Research and experimental verification on low-frequency long-range underwater sound propagation dispersion characteristics under dual-channel sound speed profiles in the Chukchi Plateau

The dual-channel sound speed profiles of the Chukchi Plateau and the Canadian Basin have become current research hotspots due to their excellent low-frequency sound signal propagation ability. Previous research has mainly focused on using sound propagation theory to explain the changes in sound signal energy. This article is mainly based on the theory of normal modes to study the fine structure of low-frequency wide-band sound propagation dispersion under dual-channel sound speed profiles. In this paper, the problem of the intersection of normal mode dispersion curves caused by the dual-channel sound speed profile (SSP) has been explained, the blocking effect of seabed terrain changes on dispersion structures has been analyzed, and the normal modes has been separated by using modified warping operator. The above research results have been verified through a long-range seismic exploration experiment at the Chukchi Plateau. At the same time, based on the acoustic signal characteristics in this environment, two methods for estimating the distance of sound sources have been proposed, and the experiment data at sea has also verified these two methods.Comment: 30 pages, 18 figure

Research and experimental verification on low-frequency long-range sound propagation characteristics under ice-covered and range-dependent marine environment in the Arctic

At present, research on sound propagation under the Arctic ice mainly focuses on modeling and experimental verification of sound propagation under sea ice cover and unique sound velocity profiles. Among them, the main research object of concern is sound transmission loss, and this article will delve into the time-domain waveform and fine dispersion structure of low-frequency broadband acoustic signals. Firstly, based on the theory of normal modes, this article derives the horizontal wavenumber expression and warping transformation operator for refractive normal modes in the Arctic deep-sea environment. Subsequently, based on measured ocean environmental parameters and sound field simulation calculations, this article studied the general laws of low-frequency long-range sound propagation signals in the Arctic deep-sea environment, and elucidated the impact mechanism of environmental factors such as seabed terrain changes, horizontal changes in sound velocity profiles (SSPs), and sea ice cover on low-frequency long-range sound propagation in the Arctic. This article validates the above research viewpoint through a sound propagation experiment conducted in the Arctic with a propagation distance exceeding 1000km. The marine environment of this experiment has obvious horizontal variation characteristics. At the same time, this article takes the lead in utilizing the warping transformation of refractive normal waves in the Arctic waters to achieve single hydrophone based separation of normal waves and extraction of dispersion structures, which is conducive to future research on underwater sound source localization and environmental parameter inversion based on dispersion structures.Comment: 46 pages, 35 figure

Sequential fault detection for sealed deep groove ball bearings of in-wheel motor in variable operating conditions

Sealed deep groove ball bearings (SDGBBs) are employed to perform the relevant duties of in-wheel motor. However, the unique construction and complex operating environment of in-wheel motor may aggravate the occurrence of SDGBB faults. Therefore, this study presents a new intelligent diagnosis method for detecting SDGBB faults of in-wheel motor. The method is constructed on the basis of optimal composition of symptom parameters (SPOC) and support vector machines (SVMs). SPOC, as the objects of a follow-on process, is proposed to obtain from symptom parameters (SPs) of multi-direction. Moreover, the optimal hyper-plane of two states is automatically obtained using soft margin SVM and SPOC, and then using multi-SVMs, the system of intelligent diagnosis is built to detect many faults and identify fault types. The experiment results confirmed that the proposed method can excellently perform fault detection and fault-type identification for the SDGBB of in-wheel motor in variable operating conditions

Determination of the downwelling diffuse attenuation coefficient of lakewater with the sentinel-3A OLCI

