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As the chart datum of China, the lowest normal low water (LNLW) was calculated using three tidal constituents, major (Q1, O1, P1, K1, N2, M2, S2 and K2), shallow water (M4, MS4 and M6) and long-period tidal (Sa and Ssa). The construction of a tidal datum is mainly concerned with improvements in the major tidal constituents, and the contribution of the long-period tidal component has been generally neglected. In this study, long-term tide gauge observations and multi-mission satellite altimetry data were used to investigate the spatial distribution of the long-period tidal contribution in Chinese seas and analyze the relative long-period tidal contribution rate into four regions. The results showed that the mean contribution in Chinese seas is 7.63 %, with the largest contribution in the Bohai Sea (11.33 %) and smallest in the East China Sea (5.27 %). Differences between tide gauge and satellite-derived results were compared in detail. The Sa and M2 tidal amplitudes are the main factors affecting the long-period tidal contribution to the LNLW. The relative long-period tidal contribution can be up to 34.18 % when tide gauge observations record small M2 and large Sa amplitudes. These results indicate that the long-period tidal constituent cannot be neglected in the establishment of the LNLW datum. Therefore, to improve tidal datum precision, precise extraction and accuracy assessments of long-period tidal constituents should be a research focus. [ABSTRACT FROM AUTHOR]

In the process of synthetic aperture radar image noise suppression by the bi‐dimensional empirical mode decomposition (BEMD) algorithm, the edge effect is a key problem in the BEMD operation. To weaken this effect, an improved BEMD‐kernel principal component analysis (BEMD‐KPCA) method of image denoising is proposed in this study. Experimental results show that the BEMDKPCA algorithm has a good capability of improving edge effects in the BEMD decomposition process and satisfying the requirement of the reliable decomposition results. Compared with the traditional BEMD method, the proposed approach has a good effect on suppressing speckle noise. Additionally, the denoised image from the decomposed components of the IMFs processed by the BEMD‐KPCA method sufficiently preserves the edge and detail information, confirming its high coherency with the original image. [ABSTRACT FROM AUTHOR]

Sea ice type is one of the most sensitive variables in Arctic sea ice monitoring, and it is important for the retrieval of ice thickness. In this study, we analyzed various waveform features that characterize the echo waveform shape and Sigma0 (i.e., backscatter coefficient) of CryoSat-2 synthetic aperture radar altimeter data over different sea ice types. Arctic and Antarctic Research Institute operational ice charts were input as reference. An object-based random forest (ORF) classification method is proposed with overall classification accuracy of 90.1%. Accuracy of 92.7% was achieved for first-year ice (FYI), which is the domain ice type in the Arctic. Accuracy of 76.7% was achieved at the border of FYI and multiyear ice (MYI), which is better than current state-of-the-art methods. Accuracy of 83.8% was achieved for MYI. Results showed the overall accuracy of the ORF method was increased by ∼8% in comparison with other methods, and the classification accuracy at the border of FYI and MYI was increased by ∼10.5%. Nevertheless, ORF classification performance might be influenced by the selected waveform features, snow loading, and the ability to distinguish sea ice from leads. [ABSTRACT FROM AUTHOR]

In this study, to meet the need for the accurate prediction of sea level anomaly (SLA), a hybrid model is proposed. In this model, empirical mode decomposition is combined with singular spectrum analysis and least-squares extrapolation to predict satellite-derived SLA. Each intrinsic mode function series of an empirical mode decomposition is decomposed and reconstructed using singular spectrum analysis. The reconstructed components and the residual series are predicted using least-squares extrapolation. This hybrid model was used for satellite-derived SLAs that were obtained using multi-mission along-track satellite altimetry data from September 1992 to January 2018, and the prediction errors for 3 years lead times were analysed. The observations and predictions of the principal components for annual or interannual periods correlated well, and the proposed hybrid model effectively predicted the SLAs. For the 3 years lead time predictions, the mean absolute error and root-mean-square error were 1.03 and 1.32 cm, respectively, which were less than those reported for existing methods. [ABSTRACT FROM AUTHOR]

