Purple Urine Bag Syndrome Amelioration without Antibiotic Therapy.

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High-sensitivity optical fiber probe for simultaneous measurement of chloride ions and temperature.

A fiber optic probe for the simultaneous measurement of chloride ions and temperature is presented. The Ag/alginate composite film is used as the reflective surface of the Fabry-Perot interferometer (FPI) and is a sensitive film for the adsorption of chloride ions. The experimental results show that the Fabry-Perot (FP) response sensitivity is approximately 1.4689 nm/µM as the chloride ion concentration changes from 1 to 9 µM, but the fiber Bragg grating (FBG) is insensitive to chloride ions. When the temperature is changed from 35°C to 80°C, the response sensitivities of the FP and the FBG are about 0.7 and 0.01115 nm/°C, respectively.

Antiepileptic drug concentration detection based on Raman spectroscopy and an improved snake optimization-convolutional neural network algorithm.

A method for measurement of antiepileptic drug concentrations based on Raman spectroscopy and an optimization algorithm for mathematical models are proposed and investigated. This study uses Raman spectroscopy to measure mixed antiepileptic drugs, and an Improved Snake Optimization (ISO)-Convolutional Neural Network (CNN) algorithm is proposed. Raman spectroscopy is widely used in the identification of pharmaceutical ingredients due to its sharp peaks, no pre-treatment of samples and non-destructive detection. To analyze the spectral data precisely, a machine learning method is used in this paper. The ISO algorithm is an improved intelligent swarm algorithm in which the method of generating random solutions is improved, which can ensure that a comprehensive local search of the model is performed, the global search capability is maintained at a later stage, and the convergence speed is accelerated. In this study, 360 groups of oxcarbazepine, carbamazepine, and lamotrigine drug mixtures are measured using Raman spectroscopy, and the raw spectral data after pre-processing are trained and evaluated using ISO-CNN algorithms, and the results are compared and analyzed with those obtained from other algorithms such as the Northern Goshawk Optimization algorithm, Chameleon Swarm Algorithm, and White Shark Optimizer algorithm. The results show that the best ISO-CNN algorithm training is achieved for oxcarbazepine, with a determination coefficient and root mean square error of 0.99378 and 0.0295 for the validation set, and 0.99627 and 0.0278 for the test set. The overall results suggest that Raman spectroscopy combined with machine learning algorithms can be a potential tool for drug concentration prediction.

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.

All-fiber LP01-LP11 ultra-broadband mode converters based on T-superimposed long period gratings in PCF.

In order to cover the bandwidth of optical fiber communication, a LP01-LP11 ultra-broadband mode converter based on triple superimposed long period grating in PCF is proposed and demonstrated. The transmission spectra of the D-SLPG with gratings pitches and the T-SLPG were simulated and analyzed. The simulation results on the D-SLPG indicate that the 3 dB bandwidth of the D-SLPG is more than 1.5 times than the 3 dB bandwidth of the independent LPG and the 3 dB bandwidth of T-SLPG approaches 2.6 times as much as the independent LPG. In the experiment, the mode converter based on PCF-T-SLPG covers the wavelength of S + C + L with 3 dB bandwidth of 121 nm from 1498 nm to 1619 nm. In addition, the mode converter based on PCF-T-SLPG can accomplish ultra-broadband transmission in any wavelength by adjusting the period of gratings.

Development and validation of a model to predict acute kidney injury following wasp stings: A multicentre study.