The Ocean and Land Color Imager (OLCI) on the Sentinel-3A satellite, which was launched by the European Space Agency in 2016, is a new-generation water color sensor with a spatial resolution of 300 m and 21 bands in the range of 400-1020 nm. The OLCI is important to the expansion of remote sensing monitoring of inland waters using water color satellite data. In this study, we developed a dual band ratio algorithm for the downwelling diffuse attenuation coefficient at 490 nm (Kd(490)) for the waters of Lake Taihu, a large shallow lake in China, based on data measured during seven surveys conducted between 2008 and 2017 in combination with Sentinel-3A-OLCI data. The results show that: (1) Compared to the available Kd(490) estimation algorithms, the dual band ratio (681 nm/560 nm and 754 nm/560 nm) algorithm developed in this study had a higher estimation accuracy (N = 26, coefficient of determination (R2) = 0.81, root-mean-square error (RMSE) = 0.99m-1and mean absolute percentage error (MAPE) = 19.55%) and validation accuracy (N = 14, R2= 0.83, RMSE = 1.06 m-1and MAPE = 27.30%), making it more suitable for turbid inland waters; (2) A comparison of the OLCI Kd(490) product and a similar Moderate Resolution Imaging Spectroradiometer (MODIS) product reveals a high consistency between the OLCI and MODIS products in terms of the spatial distribution of Kd(490). However, the OLCI product has a smoother spatial distribution and finer textural characteristics than the MODIS product and contains notably higher-quality data; (3) The Kd(490) values for Lake Taihu exhibit notable spatial and temporal variations. Kd(490) is higher in seasons with relatively high wind speeds and in open waters that are prone to wind- and wave-induced sediment resuspension. Finally, the Sentinel-3A-OLCI has a higher spatial resolution and is equipped with a relatively wide dynamic range of spectral bands suitable for inland waters. The Sentinel-3B satellite will be launched soon and, together with the Sentinel-3A satellite, will form a two-satellite network with the ability to make observations twice every three days. This satellite network will have a wider range of application and play an important role in the monitoring of inland waters with complex optical properties

Inversion of inherent optical properties in optically complex waters using sentinel-3A/OLCI images: A case study using China\u27s three largest freshwater lakes

Inherent optical properties (IOPs) play an important role in underwater light field, and are difficult to estimate accurately using satellite data in optically complex waters. To study water quality in appropriate temporal and spatial scales, it is necessary to develop methods to obtain IOPs form space-based observation with quantified uncertainties. Field-measured IOP data (N = 405) were collected from 17 surveys between 2011 and 2017 in the three major largest freshwater lakes of China (Lake Chaohu, Lake Taihu, and Lake Hongze) in the lower reaches of the Yangtze River and Huai River (LYHR). Here we provide a case-study on how to use in-situ observation of IOPs to devise an improved algorithm for retrieval of IOPs. We then apply this algorithm to observation with Sentinel-3A OLCI (Ocean and Land Colour Instrument, corrected with our improved AC scheme), and use in-situ data to show that the algorithm performs better than the standard OLCI IOP product. We use the satellite derived products to study the spatial and seasonal distributions of IOPs and concentrations of optically active constituents in these three lakes, including chlorophyll-a (Chla) and suspended particulate matter (SPM), using all cloud-free OLCI images (115 scenes) over the lakes in the LYHR basin in 2017. Our study provides a strategy for using local and remote observations to obtain important water quality parameters necessary to manage resources such as reservoirs, lakes and coastal waters

A Pre-trained Data Deduplication Model based on Active Learning

In the era of big data, the issue of data quality has become increasingly prominent. One of the main challenges is the problem of duplicate data, which can arise from repeated entry or the merging of multiple data sources. These "dirty data" problems can significantly limit the effective application of big data. To address the issue of data deduplication, we propose a pre-trained deduplication model based on active learning, which is the first work that utilizes active learning to address the problem of deduplication at the semantic level. The model is built on a pre-trained Transformer and fine-tuned to solve the deduplication problem as a sequence to classification task, which firstly integrate the transformer with active learning into an end-to-end architecture to select the most valuable data for deduplication model training, and also firstly employ the R-Drop method to perform data augmentation on each round of labeled data, which can reduce the cost of manual labeling and improve the model's performance. Experimental results demonstrate that our proposed model outperforms previous state-of-the-art (SOTA) for deduplicated data identification, achieving up to a 28% improvement in Recall score on benchmark datasets

