Research on Quantitative Evaluation of Wax Deposition Based on Distributed Optical Fiber Sensing Signal Inversion

Mass measurement is the main outcome of mass spectrometry-based proteomics yet the potential of recent advances in accurate mass measurements remains largely unexploited. There is not even a clear definition of mass accuracy in the proteomics literature, and we identify at least three uses of this term: anecdotal mass accuracy, statistical mass accuracy, and the maximum mass deviation (MMD) allowed in a database search. We suggest using the second of these terms as the generic one. To make the best use of the mass precision offered by modern instruments we propose a series of simple steps involving recalibration of the data on "internal standards" contained in every proteomics data set. Each data set should be accompanied by a plot of mass errors from which the appropriate MMD can be chosen. More advanced uses of high mass accuracy include an MMD that depends on the signal abundance of each peptide. Adapting search engines to high mass accuracy in the MS/MS data is also a high priority. Proper use of high mass accuracy data can make MS-based proteomics one of the most "digital" and accurate post-genomics disciplines.

Wax deposition seriously affects the safe and economic operation of pipelines. Mastering the variation laws of wax deposition thickness is the premise of formulating reasonable pigging schemes. Although the GM (1,1) model (a kind of gray model) is an effective method for predicting wax deposition thickness on pipe walls, its prediction accuracy is easily affected by the smoothness of the original sequence. The improved GM (1,1) was established by introducing the idea of translation transformation, and an optimal weighted combination model based on the traditional gray model and a logarithmic function model was proposed. The differences in the predicted results of the established models were compared and analyzed through indoor wax deposition experimental data. The research results indicate that the optimal weighted combination model has the highest fitting accuracy, followed by the logarithmic function model and the improved GM (1,1), while the fitting accuracy of the traditional gray model is poor. When the number of modeling samples is five, the average relative error and root mean square error of the prediction results of the optimal weighted combination model are 1.313% and 0.021, respectively, which shows the highest prediction accuracy. When the number of modeling samples is six, the average relative error and root mean square error of the optimal weighted combination model are 2.143% and 0.031, respectively, and its prediction accuracy is still the highest. Overall, the optimal weighted combination model has the advantages of high accuracy and easy implementation, and has strong promotion and application value.

Stroke patients often suffer from spasticity. Before treatment of spasticity, there are often practical demands for objective and quantitative assessment of muscle spasticity. However, the common quantitative spasticity assessment method, the tonic stretch reflex threshold (TSRT), is time-consuming and complicated to implement due to the requirement of multiple passive stretches. To evaluate spasticity conveniently, a novel spasticity evaluation method based on surface electromyogram (sEMG) signals and adaptive neuro fuzzy inference system (i.e., the sEMG-ANFIS method) was presented in this paper. Eleven stroke patients with spasticity and four healthy subjects were recruited to participate in the experiment. During the experiment, the Modified Ashworth scale (MAS) scores of each subject was obtained and sEMG signals from four elbow flexors or extensors were collected from several times (4–5) repetitions of passive stretching. Four time-domain features (root mean square, the zero-cross rate, the wavelength and a 4th-order autoregressive model coefficient) and one frequency-domain feature (the mean power frequency) were extracted from the collected sEMG signals to reflect the spasticity information. Using the ANFIS classifier, excellent regression performance was achieved [mean accuracy = 0.96, mean root-mean-square error (RMSE) = 0.13], outperforming the classical TSRT method (accuracy = 0.88, RMSE = 0.28). The results showed that the sEMG-ANFIS method not only has higher accuracy but also is convenient to implement by requiring fewer repetitions (4–5) of passive stretches. The sEMG-ANFIS method can help stroke patients develop proper rehabilitation training programs and can potentially be used to provide therapeutic feedback for some new spasticity interventions, such as shockwave therapy and repetitive transcranial magnetic stimulation.

