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The first rare-earth borate crystal CsBa2 ScB8 O16 with both zero-dimensional [B3 O6] units and one-dimensional B--O chain structures has been synthesized by the high-temperature solution method. It crystallizes in the triclinic crystal system with the P1 space group, with lattice parameters of a =6.698 5 Å, b =7.216 Å, c =14.798 Å, α =97.563°, β =95.226°, γ = 95. 546°, Z = 2. In this compound, [BO3] and [B4 O9] units are connected by sharing oxygen atoms to form a onedimensional chain [B5 O10]∞. Octahedrally coordinated [Sc(1)O6] groups and the one-dimensional chain [B5 O10]∞ are further connected by sharing oxygen atoms to form two-dimensional layer [Sc(1) (B4 O9)]∞. Interlayers are linked by octahedrally coordinated [Sc(2)O6] groups, which share oxygen atoms to form a three-dimensional Sc--B--O framework. Isolated [B3 O6] units are filled in the three-dimensional channels with Cs+ and Ba2 + to balance the charges. In order to further explore the novelty of the structure of CsBa2 ScB8 O16, we compared it with other rare-earth borates containing alkali or alkaline-earth metals, and discuss the effect of the cation-to-boron molar ratio (n(A) / n(B)) on the degree of polymerization of B--O units, as well as the dimensionality of the B--O anion framework. Furthermore, the first-principles calculations, infrared spectrum, UV-vis-NIR diffuse reflectance spectrum and the thermal analysis of the compounds have also been performed. Property measurements show that CsBa2 ScB8 O16 exhibits short UV absorption edge (< 190 nm) and moderate birefringence (0. 072@1 064 nm). The optical properties of the compound are mainly contributed by the [B3 O6], [B5 O10] units, and [ScO6] octahedra. [ABSTRACT FROM AUTHOR]

Addressing the complexity of cigarette ingredients and the challenges in brand identification, a cigarette brand identification model based on Projection Pursuit based on Fisher's Criterion (PPF) and hyperspectral technology was proposed. Firstly, the PPF projection algorithm was employed to conduct spectral similarity analysis on the original spectra as well as those processed with Multiplicative Scatter Correction (MSC), first-order derivative (1st D), and second-order derivative (2ndD). Secondly, feature wavelength selection was carried out using Successive Projections Algorithm (SPA) and Genetic Algorithm (GA) to enhance the classification performance in subsequent steps. Finally, cigarette brand identification models were established using Support Vector Machine (SVM) and Extreme Gradient Boosting (XGBoost) algorithms, and the personality of the model was evaluated. The results indicated that, in the combination of hyperspectral technology and the PPF projection algorithm, the second-order derivative (2nd D) served as the optimal band processing method, significantly enhancing the spectral separability. The recognition model established based on 2ndD-SPA-SVM emerged as the best model, achieving an overall classification accuracy of 94. 58% and 92. 50% for the training and test sets respectively. Moreover, a greater difference in the proportion of leaf silk and expanded silk among different brand formulations led to a lower similarity value between categories and a higher recognition accuracy. This model can provide a new efficient, fast, accurate, and non-destructive classification and discrimination method for cigarettes of different grades. It offered theoretical support for the application of hyperspectral technology in cigarette brand maintenance, tobacco blend formulation design, and other scenarios. [ABSTRACT FROM AUTHOR]

Pest and diseases have seriously threatened to the yield and quality of citrus. This study aims to explore the effect of citrus diseases on SPAD value using identification model by near-infrared spectroscopy with chemometrics. Leaf spectra and physicochemical indexes were further determined to detect the citrus diseases. The NIR spectra of citrus leaves with normal, canker and emery derma were obtained to measure the physicochemical indices by portable NIR spectrometer. The spectrum analysis showed that there was a reflection peak in the leaves near 553 nm, indicating the reflection peak after the chlorophyll saturated the high-frequency absorption of light. The maximum reflection peak of normal leaves was about 0.21, whereas, the lowest was about 0.13 in the leaves with the sand skin disease. The reflectance of normal leaves was higher than that of diseased leaves at 750-900 nm. The reason was that the reflectance decreased to enlarge the leaf cell cavities under disease stress. The variance was analyzed for two physical and chemical indexes. There were significant differences in the SPAD value indexes of the three types of citrus leaves, while no significant difference was found in fresh quality indexes. A qualitative and quantitative model of citrus diseases was also established to clarify the relationship with leaf spectrum. According to the ratio of 2:1, 212 blades were randomly divided into two dataset with 145 samples in training set and 67 samples in verification set. The SPA and PCA were used to screen the spectral variables. 17 and 20 optimal variables were selected to combine the RF and LWPLS. The comparison showed that the best detection was achieved in the qualitative model of LWPLS for three types of disease leaves using 401 variables of the original spectrum, with the accuracy of 94.03%. While the low accuracy of 77.61% was found in the qualitative model of RF for three types of disease leaves using SPA screening 17 variables. The main reason was that a few sensitive variables were eliminated, although the characteristic variables represented the most information of citrus leaves. A numerical model was also constructed to further verify whether the leaf diseases affected the SPAD value of normal and diseased leaves. The best performance was achieved in the LWPLS model in the normal blade SPAD value analysis with 401 spectral variables as the input. The RMSEP of LWPLS quantitative model for normal, canker and emery was 4.46%, the model had better prediction accuracy for SPAD value of leaves, R² and RPD were 0.93 and 2.19, respectively. NIR spectroscopy with chemometrics was feasible to identify the citrus diseases on physicochemical indexes of leaves. The finding can provide an important reference for the real-time field detection of citrus diseases. [ABSTRACT FROM AUTHOR]

The rapid development of modern completely artificial light-controlled plant growth requires the light source increasingly stringent. Blue-violet phosphor with high luminous intensity and good thermal stability is the key material for plant growth. Herein, narrow-band blue-violet Zn3B2O6 : xBi3+ (0 ≤x ≤0. 03) phosphor with excellent thermal stability was synthesized by solid-state reaction. The results of XRD and energy spectra analysis show that Bi3+ successfully entered Zn3B2O6 lattice. The fluorescence spectrum of Zn3B2O6 : Bi3+ phosphor shows narrow-band blue-violet light at 430 nm (³P1~¹S0), and the full width at half maximum is only 56 nm. The optimal Bi3+ doping concentration is 0.02. According to the excitation spectrum shape and the lifetime decay behavior, it is proved that Bi3+ only occupies Zn sites in Zn3B2O6 . The emission peak intensity and integrated area of Zn3B2O6: 0.02Bi3+ phosphor at 423 K are both 85% of those at room temperature, indicating that the sample has excellent thermal stability and may have potential application in high-temperature devices. The emission spectrum of Zn3B2O6 :0.02Bi3+ phosphor accounts for 70.4% and 84.6% of the absorption spectra (370~525 nm) of chlorophyll a/ b, respectively, indicating the potential application of as-prepared blue-violet phosphors in the field of plant growth. [ABSTRACT FROM AUTHOR]

Objectives: Length of day variation (ΔLOD) predictions play an important role in tracking and navigation of deep-space detector, precise determination of artificial satellite orbit and climate forecasting. In full consideration of the time-varying characteristics of ΔLOD, we present the application of a hybrid technique for predicting ΔLOD. Methods: The ΔLOD predictions are generated by means of the combination of singular spectrum analysis (SSA) extrapolation for the linear trend, annual and semiannual oscillations in ΔLOD based on an iterative interpolation strategy, and autoregressive integrated moving average (ARIMA) stochastic prediction of SSA remaining residuals, referred to as SSA+ARIMA. In order to evaluate the effectiveness of this approach, the ΔLOD predictions up to 365 d into the future are calculated year-by-year for the 2-year period from Jan. 1,2000 to Dec. 31, 2001 using the data covering the previous 10 years from International Earth Rotation and Reference Systems Service C04 series. The prediction results are analyzed and compared with those obtained by machine learning methods such as back propagation neural network (BPNN), general regression neural network (GRNN) and Gaussian process (GP). Results: It is shown that the accuracy of the predictions are better than that by machine learning methods in terms of the mean absolute error (MAE) of predictions, especially for medium and long-term predictions. Compared with the predictions obtained by the BPNN, GRNN and GP, the MAE of the proposed SSA+ARIMA predictions up to 30 d and 365 d in future is reduced by 39% and 61%, respectively. Conclusions: The proposed method provides a new solution for ΔLOD prediction. [ABSTRACT FROM AUTHOR]

First, polyacrylonitrile/polyvinyl pyrrolidone (PAN/PVP) fibrous membrane was prepared by electrospinning, and then porous polyacrylonitrile (PPAN) fibrous membrane was obtained by water impregnation. Fourier transform infrared (FTIR) spectra and thermogravimetric analysis (TGA) were used to explore the mechanism of fiber pore-forming, and X-ray photoelectron spectroscopy (XPS) was used to study the interaction between PAN and PVP molecules in the porous fibrous membrane. At the same time, the effects of mass ratio of PAN to PVP on the morphology, surface area, wettability, mechanical properties, and oil/water separation performance of fibrous membrane were investigated, and the optimal mass ratio was determined. Results showed that when m(PAN)/m(PVP) was 1∶2, PPAN fibrous membrane showed higher mechanical properties. The separation flux of the n-hexane/water mixture was (46 318 ± 3 879) L/(m2·h·bar) (1 bar=0.1 MPa), and the efficiency was (96.01 ± 0.38)%. It also realized the efficient separation of different kinds of oil/water mixtures. In addition, PPAN fibrous membrane showed excellent cyclic separation performance, and the flux loss rate was only 8.9% after ten cycles of separation. [ABSTRACT FROM AUTHOR]