We have previously reported an aqueous extract of fermented barley with Lactobacillus plantarum dy-1 (LFBE) has more efficient anti-obesity effect compared with that of Saccharomyces cerevisiae. To further explore associated effects of LFBE on body weight and body fat distribution, and lipid profiles related metabolic outcomes, serum metabolites were analysed using LC-MS-MS and partial least-squares-discriminant analysis (PLS-DA). Obese and lean groups were clearly discriminated from each other on PLS-DA score plot and major metabolites contributing to the discrimination were assigned as lipid metabolites (fatty acids), lipid metabolism intermediates (choline, betaine, carnitine and butyryl-carnitine), amino acids and citric acid. A high-fat diet increased lipid metabolites and decreased lipid metabolism intermediates, indicating that abnormal lipid metabolism induced by a high-fat diet resulted in fat accumulation via decreased β-oxidation. But LFBE can inhibit fat accumulation by reducing lipid metabolites and increasing lipid metabolism intermediates. Furthermore, the level changes of these metabolites can be used to assess the risk of obesity and the therapeutic effect of obesity management. [ABSTRACT FROM AUTHOR]

Abstract: A 150‐d feeding experiment was conducted to assess the effect of the dietary energy to protein ratio (E/P ratio) on growth, biological indices, proximate composition, and amino acid profiles in the muscle of Varicorhinus macrolepis. Six experimental diets were formulated with different E/P ratios (30.51, 33.90, 37.82, 42.34, 47.70, and 54.09 kJ/g). In the experiment, 360 fish with a mean weight of 8.75 g (SD, 0.59) were randomly divided into six groups (three replicates per group, 20 fish per replicate) and fed three times daily with the experimental diet. Results showed that when the dietary E/P ratio was 37.82 kJ/g, Varicorhinus macrolepis tended to gain the highest specific growth rate. With a change in the increment of dietary E/P ratio, the condition factor, intestosomatic index, hepatosomatic index, and viscerosomatic index all changed in the quadric polynomial regression model. Muscle crude lipid content showed a tendency of leveling‐off first and then increased with increasing dietary E/P ratio. Varicorhinus macrolepis gained the highest concentration of total amino acids and essential amino acids when the dietary E/P ratio was 37.82 kJ/g. Except for methionine + cysteine, phenylalanine + tyrosine, and the amino acid score of lysine, the amino acid scores and chemical scores of the other essential amino acids were highest when the dietary E/P ratio was 37.82 kJ/g. These results suggest that Varicorhinus macrolepis obtained optimal growth and muscle amino acid composition when the dietary E/P ratio was 37.82 kJ/g. [ABSTRACT FROM AUTHOR]

Water vapor plays an important role in climate change; thus, studying the spatial distribution and temporal variation of precipitable water vapor (PWV) in the coastal regions of China would help researchers to understand the climate characteristics of those regions. In this paper, 6-year 1-h interval PWV were derived from 27 Global Positioning System stations observations of Chinese coastal GPS observation network, surface meteorological data and European Center for Medium-Range Weather Forecasts (ERA-Interim) reanalysis products. The present study provides the use of these data to investigate the spatial-temporal variability of water vapor throughout the coastal regions of China. Latitude is the main factor affecting the spatial distribution of GPS-derived PWV; that is, PWV decreased by about 1.5 mm for each 1° increase of latitude. For regions at the same latitude, a region that is relatively close to the ocean will have a higher content of PWV. The PWV in the southeastern and southwestern coastal regions of China is significantly higher in summer; this may be influenced by the southeastern and southwestern water vapor inflow corridors. The PWV obviously varies monthly, reaching a minimum in January; however, the timing of the maximum varied but usually appeared in June, July or August and was affected by the monsoons. The PWV varies largely between summer and winter with a larger gradient of change in PWV with latitude in winter than in summer. The positive correlation coefficient between PWV and the surface temperature varied in different seasons; this is related to the changes of temperature and the horizontal motion of water vapor. Use of the Fast Fourier Transform method showed that the PWV time series data have multi-scale characteristics. The amplitude and phase of the PWV time series in annual, semiannual, four month and seasonal cycles were extracted through harmonic wave analysis. The amplitude of four month and seasonal cycles did not pass significance testing. The maximum amplitude of the annual PWV cycle is located in mid-latitudes with 22.09 mm, which gradually decreases towards high latitude and equator area. The peak time of annual PWV cycle occurs in July, which does not agree with the timing of the maximum monthly average PWV. The semiannual cycle amplitudes is between 0.42 and 6.32 mm, with no significant correlation between their distribution and latitude. The peak time of semiannual PWV cycles is during about January–March and July–September, and the peak time gradually becomes late from north to south. [ABSTRACT FROM AUTHOR]