Explore the vast range of titles available.

Monitoring and maintaining the health of wind turbine blades has long been one of the challenges facing the global wind energy industry. Detecting damage to a wind turbine blade is important for planning blade repair, avoiding aggravated blade damage, and extending the sustainability of blade operation. This paper firstly introduces the existing wind turbine blade detection methods and reviews the research progress and trends of monitoring of wind turbine composite blades based on acoustic signals. Compared with other blade damage detection technologies, acoustic emission (AE) signal detection technology has the advantage of time lead. It presents the potential to detect leaf damage by detecting the presence of cracks and growth failures and can also be used to determine the location of leaf damage sources. The detection technology based on the blade aerodynamic noise signal has the potential of blade damage detection, as well as the advantages of convenient sensor installation and real-time and remote signal acquisition. Therefore, this paper focuses on the review and analysis of wind power blade structural integrity detection and damage source location technology based on acoustic signals, as well as the automatic detection and classification method of wind power blade failure mechanisms combined with machine learning algorithm. In addition to providing a reference for understanding wind power health detection methods based on AE signals and aerodynamic noise signals, this paper also points out the development trend and prospects of blade damage detection technology. It has important reference value for the practical application of non-destructive, remote, and real-time monitoring of wind power blades.

In recent years, the size of wind turbine blades has increased, underscoring the critical importance of monitoring their structural health. This study explores the use of noise emitted during wind turbine operation for the assessment of blade structural integrity. During sound acquisition, the wind sound, pneumatic sound and mechanical sound are recorded together to form the wind turbine sound signal. Considering the computational challenges of spectral subtraction under extreme noise intensities, a pretrained sound source separation neural network was used to distinguish between random wind noise and mechanical noise in wind turbine sound signals. In this paper, the short-time Fourier transform (STFT) time-frequency diagrams of signals processed using the spectral subtraction method are compared with those processed by combining the source separation model and spectral subtraction. The results reveal that the combined approach provides a more detailed representation in the time-frequency diagrams. Additionally, the mel-scale frequency cepstral coefficients (MFCCs) algorithm is utilized for feature extraction in the experimental dataset, forming training and test sets for the normal and abnormal datasets. To carry out damage detection, the ResNet50 deep residual neural network model is employed. The training results of the same network model were evaluated using the datasets obtained from the four different denoising schemes in the experiments and a 95% confidence level assessment metric. The analysis of the 95% confidence intervals reveals that the proposed sound source separation model combined with the traditional spectral subtraction denoising algorithm is effective in reducing the noise of wind turbine sound signals and performs well in identifying the anomalous sound generated by blade damage. Under this approach, the 95% confidence intervals of the model training set accuracies range from 0.926 to 0.965, while the confidence intervals of the test set accuracies range from 0.869 to 0.931.

Road cracks affect traffic safety. High-precision and real-time segmentation of cracks presents a challenging topic due to intricate backgrounds and complex topological configurations of road cracks. To address these issues, a road crack segmentation method named EGA-UNet is proposed to handle cracks of various sizes with complex backgrounds, based on efficient lightweight convolutional blocks. The network adopts an encoder-decoder structure and mainly consists of efficient lightweight convolutional modules with attention mechanisms, enabling rapid focusing on cracks. Furthermore, by introducing RepViT, the model’s expressive ability is enhanced, enabling it to learn more complex feature representations. This is particularly important for dealing with diverse crack patterns and shape variations. Additionally, an efficient global token fusion operator based on Adaptive Fourier Filter is utilized as the token mixer, which not only makes the model lightweight but also better captures crack features. Finally, to demonstrate the method’s effectiveness and accuracy, we compare the proposed approach with some existing methods on three public datasets. Experimental results demonstrate that the proposed method outperforms existing approaches in detecting cracks of diverse shapes and sizes within complex backgrounds, satisfying the requirements for both high precision and real-time segmentation.