BACKGROUND: Chameleon diamond is a kind of color diamond with photochromic and thermochromic phenomenon. If the chameleon diamond is placed in a dark room or heated for a long time, it can change from greenish yellow to fancy yellow, and return to its original color when the diamond cools. Previous studies on chameleon diamond mainly focus on spectral characteristics. Through testing a large number of samples, the gemological and spectral characteristics of chameleon diamond are summarized. The characteristics of chameleon diamond, including persistent yellow phosphorescence, 480nm absorption band, mainly the A-aggregate of nitrogen, and proposed a model to explain the thermochromic and photochromic phenomenon of chameleon diamond.The characteristics of chameleon diamond discovered by previous studies are also found in a few yellow diamonds. Diamonds colored by the 480nm band shows yellow fluorescence and phosphorescence under short wave ultraviolet light, low nitrogen content, high concentrations of defects related to hydrogen and nickel, but no obvious thermochromic and photochromic phenomena. Previous studies of the chameleon diamond and similar yellow diamonds were separated and did not compare the two similar diamonds. OBJECTIVES: To ascertain the difference of spectral characteristics and defect types between chameleon diamonds and similar yellow diamonds and then analyze the causes of thermochromic phenomenon of chameleon diamond, and have a deeper comprehend of the possible structure of the center responsible of the chameleon effect. METHODS: Firstly, two chameleon diamond samples and four similar yellow diamond samples were collected to observe the color changes of the diamonds before and after heating. Top illumination and scattering illumination were used to observe the internal characteristics and color distribution of the samples. The fluorescence and phosphorescence characteristics of diamonds at long wave （365nm） and short wave （254nm） were observed by ordinary fluorescent lamp. The fluorescence and phosphorescent characteristics and growth structure of the samples under ultra-violet light were observed by Diamond View MT. Secondly, in order to investigate the causes of thermochromism in chameleon diamond, the absorption characteristics of diamond samples in the range of ultraviolet visible light were collected before and after heating, and compared with those of similar yellow diamonds. Analyze the differences in absorption spectra between chameleon diamond and similar yellow diamond, explore the causes of thermochromic phenomena. Finally, infrared spectrometer was used to collect the infrared spectrum of diamond samples and analysis of the types and impurity elements of diamond samples. Through the comparative analysis of the infrared spectrum of samples, the differences in the types and contents of nitrogen and hydrogen impurities between chameleon diamond and similar yellow diamond were obtained.The laser Raman spectrometer was used to collected PL spectra of the samples at liquid nitrogen temperature by using 473nm,532nm and 830nm lasers. By comparing the PL characteristics of chameleon diamond and similar yellow diamond, the differences between the two defects were obtained. RESULTS: The color distribution of the 6 diamond samples was uniform. After heating, the chameleon diamond samples changed color from yellow-green to yellow, similar yellow diamonds had no obvious color change. Under long and short wavelength UV light, the samples were yellow fluorescence without phosphorescence. The performance of the samples in this study is consistent under long and short wavelength ultraviolet light, but it is different from the previous study results that the chameleon diamond has persistent yellow phosphorescence, which further indicates that the chameleon effect is not directly related to the phosphorescence. Under ultra-violet light, the samples were all green fluorescence with irregular patterns which were markedly similar to diamonds colored by the480nm band. Chameleon diamonds showed moderate green phosphorescence. All the samples have C defect absorption continuum combines with of 480nm absorption band. The specific wide absorption band of 650-800nm is obvious in chameleon diamond. After being heated, the 480nm and 650-800nm absorption band of chameleon diamond completely disappeared. However, the 480nm absorption band of similar yellow diamonds is weakened or has no obvious change. Therefore, the poor thermal stability of 480nm and 650-800nm absorption band is responsible for the thermochromic phenomenon of chameleon diamonds. The infrared absorption characteristics of chameleon diamond mainly show A-center absorption, isolated nitrogen and hydrogen-related features can be seen. In addition to the characteristic 1430cm-1 wide absorption which has a different origin than the platelet-related1430cm-1 feature in Cape diamond. The similar yellow diamond has higher nitrogen content, and the degree of nitrogen aggregation is higher, and does not show any type Ib character. The broadband emission of all samples occurred mainly as one band centered at 700nm, this band consists of a dozen of peaks from 595nm to 725nm in steps of about 10nm.Additionally sharp peaks at 753,771,799,818,838,845nm present in the chameleon diamond samples. This spectrum is virtually identical to that reported previously for diamonds containing the 480nm absorption band. Chameleon diamonds and similar yellow diamonds all contain nitrogen-vacancy, nickel-nitrogen related defects, but the amount of defects are different. Chameleon diamonds cannot be effectively distinguished based on the PL spectral. The PL spectrum analysis showed that the nickel impurity was responsible for the green fluorescence of the samples under ultra-violet light. CONCLUSIONS: This study focuses on the comparative analysis of gemology, UV-visible absorption, infrared absorption and photoluminescence characteristics of chameleon diamond and its similar yellow diamond. Chameleon diamond is Ia diamond, the total nitrogen, as well as the A-center concentration, is relatively low, and show some type Ib character, such as 1240cm-1 absorption. Chameleon diamond has 480nm and 650-800nm absorption bands, that were responsible for chameleon effect. The characteristics of chameleon diamond in this study are roughly same as those in previous studies. Absorption bands of 650-800nm and absorption peaks of1430cm-1 （unrelated to platelet） are the main differences between the chameleon diamond and similar yellow diamond sample. It is inferred that the absorption band of 650-800nm and the absorption peak of 1430cm-1 are related to the center causing the thermochromic phenomenon. Although similar yellow diamond samples also have 480nm center, their thermal stability is different from that of chameleon diamonds. The reason for this phenomenon is unknown, therefor further studies are needed. [ABSTRACT FROM AUTHOR]

Objectives: Backscatter coefficient (σ 0) is one of observations of satellite radar altimetry, which is widely used in the processes of surface state monitoring, snow thickness inversion, data calibration and verification of satellite altimeters, and other fields. The geophysical data record data of Jason-2 is used to extract and isolate the Ku-band σ 0 data of the Tibetan Plateau (TP). Methods: Taking the GlobeLand30 2020 version data as the basis for surface classification, σ 0 is given surface attributes by latitude and longi‑ tude data. And we obtain the time-varying sequences of σ 0 under different types of surface features from De‑ cember 2008 to September 2016. The singular spectrum analysis principle is used to extract the σ 0 time change trend and period information, and the period results are analyzed by fast Fourier transform. Results: The results show that the σ 0 is higher in waters and wetland areas, and is lower in permanent snow and ice areas. There are multiple period signals of σ 0 in the TP. Conclusions: The surface properties of the artificial surfaces, bare land, and shrubland area are stable, and the annual σ 0 change is not significant. The other re‑ gions have significant annual and semi-annual cycles of σ 0 variability, and the amplitude of variability is not consistent across regions, with different regions corresponding to different changes in σ 0 trends. [ABSTRACT FROM AUTHOR]

Global navigation satellite system reflectometry(GNSS-R)has shown great potential in surface and marine environment parameters monitoring, with the advantages of extensive data sources, low cost, high spatial and temporal resolution, and has become an important approach for sea surface height(SSH)inversion. The existing research mainly focuses on the short-term GPS sea surface inversion within 3-6 months, which is difficult to reflect the seasonal variation and interannual characteristics of SSH. Meanwhile, only the influence of vertical velocity is considered in the dynamic sea surface correction, and the vertical acceleration of sea surface fluctuation is ignored, resulting in the poor estimation accuracy of low and high tide levels. For the above issues, a shore-based tracking station BRST in France was taken as an example, and the reflected signals of 3 consecutive years from BDS/GPS/GLONASS/Galileo system were used to estimate SSH. Based on Lomb-Scargle spectrum analysis and second-order dynamic tide level correction model, a robust regression strategy was adopted. The estimation results were compared with tide gauge records to analyze the trend of sea level change. The results show that the inversion results of GNSS-R technology are in good agreement with tide gauge records, and the inversion accuracy has a tendency to improve year by year, with a root mean square error(RMSE)of 7.57 cm and a correlation coefficient of 0.935 for the three-year daily average; the seasonal variation of SSH is obvious, the average SSH is high in autumn and winter, and low in summer, and the seasonal variation of SSH is opposite to that of temperature; the differences of amplitude for nine tidal constituents including M2, S2, K1, O1, N2, K2, P1, Q1 and M4, range from 0.06 cm to 6.76 cm with the mean absolute error(MAE)of 1.60 cm, and the differences of phase range from 0.03° to 6.96° with the MAE of 2.45°. It is further verified that the reliability of GNSS-R technology in monitoring SSH change in the frequency domain. [ABSTRACT FROM AUTHOR]

This paper considered the Schrödinger equation with different types of nonlocal integral term coefficients. By using the algebraic analysis method and the spectrum theory of differential equations, the existence of solutions was transformed into the existence of roots for some algebraic equations, the shape of the corresponding curve for the solution of the one-dimensional case. Compared with the results on the existence of solutions in the references, improved results were derived with the help of simpler methods. In order to promote the combination of theory and practice, as the auxiliaries, some curves of solutions were set in the progress of proving main results. [ABSTRACT FROM AUTHOR]

Single-view 3D reconstruction based on deep learning is a research hot spot at present. In order to discover more high-frequency details, SDF-SRN algorithm introduces positional encoding, but neural network is easy to overfit without accurate supervision, and reconstructs uneven surface. To solve the problem, this paper proposed the network model based on sparse feature. The model enabled the network that preferred to overfitting to predict high-frequency residual by residual learning. The feature extraction network extracted sparse features and the global features. Then one hypernetwork took the sparse features as input and generated prediction shallow head. This shallow head predicted low-frequency part of signed distance function. Another hypernetwork took global features as input and generated another shallow head. This shallow head predicted high-frequency residual. It fused two predictions of shallow heads into final signed distance function. Spectrum analysis shows that the design purpose of network is achieved. Compared with other smooth surface reconstruction schemes, the network can achieve smoother surface reconstruction with enough details. It overcomes the overfitting of SDF-SRN. The qualitative and quantitative comparison with other advanced single-view reconstruction approaches show the superiority of the proposed approach. [ABSTRACT FROM AUTHOR]

Gear fault detection based on vibration analysis has been proven to be effective in fault identification. However， the extraction and identification of vibration signals characterizing early wear have not been solved well. This paper proposes a method for early wear diagnosis of gears， combining variational mode decomposition (VMD) based on spectrum correlation analysis with kernel support vector machine (SVM). For weak gear vibration signals that can characterize early wear， the modal numbers are initialized by an approximate complete reconstruction criterion. Meanwhile， the frequency corresponding to the maximum value of the signal power spectral density is used to initialize the center frequency of the VMD method. It is used to effectively extract gear wear information from gear vibration signals and then be combined with kernel support vector machines for early wear diagnosis of gears. The experimental results show that the proposed method can effectively overcome the problem that the modal numbers cannot be preset with large background noise， be with better robustness to noisy situations， and achieve a diagnostic accuracy of 94.4%， which provides a solution for early wear detection of gears. [ABSTRACT FROM AUTHOR]

Aiming at the urgent demands of intelligent fault diagnosis for the key components of helicopter main reducers， the dynamic response characteristics of the system under the typical fault state are analyzed from the perspective of dynamic modeling， which is conducive to deepening the understanding of fault mechanism and advanced diagnosis algorithm. The fault dynamic models considering multi-nonlinear factors are developed for the typical components of a helicopter main reducer using the lumped-parameter method， and the multi-body software is applied for simulation modeling and verification. The results show that the modulation phenomenon between meshing frequency and local fault characteristic frequency is clear and the circumferential responses of the planet gear are obviously greater than the translational responses. these models can effectively reveal the vibration characteristics under typical faults and provide guidance for fault diagnosis of the helicopter transmission system. [ABSTRACT FROM AUTHOR]

