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Accurate and fast identification of vibration signals detected based on the phase-sensitive optical time-domain reflectometer (Φ-OTDR) is crucial in reducing the false-alarm rate of the long-distance distributed vibration warning system. This study proposes a computer vision-based Φ-OTDR multi-vibration events detection method in real-time, which can effectively detect perimeter intrusion events and reduce personnel patrol costs. Pulse accumulation, pulse cancellers, median filter, and pseudo-color processing are employed for vibration signal feature enhancement to generate vibration spatio-temporal images and form a customized dataset. This dataset is used to train and evaluate an improved YOLO-A30 based on the YOLO target detection meta-architecture to improve system performance. Experiments show that using this method to process 8069 vibration data images generated from 5 abnormal vibration activities for two types of fiber optic laying scenarios, buried underground or hung on razor barbed wire at the perimeter of high-speed rail, the system mAP@.5 is 99.5%, 555 frames per second (FPS), and can detect a theoretical maximum distance of 135.1 km per second. It can quickly and effectively identify abnormal vibration activities, reduce the false-alarm rate of the system for long-distance multi-vibration along high-speed rail lines, and significantly reduce the computational cost while maintaining accuracy.

The exhaust gas temperature (EGT) baseline of an aeroengine is key to accurately analyzing engine health, formulating maintenance decisions and ensuring flight safety. However, due to the complex performance characteristics of aeroengine and the constraints of many external factors, it is difficult to obtain accurate non-linear features between various operating factors and EGT. In order to diagnose and forecast aeroengine performance quickly and accurately, four data-driven baseline prediction frameworks for EGT are proposed. These baseline frameworks took engine operating conditions and operating state control parameters as input variables and EGT as predicted output variables. The original data were collected from CFM56-5B engine ACARS flight data. Four typical machine learning methods, including Generalized Regression Neural Network (GRNN), Radial Basis Neural Network (RBF), Support Vector Regression (SVR) and Random Forest (RF) are trained to develop the models. Four aeroengine EGT baseline models were validated by comparing the after-flight data of another engine. The results show that the developed GRNN models have the best accuracy and computational efficiency compared with other models, and their RE and CPU calculation time on the verification set are 1.132 × 10−3 and 3.512 × 10−3 s, respectively. The developed baseline prediction frameworks can meet the needs of practical engineering applications for airlines. The methodologies developed can be employed by airlines to predict the EGT baseline for the purpose of engine performance monitoring and health management.

Two floating treatment wetlands (FTWs) in experimental tanks were compared in terms of their effectiveness on removing nutrients. The results showed that the FTWs were dominated by emergent wetland plants and were constructed to remove nutrients from simulated urban stormwater. Iris pseudacorus and Thalia dealbata wetland systems were effective in reducing the nutrient. T . dealbata FTWs showed higher nutrient removal performance than I. pseudacorus FTWs. Nitrogen (N) and phosphorous (P) removal rates in water by T. dealbata FTWs were 3.95 ± 0.19 and 0.15 ± 0.01 g/m 2 /day, respectively. For I. pseudacorus FTWs, the TN and TP removal rates were 3.07 ± 0.15 and 0.14 ± 0.01 g/m 2 /day, respectively. The maximum absolute growth rate for T . dealbata corresponded directly with the maximum mean nutrient removal efficiency during the 5th stage. At harvest, N and P uptak of T . dealbata was 23.354 ± 1.366 g and 1.489 ± 0.077 g per plant, respectively, approximate twice as high as by I. pseudacorus .

To weaken the effect of receiver location error on localization accuracy and make the localization model closer to the practical scenario, this paper considers the receiver location errors, usually neglected in prior studies into the measurement model, and proposes an algebraic method for locating a moving source using time difference of arrival (TDOA), frequency difference of arrival (FDOA), and differential Doppler rate measurements. The proposed method is based on the pseudo-linear set of equations and two-step weighted least square estimator. Only noise values of receiver locations and three types of positioning measurements are available for processing. In addition, a new Cramér-Rao lower bound (CRLB) combining TDOA, FDOA, and differential Doppler rate in the presence of receiver location errors is also derived in this paper. Theoretical analysis and simulation results both indicate that the proposed method can attain CRLB at a moderate noise level, avoid the rank deficiency problem efficiently, and achieve a significant improvement over the existing methods.