As deep learning networks deepen, detecting multi-scale subtle defects is a challenging task in road images with complex background, due to some fine features gradually disappearing, which significantly increases the difficulty of extracting these fine features. To address this problem, an SCB-AF-Detector is proposed, which combines space-to-depth convolution with bottleneck transformer and employs enhanced asymptotic feature pyramid network to fuse features. Firstly, an SCB-Darknet53 backbone network is designed, which integrates SPD-Conv structure and bottleneck transformer to effectively extract the subtle and distant defect features in complex background. And then, asymptotic feature pyramid network is developed, which first fuses the two shallow semantic features of the backbone network, and then fuses the deep semantic features. In this way, the subtle features in the shallow layer can be preserved, and the deep semantic features can be extracted. Finally, experiments are carried out on the Iran Road Disease Dataset (IRRDD), which contains 25,000 road images. The results show that the proposed method achieves 90.8% (Precision), 95% (Recall) and 75.2% (mAP) in the classification and detection of multi-scale subtle defects respectively, which meets the high-precision detection requirements of road defects.

The rapid spread of coronavirus SARS-CoV-2 greatly threatens global public health but no prophylactic vaccine is available. Here, we report the generation of a replication-incompetent recombinant serotype 5 adenovirus, Ad5-S-nb2, carrying a codon-optimized gene encoding Spike protein (S). In mice and rhesus macaques, intramuscular injection with Ad5-S-nb2 elicits systemic S-specific antibody and cell-mediated immune (CMI) responses. Intranasal inoculation elicits both systemic and pulmonary antibody responses but weaker CMI response. At 30 days after a single vaccination with Ad5-S-nb2 either intramuscularly or intranasally, macaques are protected against SARS-CoV-2 challenge. A subsequent challenge reveals that macaques vaccinated with a 10-fold lower vaccine dosage (1 × 10 10 viral particles) are also protected, demonstrating the effectiveness of Ad5-S-nb2 and the possibility of offering more vaccine dosages within a shorter timeframe. Thus, Ad5-S-nb2 is a promising candidate vaccine and warrants further clinical evaluation. A vaccine protecting from SARS-CoV-2 infection is needed. Here the authors generate a replication-incompetent adenovirus based vaccine expressing SARS-CoV-2 spike, show protection from infection in non-human primates, and analyze the immune response after intramuscular and intranasal vaccination.

As the depth of coal mining in China continues to increase, the fracturing of coal rock masses has an increasingly complex impact on the surrounding rock roadways. The majority of the mine’s roadways run through coal rock masses with hard roofs and soft bottoms, which typically exhibit complex dynamic behaviour. To further research the mechanical behaviour and fracture evolution of coal rock masses under hard-roof and soft-floor conditions, the study is based on the majority of working faces in a mine, which have hard roofs and soft floors. Uniaxial compression tests were utilized to study the mechanical properties of coal rock masses under hard-roof and soft-floor circumstances, using acoustic emission monitoring, whole-process imaging technologies, and fractal dimension analysis. The experimental results are as follows: The uniaxial compressive strength of the coal rock mass is significantly higher than that of its weakest component. The results of the experiment are as follows: The uniaxial compressive strength of coal rock mass is significantly higher than that of its weakest component. Samples with different soft rock strengths exhibited dissipated energy greater than the accumulated energy before the stress maximum, accompanied by volume expansion and the formation of shear surfaces. Samples with higher soft rock strengths tend to exhibit brittle failure, while weaker samples show stress-softening behaviour. Internal fracture complexity varies amongst samples with varying soft rock strengths. A fractal study of the acoustic emission parameters was carried out utilizing MATLAB programming. The fractal analysis results show that acoustic emission ringing counts and energy time series of coal rock masses under hard-roof and soft-floor settings have good fractal properties. The fractal analysis results show that acoustic emission ringing counts and energy time series of coal rock masses under hard-roof and soft-floor settings have good fractal characteristics. Acoustic emission ringing counts tend to have a larger correlation dimension than acoustic emission energy. However, while the sample is fracturing on a vast scale, the ringing count correlation dimension fluctuates very little. The correlation dimension distribution of samples with lower strength is more concentrated after the stress maximum, implying that the deformation and fracturing of the floor rock in highways under hard-roof and soft-floor circumstances are more complex. Both the correlation dimension D of acoustic emission ringing counts and energy indicate a continuous fall before peak stress, which can be used to anticipate coal rock mass fracture. This study, based on the mechanical behaviour and fracture evolution of coal rock masses under hard-roof and soft-floor conditions, provides a foundation for disaster avoidance by controlling the stability and structural deformation of floor rock in hard-roof and soft-floor highways.