Based on water stage prediction-correction method, an 1D-3D coupled method is proposed to accurately simulate the surge wave caused by the transient process of hydropower stations in open channel, overcoming the inability of low-dimensional models to fully resolve the horizontal and vertical fluctuation of the surge wave, and low computational efficiency of high-dimensional models. Typical verification cases, including the tidal wave motion, the partial dam break flow and the open channel transient flow are simulated to analyze the accuracy of the proposed method. The simulated results are in good agreement with asymptotic theoretical solution and those yielded by commercial software. To futher demonstrate its capability in practical cases, the surge wave triggered by the transient process of the Gezhouba hydropower station is simulated. The maximum and minimum surge wave heights including its attenuation characteristics are analyzed. The results are in good agreement with those yielded by spectrum analysis, showing that the proposed method is capable of simulating practical transient flows in open channel. [ABSTRACT FROM AUTHOR]

This paperanalyzed the relationship between the frequency phase difference of the ultrasonic transducer and the offset value.Considering the fact thatthe classic controller constructed bythe time domain model has not high applicability, this research proposed a frequency tracking system of ultrasonic power supply based on the fuzzy PI control.Also, a time-domain model for molding the current and voltage at both ends of the transducer was presented.The phase difference between the two can be obtained by spectrum analysis methodandthe simulation analysis of ultrasonic power frequency trackingcan bemade.The ultrasonic power frequency tracking system was constructed and analyzed.The initial resonance frequency wasmeasured under normal working voltage.Simulation analysis and experimental data showed the effectiveness of the method.This study found that compared with the traditional control method, the fuzzy PI method can improve the control accuracy of the system, and consequently it can improve the service life and operating efficiency of the ultrasonic transducer. [ABSTRACT FROM AUTHOR]

Aiming at the problem of phosphorus emission pollution caused by the widespread use of phosphorus-based corrosion and scale inhibitors in industrial production circulating cooling systems, maleic anhydride is used as the main raw material, calcium hydroxide is used as the initiator, and a “one-step” synthesis process is used to synthesize a phosphorus-free corrosion and scale inhibitor, through infrared spectrum analysis and nuclear magnetic resonance spectrum analysis, which shows that the synthesized phosphorus-free corrosion and scale inhibitor is polyepoxysuccinic acid(PESA). Through infrared spectrum analysis and nuclear magnetic resonance spectrum analysis, it is shown that the synthesized product is PESA. The corrosion and scale inhibition performance of PESA under different conditions was studied by rotating coupon method and calcium carbonate deposition method. The results show that the dosage of PESA is 110 mg/L, the circulating water temperature is lower than 45 ℃, and the pH is neutral or alkaline. Under normal conditions, when the water flow speed is less than 85 r/min, the corrosion rate of PESA to A3 carbon steel is less than 0.248 mm/a, the maximum corrosion inhibition rate can reach 60.9%, and the maximum scale inhibition rate is nearly 100%. At the same time, the corrosion and scale inhibition mechanism of PESA was analyzed by means of conductivity meter, infrared spectroscopy, scanning electron microscope, etc. The results show that PESA achieves scale inhibition performance through dispersion, chelation and lattice distortion. A protective film is formed on the surface to prevent the transfer of corroded electrons, thereby preventing metal corrosion. [ABSTRACT FROM AUTHOR]

The surface modification of cordierite was carried out with mullite whiskers grown by the molten salt method. Using cordierite/mullite whiskers composite material as the matrix, LaCoO3 monolithic cDPF (C/M/LC) and LaCo0.9Mn0.1O3 monolithic cDPF (C/M/LCM) was prepared by the sol-gel impregnation method. The monolithic catalysts were characterized by means of microscopy (SEM),energy spectrum analysis (EDS),X-ray diffraction (XRD),H2 temperature programmed reduction (H2-TPR) and other means. The results showed that the catalyst was uniformly distributed on the support, and the introduction of Mn can significantly improve the catalytic activity of the catalyst without changing the structure of the perovskite itself. The ultrasonic vibration treatment showed that the catalyst layer and the carrier were firmly bonded, and after 60 min of ultrasonic treatment, the weight loss was 4.6%.The performance test results show that among the prepared catalysts, LaCo0.9Mn0.1O3 monolithic cDPF has higher catalytic activity for soot, and its T10,T50 and T90 are 329,417,516 ℃ respectively. [ABSTRACT FROM AUTHOR]

BACKGROUND: Through visiting the jadeite market, the existence of inorganic material-filled jadeite is known, but there is a lack of identification basis due to rare references. OBJECTIVES: To understand the identification characteristics of inorganic material-filled jadeite. METHODS: Two types of inorganic materials, water glass and silica sol, were used to fill low-grade jadeite in the simulation experiment. Conventional gemological tests, fluorescence image observation with DiamondViewTM, Fourier transform infrared (FTIR) spectroscopy, and laser-induced breakdown spectroscopy (LIBS) were used to test the inorganic filled jadeite samples. RESULTS: The transparency, color, density and structure of jadeite were improved after filling. Under the DiamondViewTM, the jadeite particles in the sample showed green fluorescence, and the filling around the cracks and between the particles displayed blue fluorescence with non-uniform distribution. Mid-infrared reflectance spectrum analysis showed that the spectra of silica sol and water-glass were slightly different from each other. The absorption peaks of the samples filled with inorganic materials at 1162 cm-1, 1070 cm-1, 949 cm-1, 579 cm-1, 529 cm-1 and 470 cm-1 gradually weakened, and the peak shape gradually became smooth or disappeared. In addition, the differences between the jadeite and inorganic filled jadeite can be determined by the near-infrared spectral morphology and the shape of the absorption peak changed in the range of 7062 cm-1, 5204 cm-1 and 4537 cm-1. The laser-induced breakdown spectroscopy demonstrated that the content of the silicon in the jadeite filled with silicon sol or sodium and potassium water-glass was higher. The high potassium content was an important feature for the jadeite filled by sodium and potassium water-glass. CONCLUSIONS: The samples effect of the simulation experiment needs to be improved, but the identification characteristics of the filled jadeite with inorganic materials are recognized, which has caused a further breakthrough in the enhancement and treatment of jadeite identification. [ABSTRACT FROM AUTHOR]

Many existing arrhythmia researches focus on the separation of different frequency characteristic components in the ECG signal. However, the contribution of different subsequences to the final target decision-making is lack of research and analysis. In order to enhance the impact of high-contribution subsequences on the classifier, a recognition method combining variable weight singular spectrum analysis and deep learning is proposed. Multiple subsequences are obtained through singular spectrum analysis. The Gini coefficient under the random forest is calculated by the singular value of each sequence and used as the weight. The sequence samples with variable weights are used to train the neural network model, which can mine useful information more efficiently and further improve the recognition accuracy. The accuracy rate of final arrhythmia recognition is 98. 35%, and Macro-Fl is 97. 95%. Compared with the traditional fixed weight, the proposed recognition method of variable weight has a significant improvement in various performance indicators. [ABSTRACT FROM AUTHOR]

Objectives: There are obvious seasonal variations in the GPS height time series, which affect the improvement of precision and can be corrected by both mathematical modelling and geophysical mechanisms. Compared to least square fitting, singular spectrum analysis (SSA) can extract random seasonal signals effectively through signal reconstruction, which is unaffected by the assumed sinusoidal waves. According to the elastic loading theory, the gravity recovery and climate experiment (GRACE) can be used to calculate the vertical surface displacement caused by changes in terrestrial water storage. Methods: This paper mainly studies the feasibility of correcting the seasonal variations in GPS heights using SSA and GRACE inversion results. The height time series of 27 GPS stations in Australia with a time span of from 5 to 10 years were chosen and combined with GRACE simultaneous inversions. Results: Because the spatial resolutions of GRACE are coarse and the loading displacement is much more sensitive to near‑field mass changes than far‑field ones, the amplitudes of GRACE ‑inferred hydrological loading deformations are significantly smaller than GPS. The weighted root mean square (WRMS) are reduced at 22 stations after GRACE ‑inferred displacement corrections, and the correlation coefficients between deformations estimated by GPS and GRACE range from 0.12 to 0.78 with a mean value of 0.43, indicating that GPS and GRACE results have good consistency and correlation. SSA is used to extract the annual signals of vertical displacements derived from GPS and GRACE, and contribution rates of singular spectral variance of annual signals are 21.60% and 34.48%, respectively, expressing that annual signals are the main components of GRACE inferred results. Geographical climatic conditions have a significant impact on the consistency of annual signals derived from GPS and GRACE. Compared with the arid areas in central and western Australia, the amplitude and phase of annual signals derived from GPS and GRACE are more consistent in the northern region with seasonal rainfall. Furthermore, cross wavelet transform (XWT) finds that the vertical displacement series derived from GPS and GRACE of each station have a significant resonance period of one year. The circular average phase angles of GPS/GRACE at the period closet to 1 cycle per year (cpy) outside the cone of influence range from − 74.03° to 67.23° . The mean XWT ‑ based semblances range from 0.28 to 0.99 with an average value of 0.79, showing that there is a significant positive correlation between the annual variations derived from GPS and GRACE. Conclusions: Overall, GRACE‑inferred deformations can explain the annual variations of GPS‑derived displacements, particularly in areas with high hydrological loading. It is possible to correct the annual signals of GPS heights by GRACE inversions, but the effect is not as good as the SSA‑filtered annual signals. [ABSTRACT FROM AUTHOR]

In order to study the influence of different delay times on underground shallow hole blasting, numerical simulation software LS-DYNA was used to study the operation mode of underground shallow hole blasting with precise delay and hole-by-hole initiation. Based on the research status at home and abroad, five kinds of continuous uncoupled charged with different delay times of 15,20,25,30 and 35 ms were used for shallow-hole blasting. The field fine delay by hole initiation test was carried out. The blasting vibration velocity was collected by blasting vibration tester, and the velocity waveform was explored and analyzed. It was found that the vibration velocity peak at 25 ms with delay time decreased by 27%,8%,29% and 52% respectively, compared with the peak velocity at 15,20,30 and 35 ms. The research shows that the delay time is 25 ms, and the vibration reduction is the most pronounced. Reasonable delay time can effectively reduce the blasting vibration intensity and ensure the safety of surrounding rock masses. [ABSTRACT FROM AUTHOR]

