A Method for Real-Time Measurement of the Vertical Vortex at Flood Discharge Outlets Using Ultrasonic Sensors.

In this study, ultrasonic sensors were used to measure the vertical vortex at flood discharge outlets in real time, and numerical simulations and model experiments were conducted. When a sound signal passes through a vortex, its propagation characteristics will change, which helps to determine the existence of the vortex. Moreover, its characteristic parameters can be obtained through inversion. In this paper, first, the theories of acoustic measurement methods were introduced and their feasibility was verified through a comparison between Particle Image Velocimetry (PIV) measurement and numerical simulation results. Then, the Computational Fluid Dynamics (CFD) method was used to simulate the vertical vortex at the flood discharge outlets of hydraulic structures and the simulation data were restored to the actual size at scale. Finally, acoustic numerical simulations of actual vortex data were conducted, and ultrasonic sensors were used to measure the velocity of a simplified vertical vortex model under laboratory conditions. The research results indicate that the acoustic measurement method proposed in this article is effective in the measurement of the characteristic parameters of vertical vortex with a core radius of 0.03~0.05 m and a maximum tangential velocity of 0.5 m/s, the measurement error of the maximum tangential velocity is within 10%.

Yield-Adjusted Operation for Convolution Filter Denoising.

A perfect denoising for measurement shall remove noise, while keeping signal truth, so it is a dual-objective optimization of the signal yield and the noise residue. The frequency difference between the noise and signal is the basis of band-limited filter denoising. The root cause for the sharp peak denoise distortion is the insufficient spectrum sampling because of the scattered frequency distribution, which makes it hard to achieve dual-objective optimization. Thus, this article proposes a four-step operation of the signal yield adjustment for beyond the band-limited system. The first step is identifying the signal and noise levels in raw data, then adjusting the sampling density of high-signal level areas and enriching it by linear interpolation, then smoothing the reshaped profile, which is friendly to the filter, and finally, restoring the deformed one to its original form. An executable script function has fully achieved the whole operation. Some actual sharp spectra (Raman, NMR, laser-induced breakdown spectroscopy, and X-ray diffraction) make a comparison between the way with the Savitzky-Golay (SG) method and wavelet (multi-scale) denoising. The results show that all the effects are better than those of the SG filter, all estimations of the yield of signals are more than 99%, and the residue of noise is less than 10%. With multi-scale denoising, this operation is more targeted and gets more rational spectrum profiles─noise reduction without spectrum distortion.

The Association of an SNP in the EXOC4 Gene and Reproductive Traits Suggests Its Use as a Breeding Marker in Pigs.

In mammals, the exocyst complex component 4 (EXOC4) gene has often been reported to be involved in vesicle transport. The SNP rs81471943 (C/T) is located in the intron of porcine EXOC4, while six quantitative trait loci (QTL) within 5-10 Mb around EXOC4 are associated with ovary weight, teat number, total offspring born alive, and corpus luteum number. However, the molecular mechanisms between EXOC4 and the reproductive performance of pigs remains to be elucidated. In this study, rs81471943 was genotyped from a total of 994 Duroc sows, and the genotype and allele frequency of SNP rs81471943 (C/T) were statistically analyzed. Then, the associations between SNP rs81471943 and four reproductive traits, including number of piglets born alive (NBA), litter weight at birth (LWB), number of piglets weaned (NW), and litter weight at weaning (LWW), were determined. Sanger sequencing and PCR restriction fragment length polymorphism (PCR-RFLP) were utilized to identify the rs81471943 genotype. We found that the genotype frequency of CC was significantly higher than that of CT and TT, and CC was the most frequent genotype for NBA, LWB, NW, and LWW. Moreover, 5'-deletion and luciferase assays identified a positive transcription regulatory element in the EXOC4 promoter. After exploring the EXOC4 promoter, SNP -1781G/A linked with SNP rs81471943 (C/T) were identified by analysis of the transcription activity of the haplotypes, and SNP -1781 G/A may influence the potential binding of P53, E26 transformation specific sequence -like 1 transcription factor (ELK1), and myeloid zinc finger 1 (MZF1). These findings provide useful information for identifying a molecular marker of EXOC4-assisted selection in pig breeding.

