Purple Urine Bag Syndrome Amelioration without Antibiotic Therapy.

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A detection method of typical toxic mixed red tide algae in Qinhuangdao based on three-dimensional fluorescence spectroscopy.

Red tides occur every year in the Qinhuangdao sea area of China, including a variety of toxic algae and non-toxic algae. Toxic red tide algae have caused great damage to the marine aquaculture industry in China and seriously endangered human health, but most of non-toxic algae are important bait for marine plankton. Therefore, it is very important to identify the type of mixed red tide algae in Qinhuangdao sea area. In this paper, three-dimensional fluorescence spectroscopy and chemometrics were applied to the identification of typical toxic mixed red tide algae in Qinhuangdao. Firstly, the three-dimensional fluorescence spectrum data of typical mixed red tide algae in Qinhuangdao sea area were measured by f-7000 fluorescence spectrometer, and the contour map of algae samples was obtained. Secondly, the contour spectrum analysis is carried out to find the excitation wavelength of the peak position of the three-dimensional fluorescence spectrum and form the new three-dimensional fluorescence spectrum data selected by the feature interval. Then, the new three-dimensional fluorescence spectrum data are extracted by principal component analysis (PCA). Finally, the feature extraction data and the data without feature extraction are used as the input of the genetic optimization support vector machine (GA-SVM) and particle swarm optimization support vector machine (PSO-SVM) classification models, respectively, to obtain the classification model of mixed red tide algae, and the two feature extraction analysis methods and two classification algorithms are compared. The results show that the classification accuracy of the test set using the principal component feature extraction and GA-SVM classification method is 92.97 %, when the excitation wavelengths are 420 nm, 440 nm, 480 nm, 500 nm and 580 nm, and the emission wavelengths are 650-750 nm. Therefore, it is feasible and effective to apply the three-dimensional fluorescence spectrum characteristics and genetic optimization support vector machine classification method to the identification of toxic mixed red tide algae in Qinhuangdao sea area.

Identification of ichthyotoxic red tide algae based on three-dimensional fluorescence spectra and particle swarm optimization support vector machine.

Acccurate identification whether red tide has ithyotoxicity is very significant for microalgae monitoring. In order to realize the rapid and non-destructive detection of ichthyotoxic red tide algae, a detection method combining three-dimensional (3D) fluorescence spectrum and particle swarm optimization support vector machine (PSO-SVM) was developed to monitor the ichthyotoxic red tide algae with cell concentrations from 104 cells/mL to 106 cells/mL. The contour maps contracted form three-dimensional fluorescence spectra of six common species of ichthyotoxic algae and eight common species of non-ichthyotoxic algae,which are analyzed to select the optimal emission and excitation wavelength span. The new feature data are acquired by using the emission spectrum data at 480 nm and 510 nm excitation wavelengths. The new feature data are used as the input of particle swarm optimization support vector machine to establish the optimal classification model of ichthyotoxic algae, which achieves an classification accuracy of 100% for the test set. The optimal classification model is successfully applied to identify the ichthyotoxicity of different algae including Heterosigma akashiwo, Chattonella marina, Phaeocystis globosa, Prorocentrum donghaiense, Karenia dunnii, Isoscelina galbana, Isosceles globosa and Skeletonema costatum.

Identification of paralytic shellfish poison producing algae based on three-dimensional fluorescence spectra and quaternion principal component analysis.

In view of the problem of the paralytic shellfish poison producing algae on-line measurement and identification, a new feature extraction method of paralytic shellfish poison producing algae measurement and identification based on quaternion principal component analysis (QPCA) is investigated. The three-dimensional (3D) fluorescence spectra of three common species of paralytic shellfish poison producing algae and eight species common of non paralytic shellfish poison producing algae are analyzed. The quaternion parallel representation model of algae three-dimensional fluorescence spectrum data is established, then the features of quaternion principal component is extracted to use as the input of k-nearest neighbor (KNN) classifier, and the identification of paralytic shellfish poison producing algae is realized by the three-dimensional fluorescence spectra coupled with quaternion principal component analysis. The results show that under the quaternion parallel representation model, the recognition accuracy rate of multiplication feature, modulus feature and summation feature is 90%, 95% and 100% respectively. Compared with that of the principal component analysis feature extraction method, the recognition accuracy rate in pure samples by summation feature of quaternion principal component is improved by 10%. This study provides an experimental basis for the accurate monitoring technology of three-dimensional fluorescence spectrum of paralytic shellfish poison producing algae.