Unmanned Aerial Vehicle (UAV) remote sensing can be widely used to capture large-scale high overlapping and high spatial resolution images from the ground in a low-cost way. However, the UAV Red-Green-Blue (RGB) images are usually obtained, because the UAV sensors can only contain the wavebands with the visible light range, such as the red, green, and blue. It is difficult to extract the feature information from the UAV RGB images because of the fewer wavebands. Taking the UAV RGB orthophoto image as the research object, an image extraction was proposed to integrate the point cloud matching with the Hue-Saturation-Intensity (HSI) color component. Firstly, the high overlapping ground images were acquired by UAV, thereby constructing the point cloud matching using dense matching, where a digital surface model was generated. Then, the ground points and non-ground points in the study area were obtained by a cloth simulation filtering. Specifically, the ground points were interpolated to generate the digital elevation model. As such, a normalized digital surface model was obtained via the difference between the digital surface model and digital elevation model. Secondly, a saturation-red ratio index was established to extract the bare land using the color components of HSI color space and visible light bands. A bare land extraction was then carried out to verify the model. Thirdly, the expansion index was constructed by multiplying the formula of saturation-red ratio index by the standard deviation of the red band to extract the water with the sediment. Finally, two models and three indexes were achieved to classify the UAV RGB orthophoto images in the study area, including the digital elevation model, the normalized digital surface model, the visible-band difference vegetation index, the saturation-red ratio index, and the expansion index. Afterwards, an experiment was then carried out to verify the extraction model. The results showed that the saturation-red ratio index presented a higher accuracy in the bare land extraction from the UAV RGB images. The normalized digital surface model performed better to obtain the key feature that distinguished from the trees and grasslands using the matching of point cloud interpolation. Furthermore, the river and bare land were successfully extracted, using the saturation-red ratio and the expansion index under the HSI color components, while the digital elevation model features that obtained from the point cloud matching. The overall accuracy of the rule-based classification was 91.11%, where the Kappa coefficient was 0.895. In the classification, the highest producer’s accuracy and user’s accuracy were the buildings and rivers, which reached 96.25% and 99.15%, respectively. Moreover, there was an outstanding elevation difference in the high-precision digital elevation model, digital surface model, and normalized digital surface model. Additionally, the saturation-red ratio and expansion indexes were composed of the visible light band and the color component of saturation, particularly for the extraction of bare land and sand water. Consequently, it can be feasible to combine the point cloud matching and the color components of the HSI color space in the extraction of UAV RGB orthophoto image information. [ABSTRACT FROM AUTHOR]

CdZnTe detectors play an important role in international astronomy research. In this paper, the spectra response of CdZnTe planar detector for γ-ray was numerically simulated using GEANT4 Monte-Carlo simulation toolkit, and the influence of the factors, such as transport properties of electrons and holes, applied bias voltage, thickness of detector and other factors were studied. The calculated results show that when the charge collection efficiency ( CCE) of electron is high, the dependence of energy resolution on the ratio of electron and hole mobility-lifetime product ((μτ)e/ (μτ)h) is strong, and the resolution increases with the decrease of the ratio. The energy resolution and CCE of carrier can be improved by raising up the bias. When the CCE of electron is high, increasing thickness can weaken the hole signal contribution to the total induced charge, and improve the energy resolution. The value of (μτ)eE /d can be used to evaluate CCE of planar CdZnTe detectors for rays of low energy, and the corresponding relationship was calculated. [ABSTRACT FROM AUTHOR]

BACKGROUND: Niobium is a strategic metal with wide applications and plays a very important role in modern steel technology. The average content of Nb2O5 in a niobium-rare earth ore is 0.0855%, and the content of total rare earth (REO) is 1.03%, which is close to the minimum industrial grade requirement of niobium ore. Niobium minerals and rare earth minerals have the characteristics of small particles and complex intergrowths, which makes them difficult to find under a polarized microscope. OBJECTIVES: To investigate the forms of niobium and rare earth elements. METHODS: Electron probe backscatter image, energy spectrum analysis and electron probe spectrum quantitative analysis were used to analyze niobium and rare-earth ore. RESULTS: Niobium mainly existed in the form of niobite and niobium-bearing rutile. The average content of Nb2O5 in niobite was 78.26% and that in niobium-bearing rutile was 5.26%. Rare earth elements mainly existed in the form of monazite, bastnaite and bastnaesite. The average content of rare earth elements (REO) in monazite, bastnaite and bastnaesite was 64.84%, 57.52% and 70.61%, respectively. Niobium and rare-earth minerals were distributed and dispersed. They were mostly trapped in the gangue minerals such as potassium feldspar, calcite and biotite. CONCLUSIONS: The types and characteristics of the main niobium and rare earth minerals in the ore deposit were identified. This study has important guiding significance for the comprehensive utilization of niobium and rare earth deposits in the future. [ABSTRACT FROM AUTHOR]

As an important economic tree species in China, tea trees play a critical role in promoting economic development. However, the expanding area of tea plantations in China over the recent years has reduced the biodiversity of the ecosystem and accelerated ecological problems. Identifying the spatial distribution of the tea plantation accurately is important for the monitoring of the tea plantation as well as the development of the tea industry. In this study, Wangzhai Town, located in the central of Wuyi County in Zhejiang Province, was chosen as the study area, and Gaofen-1 (GF-1) data on December 28, 2017, and all available Sentinel-2 data in 2017 were downloaded to study the tea plantations extraction method. The spectral and texture features of tea plantations are similar with crops on the high spatial resolution remote sensing image which results in the mixture of the tea plantations and crops. The Normalized Difference Vegetation Index (NDVI) time series data can reflect the phenological changes of the tea trees and crops, which can be used to improve the separability of tea plantations and crops. In this study, a phenology-based approach was applied to the NDVI time series to distinguish tea plantations from crops by comparing the similarity of the seasonal changes of NDVI time series. Due to the influence of cloud and fog on the satellite image, the complete Sentinel-2 time series data is difficult to be obtained. Therefore, the high temporal resolution Moderate-resolution Imaging Spectroradiometer (MODIS) reflection product (MCD43A4) in 2017 was used to reconstruct the complete Sentinel-2 NDVI time series data (5 m spatial resolution, 5 d temporal resolution) based on Bayesian Maximum Entropy (BME) spatial-temporal fusion algorithm. Then, the Dynamic Time Wraping (DTW) distance was calculated based on the DTW algorithm to characterize the similarity of NDVI time series between the tea plantation samples and other pixels to be classified. Combining the advantages of GF-1 in spatial details and the high temporal resolution of reconstructed Sentinel-2 NDVI time series in capturing the difference of growth process of tea trees, the tea plantation was identified based on random forest algorithm. Two identification schemes: 1) spectral and texture features based on GF-1; 2) spectral, texture features based on GF-1 and DTW distance were set up to investigate the impact of NDVI time series on the tea plantation identification. The results showed that: 1) the reconstructed NDVI time series data derived from MODIS and Sentinel-2 based on spatio-temporal fusion algorithm is able to capture the seasonal dynamics of different objects, and the DTW distance can capture the difference of NDVI time series data between crops and tea plantations and can be used for tea plantations extraction; 2) the accuracy、 error rate、precision、recall and F1score were 96.91%、3.09%、89.00%、83.09% and 0.86 respectively combining texture and spectral information of GF-1 and Sentinel-2 NDVI time series information, which were 94.72%、5.28%、73.09%、84.61% and 0.78 respectively without Sentinel-2 NDVI time series information. Overall, the results of combining GF-1 and Sentinel-2 time series data performed better than that only based on the spectral and texture features of GF-1. These results confirmed that combining high spatial resolution with high temporal resolution time series remote sensing data is an effective method to improve the identification accuracy of tea plantations. [ABSTRACT FROM AUTHOR]

The Jianzha Basin is located in the northeastern margin of Tibetan Plateau, and contains a thick sequence of Cenozoic sediments, which is composed of the sequence of eolian red clay with a bit part fluviolacustrine deposits. The Jiarang section in Jianzha Basin is an ideal section for cyclostratigraphy, because of its high resolution on magnetostratigraphy and environmental magnetism. Based on the systematic cyclostratigraphy of 3 - 361 m sedimentary strata of Late Miocene in Jiarang section, the absolute astronomical time scale of Jiarang section was established, and the dominant period of earth orbital parameters affecting the paleoclimate evolution in Jianzha Basin was extracted, in order to provide basic data for the further comparison between land and sea, and the paleoclimatic and environmental evolution. The result of spectrum analyses of frequency-dependent magnetic susceptibility (Xfd) in depth domain shows that there is a stable 26 m sedimentary cycle representing long eccentricity period in the 3 - 361 m interval, and the sedimentary cycles representing other astronomical orbital periods are also present, but not stable. Based on the identified sedimentary cycles, the long eccentricity period was used to tune frequency-dependent magnetic susceptibility data series in depth domain of 3 - 361 m. Combined with the magnetostratigraphic chronological framework of Jiarang section, the absolute astronomical time scale of 11. 758 - 5. 890 Ma was established. The astronomical tuning for frequency-dependent magnetic susceptibility data series in time domain is consistent with the paleomagnetic age, which indicates that the astronomical time scale is reliable. The spectrum analyses for frequency-dependent magnetic susceptibility and marine oxygen isotope records show that there are obvious periodic shifts in about 7. 2 Ma. This record reveals that the East Asian summer monsoon (EASM) is dominated by 41 ka obliquity period before 7. 2 Ma, and controlled by 100 ka short eccentricity period after 7. 2 Ma. [ABSTRACT FROM AUTHOR]

Uncaria rhynchophylla contains large amount of alkaloids and abounds in natural resources, and has the function of heat-clearing for calming liver and wind-calming for calming frightened. In order to make clear the material basis of stems and leaves of U. rhynchophlla, in this paper, the chemical constituents of stems and leaves from U. rhynchophylla and their antitumor activities were studied. The extracts were isolated and purified by using silical gel, Sephadex LH-20, semi-HPLC, and the structures of the compounds were identified by physicochemical property and spectrum analysis. The antitumor activities of chemical constituents against K562, HEL cell lines were assessed by MTT method. Seven compounds were isolated and identified from stems of U. rhynchophylla as 3, 4, 5-trimethoxyphenol(1),scutellarin(2), isohydrohydrogenine(3), dehydroramine(4), Vallesiachotamine(5), isorhynchophylline(6), rhynchophylline(7). Twelve compounds were isolated and identified from leaves of U. rhynchophylla as octadecanol(8), β-sitosterol(9), tridecanoicacid(10), 2-methyl-5,7-dihydroxy-chroone-7-O-β-D-glucopyranoside(11), oleanolic acid(12), quercetin(13), ivy aglycone(14), kaempferol(15),(6R, 9R )-9-hydroxymegastigman-4-en-3-one(16), ursolic(17), epicatechin(18), emodin methylether(19). The results of antitumor activities were as follows:(1)Compounds 3 and 5 inhibited the activities of HEL cell lines, and IC50 values were 17.96 and 73.01 μg·mL-1; Compound 5 inhibited the activities of K562 cell lines, and IC50 values were 16.45 μg·mL-1, which indicate that some compounds from stems of U. rhynchophylla have certain antitumor activities.(2)Compounds 1, 8,10 and 16 were isolated from this plant for the first time. This study shed the scientific light on reasonable utilization and sustainable development of plant resources. [ABSTRACT FROM AUTHOR]

