer (CFRP) laminates have been widely coated on aged and damaged structures for recovering or enhancing their structural performance. The health conditions of the coated composite structures have been given high attention, as they are critically important for assessing operational safety and residual service life. However, the current problem is the lack of an efficient, long-term, and stable monitoring technique to characterize the structural behavior of coated composite structures in the whole life cycle. For this reason, bare and packaged fiber Bragg grating (FBG) sensors have been specially developed and designed in sensing networks to monitor the structural performance of CFRP-coated composite beams under different loads. Some optical fibers have also been inserted in the CFRP laminates to configure the smart CFRP component. Detailed data interpretation has been conducted to declare the strengthening process and effect. Finite element simulation and simplified theoretical analysis have been conducted to validate the experimental testing results and the deformation profiles of steel beams before and after the CFRP coating has been carefully checked. Results indicate that the proposed FBG sensors and sensing layout can accurately reflect the structural performance of the composite beam structure, and the CFRP coating can share partial loads, which finally leads to the downward shift in the centroidal axis, with a value of about 10 mm. The externally bonded sensors generally show good stability and high sensitivity to the applied load and temperature-induced inner stress variation. The study provides a straightforward instruction for the establishment of a structural health monitoring system for CFRP-coated composite structures in the whole life cycle. Full article

Path planning for picking safflowers is a key component in ensuring the efficient operation of robotic safflower-picking systems. However, existing single-arm picking devices have become a bottleneck due to their limited operating range, and a breakthrough in multi-arm cooperative picking is urgently needed. To address the issue of inadequate adaptability in current path planning strategies for dual-arm systems, this paper proposes a novel path planning method for dual-arm picking (LTSACO). The technique centers on a dynamic-weight heuristic strategy and achieves optimization through the following steps: first, the K-means clustering algorithm divides the target area; second, the heuristic mechanism of the Ant Colony Optimization (ACO) algorithm is improved by dynamically adjusting the weight factor of the state transition probability, thereby enhancing the diversity of path selection; third, a 2-OPT local search strategy eliminates path crossings through neighborhood search; finally, a cubic Bézier curve heuristically smooths and optimizes the picking trajectory, ensuring the continuity of the trajectory’s curvature. Experimental results show that the length of the parallelogram trajectory, after smoothing with the Bézier curve, is reduced by 20.52% compared to the gantry trajectory. In terms of average picking time, the LTSACO algorithm reduces the time by 2.00%, 2.60%, and 5.60% compared to DCACO, IACO, and the traditional ACO algorithm, respectively. In conclusion, the LTSACO algorithm demonstrates high efficiency and strong robustness, providing an effective optimization solution for multi-arm cooperative picking and significantly contributing to the advancement of multi-arm robotic picking systems. Full article

This paper proposes a harmonic screening-based method for calculating the cogging torque of the axial flux permanent magnet synchronous motor. The magnetic field energy in the air gap is derived from the air gap flux and the magnetomotive force of rotor. The cogging torque is then obtained using the energy-based method. Compared with finite element analysis, the proposed approach is significantly faster while maintaining high accuracy. It is particularly effective for scenarios involving stator staggering, which can facilitate quick calculation of cogging torques of many different staggering angles, offering rapid insights into motor performance during the initial design. The method achieves a similarity accuracy with FEA results and reduces computation time, demonstrating both its efficiency and reliability. Full article

Marine coral sand–clay mixtures (MCCM) are promising green fill materials in civil engineering projects, where their strength characteristics play a vital role in ensuring structural safety and stability. To investigate these properties, a series of triaxial shear tests were performed under diverse conditions, including variations in asperity spacing, asperity height, the number of reinforcement layers, confining pressure, and axial strain. This experimental campaign yielded a robust strength dataset for MCCM. Utilizing this dataset, several predictive models were developed, including a standard Support Vector Machine (SVM), an SVM optimized via Genetic Algorithm (GA-SVM), an SVM enhanced by Particle Swarm Optimization (PSO-SVM), and a hybrid model incorporating Logical Development Algorithm preprocessing a SVM model (LDA-SVM). Among these models, the LDA-SVM model exhibited the best performance, achieving a test RMSE of 1.67245 and a correlation coefficient (R) of 0.996, demonstrating superior prediction accuracy and strong generalization ability. Sensitivity analyses revealed that asperity spacing, asperity height, and confining pressure are the most influential factors affecting MCCM strength. Moreover, an explicit empirical equation was derived from the LDA-SVM model, allowing practitioners to estimate strength without relying on complex machine learning tools. The results of this study offer practical guidance for the optimized design and safety evaluation of MCCM in civil engineering applications. Full article