To improve the current indoor positioning algorithms, which have insufficient positioning accuracy, an ultra-wideband (UWB) positioning algorithm based on the Levenberg-Marquardt algorithm with improved Kalman filtering is proposed. An alternative double-sided two-way ranging (ADS-TWR) algorithm is used to obtain the distance from the UWB tag to each base station and calculate the initial position of the tag by the least squares method. The Levenberg-Marquardt algorithm is used to correct the covariance matrix of the Kalman filter, and the improved Kalman filtering algorithm is used to filter the initial position to obtain the final position of the tag. The feasibility and effectiveness of the algorithm are verified by MATLAB simulation. Finally, the UWB positioning system is constructed, and the improved Kalman filter algorithm is experimentally verified in LOS and NLOS environments. The average X-axis and the Y-axis positioning errors in the LOS environment are 6.9 mm and 5.4 mm, respectively, with a root mean square error of 10.8 mm. The average positioning errors for the X-axis and Y-axis in the NLOS environment are 20.8 mm and 18.0 mm, respectively, while the root mean square error is 28.9 mm. The experimental results show that the improved algorithm has high accuracy and good stability. At the same time, it can effectively improve the convergence speed of the Kalman filter.

Various models have been proposed to estimate the degree of backscatter in Synthetic Aperture Radar (SAR) images. However, it is still necessary to calibrate these models based on the characteristics of different study areas and to propose new models to achieve the highest possible accuracy in estimating the backscattering coefficient () SAR. In this study, three empirical models, including Champion, Sahebi and Zribi/Dechambre, were initially calibrated for two SAR datasets (i.e. The Airborne Synthetic Aperture Radar (AIRSAR) and Canadian Space Agency radar satellite (RADARSAT-1)) acquired over two bare soil study areas with various soil characteristics. The Zribi/Dechambre model was then modified by revising the roughness parameter to obtain higher accuracy in estimating over a larger range of incidence angles (θ). A new empirical model was also proposed by combining the four parameters of Soil Moisture (SM), standard deviation of surface height -root mean square- (rms), correlation length (l), and θ. To this end, the most appropriate form of the regression model was investigated and used for each of these parameters to obtain the highest correlation between the in-situ data and values. A comparison of the empirical models showed that the modified Zribi/Dechambre had the highest accuracy in predicting values with the Root Mean Square Errors (RMSE) of 1.20 dB and 1.59 dB over Oklahoma and Quebec, respectively. Furthermore, coefficients values of the new proposed model remained stable in the two datasets unlike the other investigated models. In this study, the effects of l on the accuracy of the new proposed model were also assessed. It was concluded that l had a considerable impact on the accuracy of the proposed model and including this parameter can improve the accuracy by up to 1 dB.

The system uses STC12C5A60S2 microcontroller as control core of the system, the use of special ADE7763 energy metering IC to collect data to construct a developed a high precision, small water treatment equipment data acquisition.Collect real-time data storage and transmission timing via GSM network to the host computer, the system has high accuracy, good stability.

Summary Accurate prediction of wax deposition is of vital interest in digitalized systems to avoid issues that interrupt the flow assurance during production of hydrocarbon fluids. The present investigation aims at establishing rigorous intelligent schemes for predicting wax deposition under extensive production conditions. To do so, multilayer perceptron (MLP) optimized with Levenberg-Marquardt algorithm (MLP-LMA) and Bayesian Regularization algorithm (MLP-BR) were established using 88 experimental measurements. The obtained results showed that MLP-LMA achieved the best performance with an overall root mean square error of 0.2198 and a coefficient of determination (R²) of 0.9971. The performance comparison revealed that MLP-LMA outperforms the prior approaches in the literature.

Objective To establish an online quantitative analysis model for moisture content assay of hydroxychloroquine sulfate particles by near infrared (NIR) spectroscopy. Methods The NIR spectra were collected in real time when the material particles were dried in the fluidized bed. Meanwhile the water content of the particles was measured with the standard moisture tester. The multiplicative signal correction (MSC) and first derivative followed by Karl Norris smoothing were used for spectra pretreatment. Two spectral range (4 935−5 336 cm−1 and 6 911−7 297 cm−1) were selected for the quantitative model with the partial least squares (PLS) regression. Results The quantitative calibration model had good correlation coefficients with Rc value=0.952 9 and Rp value=0.936 6. The root mean square error of calibration (RMSEC) was 0.408 and the root mean square error of prediction error (RMSEP) was 0.435. The ratio of standard deviation of validation set to prediction standard deviation (RPD) was 5.18. There was no significant difference between the predicted value and the reference value by t test when the established model was applied in large-scale production. Conclusion The online model established for monitoring water content has high accuracy and stability, which can be applied in industrial scale process to monitor the particle moisture in real time.