Aiming at the serious problem of tubing corrosion in huff and puff well, the corrosion. products of carbon dioxide stimulation wells were analyzed. The corrosion type of huff and puff oil well are determined as carbon dioxide corrosion. by using methods of microscopic morphology analysis. energy spectrum analysis. metallographic analysis and X-ray diffraction analysis. Basing on the study of corrosion law. a comprehensive corrosion prevention and control technology thought was put forward, which includes liquid corrosion inhibitor. solid corrosion inhibitor and other chemical corrosion protection technology as primary. deepening liner protection technology. sacrificial anode protection technology and tubing coating protection technology as auxiliary. The reasonable dosage concentration and dosage method of corrosion inhibitors were determined through experiments. Field tests were carried out in 69 wells, and the oil well lifetime has been extended from 260 days to 400 days, which verifies the effectiveness of the comprehensive corrosion prevention and control technology. [ABSTRACT FROM AUTHOR]

Aiming at the problem that it is difficult to extract the fault features of the main shaft bearing of turboshaft engines， and that the background noise has a large interference， an improved second-order cyclostationary blind deconvolution (CYCBD) method is proposed to extract the fault feature frequency under strong noise background. In this method， particle swarm optimization (PSO) is used to optimize the filter length parameters for the CYCBD method. Firstly， the signal model is established based on the vibration characteristics of the fault bearing， and then the envelope spectrum fault feature ratio (FFR) is maximized by PSO algorithm. The optimal filter length parameter is input into the CYCBD method， and the envelope spectrum of the filtered signal is analyzed to extract the fault feature frequency. Finally， the proposed method is applied to the measured signal， and the efficiency and accuracy of fault feature extraction are improved compared with the traditional envelope spectrum analysis， which verifies the effectiveness of the method proposed. [ABSTRACT FROM AUTHOR]

Precise irrigation has been a highly urgent need to realize a quick, accurate, and non-destructive acquisition for the distribution of moisture content in reclaimed soil, particularly in coal mining subsidence areas. Consequently, traditional measurement can be replaced with Ground-Penetrating Radar (GPR), a new type of near-earth microwave remote sensing with large-scale continuous detection, fast, non-destructive, and low-cost. GPR has also been widely used in the detection of water content in soil. However, GPR is rarely used in the detection of water content in reclaimed soil, due to the complex composition of reclaimed soil and the fast attenuation of electromagnetic waves in cohesive soil. Fortunately, spectrum analysis can convert the radar data (signal change) from the time domain to the frequency domain. Energy distribution of signal frequency can be used to alleviate a large wave speed error under different media. In this study, the soil of a subsided reclamation area in Huaibei City, Anhui Province of China was taken as the research object. A control group was set up in the surrounding normal areas. The Chirp-Z-transform (CZT) was used to find the characteristic frequency. Rayleigh scattering was selected to analyze the characteristic spectrum response of radar signals under volumetric water content in soil. Various regressions were used to establish inversion models of water cut. The results showed that the frequency of peak (FP) using the Rayleigh scattering had an excellent correlation with the volumetric water content of reclaimed soil, where the correlation coefficient reached 0.90, indicating that the radar signal FP was feasible to invert the soil volumetric water content of the reclaimed area. Moreover, the CZT effectively identified the characteristic frequency in a higher resolution without changing the original frequency spectrum. The correlation coefficient between FP and soil moisture content was improved from 0.77 to 0.93, compared with the fast Fourier transform (FFT). In addition, the spectrum of radar signal in the reclaimed area demonstrated a greater drift than that in the non-reclaimed area under the same change of water content. The bulk density was also one of the factors that affect the radar signal. The highest accuracy was achieved in the exponential regression, where the verification precisions were R2 =0.84, and RMSE=1.97%, showing that the frequency spectrum analysis was an effective way to invert the moisture content of cultivated soil. When large construction equipment was used to compact the reconstructed soil, there will be a larger influence on the distribution of soil moisture content and the fertility of reclaimed soil. The findings can provide an insightful theoretical basis and technical support for rapidly monitoring the soil quality and precision irrigation in reclaimed areas. [ABSTRACT FROM AUTHOR]

In order to effectively extract useful information from time series of the global naviga- tion satellite system(GNSS) sites, and improve the modeling accuracy of coordinate time series, this paper proposes a nonlinear motion modeling method combining wavelet decomposition and singular spectrum analysis. Experiments were carried out using global positioning system(GPS) vertical coordinate time series of 11 stations around the world from 1999 to 2018. Methods: Firstly, the coordinate time series is decom-posed into different scales by wavelet decomposition.And then the singular spectrum analysis(SSA) is per- formed on the high-frequency part and the low -frequency part of each layer. Finally, combine the fitting value that is the fitting coordinate time series, and the fitting effect of the new method was evaluated. Results: The results show that, compared with the simple singular spectrum analysis method, the new method can extract useful information such as trend and period more accurately from the limited scale of the time series with noise. And reduce the partial period items in the singular spectrum analysis method to some extent, for example, the seasonal period item and the monthly period item are regarded as the probability of noise rejec- tion, and the modeling accuracy is improved by 26%. Conclusions: The purpose is to propose a nonlinear motion modeling method based on wavelet decomposition and singular spectrum analysis, so as to improve the modeling accuracy of coordinate time series. [ABSTRACT FROM AUTHOR]

MODIS Precipitable Water Vapor (PWV) has high spatial resolution but is easily affected by environmental factors such as clouds and rain,and its accuracy is not high.GNSS PWV has low spatial resolution but has advantages of all-weather,weather-free,and high accuracy.Previous researches have shown a significant linear correlation between the two.Combining the advantages of both,correcting MODIS PWV based on GNSS PWV data can obtain PWV for large area with high precision.Yet traditional linear regression correction model does not take into account the deterioration of the linear correlation due to influences from environmental factors such as clouds and aerosols.To address this,a non-linear term of Day of Year is added to traditional linear model thus construct a new correction model.The GNSS tropospheric delay and MODIS near-infrared data in Hong Kong during 2017-2019 are used to verify the proposed correction model,and the result of linear residual spectrum shows a significant annual cycle.Compared with traditional correction model,the new model is significantly improved in aspects of average absolute error,average relative error,root mean square error,and fitting,which verifies the new model in feasibility and accuracy for MODIS PWV correction. [ABSTRACT FROM AUTHOR]

Using the high-dimensional complex chaotic system in the chaotic ultra wideband signal can enhance the security of the signal,but reduce the easy implementation of the chaotic system.In view of the contradiction between the complexity and the easy realization of the chaotic system used in the chaotic ultra wideband(UWB),this paper designed a chaotic system with low dimension and easy to implement by using two one-dimensional chaotic maps with easy implementation based on chaos branch switching and embedding.Lyapunov exponent analysis shows that it is chaotic in a wide range of parameters.The analysis shows that the signal generated by the system cannot reflect the definite mapping relation,which not only increases the complexity of chaotic signal,but also makes the system easy to implement.The analysis also shows that this chaotic signal has good correlation.Therefore’ this paper also generated more secure chaotic time hopping UWB signal based on this chaotic system.At the same time,through the comparative analysis of the power spectrum of the chaotic UWB signal and the general pseudo-random time hopping UWB signal,it contains less discrete spectrum which can produce interference to other transmission systems,and also makes the chaotic UWB signal have more extensive application fields. [ABSTRACT FROM AUTHOR]

The proanthocyanidin (PC)-enhanced polyethylene glycol (PEG)-lysozyme (LZM) hydrogel (PEG-LZM-PC) was prepared based on PEG-LZM hydrogel by post-soaking with PC. The morphology, structure and mechanical properties of PEG-LZM-PC were characterized by scanning electron microscope (SEM), Fourier-transformed infrared (FT-IR) spectroscopy and universal testing instruments. Antibacterial ability of PEG-ZEM-PC was evaluated by plate coating method. The effect of PEG-LZM-PC on the expression of inflammatory factors in immune cells was evaluated by quantitative reverse transcription PCR (RT-qPCR). The results showed that the strength and toughness of PEG-LZM-PC were significantly improved compared with PEG-LZM; the antibacterial performance of PEG-LZM-PC in vivo and in vitro was pretty good; and the inflammatory response caused by lipopolysaccharide (LPS) stimulation was inhibited by PEG-LZM-PC, which is expected to promote wound healing. [ABSTRACT FROM AUTHOR]

For traditional data fusion algorithms, the fusion performance of high-noise, large-scale and complex-structure time series data is poor. A hybrid neural network data fusion algorithm (i.e. the SCLG algorithm) was proposed to solve this problem. Firstly, the time series data were decomposed and reconstructed with the singular spectrum analysis algorithm to eliminate noise. Secondly, the spatial and short-term characteristics of the data were extracted by means of the deep convolutional neural network. Thirdly, the long short-term memory neural network and the gated recurrent unit neural network were introduced to extract data features in the time dimension. Finally, the fully connected layer was applied to integrate the main information and output the final decision. The experimental results from the SP&500 and AQI data sets show that, the proposed algorithm is superior to DCNN, CNN-LSTM and FDL in terms of fusion performance and stability. [ABSTRACT FROM AUTHOR]