Hepatitis E virus (HEV) is the major pathogen of viral hepatitis. HEV causes high mortality in pregnant women. Its infection during pregnancy usually leads to fulminant hepatic failure, spontaneous abortions, premature delivery, or stillbirth. Vertical transmission of HEV has been reported, but the pathogenesis during pregnancy remains largely elusive. Pregnant rhesus macaques were infected with HEV to explore the pathogenesis of genotype 4 HEV infection during pregnancy. Active HEV infections were established with shedding viruses in the feces and blood, and elevated liver enzymes. Notably, higher viral titers and longer durations of HEV infection were found in HEV-infected pregnant rhesus macaques than in non-pregnant macaques. Premature delivery and fetal death occurred in one of the HEV-infected pregnant rhesus macaques. HEV RNA was detected in the liver, spleen, kidneys, and intestines of the dead fetus. This result strongly indicated vertical HEV transmission from mother to fetus. Maternal-transferred antibodies were observed in one of the babies with poor protection. The expressions of interferon-stimulated genes (ISGs) related to HEV infection were completely different between pregnant and non-pregnant rhesus macaques. During pregnancy, impaired innate immune responses, reduced progesterone levels, and shifts in immune states may aggravate HEV infection and result in adverse pregnancy outcomes.

Background This meta-analysis aimed to evaluate the effectiveness of lymphadenectomy on survival and recurrence in patients with early-stage epithelial ovarian cancer (eEOC). Methods Relevant studies were searched from four online databases. Hazard ratios (HRs) with 95% confidence intervals (CIs) or risk ratios (RRs) with 95% CIs were used to evaluate the effects of lymphadenectomy on overall survival (OS), progression-free survival (PFS), and recurrence rates. A subgroup analysis was performed to explore the sources of heterogeneity, followed by sensitivity and publication bias assessments. Results Fourteen articles involving 22,178 subjects were included. Meta-analysis revealed that lymphadenectomy was significantly associated with improved OS (HR = 0.72; 95% CI:0.61, 0.84; P  < 0.001), improved PFS (HR = 0.74; 95% CI: 0.67, 0.80; P  < 0.001), and reduced recurrence rates (RR = 0.72; 95% CI: 0.60, 0.85; P  < 0.001). Subgroup analysis showed that factors including area, histology, and source of the control group were significantly related to improved OS and PFS in patients with eEOC. Sensitivity analysis showed that the combined results were stable and reliable, and no significant publication bias was observed. Conclusions Patients with eEOC can benefit from lymphadenectomy, with improved survival outcomes (OS and PFS) and a lower recurrence rate. Highlights 1. The clinical outcomes of eEOC patients who did and did not undergo lymphadenectomy were compared. 2. Lymphadenectomy was associated with better survival outcomes in patients with eEOC. 3. Lymphadenectomy was associated with a lower recurrence rate in patients with eEOC.

Covering greenhouses and agricultural fields with photovoltaics has the potential to create multipurpose agricultural systems that generate revenue through conventional crop production as well as sustainable electrical energy. In this work, we evaluate the effects of wavelength-selective cutoffs of visible and near-infrared (biologically active) radiation using transparent photovoltaic (TPV) absorbers on the growth of three diverse, representative, and economically important crops: petunia, basil, and tomato. Despite the differences in TPV harvester absorption spectra, photon transmission of photosynthetically active radiation (PAR; 400–700 nm) is the most dominant predictor of crop yield and quality. This indicates that different wavebands of blue, red, and green are essentially equally important to these plants. When the average photosynthetic daily light integral is > 12 mol m –2  d –1 , basil and petunia yield and quality is acceptable for commercial production. However, even modest decreases in TPV transmission of PAR reduces tomato growth and fruit yield. These results identify crop-specific design requirements that exist for TPV harvester transmission and the necessity to maximize transmission of PAR to create the most broadly applicable TPV greenhouse harvesters for diverse crops and geographic locations. We determine that the deployment of 10% power conversion efficiency ( PCE ) plant-optimized TPVs over approximately 10% of total agricultural and pasture land in the U.S. would generate 7 TW, nearly double the entire energy demand of the U.S.