[Multi Spectral Detection of Ethanol Content in Gasoline Based on SiPLS Feature Extraction and Information Fusion].

The ethanol content in ethanol gasoline was detected with ultraviolet/visible(UV/vis) and near-infrared (NIR) spectroscopy while information fusion technology and synergy interval PLS(SiPLS) algorithm were used as the feature extraction method with the establishment of partial least squares(PLS) regression model. Using the information fusion theory, UV/vis and NIR spectra were used for data fusion, the data level fusion (Low level data fusion, LLDF) and feature level fusion(Mid-level data fusion, MLDF) model were established. The results were compared with the single source modelwith low level data fusion before vector normalization(LLDF-VN1) selected for the optimal model. Finally, the optimal model was tested using the spectral data collected from the samples of high ethanol content and commercial gasoline. The results showed that both UV/vis and NIR can be used to detect and provide good prediction results, whereas direct fusion of the UV/vis and NIR spectral data provided the best results in the regression model based on the calibration set, with the highest correlation coefficient rc, the smallest Biasc and RMSECV values, as 0.999 9, 0.125 8 and 0.000 6, respectively. And the prediction effect of the model of LLDF-VN1(low level data fusion before vector normalization) was the best, r(p)=0.999 1，Bias(p)=0.352 7，RMSEP=-0.073 8. In the verification of the optimal model (LLDF-VN1) by the self distribution solution, rp=0.999 7, Bias(p)=0.102 2, RMSEP=0.329 1; and that for gasoline sold on market, r(p)=0.990 1, RMSEP=0.675 1, Bias(p)=0.892 7, respectively. It showed that the data level fusion based on UV/vis and NIR spectral information could be used to detect the content of ethanol in ethanol-gasoline quickly and accurately, achieving a wide range of ethanol concentration detection, which laid a foundation for further realization of the rapid detection of substances in the blended fuel oil.

[An Extraction and Recognition Method of the Distributed Optical Fiber Vibration Signal Based on EMD-AWPP and HOSA-SVM Algorithm].

Given that the traditional signal processing methods can not effectively distinguish the different vibration intrusion signal, a feature extraction and recognition method of the vibration information is proposed based on EMD-AWPP and HOSA-SVM, using for high precision signal recognition of distributed fiber optic intrusion detection system. When dealing with different types of vibration, the method firstly utilizes the adaptive wavelet processing algorithm based on empirical mode decomposition effect to reduce the abnormal value influence of sensing signal and improve the accuracy of signal feature extraction. Not only the low frequency part of the signal is decomposed, but also the high frequency part the details of the signal disposed better by time-frequency localization process. Secondly, it uses the bispectrum and bicoherence spectrum to accurately extract the feature vector which contains different types of intrusion vibration. Finally, based on the BPNN reference model, the recognition parameters of SVM after the implementation of the particle swarm optimization can distinguish signals of different intrusion vibration, which endows the identification model stronger adaptive and self-learning ability. It overcomes the shortcomings, such as easy to fall into local optimum. The simulation experiment results showed that this new method can effectively extract the feature vector of sensing information, eliminate the influence of random noise and reduce the effects of outliers for different types of invasion source. The predicted category identifies with the output category and the accurate rate of vibration identification can reach above 95%. So it is better than BPNN recognition algorithm and improves the accuracy of the information analysis effectively.

[Refractive Index Insensitive Temperature Sensor Based on Cascading Single Mode Fiber with Few Mode Fiber].

A refractive index insensitive temperature sensor is proposed base on cascading single mode fiber with few mode fiber(FMF). During the sensor preparation, the splicing current is set to 100 mA, and a section of FMF is no core-offset splicing between two single-mode fibers. Therefore, it can motivate the transmission mode preferably and form optical fiber Mach-Zehnder interferometer. The mode phase difference in FMF will be changed according to the outside environment. It will cause interference fringe shift. The parameter to be measured can be achieved by detecting the amount shift of interference spectrum. The FMF can transmit four modes with LP01, LP11, LP21, LP02. The transmission spectrum is also analyzed, which shows that they have two modes of LP01 and LP11 in sensor with the length of 81.5 mm. In the refractive index and temperature sensing experiment, the cascading FMF sensor with the length of 81.5 mm is used. The results show that the transmission spectrum of sensor appears obvious blue shift as temperature is increasing, the temperature sensitivity can be up to -85.9 pm·â„ƒ-1 within the range of 27.6~93.8 ℃ with good linearity. The refractive index sensitivity is 3.697 34 nm·RIU-1 within the range of 1.347 1~1.443 9. There is no obvious shift phenomenon in the transmission spectrum with the feature of refractive index insensitive. Therefore, compared with the traditional cladding mode and multimode interferometric fiber-optic sensor, the proposed sensor based on FMF is easier to control and analyze transmission mode has the advantages of simple structure, easy process and high sensitivity. It can avoid cross-sensitivity between temperature and refractive index measurement. Thus, it can be used for temperature detection of power system, biomedicine, aerospace and other fields.