Meat and bone meal can be widely used as an important protein feed raw material in breeding industry, because of its high content of protein and biological value. However, the feed safety issues occurred frequently around the world, such as mad cow disease and sheep pruritus. There is an urgent need to develop an identification technology with a simple, reliable, reasonable, and efficient way to distinguish the specific species of meat and bone meal. In this study, Raman spectroscopy and chemometrics methods were used to establish a systematic approach for the identification of species in meat and bone meal. The proposed method was based on the difference in Raman spectral characteristics of bone protein. This is due mainly to the composition and structure of bone proteins vary with species, while the bone protein was also the dominated component of meat and bone meal. A total of 87 source-reliable samples of meat and bone meal were analyzed, including 23 porcine, 22 poultry, 20 bovine, and 22 ovine in the measurement. Given the extraction procedures of proteins were complex, the bone protein was enriched via the extracting bone particles, and then ground into powder. All the powdered bone particles were scanned by the Raman spectrometer from 400 to 3 600 cm-1, where the Raman characteristics of bone protein, including 3200-2800, 1800-1200 cm-1, and 900-800 cm-1, were selected for further analysis. A three-step protocol was established for discriminant analysis combined with partial least squares method. The first model was used for the identification of poultry and mammal (porcine, bovine and ovine) meat and bone meal, where six latent variables were selected to establish the partial least square-discriminant analysis (PLS-DA) model. The specific fingerprint characteristic bands were at 1659, 2930, 2852, 1246 cm-1, and 1455 cm-1, respectively. Both the sensitivity and specificity of discriminant models were achieved at 100%, indicating that the PLS-DA discriminant analysis model based on the Raman characteristic band of bone protein can be well suited to distinguish poultry and mammal (porcine, bovine and ovine) meat and bone meal. The second model was used for the identification of non-ruminant (porcine) and ruminant (bovine and ovine) meat and bone meal, where ten latent variables were selected to establish a PLS-DA model according to the Raman characteristic bands of bone protein. The fingerprint characteristic bands were located at 2852, 1659 cm-1, and 1246 cm-1, respectively. Both the sensitivity and specificity of discriminant model can also be 100%, indicating that the PLS-DA discriminant analysis model can be used to well distinguish non-ruminant (porcine) and ruminant (bovine and ovine) meat and bone meal. The third model was used for the identification of bovine and ovine meat and bone meal, where eleven latent variables were selected to establish a PLS-DA model. Their fingerprint characteristic bands were at 878, 853, 2930, 2852, 1246, 1455 cm-1, and 1659 cm-1, respectively. Both the sensitivity and specificity of discriminant model can be obtained over 93.8%, indicating that the PLS-DA discriminant analysis model can be used to well distinguish bovine and ovine meat and bone meal. The findings can be contributed much to the rapid development of a portable Raman spectrometer system for species-specific identification of meat and bone meal in feed industry. [ABSTRACT FROM AUTHOR]

A hyperspectral reflectance can be expected as an alternative method to quickly predict the concentration of heavy metals in soil at present. However, excessive band counting of hyperspectral data can lead to information redundancy during the quantitative inversion of surface parameters, and thereby to make it difficult to extract valid data from the spectrum. In this study, two aspects were addressed to solve spectral redundancy during the inversion of Pb content in soil: One is to extract the characteristics of iron oxides from the soil spectrum, according to the adsorption mechanism of Pb element in soil, in order to reduce data redundancy, while enhance the mechanism of inversion. Another is to propose an Improved Genetic Algorithm (IGA), in order to deal with the premature convergence of traditional Genetic Algorithm (GA), further to improve the extracting ability of algorithm for the required spectrum. Taking the Xiong’an New Area near Beijing, a national new-district in China, as the research region, the field experiment was carried out to quickly collect the Pb content of soil in the farmland, resulting from the rapid construction and developing facilities in recent years. 70 soil samples from the Xiong’an farmland were selected to establish the inversion model of Pb content. SVC HR1024I type ground object spectrometer was used to measure the raw hyperspectral reflectance of soil samples according to the national standard procedure in field farmland. After that, the collected soil samples were transferred to the laboratory for chemical analysis, particularly for the Pb content. Before modeling, the methods, including Savitzky-Golay filter, envelope removal and first-order difference, were used to pretreat with segmented the curves of soil reflectance. Partial Least Squares Regression (PLSR) was selected to serve as inversion model, in order to well deal with the strong correlation between hyperspectral bands. The models were also constructed in terms of full spectrum and characteristic bands of iron oxides, to verify the effectiveness of extracting method from characteristic bands of iron oxides during the inversion process. In full spectrum, the average values of R2, RPD, RMSE in the IGA-PLSR models were 0.440, 1.366, and 4.012 mg/kg, respectively. In the characteristic bands of iron oxides, the average values of R2, RPD, RMSE in the IGA-PLSR models were 0.822, 2.377, and 2.221 mg/kg, respectively. It infers that the models using the characteristic bands of iron oxides were much better than those using the full spectrum, indicating that the extraction for the characteristic bands of iron oxides can be contributed to reduce the redundancy of spectral information, and thereby to significantly improve the accuracy of the inversion model. Consequently, a final inversion model was selected from the optimal model for the improvement of inversion algorithm, based on the utilization of the characteristic bands of iron oxides to construct the GA-PLSR and IGA-PLSR models with different iterations. In the GA-PLSR models, the average values of R2, RPD, RMSE in the optimal model were 0.788, 2.140, and 2.447 mg/kg, respectively, whereas, the IGA-PLSR model achieved the highest accuracy, where the average values of R², RPD, RMSE were 0.837, 2.403, and 2.063 mg/kg, respectively. Therefore, the optimal model of IGA-PLSR was chosen to estimate the Pb content in soil for the Xiong’an farmland area. The IGA-PLSR method has demonstrated better performance in the inversion of Pb content in soil, indicating that the proposed method was valid to improve genetic algorithm. At last, a visualized profile can be conducted for the changes of RMSE during the 1000 iterations of two models. The visualization revealed that the RMSE in the GA-PLSR model decreased monotonously during the operation process, whereas, the RMSE in the IGA-PLSR model fluctuated greatly at the beginning of the iteration, showing a certain probability to jump out from the local solution area to a better solution area at the later stage, while meaning that the problem of “premature convergence” was solved properly. This study revealed that two aspects, including the extraction of iron oxides spectral features associated with Pb absorption, and the improved algorithm of bands selection, both can be used to enhance the accuracy of Pb content estimation in soil. The proposed algorithm can provide a sound reference to real-time monitor the variation of heavy metal content in soil for Xiong’an area, China. [ABSTRACT FROM AUTHOR]

Tobacco extract has good insecticidal activity. In order to improve the stability of tobacco extract, microcapsules were prepared by complex coacervation with tobacco extract as the core material and gelatin and gum arabic as the wall material. The preparation method of microcapsules was optimized by the response surface method with the embedding rate as the evaluation index. The surface morphology, the particle size measurement, the infrared spectrum analysis, the thermal stability analysis and the indoor virulence determination of microcapsules were carried out. The results showed the highest embedding rate of 58.93% was obtained when the following conditions were used: emulsifier mass fraction 4%, gelatin mass fraction 2.05%, gum arabic mass fraction 3%, wall core mass ratio 1.34 : 1.00 and complex coacervation reaction time of 36 min. The actual embedding rate of microcapsules under these conditions was 58.42% ± 0.12%, which was close to the theoretical value. The microcapsules prepared by this model exhibted smooth surface, good capsule genicity with the particle size distribution of 4.72-17.05 μm and median diameter of 8.63 μm.Infrared characteristic peaks of tobacco extracts were preserved at 877.55 cm−1 and 2 922.13 cm−1. Microencapsulation can improve the thermal stability of tobacco extract. Indoor virulence test showed that the LC50 value of tobacco extract microcapsules was 20.19 mg/mL, which was equivalent to the virulence level of the tobacco extract, indicating that microencapsulation didn’t reduce the insecticidal effect of tobacco extracts. To conclude, the present study can provide a reference for the optimization of tobacco extract microcapsules preparation process. [ABSTRACT FROM AUTHOR]

To study on chemical constituents and bioactivity of Larix gmelinii needles, separation and purification of ethyl acetate extract from Larix gmelinii needles were carried out by silica gel column chromatography，semi-preparative high performance liquid chromatography，and re-crystallization. The structures of isolated compounds wrere identified by analysis of spectra of MS，NMR，and compared wdth the reported conferences. The antibacterial activity of the extract ■was also tested. The results 'were as follo'ws: Compounds 1-15 wrere isolated from the ethyl acetate extract and identified as Larixol ( 1)，( 2R)-5, 4,-dihydroxy-6-methyl-7-methoxy flavone ( 2 ), 2', 4,-dihydroxy-4, 6,-dimethoxydihydro- chalcone (3) , 2,,4-dihydroxy-4,,6,-dimethoxy-chalcone (4) , 2,,4,-dihydroxy-4,6,-dimethoxy-chalcone (5) , isorham- netin (6)，4'，5-dihydroxy-7-methoxy-8-methyl flavone (7)， kaempferol (8)， p-sitosterol (9)， stigmasterol (10)， daucosterol (11)，vanillic acid (12)，hydroxyl-benzoic acid (13)， dimethyl - matairesinol (14)，and 15-nonacosanol (15). Compounds 2，4，5，and 7 were isolated from the genus Pinus for the first time. The result of antibacterial activity indicated that the inhibition rates of ethyl acetate extract to Escherichia coli，Bacillus subtilis，B. cereus，and Staphylococcus aureus were 55%-70%，53%-72%，61%-71%，33%-65%，respectively，at the concentration range of 5-100 mg • mL 1. The results of this study wall provide a theoretical basis for further study on the chemical composition and pharmacological activity of Larix gmelinii. [ABSTRACT FROM AUTHOR]

Unmanned Aerial Vehicle (UAV) remote sensing can obtain high-resolution images at low cost and high efficiency. However, there is rare research on the use of UAV remote sensing images to extract information from impervious surfaces. The difficulty of the research lies in that for the high-resolution UAV red-green-blue images, there is currently not an exclusive impervious surface index that can be applied to the extraction of impervious surface information. To address this problem, this study established the green-blue spectral feature space in the blue and green bands of the visible-bands. Under this spectral feature space, the green-blue impervious surface index was proposed to effectively separate soil-vegetation pixels and impervious surface pixels. The process of constructing the index was as follows. Firstly, the feature points were divided into impervious surface points and pervious surface points in the green-blue spectral feature space. Secondly, the least-squares fitting was conducted on the impervious surface points and the previous surface points. Then, the impervious surface line and the soil line were obtained, and a reference line was constructed between the two straight lines. Finally, the distance from the feature point to the reference line was used as the expression of the index. To verify the accuracy of the difference between the green-blue impervious surface index and other impervious surface indexes applied to satellite remote sensing images, comparison and analysis were performed on the extraction of the impervious surface by perpendicular impervious index, ratio resident-area index and green-blue impervious surface index. At the same time, the experiment was conducted using the UAV orthophoto image data of Hongya county in Meishan city to extract the impervious surface. The analysis was conducted to investigate the effect of the green-blue impervious surface index on the extraction of the impervious surface information in the UAV remote sensing image. The experimental results showed that: 1) The green-blue impervious surface index constructed the blue and green bands as the feature space had the same accuracy as the perpendicular impervious index and ratio resident-area index based on the blue and near-infrared bands in terms of the impervious surface extraction, and the overall accuracy reached over 94%. 2) The green-blue impervious surface index showed strong applicability and replaced the indexes like perpendicular impervious index, ratio resident-area index and biophysical composition index in the images lacking near-infrared, mid-infrared, and thermal-infrared bands. The green-blue impervious surface index was used as a remote sensing index to extract visible-bands on the impervious surfaces. 3) As a visible light wave impervious surface extraction index used in UAV remote sensing, the green-blue impervious surface index could not only effectively distinguish between soil and impervious surfaces, but also utilized the characteristics of easy-to-obtain terrain features by UAV remote sensing. For the problem of misclassification, the overall accuracy of the extraction results by the green-blue impervious surface index reached 96.95%, and the Kappa coefficient was 0.936 1. The green-blue impervious surface index constructed based on the green-blue spectral feature space effectively separated soil pixels and extracted high-precision urban impervious surfaces from UAV remote sensing images. For satellite imagery, the existing impervious surface index had a good performance in extracting the surfaces, while the proposed green-blue impervious surface index was more suitable for UAV remote sensing. At present, extracting urban impervious surfaces from UAV remote sensing images has gradually become an important application. [ABSTRACT FROM AUTHOR]