In order to solve the problems of inefficient allocation of teaching resources and inaccurate recommendation of learning paths in higher education, this paper proposes a smart education optimization model (SEOM) by combining the improved random forest algorithm (RFA) based on adaptive enhancement mechanism and the Graph Neural Network (GNN) algorithm. The public data and information such as the national higher education intelligent education platform are collected, and SEOM is trained and verified. The results show that SEOM has high accuracy and generalization ability in three different teaching scenes: online mixed teaching, personalized teaching and project-based teaching. The Root Mean Square Error (RMSE) value in cross-validation is between 0.2 and 0.5, and the Mean Absolute Error (MAE) value is between 0.1 and 0.5. SEOM shows strong stability when dealing with multidimensional educational resources and complex teaching modes. The accuracy rate remains at 85-97%, indicating its reliability in personalized learning path recommendation. Further analysis shows that the chi-square freedom ratio is between 1.0 and 2.5, the fitting index and the adjusted fitting index are both above 0.85, and the comparative fitting index is close to 0.95, which shows that SEOM has high accuracy and rationality in capturing the dependence of knowledge points in different teaching modes. The Root Mean Square Residual (RMR) and Root Mean Square Error of Approximation (RMSEA) are both below 0.05, which indicates that SEOM has small residual and strong scene adaptability. In addition, in the abnormal network environment, the resource allocation efficiency of SEOM is above 60%, and the Shapley value is between 0.1 and 0.4, which shows that SEOM can adapt to the change of network environment and the resource allocation effect is still obvious. Generally speaking, SEOM can optimize the allocation of educational resources and recommend learning paths in a complex environment, and effectively improve the intelligence and efficiency of teaching decision-making, especially for university administrators and educational technology developers.

Estimation of daily and total lactation milk yield of Chios ewes from single morning or evening records

Objective: The present study is concerning qualitative and quantitative detection of Poria cocos quality based on FT-near infrared( FT-NIR) spectroscopy combined with chemometrics. Method: The Poria cocos polysaccharides contents were determined by UV. Transmission mode was used in the collection of NIR spectral samples. The pretreatment method was first derivation and vector normalization. Then principal component analysis( PCA) was used to build classification model and partialleast square( PLS) to build the calibration model. Result: The results showed that conventional criteria such as the R,root mean square error of calibration( RMSEC),and the root mean square error of prediction( RMSEP) are 0. 944 0,0. 072 1 and 0. 076 2,respectively. the misclassifiedsample is 0 using the qualitative model built by PCA. Conclusion: The prediction models based on NIR have a better performance with high precision,good stability and adaptability and can be used to predict the polysaccharose content of Poria cocos rapidly,which can provide a fast approach to discriminate the different parts of Poria cocos.

The scientific and accurate prediction of suspended sediment concentrations is of great importance for river management in the lower reaches of the Yellow River and for the scheduling of water conservancy projects in the upper and middle reaches. In order to solve the influence of the non-linear and non-smooth characteristics of the suspended sediment concentration series in the lower Yellow River on the prediction results and improve the prediction accuracy, this paper proposes a coupled model based on Complementary Ensemble Empirical Mode Decomposition (CEEMD) and non-linear autoregressive (NAR) model. Take the predicted suspended sediment concentrations in the lower reaches of the Yellow River at the Huayuankou hydrographic station as an example. The accuracy and stability of the coupled CEEMD-NAR model were verified through the Gaocun and Lijin hydrological stations. The CEEMD-NAR model predicted suspended sediment concentrations with a Nash-Sutcliffe efficiency (NSE) factor of 0.93. The three statistical evaluation indicators of the CEEMD-NAR model, mean absolute error (MAE), mean relative error (MRE), and root mean square error (RMSE) were 2.12kg/m3, 1.07, and 3.75kg/m3 respectively. In contrast to the NAR, EMD-NAR, and EEMD-NAR models, the coupled CEEMD-NAR model has good stability and high prediction accuracy and can be used in non-linear, non-smooth suspended sediment concentration long series prediction.