Improve the performance of independent component analysis by mapping the spectrum to an orthogonal space.

Independent Component Analysis (ICA) has attracted chemists recently, for its charm can separate the independent signals from a mixed system and does not need prior knowledge. However, its dissatisfactory performance for the chemical measured signal is still blocking the practicability. Thus, this paper summarized the ICA processing path from the establishment of rectangular coordinates in linear space to the determination of the corresponding relation between the coordinate system and real components. The primary cause of the deviation between the ICA results and the chemical measurements is that the measuring signal was subject to uncertainty. Besides, uncertainty made the deviation of source signal from the statistical independence assumption, or in other words, it appeared to be nonorthogonal. For this key, it proposed to map the measured value to the high-order derivative space, use the derivative to narrow the peak width, reduce the influence of uncertainty, and improve the separation performance of ICA to chemical measurement signal, such as the spectrum. Actual cases of this paper showed that when up to 6th order, the separating results had been perfect for IR spectra, and even for homologs isomers.

FoxA2 and p53 regulate the transcription of HSD17B1 in ovarian granulosa cells of pigs.

The oestrogens have been highly implicated in the fertility of female animals. It is widely known that the oestrogens are primarily synthetized by the ovarian granulosa cells (GCs), and the final and essential step of this process is to catalyse the oestrone to the more active oestradiol by the protein coded by hydroxysteroid 17-beta dehydrogenase 1 (HSD17B1) gene. However, the molecular mechanism regarding the transcription of HSD17B1 remains to be fully elucidated in ovarian GCs. In this study, the 5'-deletion, luciferase assay and chromatin immunoprecipitation (ChIP) were utilized to explore the molecular regulation of transcription of HSD17B1 with the porcine ovarian GCs as the cellular model. After the deletions with -2105 to -1754 bp, -1753 to -1429 bp, -1430 to -1081 bp and -1082 to -730 bp, the relative luciferase activity of HSD17B1 promoter did not change significantly, but the deletion of -731 to -332 bp significantly increased the relative luciferase activity of HSD17B1 promoter, and an insertion (GTTT) that might raise the transcription of HSD17B1 was identified at -401 bp of HSD17B1. These findings suggested the region from -731 to +38 bp was the core promoter of HSD17B1, and the region between -731 to -332 bp might be a silence element for HSD17B1. Furthermore, the forkhead box A2 (FoxA2) directly bound at -412 to -401 bp to negatively but p53 bound at -383 to -374 bp to positively regulate the transcription and translation of HSD17B1 in ovarian GCs. These findings will improve our understanding on HSD17B1-mediated oestrogens and provide useful information for further investigations into fertility of females.

Blind source separation of coexisting background in Raman spectra.

Due to the Raman signal coexists with other scatter spectra which leads to the low ratio of the wanted signal and high background, the appropriate method should be applied to enhance this ratio. The nature of raw spectra is a multi-source system, so its determinacy must be ensured by multi-input. Besides, the faithfulness of output should be provided. Then, the huge fall within the frequencies of Raman and background almost satisfies separating demand for independent component analysis (ICA), and this analysis can give help to the achievement of the two type signals classing and estimate the optimal number of source and match ICA output signals to Raman or background. Thus, based on ICA and the mixing-entropy criteria, the background and Raman adapting calibration kit (BRACK) method is proposed, which is a kind of multiple raw spectral inputs and multiple output (MIMO) method. This method firstly divides the raw data into two parts of Raman and background by ICA, identifies Raman signal by entropy criterion, then restores the part of Raman signal. BRACK method obtains several advantages, for instance, well-adapted, no need for any additional option or extra-intervention, high fidelity, and no unwanted external information. In principle, the correction of background and Raman signals can be expected to be completed by BRACK method.

[Correction Multiplicative Effects in Raman Spectra through Vector Angle Transformation].