This paper proposed a new speech concealment algorithm for many speech concealment algorithms with small hidden capacity and bad transparency. Firstly, it compressed and encoded the concealed information by G. 729A, and then applied the lifting wavelet transform and singular spectrum analysis to the audio information hiding. Experiments show that the audio file embedded in the hidden information by the algorithm not only has a significant improvement in the hidden information capacity, but also has good transparency, and can also resist certain attacks. The algorithm in concealed capacity, transparency and robustness reaches a better balance. [ABSTRACT FROM AUTHOR]

Nowadays, UAV (unmanned aerial vehicle) remote sensing has been widely used in various research fields, due to its incomparable advantages over traditional satellite remote sensing, such as lower cost, fast image access, and high spatial resolution, and so on. But most of the vegetation indices are constructed based on visible bands and near-infrared bands of satellite remote sensing images, and few of them are constructed only based on visible-light bands. Thus, it is necessary to construct a universal vegetation index that is suitable for the visible-light bands of UAV images. According to the analysis of the spectral characteristics of 6 kinds of typical features based on regions of interest in visible-light images from UAV images, this paper proposed a new vegetation index based on red, green and blue bands, named Excess green-red-blue difference index (EGRBDI). The formula of EGRBDI was that the sum value between the square of 2 times green band and the product of red and blue bands divided the difference value of them. The value range of EGRBDI was the interval [-1, 1]. To determine the accuracy and reliability of EGRBDI, 18 kinds of vegetation indices had been studied in this paper, such as CIVE GLI, ExG, and so on. The overlap between different object types was obtained by calculating the mean value and 1-fold standard deviation of vegetation indices. The results showed that EGRBDI, GLI, ExG, g, CIVE, RGBVI, and V-MSAVI had no overlap between vegetation and non-vegetation information, while other vegetation indices appeared the different degree of overlap. Moreover, EGRBDI effectively enlarged the identification range of vegetation information and reduced the identification range of non-vegetation information. When the grey histogram of vegetation index existed distinct bimodal peaks, the corresponding discrimination performance of ground features was relatively strong. Therefore, the quantized interval of gray histograms should be normalized to the interval [0, 255] for the comparative analysis between the indices. Results of the analysis concluded that EGRBDI, GLI, ExG, g, CIVE, RGBVI, and V-MSAVI had distinct bimodal-peak characteristics and scarcely appeared thorn peaks in the histogram, but the others had either no obvious bimodal peaks or obvious thorn peaks. To determine the thresholds of vegetation information identification, the bimodal histogram method and the maximum entropy method were used to determine the threshold of each vegetation index and got the optimal threshold of each vegetation index by the precision comparison method. The accuracy evaluation results revealed that GBRI and ExB obtained higher classification accuracy by the maximum entropy method than the bimodal histogram method. WI and VEG had the same accuracy between the two methods, and the other 15 indices did better on the bimodal histogram method. Therefore, the maximum entropy method was used to determine the thresholds of GBRI and ExB, while the other indices used the bimodal histogram method to determine their thresholds in this paper. Through the comparative analysis of the experimental results, it could be found that EGRBDI was generally better than the other 18 algorithms and had a great advantage in the case of the low vegetation coverage, which had a total accuracy of 97.67% and a Kappa coefficient of 0.9415. Another 3 UAV images had been used to extract vegetation information of top 5 higher precision indices to further verify the suitability in the area of various ground subjects and used 400 random points to evaluate the vegetation extraction accuracy. The accuracy of the vegetation and non-vegetation information was not less than 90%. The total accuracy in the 3 study areas was higher than 93%. Additionally, the Kappa coefficient was greater than 0.85. The results showed that EGRBDI had been less affected by ground subjects and shadows, and it had better applicability, reliability, and accuracy of vegetation extraction. [ABSTRACT FROM AUTHOR]

Nitrogen is a key plant nutrient and its deficiency or surplus could inhibit plant growth and reduce crop yield. Over the past decade, remote sensing has been increasingly used to diagnosis nitrogen deficiency in crop. Taking rice grown in cold region in northeast China as an example, this paper studies the relationship between nitrogen content in japonica rice and the difference between spectral reflectance based on data measured from field. This relationship was used to inversely estimate nitrogen deficiency in the rice based on hyperspectral images. In our analysis, the nitrogen content producing the highest yield was defined as standard nitrogen content and its associated spectral reflectance was defied as standard spectral reflectance. The difference between real nitrogen content and the standard nitrogen content, as well as the difference between the real spectral reflectance the standard spectral reflectance, were calculated respectively. The difference in spectral reflectance was dimensionally reduced using the discrete wavelet multi-scale decomposition, continuous projection method (successive projections algorithm, SPA) and vegetation index construction. The characteristic bands screened by SPA were 459、460、475、 671、723、874 和 996 nm. Analysis showed that when the discrete wavelet multi-scale decomposition was used to reduce the dimension, the Sym8 wavelet mother function worked best when it was decomposed at the seventh layer. Comparing DVI, NDVI and RVI vegetation index found that the determination coefficient of the DVI index and nitrogen deficiency was significantly higher than that of NDVI and RVI index. The three indexes were used as input to the partial least squares (PLSR), the extreme learning machine (ELM) and genetic algorithm optimization extreme learning machine (GA-ELM). The GA-ELM model was most accurate with the R² being 0.7062 for the training set and 0.7594 for the verification set; their associated RMSE was 0.5099mg/g and 0.4276mg/g respectively. The GA-ELM model based on the optimal vegetation index was least accurate, with the R² for the training set and the verifying set being 0.6615 and 0.6509 respectively; their associated RMSE was 0.4415mg/g and 0.5312mg/g. Overall, GA-ELM improved stability and predictability of the model compared with PLSR and ELM. It can thus be used as a new method to detect nitrogen content in rice leaf, and has important implication in precision fertilization. [ABSTRACT FROM AUTHOR]

Rapid and accurate estimation of crude protein (CP) content in pasture plays an important role in the monitoring and management of the forage growth on a wide range of grassland. Crude protein contents of pastures are ideal for characterization using hyperspectral data. In view of the limitation of ground and satellite remote sensing, in this paper, we attempted to achieve an accurate estimation of CP content in forage by unmanned aerial vehicle (UAV)-based hyperspectral remote sensing images with high spatial resolution of the pasture canopy. Although the hyperspectral data of the forage have a large number of bands, the reflectance of the canopy spectrum at each band contains information of various parameters which are from atomic level to plant community level. So, when estimating a physicochemical parameter using the spectral data, we may achieve low prediction accuracy because the spectra are affected by other parameters. Compared with the original spectra, multi-granularity spectral features can provide more sensitive features for inversion of chemical parameters. More importantly, multi-granularity spectral features can extract some hidden weak spectral information, which is of great significance for inversion of low-content physical and chemical indicators. However, in current inversion methods of pasture CP content, there is a lack of effective utilization of spectral multi-granularity information. In view of this, we first proposed a novel multi-granularity spectral feature extraction approach named multi-granularity spectral segmentation (MGSS) to segment each canopy spectrum into multiple spectral features. Second, by using the sequential forward selection method, sensitive components of each feature under a granularity can be selected. Finally, based on these selected components of different features, the inversion models of pasture CP content can be established using two regression methods, i.e., the stepwise multivariate linear regression (SMLR) and the partial least squares regression (PLSR). Taking a typical meadowland in Qinghai Plateau as an example, the detailed experimental analyses have been conducted. Results showed that under the same quantity sensitive components for MGSS and sensitive bands for the raw spectra, on the estimation accuracy of pasture CP content, MGSS was superior to the raw spectra. So the validity of MGSS in improving the accuracy of hyperspectral estimation of CP content in forage was verified. Specifically, under Granularity 23 (G23) of MGSS, the PLSR model achieved the best performance. Its determining coefficient (R²) was 0.937 which was 0.06 higher than that of the optimal model of the raw spectra. And the root mean square error (RMSE) and the mean relative error (MRE) were 1.906 (g/m²) and 8.82%, respectively, which were 0.75 (g/m²) and 1.37 percentage points lower than those of the optimal model of raw spectra. Moreover, on the single and combined components sensitive to CP content in forage, there were three characteristics among the selected components of MGSS, i.e., the agglomeration within the Red Edge range, the dispersion of non-Red Edge range, and the sparsity of strongly sensitive components, which can be helpful for selecting sensitive components. In conclusion, the proposed MGSS achieved the high performance estimation of CP content in forage by UAV hyperspectral imagery. And compared with the raw spectra, MGSS had better performance. This study provides a new technical means for the accurate estimation of CP content in grasslands in large areas. [ABSTRACT FROM AUTHOR]

A detailed study on a high-iron red mud from Shandong province in the perspective of process mineralogy were conducted through chemical analysis, XRD, optical microscope, laser particle size analysis, energy spectrum analysis and BPMA automatic analyzer. The results show that the red mud has a fine particle size, and the chemical composition and mineral ( phase) composition are complex. The main minerals ( phases) in red mud are particulate silicon slag, hematite, iron-aluminum oxide, limonite, as well as trace amounts of sodium aluminosilicate, iron-titanium oxide, quaitz, gibbsite, bauxite, feldspar, and mica, etc. Iron occurs in the form of hematite, limonite, iron-aluminum oxide and particulate silicon slag. There is a large amount of particulate silicon slag in red mud, but the mineral ( phase) particle size is fine. The key to taking full advantage of the red mud is how to make use of iron, aluminum, titanium and sodium in the particulate silicon slag. [ABSTRACT FROM AUTHOR]

The spectrum of the image reflects the distribution of image energy in the frequency domain. The ratio of the high and low frequency components of the spectrum reflects the distribution of edge information and smooth information in the image. Through statistical analysis of the spectral distribution of dear and blurred images, this paper proposes an image clarity and fuzzy discrimination method based on sub-band spectral information. First, the spectrum energy outside the frequency radius is calculated, and the characteristic curve of the residual energy of the spectrum is obtained. Secondly, according to the residual energy curve, the normalized fuzzy judgment parameters are obtained. When the fuzzy parameter is less than a certain threshold, the image is judged to be a blurred image. The experimental results show that the parameter proposed in this paper can quantitatively measure the degree of image blur, and has the advantages of fast and simple judgment process and high classification discrimination. [ABSTRACT FROM AUTHOR]