In this review, we aim to provide a comprehensive overview of the roles of fungi in horticultural crops. Their beneficial roles and pathogenic effects are investigated. In addition, the recent advancements in fungal detection and management strategies (especially the use of spectral analysis) are summarized. Beneficial fungi, including plant growth-promoting fungi (PGPF), ectomycorrhizal fungi (ECM), and arbuscular mycorrhizal fungi (AMF), enhance nutrient uptake, promote root and shoot development, improve photosynthetic efficiency, and support plant resilience against biotic and abiotic stresses. Additionally, beneficial fungi contribute to flowering, seed germination, and disease management through biofertilizers, microbial pesticides, and mycoinsecticides. Conversely, pathogenic fungi cause significant diseases affecting roots, stems, leaves, flowers, and fruits, leading to crop yield losses. Advanced spectral analysis techniques, such as Fourier Transform Infrared Spectroscopy (FTIR), Near-Infrared Spectroscopy (NIR), Raman, and Visible and Near-Infrared Spectroscopy (Vis-NIR), alongside traditional methods like Polymerase Chain Reaction (PCR) and Enzyme-Linked Immunosorbent Assay (ELISA), have shown promise in detecting and managing fungal pathogens. Emerging applications of fungi in sustainable agriculture, including biofertilizers and eco-friendly pest management, are discussed, underscoring their potential to enhance crop productivity and mitigate environmental impacts. This review provides a comprehensive understanding of the complex roles of fungi in horticulture and explores innovative detection and management strategies. Full article

Avian infectious bronchitis virus (IBV) infection has caused significant economic losses to the poultry industry. Unfortunately, there is currently no effective cure for this disease. Understanding the pathogenic mechanism is crucial for the treatment of the disease. Studying the pathogenic mechanism of IBV based on metabolomics analysis is helpful for identifying antiviral drugs. However, studies on metabolomics analysis of IBV infection have been relatively limited, particularly without metabolomics analysis in sera after IBV infection. In this study, 17-day-old SPF chicks were infected with the IBV GX-YL5 strain, and serum samples were collected 7 days post-infection (DPI) for metabolomics analysis using ultraperformance liquid chromatography tandem mass spectrometry (UPLC-MS/MS). A total of 143 differential metabolites were identified across 20 metabolic pathways, with the phenylalanine pathway showing the most significant changes. The level of cinnamic acid (CA), an upstream metabolite in the phenylalanine pathway, was notably increased following IBV infection. To investigate the antiviral effects of CA, chicken embryo kidney (CEK) cells and SPF chicks infected with IBV were treated with different concentrations of CA to assess its effect on viral replication. The results demonstrated that CA at 25 μg/mL effectively inhibited IBV replication in vitro; meanwhile, CA at 50 μg/mL and 25 μg/mL effectively inhibited IBV replication in vivo. Molecular docking and molecular dynamics simulation studies showed that CA interacts with the N domains of the IBV nucleocapsid (N) protein. In conclusion, the serum metabolite CA is significantly elevated following IBV infection and demonstrates remarkable antiviral effects both in vitro and in vivo, providing a promising avenue for the development of antiviral therapies to combat IBV infection. Full article

Gypsum and salt rocks have been proven to act as seals for abundant oil and gas reserves on a global scale, with significant potential for hydrocarbon preservation and evolution. Notably, the sedimentary dynamics of non-evaporitic gypsum in terrestrial half-graben basins remain underexplored, particularly regarding its genetic link to hydrocarbon accumulation in interbedded mudstones. This study is based on the Zhanhua Sag, in which thick-layered gypsum rocks with dark mudstone are deposited. The gypsum crystals show the intermittent deposition characteristics. The cumulative thickness of the gypsum-containing section reaches a maximum of over 110 m. The spatial distribution of gypsum thickness correlates strongly with the location of deep-seated faults. The strontium and sulfur isotopes of gypsum indicate deep hydrothermal fluids as mineral sources, and negative oxygen isotope excursions also suggest that gypsum layers precipitated in situ from hot brine. Total organic carbon and Rock-Eval data indicate that the deep-lake gypsum rock system has excellent hydrocarbon potential, especially in the mudstone interlayers. This study developed a depositional model of deep-lake gypsum rocks with thermal brine genesis in half-graben basins. The gypsum-bearing system is rich in mudstone interlayers. These gypsum–mudstone interbeds represent promising targets for shale oil exploration after the initial breakthrough during the extraction process. These insights provide a theoretical framework for understanding gypsum-related petroleum systems in half-graben basins across the globe, offering guidance for hydrocarbon exploration in analogous sedimentary environments. Full article

Wake steering has emerged as a promising strategy to mitigate turbine wake losses, with existing research largely focusing on the aerodynamic optimization of yaw angles. However, many prior approaches rely on static look-up tables (LUTs), offering limited adaptability to real-world wind variability and leading to non-optimal results. More importantly, these energy-focused strategies overlook the mechanical implications of frequent yaw activities in pursuit of the maximum power output, which may lead to premature exhaustion of the yaw system’s design life, thereby accelerating structural degradation. This study proposes a supervisory control framework that balances energy capture with structural reliability through three key innovations: (1) upstream-based inflow sensing for real-time capture of free-stream wind, (2) fatigue-responsive optimization constrained by a dynamic actuation quota system with adaptive yaw activation, and (3) a bidirectional threshold adjustment mechanism that redistributes unused actuation allowances and compensates for transient quota overruns. A case study at an offshore wind farm shows that the framework improves energy yield by 3.94%, which is only 0.29% below conventional optimization, while reducing yaw duration and activation frequency by 48.5% and 74.6%, respectively. These findings demonstrate the framework’s potential as a fatigue-aware control paradigm that balances energy efficiency with system longevity. Full article