In view of the problems that exoskeleton robots are always disturbed by the outside world during operation, resulting in deviation from the preset target position, this paper designs a spatial angle detection device, and proposes and constructs a spatial angle detection strategy and mathematical model based on the device. The device is prepared from a Bowden cable and four optical waveguides, which has the characteristics of flexibility and coupled deformation, and shows good anti-interference ability and stability. In this study, one optical waveguide is placed inside the Bowden cable, and three optical waveguides are closely attached to the Bowden cable in a circular array. When the device is bent, the bending angle and direction of the device are detected according to the voltage values and voltage differences between the three optical waveguides and the central optical waveguide. Through the bending angle calibration experiment, the mathematical model of the bending angle, bending direction and voltage value of the device is obtained, and the R-square is higher than 0.99. Finally, compared with the angle detected by IMU, the experimental results show that the root mean square error of the device is 7.20°, and the relative error is less than 14.40%. It has high accuracy and stability, and can real-time and efficiently detect the spatial angle between the joints of the exoskeleton robot.

With the ever increasing importance of testing drug quality, rapid analytical methods are needed for supervision of Chinese herbal medicines. Near-infrared spectroscopy is one of the most powerful tools in quality assessment of Chinese herbal medicines. In this work, near-infrared spectroscopy was applied to develop a rapid method for quantitative determination of typhaneoside and isorhamnetin-3-O-glucoside in different processed products of Pollen Typhae. A total of 71 batches of samples were collected from different regions in China. After acquisition of near-infrared spectra, different pre-processing methods were compared, and a competitive adaptive reweighted sampling algorithm was used to perform the variable selection. Then a partial least squares regression algorithm was applied to build the quantitative models. The root mean square error of calibration, root mean square error of cross validation, and root mean square error of prediction were 0.0190, 0.0364, and 0.0158%, respectively, for a quantitative model of typhaneoside. The root mean square error of calibration, root mean square error of cross validation, and root mean square error of prediction were 0.0190, 0.0377, and 0.0170%, respectively, for a quantitative model of isorhamnetin-3-O-glucoside. Moreover, the relative prediction deviation values of both quantitative models were larger than 3, indicating good performance of the partial least squares (PLS) models. The results demonstrated that high accuracy prediction of typhaneoside and isorhamnetin-3-O-glucoside could be obtained by near-infrared spectroscopy, to allow an alternative method for quality assessment of different processed products of Pollen Typhae.

The structure and intrinsic activities of conserved STAS domains of the ubiquitous SulP/SLC26 anion transporter superfamily have until recently remained unknown. Here we report the heteronuclear, multidimensional NMR spectroscopy solution structure of the STAS domain from the SulP/SLC26 putative anion transporter Rv1739c of Mycobacterium tuberculosis. The 0.87-Å root mean square deviation structure revealed a four-stranded β-sheet with five interspersed α-helices, resembling the anti-σ factor antagonist fold. Rv1739c STAS was shown to be a guanine nucleotide-binding protein, as revealed by nucleotide-dependent quench of intrinsic STAS fluorescence and photoaffinity labeling. NMR chemical shift perturbation analysis partnered with in silico docking calculations identified solvent-exposed STAS residues involved in nucleotide binding. Rv1739c STAS was not an in vitro substrate of mycobacterial kinases or anti-σ factors. These results demonstrate that Rv1739c STAS binds guanine nucleotides at physiological concentrations and undergoes a ligand-induced conformational change but, unlike anti-σ factor antagonists, may not mediate signals via phosphorylation.