In order to fully explore useful spectral and image information of hyperspectral images, a method of extracting the deep feature of hyperspectral images based on stacked auto-encoder (SAE) was investigated in this study, then the extracted deep feature was used to establish support vector regression (SVR) model to realize non-destructive detection of protein content in rice. Firstly, 420 rice samples (70 in each group) were placed in 6 groups at different storage time (0, 24, 48, 72, 96 and 120 h) under the condition of high temperature (45 ℃) and high humidity (95% relative humidity). Secondly, the hyperspectral images (400-1 000 nm) of 6 group rice samples with different protein content were collected by the hyperspectral image acquisition system. After hyperspectral images collection, the Kjeldahl method for determination of nitrogen was used to detect the protein content of rice samples. According to the chemical detection results, the protein content of the rice samples decreased from 7.78 to 7.62 g/(100 g) with the increase of storage time. Thirdly, in order to separate samples from background, the threshold segmentation method was used to obtain the sample mask in ENVI software, then the mask was applied to the hyperspectral image containing only rice samples. The rice sample area was chosen as region of interest (ROI), and the average spectral data and image information of ROI was extracted respectively. Finally, SAE was used to extract deep feature of spectral data, image information and fusion data. 1) For spectral information, savitzky-golay (SG) was used to pre-process the obtained spectral data, and SAE was used to extract the deep feature, then SVR was used to establish the prediction model, and grid search method was used to optimize the kernel parameter g and penalty factor c in SVR. The results showed that the optimal scale of SAE was 478-400-290-70, and R²c (determinant coefficient of calibration set), R²p (determinant coefficient of prediction set), RMSEC (root mean square error of calibration set), RMSEP (root mean square error of prediction set) using deep feature extracted by SAE were 0.976 2, 0.939 2, 0.068 6 g/(100 g), 0.115 3 g/(100 g), respectively. 2) For image information, the RGB images in each band were extracted first, the size was 100 pixels×100 pixels. There was much redundant information in the original images, so we unified them to the gray images with 28 pixels×28 pixels, then flattened and converted them to one-dimensional column vector, after deep feature extraction by SAE on the vector, the prediction results of the model built with SVR showed that the optimal scale of SAE was 784-700-480-30, and R²c R²p, RMSEC, RMSEP modeled using deep feature extracted by SAE were 0.915 4, 0.821 0, 0.051 0 g/(100 g), 0.111 8 g/(100 g), respectively. 3) For the fusion data of spectral data and image information, the initial dimension was 1 262, the fusion data combined all the redundant information of spectral and image, so dimension reduction becomes critical. After feature extraction by SAE, the dimension was reduced to the low level and the efficiency of network training was improved. The optimal scale of SAE was 1262-550-450-30, and R²c, R²p, RMSEC, RMSEP of the model build with deep feature extracted by SAE were 0.971 0, 0.964 4, 0.077 2 g/(100 g), 0.085 1 g/(100 g), respectively. Compared with using spectral data or image information alone, the prediction effect of the model build with the fusion data was improved obviously. To summarize, the method in the paper fully fused the spectral data and image information of hyperspectral image, and then deep feature extracted by SAE improved the prediction accuracy of the model established by SVR effectively, which provides a theoretical basis for non-destructive detection of protein content in rice [ABSTRACT FROM AUTHOR]

The U-typed reduction chamber is the core reaction device of the iron suspension roaster. In order to study the fluidization characteristics, a cold experiment system of U-typed reduction chamber was established. Based on the experimental data collected by pressure fluctuation method, the following conclusions were drawn. Aeration velocity and particle size were the main factors affecting the initial fluidization velocity, while the initial bed height had little effect. With the increasing of initial bed height and aeration velocity, the pressure fluctuation in fluidization chamber became lower, but opposite result was obtained for particle size. Besides, power spectrum analysis revealed that the formation, coalescence and fragmentation of bubbles were the fundamental reasons for pressure fluctuation. The results would provide theoretical support for the design and optimization of the operating parameters during suspension magnetization roasting experiments. [ABSTRACT FROM AUTHOR]

In order to fully explore useful spectral and image information of hyperspectral images, a method of extracting the deep feature of hyperspectral images based on stacked auto-encoder (SAE) was investigated in this study, then the extracted deep feature was used to establish support vector regression (SVR) model to realize non-destructive detection of protein content in rice. Firstly, 420 rice samples (70 in each group) were placed in 6 groups at different storage time (0, 24, 48, 72, 96 and 120 h) under the condition of high temperature (45 °C) and high humidity (95% relative humidity). Secondly, the hyperspectral images (400-1 000 nm) of 6 group rice samples with different protein content were collected by the hyperspectral image acquisition system. After hyperspectral images collection, the Kjeldahl method for determination of nitrogen was used to detect the protein content of rice samples. According to the chemical detection results, the protein content of the rice samples decreased from 7.78 g/(100 g) to 7.62 g/(100 g) with the increase of storage time. Thirdly, in order to separate samples from background, the threshold segmentation method was used to obtain the sample mask in ENVI software, then the mask was applied to the hyperspectral image containing only rice samples. The rice sample area was chosen as region of interest (ROI), and the average spectral data and image information of ROI was extracted respectively. Finally, SAE was used to extract deep feature of spectral data, image information and fusion data. 1) For spectral information, savitzky-golay (SG) was used to pre-process the obtained spectral data, and SAE was used to extract the deep feature, then SVR was used to establish the prediction model, and grid search method was used to optimize the kernel parameter g and penalty factor c in SVR. The results showed that R2C(determinant coefficient of calibration set), R2P(determinant coefficient of prediction set), RMSEC (root mean square error of calibration set), RMSEP (root mean square error of prediction set) based on full bands were 0.999 6, 0.686 9, 0.010 0 g/(100 g), 0.299 8 g/(100 g), respectively, while R2C, R2P, RMSEC, RMSEP using deep feature extracted by SAE were 0.976 2, 0.939 2, 0.068 6 g/(100 g), 0.115 3 g/(100 g), respectively. 2) For image information, the RGB images in each band were extracted first, the size was 100 pixels×100 pixels. There was much redundant information in the original images, so we unified them to the gray images with 28 pixels×28 pixels, then flattened and converted them to one-dimensional column vector, after deep feature extraction by SAE on the vector, the prediction results of the model built with SVR showed that R2C, R2P, RMSEC, RMSEP based on full bands were 0.997 4, 0.667 0, 0.024 5 g/(100 g), 0.261 9 g/ (100 g), respectively, and R2C, R2P, RMSEC, RMSEP modeled using deep feature extracted by SAE were 0.915 4, 0.821 0, 0.051 0 g/(100 g), 0.111 8 g/(100 g), respectively. 3) For the fusion data of spectral data and image information, the initial dimension was 1 262, the fusion data combined all the redundant information of spectral and image, so dimension reduction becomes critical. After feature extraction by SAE, the dimension was reduced to the low level and the efficiency of network training was improved. R2C, R2P, RMSEC, RMSEP of the model based on full bands was 0.935 7, 0.842 1, 0.116 9 g/(100 g), 0.195 3 g/(100 g), respectively, and R2C, R2P, RMSEC, RMSEP of the model build with deep feature extracted by SAE were 0.971 0, 0.964 4, 0.077 2 g/(100 g), 0.085 1 g/(100 g), respectively. Compared with using spectral data or image information alone, the prediction effect of the model build with the fusion data was improved obviously. To summarize, the method in the paper fully fused the spectral data and image information of hyperspectral image, and then deep feature extracted by SAE improved the prediction accuracy of the model established by SVR effectively, which provides a theoretical basis for non-destructive detection of protein content in rice. [ABSTRACT FROM AUTHOR]

During the storage process, honeysuckle easily undergoes discoloration and mildew under the influence of temperature, humidity and microorganisms, which leads to a significant decrease of its medicinal efficacy and economic value, and even harms the health of consumers. Hence, it is necessary to monitor the quality of honeysuckle during storage. Chlorogenic acid (CGA), as the main active ingredient, is an important indicator to evaluate the quality of honeysuckle. In order to realize rapid and effective detection of CGA content in honeysuckle, 500 hyperspectral images of honeysuckle during different storage periods were collected by hyperspectral imaging (HSI) system, and then CGA content values were measured by high performance liquid chromatography (HPLC) method. Average spectral information extracted from the hyperspectral images and corresponding CGA values were used to build HSI detection models. Because of the non-uniformity of sample surface, baseline drift of instrument, random noise and light scattering, the collected hyperspectral images contained some redundant information, which could reduce the accuracy of modeling. In order to improve the prediction accuracy and efficiency of the model, six spectral preprocessing methods were used to improve the signal-to-noise ratio of the original spectrum, including Savizky-Golay filter (SG), moving average, standard normal variable (SNV), baseline correction (BC), multiplicative scatter correction (MSC), orthogonal signal correction (OSC). Comparing the effects of different pretreatment methods by establishing partial least squares regression (PLSR) models, the SNV-PLSR model obtained the best prediction accuracy with determination coefficient (R2) of 0.976 6 and root mean square error (RMSE) of 0.271 1% in prediction set, and SNV was identified as the best pretreatment method for further analysis. In order to simplify the calibration model, the uninformative variable elimination (UVE), successive projections algorithm (SPA), competitive adaptive reweighted sampling (CARS), the combination of UVE and CARS (UVE-CARS), and the combination of UVE and SPA (UVE-SPA) were used to extract characteristic wavelengths from the pre-processed spectrum by SNV method. And UVE, CARS, SPA, UVE-CARS and UVE-SPA selected 192, 51, 17, 26, 9 characteristic wavelengths from the full spectrum. Then, based on the full spectrum data and the selected characteristic variables by five variable screening methods, the linear PLSR and the non-linear BP neural network model were established. The performance of all the models were evaluated by the index of R2 for calibration set and prediction set, (RMSE) for calibration set and prediction set. The results showed that UVE-CARS algorithm could effectively eliminate useless information variables from full spectrum, and 26 characteristic wavelengths were selected from full spectrum by UVE-CARS algorithm, and the established model based on UVE-CARS algorithm had high accuracy, which was considered as the best feature wavelength screening method. The prediction results of the non-linear BP model were better than that of the linear PLSR model. In all BP model, the prediction accuracy of UVE-CARS-BP was the highest with R2 of 0.978 4 and RMSE of 0.250 3% in prediction set, respectively, and it was proved that the non-linear model was more suitable for the prediction of CGA content in honeysuckle. In conclusion, HSI technology combined with SNV-UVE-CARS-BP model can realize the rapid and non-destructive prediction of CGA content in honeysuckle during storage. [ABSTRACT FROM AUTHOR]