Cloning and Expression of a Novel GH134 β-Mannanase Gene from Thermophilic Fungus Rhizopus microsporus and Its Application in Juice Processing

A thermophilic fungus, named HBFH10, was isolated from Luzhou-flavor Daqu and identified as Rhizopus microsporus. A new β-mannanase gene (RmMan134) from Rhizopus microsporus HBHF10, was cloned using degenerate primers, expressed, and characterized. The cloned RmMan134 gene had a total length of 552 bp without introns, encoding 183 amino acids and one stop codon. The expressed enzyme was composed of one signal peptide sequence of 19 amino acid residues and the catalytic region of the GH134 family. The optimal pH and temperature for the recombinant enzyme were 6.0 and 50 ℃, respectively. It had high thermal and pH stability. Its molecular mass was estimated to be approximately 18.5 kDa, and it was a glycosylated molecule. When konjac gum was used as the substrate, the Km and Vmax values of RmMan134 were 0.66 mg/mL and 57.1 μmol/(min·mg), respectively. RmMan134 exhibited the best clarification efficiency for orange juice, resulting in a 23.8% increase in its clarity, followed apple juice (11%) and peach juice (7%). Moreover, RmMan134 significantly increased the yield of grape juice by about 7%. Overall, RmMan134 has good application prospects in juice processing

A fuzzy diagnosis of multi-fault state based on information fusion from multiple sensors

This paper presents a fuzzy diagnosis for detecting and distinguishing multi-fault state, the method is constructed on the basis of possibility theory and support vector machines (SVMs) with information fusion from multiple sensors. Non-dimensional symptom parameters (NSPs) are defined to reflect the characteristics of vibration information, and principal component analysis (PCA) is used to evaluate and select sensitive NSPs of each sensor. SVMs are employed to fuse vibration information from different sensors into an effective synthetic symptom parameter (SSP) for increasing diagnostic sensitivity, then the possibility function of the SSP is used to construct a fuzzy diagnosis for fault detection and fault-type identification by possibility theory. Practical examples of diagnosis for a roller bearing used in a test bench are given to show that multi-fault states of bearing can be identified precisely by the proposed method

A Dual-Bacterial Coupled Fermentation Strategy for Nicotinamide Mononucleotide Synthesis

In this study, a dual-bacterial coupled fermentation system containing nicotinamide nucleoside kinase (NRK) and polyphosphatase (PPK) was constructed, and the application of PPK-based ATP regeneration system in NMN production was achieved. First, engineering strains expressing NRK1 and NRK2 were constructed, and the highly active Escherichia coli BL21 (DE3)-pET28a-NRK1 was selected, with NMN yield and productivity of 5.17 g/L and 77.4%, respectively. Then, the induced expression conditions of NRK1 were optimized, and a low temperature of 16 ℃, an isopropyl-β-D-thiogalactopyranoside (IPTG) concentration of 0.7 mmol/L, an inoculation amount of 3% and an induction duration of 22 h were found to be optimal the soluble expression of NRK1 protein. The optimal synthesis conditions of NMN by E. coli BL21 (DE3)-pET28a-NRK1 were explored. It was found that after 12 h culture at 18 ℃ at an initial cell concentration of 100 g/L and a ratio of ATP to NR of 1:1.5, the highest yield of NMN of 5.73 g/L was obtained with a productivity of 85.78%. Finally, the optimal conditions that provided maximal NMN production (11.81 g/L) by coupled fermentation with E. coli BL21 (DE3) pET28a-PPK and E. coli BL21 (DE3)-pET28a-NRK1 were determined as 1:3.5, 1:2 and 16 h for ATP to NR ratio, initial cell concentration and fermentation time, respectively. The high-density dual-bacterial coupled fermentation strategy established in this study opens up a new pathway for high-efficiency, low-cost and large-scale production of NMN