Xanthoceras sorbifolia is a unique woody oilseed tree in China, and its leaves are rich in flavonoids, which are involved in plant growth, development and defense. However, the mining of flavonoid synthesis-related genes in Xanthoceras sorbifolia leaves is lacking. In this study, 226 leaves of Xanthoceras sorbifolia from eight provinces in the key distribution areas were measured for flavonoid content, and the differences in flavonoid content of Xanthoceras sorbifolia were analysed to screen out excellent seed sources and six excellent single plants with higher flavonoid content. Genome-wide association analysis (GWAS) was used to identify genes controlling the synthesis of flavonoids, and 62 significant Single nucleotide polymorphism (SNP) sites were identified, which were closely associated with 8 traits, and a total of 11 genes coding for proteins. We found that these genes mainly encode proteins such as WPP domain-associated protein (WAP) (Fragment), Protein pleiotropic regulatory locus 1 (PRL1) and Phosphomevalonate kinase, peroxisomal (PMK), etc. We found that these proteins may directly or indirectly affect the synthesis of flavonoids, which will provide a data base for molecular breeding and genetic improvement of Xanthoceras sorbifolia .

The microalgae-based technology has been developed to reduce biogas slurry nutrients and upgrade biogas simultaneously. In this work, five microalgal strains named Chlorella vulgaris , Scenedesmus obliquus , Selenastrum capricornutum, Nitzschia palea , and Anabaena spiroides under mono- and co-cultivation were used for biogas upgrading. Optimum biogas slurry nutrient reduction could be achieved by co-cultivating microalgae ( Chlorella vulgaris , Scenedesmus obliquus , and Nitzschia palea ) with fungi using the pelletization technology. In addition, the effects of different ratio of mixed LED light wavelengths applying mixed light-emitting diode during algae strains and fungi co-cultivation on CO 2 and biogas slurry nutrient removal efficiency were also investigated. The results showed that the COD (chemical oxygen demand), TN (total nitrogen), and TP (total phosphorus) removal efficiency were 85.82 ± 5.37%, 83.31 ± 4.72%, and 84.26 ± 5.58%, respectively at red: blue = 5:5 under the co-cultivation of S. obliquus and fungi. In terms of biogas upgrading, CH 4 contents were higher than 90% (v/v) for all strains, except the co-cultivation with S. obliquus and fungi at red: blue = 3:7. The results indicated that co-cultivation of microalgae with fungi under mixed light wavelengths treatments was most successful in nutrient removal from wastewater and biogas upgrading.

Aeroengine performance diagnosis technology is essential for ensuring flight safety and reliability. The complexity of engine performance and the strong coupling of fault characteristics make it challenging to develop accurate and efficient gas path diagnosis methods. To address these issues, this study proposes a novel digital twin framework for aeroengines that achieves the digitalization of physical systems. The mechanism model is constructed at the component level. The data-driven model is built using a particle swarm optimization–extreme gradient boosting algorithm (PSO-XGBoost). These two models are fused using the low-rank multimodal fusion method (LWF) and combined with the sparse stacked autoencoder (SSAE) to form a digital twin framework of the engine for performance diagnosis. Compared to methods that are solely based on mechanism or data, the proposed digital twin framework can effectively use mechanism and data information to improve the accuracy and reliability. The research results show that the proposed digital twin framework has an error rate of 0.125% in predicting gas path parameters and has a gas path fault diagnosis accuracy of 98.6%. Considering that the degradation cost of a typical flight mission for only one aircraft engine after 3000 flight cycles is approximately USD 209.5, the proposed method has good economic efficiency. This framework can be used to improve engine reliability, availability, and efficiency, and has significant value in engineering applications.