[The High Precision Analysis Research of Multichannel BOTDR Scattering Spectral Information Based on the TTDF and CNS Algorithm].

Traditional BOTDR optical fiber sensing system uses single channel sensing fiber to measure the information features. Uncontrolled factors such as cross-sensitivity can lead to a lower scattering spectrum fitting precision and make the information analysis deflection get worse. Therefore, a BOTDR system for detecting the multichannel sensor information at the same time is proposed. Also it provides a scattering spectrum analysis method for multichannel Brillouin optical time-domain reflection (BOT-DR) sensing system in order to extract high precision spectrum feature. This method combines the three times data fusion (TTDF) and the cuckoo Newton search (CNS) algorithm. First, according to the rule of Dixon and Grubbs criteria, the method uses the ability of TTDF algorithm in data fusion to eliminate the influence of abnormal value and reduce the error signal. Second, it uses the Cuckoo Newton search algorithm to improve the spectrum fitting and enhance the accuracy of Brillouin scattering spectrum information analysis. We can obtain the global optimal solution by smart cuckoo search. By using the optimal solution as the initial value of Newton algorithm for local optimization, it can ensure the spectrum fitting precision. The information extraction at different linewidths is analyzed in temperature information scattering spectrum under the condition of linear weight ratio of 1:9. The variances of the multichannel data fusion is about 0.0030, the center frequency of scattering spectrum is 11.213 GHz and the temperature error is less than 0.15 K. Theoretical analysis and simulation results show that the algorithm can be used in multichannel distributed optical fiber sensing system based on Brillouin optical time domain reflection. It can improve the accuracy of multichannel sensing signals and the precision of Brillouin scattering spectrum analysis effectively.

[A Brillouin Scattering Spectrum Feature Extraction Based on Flies Optimization Algorithm with Adaptive Mutation and Generalized Regression Neural Network].

According to the high precision extracting characteristics of scattering spectrum in Brillouin optical time domain reflection optical fiber sensing system, this paper proposes a new algorithm based on flies optimization algorithm with adaptive mutation and generalized regression neural network. The method takes advantages of the generalized regression neural network which has the ability of the approximation ability, learning speed and generalization of the model. Moreover, by using the strong search ability of flies optimization algorithm with adaptive mutation, it can enhance the learning ability of the neural network. Thus the fitting degree of Brillouin scattering spectrum and the extraction accuracy of frequency shift is improved. Model of actual Brillouin spectrum are constructed by Gaussian white noise on theoretical spectrum, whose center frequency is 11.213 GHz and the linewidths are 40-50, 30-60 and 20-70 MHz, respectively. Comparing the algorithm with the Levenberg-Marquardt fitting method based on finite element analysis, hybrid algorithm particle swarm optimization, Levenberg-Marquardt and the least square method, the maximum frequency shift error of the new algorithm is 0.4 MHz, the fitting degree is 0.991 2 and the root mean square error is 0.024 1. The simulation results show that the proposed algorithm has good fitting degree and minimum absolute error. Therefore, the algorithm can be used on distributed optical fiber sensing system based on Brillouin optical time domain reflection, which can improve the fitting of Brillouin scattering spectrum and the precision of frequency shift extraction effectively.

[The nitrate and temperature impact analysis in the detection of COD by UV spectrum].

Configured standard solution of chemical oxygen demand with potassium hydrogen phthalate was used as experimental subjects, collected ultraviolet absorption spectra of the standard solution in the range of 1,800 mg x L(-1), were collected, and PLS (partial least squares) algorithm was used to establish the correction model of different spectral region, the results showed that. The model in the spectral region of 265-310 nm had the highest correlation and smallest error; In order to eliminate the impact of nitrates and temperature on the detection of the COD , studied the changes of the UV absorption spectrum with different concentrations of sodium standard solution and different temperature. The results showed that absorption of nitrate in 208-238 nm was apparent, and the model for spectral region of 265-310 nm was free from the influence of nitrate; In the full range of spectrum, temperature rising leads to an increase in absorbance, thus the temperature compensation model was established for the different spectral region through predictive analysis.