OBJECTIVE: To establish and validate a model to predict acute kidney injury (AKI) following wasp stings. METHODS: In this multicentre prospective study, 508 patients with wasp stings from July 2015 to December 2019 were randomly divided into a training set (n = 381) and a validation set (n = 127) for internal and external validation. Risk factors were identified, and a model was established to predict the probability of AKI following multiple wasp stings using an individual nomogram and a predictive formula. The performances of the model were assessed by using the area under the curve (AUC), accuracy (ACC) of the receiver operating characteristic curve and decision curve analysis. RESULTS: The number of stings, aspartate aminotransferase >147 U/L, lactate dehydrogenase >477 U/L, time from stings to admission >12 h and activated partial thromboplastin time >49 s were demonstrated to be independent risk factors for AKI following wasp stings (all P value < 0.05) and were incorporated into the model. The performances of the model were validated (AUC = 0.950 [95% CI: 0.923 to 0.969], ACC = 0.916 and AUC = 0.953 [95% CI: 0.900 to 0.982], ACC = 0.906 in the training set and validation set, respectively). The predictive formula and the nomogram of the model could be utilized to predict AKI following wasp stings, which have sufficient accuracies, good predictive capabilities and good net benefits. CONCLUSION: The predictive formula and the individual nomogram of the model might serve as promising predictive tools to assess the probability of AKI following wasp stings.

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.

Highly sensitive temperature sensing probes based on liquid cladding elliptical micro/nanofibers.

A highly sensitive temperature probe based on a liquid cladding elliptical micro/nanofiber is proposed, which exploits a fiber loop mirror with an output port probe for remote and highly-sensitive measurements based on evanescent field coupling. The thermo-optical effective liquid cladding avoids the influence of other environmental parameters (except for temperature), while protecting the micro/nanofibers from external disturbance and contamination. This renders the sensing probe only sensitive to temperature changes, making it suitable for real-world temperature measurements. An isopropanol cladding elliptical microfiber with a diameter of 3.4 µm demonstrated a sensitivity of -16.38 nm/°C for a remote temperature measurement.

[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.

Refractive index insensitive temperature sensor based on specialty triple-clad fiber.

A refractive index (RI) insensitive temperature sensor based on specialty triple-clad fiber (STCF) is proposed. Based on coupling mode theory, the STCF can be equivalent to a rod waveguide and two tube waveguides. Then the cladding mode resonance characteristic of STCF is analyzed by calculating different mode dispersion curves, which indicates that it works only on the mode resonance from core to the fluorine-doped silica cladding, and finally a resonance wavelength can be obtained. Two straightforward experiments are performed to prove its sensing properties. Experimental results show that it has sensitivities of 72.17 pm/°C at temperature range from 35°C~95°C with characteristics of insensitive to external RI in the range from 1.3450 to 1.4607. Thus, this proposed sensor can be used for solution temperature monitoring in real time.

[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.

Hyponatraemia in patients with crush syndrome during the Wenchuan earthquake.

BACKGROUND: Although sodium disturbances are common in hospitalised patients, no study has specifically investigated the epidemiology of hyponatraemia in patients with crush syndrome. OBJECTIVES: To describe the incidence of hyponatraemia and assess its effect on outcome in patients with crush syndrome during the Wenchuan earthquake. METHODS: A retrospective study was conducted in 17 reference hospitals during the Wenchuan earthquake. We excluded patients younger than 15 years and those with missing sodium values within 3 days after being rescued from the ruins. RESULTS: Hyponatraemia (serum sodium concentration <135 mmol/l) was seen in 91/180 (50.6%) patients on admission. Compared with patients with normonatraemia, those with hyponatraemia were younger, had more severe traumatic injury and renal failure, underwent more fasciotomies, received more blood transfusion and renal replacement therapy. In the multivariable-adjusted model, the number of extremity injuries (OR=1.59, 95% CI 1.08 to 2.33) and serum creatinine (OR=1.30, 95% CI 1.07 to 1.59) were independently associated with the occurrence of hyponatraemia. Covariate adjusted multiple logistic regression analysis showed an independent mortality risk rising with hyponatraemia (OR=5.74, 95% CI 1.18 to 28.00). CONCLUSIONS: Hyponatraemia was common in the patients with crush syndrome during the Wenchuan earthquake and associated with poor prognosis. Water, commercial drinks and hypotonic intravenous fluids should be supplied carefully to patients with crush syndrome.

[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.