The linear relationship between the Raman spectral intensity and the analyte amount is frequently disrupted for a variety of complex reasons, which include these variations in laser source, focusing effect, sample scattering and refracting, so that causes poor quantitative results. As a whole, these disturbing effects can be divided to be additive and multiplicative, and the multiplicative effects are generally more difficult to be eliminated. A spectrum is a series data, also can be treated as a vector. In principle, unstable motions in spectrum intensity/amplitude corresponding to the module shifts for a vector, doesn't impact the vector direction which is the essence of the vector, so it is reasonable to rewrite the data form on module to on space angle for the same measurement. This thesis employed a data transformation to eliminate the multiplicative effects within spectra, i. e. , the spectrum signal on its amplitude has been transformed to be on the vector angles. The first step of the transformation is the selection of a stand vector which is near to the analyte and almost orthogonal to the background within the sample space; and the next step is to define a moving window, then to find out the angle between the sample vector (i. e. the transformed spectrum) and the stand vector within the window; while the window is moved along the spectrum data series, the transformation for vector angle (VA) series has been finished. The thesis has proved that an approximate linear quantitative relationship has been remained in the VA series. Multivariate calibration need full rank matrix which is combined by spectrum from variety samples, and variety VA series also can combine a full rank VA matrix, so the approximate linear VA matrix still perfectly meeting the demand for multivariate calibration. A mixed system consisted by methanol-ethanol-isopropanol has been employed to verify the eliminations to the multiplicative effects. These measuring values of the system are obtained at different Raman integral times and have remarkable multiplicative effects. In predicting results, the correlation coefficient (r) and the root mean squared error of prediction (RMSEP) from class PLS respectively are 0.911 9 and 0.110 2, and 0.906 0 and 0.100 8 are for the preprocessing by multiplicative scatter correction (MSC). In contrast, r and RMSEP under the VAPLS, presented by this thesis, respectively are 0.998 7 and 0.015 2 and are significantly better than others. The VAPLS has eliminated the multiplicative effects of Raman spectra and improved the accuracy of Raman quantitative analysis and it owes to the preprocessing of the vector angle transformation.

[Process analysis for levulinic acid generated in waste wood liquefaction by non-development thin-layer chromatography based on oblique projection].

A rapid and quantitative method is presented for multi-component process analysis, based on multi-wavelength thin-layer chromatography (TLC) scanning but without the routine development. The samples from the waste wood liquefaction process are applied on silica plates, and just the last sample of spot need to be developed for getting separated spectra. These spectra are divided into two parts of production (levulinic acid) and background, respectively, to build an oblique projection operator. The other process samples do not need to be developed repeatedly, and are scanned to collect hybrid spectra immediately. The pure production spectrum can be separated from the process spectrum by the oblique projection algorithms to realize the production quantification. It was showed that the relative errors between the determination results by this method and those by high performance liquid chromatography (HPLC) were less than 3.27%, and so the consistency is perfect.

[A new multivariate quantitative method of spectral analysis for multicomponent system].

In the spectral analysis, a large-scale application of the traditional multivariate analysis methods has been limited by both high cost and poor applicability of the calibration models. A new multivariate analysis method was proposed for multicomponent systems in the present paper. Determining MTBE content in gasoline solution by infrared spectroscopy was studied. The spectra of five kinds of gasoline and their 50 narrow distillation fractions were used to build the background library. The oblique projection algorithm was applied to the spectra of MTBE gasoline solution samples to extract the purespectral signal of MTBE in the solution. A unary linear regression curve was built between the pure spectral signals of MTBE and their concentrations with a correlation coefficient of 0.995 2 and an intercept of 0.025. Compared with the orthogonal projection algorithm method and PLS model method, a large amounts of calibration samples and complex model are no longer needed by the new method which is simpler, more accurate and with better applicability.

[Processing GC-FTIR by the blind source separation].

An analysis method for separating chromatographic overlapped peaks and purifying infrared spectra is put forward, based on the blind source separation technique and the multi-dimensional data of GC-FTIR, Using various information from hyphenated instruments, this method was used to separate completely a organic mixture, the xylene isomerism system, a problem unable to solve usually. The method can confirm the rationality of theory and algorithm and give integral explanations of the independent component analysis data. The reason for the error in quantitative analysis is discussed.