[Research on chemical oxygen demand optical detection method based on the combination of multi-source spectral characteristics].

A novel method based on multi-source spectral characteristics of the combination is proposed for chemical oxygen demand detection. First, the ultraviolet and near infrared spectrum of the actual water samples are collected respectively. After pretreatment of the spectrum data, the features of the spectrum are extracted by the nonnegative matrix factorization algorithm for training after normalization. Particle swarm and least squares support vector machines algorithm are applied to predicting chemical oxygen demand of the validation set of water samples. The effect of spectrum's base number on the predicted results is discussed. The experimental results show that the best base number of the ultraviolet spectrum is 5, the best base number of the near infrared spectrum is 2; The validation set correlation coefficient of the prediction model is 0.999 8, and the root mean square error of prediction is 3.26 mg x L(-1). Experimental results demonstrate that the nonnegative matrix factorization algorithm is more suitable for feature extraction of spectral data, and the least squares support vector machines algorithm as a quantitative model correction method of the actual water samples can get good prediction accuracy with different feature extraction methods (principal component analysis, independent component analysis), spectroscopic methods (ultraviolet spectrum method, near infrared spectrum method) and different combination pattern (data direct combination, combining data first, then feature extraction) respectively.

[The refractive index sensing characteristics of polarization maintaining microstructured optical fiber chirped grating].

The refractive index sensing characteristics of the polarization maintaining (PM) microstructured optical fiber (MOF) chirped grating was systematically investigated based on finite element method (FEM) and transfer matrix method (TMM). The chirp Bragg grating reflection spectrum was numerically analyzed with the fiber air holes injected with different refractive index medium, and the relation between the reflection spectrum area and the analyte refractive index is discussed here. The analysis results show that when the analyte refractive index increases, the reflection spectrum area will be reduced; and the detection demodulation is simplified with the light intensity demodulation. Moreover, the dependence of the reflection spectrum on the center big holes size, the chirp coefficient and the site function was studied. Since two polarization modes respond similarly to the outside perturbation, the fiber possesses high stability. The results provide the theoretical basis for the application of PM-MOF grating in the optical fiber refractive index sensor and the optical fiber label-free biosensing.

[The turbidity and pH impact analysis of low concentration water chemical oxygen demand ultraviolet absorption detection].

Configuration standard solution in the concentration range of 1 - 25 mg x L(-1) of potassium hydrogen phthalate was used as experimental subject, Ultraviolet absorption spectra was collected, the COD quantitative analysis model was established by partial least squares with different pretreatment methods and the turbidity of the compensation effect analysis was given. The results show the model uses smoothing first derivative pretreatment method, internal cross validation RMSECV root mean square value of 0.122 27, principal component number 4, the square of the prediction model correlation coefficient is 0.999 8, and the relative prediction error is in the range of 0.03%-1.7%; for 0-100 NTU's turbidity solution, the relative standard deviation RSD is 2.3% after compensation; with pH in the range of 3-10, influence can be ignored.

[Research on and application of hybrid optimization algorithm in Brillouin scattering spectrum parameter extraction problem].

This paper presents a novel algorithm which blends optimize particle swarm optimization (PSO) algorithm and Levenberg-Marquardt (LM) algorithm according to the probability. This novel algorithm can be used for Pseudo-Voigt type of Brillouin scattering spectrum to improve the degree of fitting and precision of shift extraction. This algorithm uses PSO algorithm as the main frame. First, PSO algorithm is used in global search, after a certain number of optimization every time there generates a random probability rand (0, 1). If rand (0, 1) is less than or equal to the predetermined probability P, the optimal solution obtained by PSO algorithm will be used as the initial value of LM algorithm. Then LM algorithm is used in local depth search and the solution of LM algorithm is used to replace the previous PSO algorithm for optimal solutions. Again the PSO algorithm is used for global search. If rand (0, 1) was greater than P, PSO algorithm is still used in search, waiting the next optimization to generate random probability rand (0, 1) to judge. Two kinds of algorithms are alternatively used to obtain ideal global optimal solution. Simulation analysis and experimental results show that the new algorithm overcomes the shortcomings of single algorithm and improves the degree of fitting and precision of frequency shift extraction in Brillouin scattering spectrum, and fully prove that the new method is practical and feasible.

[Quantitative analysis model of multi-component complex oil spill source based on near infrared spectroscopy].