MicroRNA profiling of 1,302 human breast tumour samples provides an overview of the miRNA landscape and its regulation, revealing context-dependent interactions, broad prognostic value of miRNA signatures and an important modulatory role for miRNAs in the biology of breast tumours devoid of copy-number aberrations. Cancer modulation by microRNAs MicroRNAs (miRNAs) are key players in cancer pathogenesis. These authors have profiled miRNA expression across a cohort of 1,302 patients with breast tumours for whom clinical follow-up information and matching genomic and messenger RNA expression data were available. The results reveal context-dependent interactions and demonstrate an important role for miRNAs in the biology and outcome of breast tumours devoid of somatic copy-number aberrations, suggesting an important modulatory role for miRNAs in this common subtype of the disease. MicroRNAs (miRNAs) show differential expression across breast cancer subtypes, and have both oncogenic and tumour-suppressive roles 1 , 2 , 3 , 4 , 5 , 6 . Here we report the miRNA expression profiles of 1,302 breast tumours with matching detailed clinical annotation, long-term follow-up and genomic and messenger RNA expression data 7 . This provides a comprehensive overview of the quantity, distribution and variation of the miRNA population and provides information on the extent to which genomic, transcriptional and post-transcriptional events contribute to miRNA expression architecture, suggesting an important role for post-transcriptional regulation. The key clinical parameters and cellular pathways related to the miRNA landscape are characterized, revealing context-dependent interactions, for example with regards to cell adhesion and Wnt signalling. Notably, only prognostic miRNA signatures derived from breast tumours devoid of somatic copy-number aberrations (CNA-devoid) are consistently prognostic across several other subtypes and can be validated in external cohorts. We then use a data-driven approach 8 to seek the effects of miRNAs associated with differential co-expression of mRNAs, and find that miRNAs act as modulators of mRNA–mRNA interactions rather than as on–off molecular switches. We demonstrate such an important modulatory role for miRNAs in the biology of CNA-devoid breast cancers, a common subtype in which the immune response is prominent. These findings represent a new framework for studying the biology of miRNAs in human breast cancer.

We developed a method, ChIP-sequencing (ChIP-seq), combining chromatin immunoprecipitation (ChIP) and massively parallel sequencing to identify mammalian DNA sequences bound by transcription factors in vivo . We used ChIP-seq to map STAT1 targets in interferon-γ (IFN-γ)–stimulated and unstimulated human HeLa S3 cells, and compared the method's performance to ChIP-PCR and to ChIP-chip for four chromosomes. By ChIP-seq, using 15.1 and 12.9 million uniquely mapped sequence reads, and an estimated false discovery rate of less than 0.001, we identified 41,582 and 11,004 putative STAT1-binding regions in stimulated and unstimulated cells, respectively. Of the 34 loci known to contain STAT1 interferon-responsive binding sites, ChIP-seq found 24 (71%). ChIP-seq targets were enriched in sequences similar to known STAT1 binding motifs. Comparisons with two ChIP-PCR data sets suggested that ChIP-seq sensitivity was between 70% and 92% and specificity was at least 95%.

The work was designed to investigate the effect of continuous positive airway pressure (CPAP) on hypertension in obstructive sleep apnea-hypopnea syndrome (OSAHS) patients and to elucidate the underlying mechanisms. We examined the effect of CPAP on blood pressure and biomarkers reflecting inflammation and oxidative stress, and investigated the correlation between changes in blood pressure and the biomarkers. CPAP significantly improved clinic, ambulatory and home blood pressure ( < 0.05). The hypotensive effect of CPAP was positively correlated with the decrease of interleukin-6, C-reactive protein, NADPH oxidase and malonaldehyde. CPAP has a significant antihypertensive effect on OSAHS patients, especially nocturnal hypertension, possibly by counteracting inflammation and oxidative stress.