Near infrared spectroscopy technology was used for quantitative analysis of the simulation of complex oil spill source. Three light petroleum products, i. e. gasoline, diesel fuel and kerosene oil, were selected and configured as simulated mixture of oil spill samples in accordance with different concentrations proportion, and their near infrared spectroscopy in the range of 8 000 -12 000 cm(-1) was collected by Fourier transform near infrared spectrometer. After processing the NIR spectra with different pretreatment methods, partial least squares method was used to establish quantitative analysis model for the mixture of oil spill samples. For gasoline, diesel fuel and kerosene oil, the second derivative method is the optimal pretreatment method, and for these three oil components in the ranges of 8 501.3-7 999.8 and 6 102.1-4 597.8 cm(-1); 6 549.5-4 597.8; 7 999.8-7 498.4 and 102.1-4 597.8 cm(-1), the correlation coefficients R2 of the prediction model are 0.998 2, 0.990 2 and 0.993 6 respectively, while the forecast RMSEP indicators are 0.474 7, 0.936 1 and 1.013 1 respectively; The experimental results show that using near infrared spectroscopy can quantitatively determine the content of each component in the simulated mixed oil spill samples, thus this method can provide effective means for the quantitative detection and analysis of complex marine oil spill source.

[Identification of spilled oil by NIR spectroscopy technology based on sparse nonnegative matrix factorization and support vector machine].

A novel method was proposed to discriminate different kinds of spilled oil. The identification of the spilled oils has great significance to developing the treatment program and tracking the source. The present method adapts to Fourier transform NIR spectrophotometer to collect the spectral data of simulation gasoline, diesel fuel and kerosene oil spills. The Sparse Nonnegative Matrix Factorization algorithm was used to extract features. Through training with 210 samples and 5-fold cross-validation, the authors constructed the qualitatvie analysis model based on support vector machine. The authors also researched the effect of the number of features and sparseness factor. The proposed method has the identification capabilities with the accuracy of 97.783 for 90 samples for validation. The present method of SNMF-SVM has a good identification effect and strong generalization ability, and can work as a new method for rapid identification of spilled oil.

[Determination of chemical oxygen demand in water using near infrared transmission and UV absorbance method].

Chemical oxygen demand (COD) is a synthetical indicator which represents the degree of organic pollution in water. The near-infrared (NIR) transmission and the UV absorbance method based on photoelectric detection technology and spectroscopy analysis have some advantages such as high precision, speed, non-contact, no secondary pollution etc compared to conventional wet chemical method. The NIR transmission spectra and UV absorbance spectra of standard solution configured with phthalate hydrogen potassium were collected respectively by MPA FTIR spectrometer (Bruker Optics Inc.) made in Germany and AvaSpec-2048-2 UV spectrometer (Avantes Inc.) made in Netherlands. After different pretreatment to the spectra, COD quantitative analysis model was established using partial least squares regression (PLS) and linear regression. The statistical analysis of COD quantitative model was implemented, and the result showed that UV absorbance method had a higher relevance but lower forecast accuracy and precision than NIR transmission method.

[Study on the determinants of prevalence in persons with overweight and obesity in rural areas of Kunming].

OBJECTIVE: This study was to explore the prevalence of overweight and obesity,and the effects of contextual and individual level determinants on them in the rural areas of Kunming city, China. METHODS: Shilin County was selected as the study site. Probability Proportional to Size (PPS) sampling method was used to select representative sample of 6006 residents aged 45 years and over from Shilin. Information was obtained from a cross-sectional survey on health. Data was analyzed using a multilevel logistic modeling. RESULTS: The prevalence rates of overweight and obesity were 12.10% and 2.15% in the study area. Males had a higher prevalence of overweight than females (13.60% vs. 10.71%). Similar situation was seen in the prevalence of obesity (2.82% vs. 1.52%). Both village level and individual level variables were associated with obesity, whereas only individual level variables were related to overweight. Elderly had lower probability of being overweight and obese than younger people with odds ratio (OR) as 0.95 (95% CI:0.83-0.97) and 0.93 (95% CI: 0.82-0.96), respectively. Males had higher probability of being overweight and obese than females: OR of 0.89 (95% CI:0.78-0.98) and OR of 0.87 (95% CI: 0.78-0.97),respectively. Individuals with lower family income had increased probability of having obesity (OR = 0.81, 95% CI: 0.73-0.95). Factor as living in a higher income village was associated with lower prevalence of obesity (OR = 0.92, 95% CI: 0.85-0.98). CONCLUSION: Interventions at village level on obesity in parallel with those at individual level were needed. Prevention and intervention on obesity should be emphasized in villages with higher income.