Sech 2 -type solitary waves and the stability analysis for the KdV-mKdV equation.

In this article, we investigated the solitary wave solutions of the KdV-mKdV equation using Hirota's bilinear method. Closed-form analytical single and multiple solitary wave solutions were obtained. Through qualitative methods and the analysis of solitary waveforms, we discovered that in addition to sech-type solitary waves, the system also contains Sech 2 -type solitary waves. By employing the trial functions method, we obtained a single Sech 2 -type solitary wave and verified its existence and stability using the split-Step Fourier Transform method. Furthermore, we use the collision of two Sech 2 -type single solitary waves to excite a stable Sech 2 -type double solitary wave. Similarly, we excite a stable triple solitary wave with three Sech 2 -type single solitary waves. This method can also be used to excite stable multiple solitary waves. It is shown that these solitary wave solutions enrich the dynamic behavior of the KdV-mKdV equation and provide methods for solving Sech 2 -type solitary waves, which hold significant theoretical value.

Full-surface defect detection of navel orange based on hyperspectral online sorting technology.

The whole-surface hyperspectral image acquisition of navel orange is particularly important for surface defect detection and quality classification. Because the light intensity at the edge of the navel orange is lower than that in the middle, the defects on the surface of the navel orange cannot be effectively identified. In this paper, a hyperspectral online sorting device for the whole-surface defects of navel orange is proposed. First of all, the image data of navel orange is collected by online detection sorting equipment and the spectral image of the characteristic wave peak of 1655.72 nm was extracted. Then, the light intensity at the edge of the navel orange is enhanced by nonuniformity correction based on quadratic curve fitting, and the light intensity correction of the navel orange is realized. Finally, the corrected image is segmented by the threshold to obtain surface defects, and the number of surface defect pixels is improved effectively compared with that before light intensity correction. Ultimately, the online sorting test is carried out, and the detection accuracy is 100%. This indicates that this method effectively improves the sensitivity of defect detection. At the same time, the dimensionality reduction of hyperspectral data is also carried out, which is conducive to improving the efficiency of online detection.

Online identification and classification of Gannan navel oranges with Cu contamination by LIBS with IGA-optimized SVM.

Elements such as minerals and heavy metals play important roles in the nutrition and safety of agricultural products. It is necessary to develop rapid, online, real-time and in situ methods for monitoring the substances in farm products. Gannan navel oranges are a unique variety of fruit, which may be affected by Cu pollution due to abundant copper mines and other factors. An online identification and classification system based on laser-induced breakdown spectroscopy (LIBS) was developed to detect possible Cu residue in Gannan navel oranges. First, transmission and classification equipment for Gannan navel oranges was built. Second, an LIBS detection module was designed. Finally, a software system for the whole online detection platform was developed based on the C# programming language. The series of operations for the online detection system, which includes the loading, transmission, detection and classification of orange samples, can be controlled. Since the navel orange has an elliptical shape, the LIBS detection module was designed with a long focal length to reduce the influence of fruit plane size fluctuation. The long focal length was optimized to 698 mm, and the depth of field was ±6 mm. Furthermore, a parameter optimization model using a support vector machine (SVM) based on an improved genetic algorithm (IGA) is proposed to improve the classification effect of Gannan navel oranges. This model avoids the over-learning or under-learning caused by improper parameter selection in the regression prediction of SVM. The IGA is used to optimize the penalty parameter c and the kernel parameter g of SVM. LIBS spectral data from two types of navel orange samples with and without Cu contamination were selected as test datasets, and the classification results were compared with those of the standard genetic algorithm-support vector machine (GA-SVM). The investigation showed that the IGA-SVM can provide better classification of navel oranges based on analysis of the LIBS spectral data, and the classification accuracy can reach 98%, which provides significant guidance for the use of LIBS to quickly realize online screening of heavy metals in agriculture products.

Online Detection of Impurities in Corn Deep-Bed Drying Process Utilizing Machine Vision.

Online detection of impurities content in the corn deep-bed drying process is the key technology to ensure stable operation and to provide data support for self-adapting control of drying equipment. In this study, an automatic approach to corn image acquisition, impurity classification and recognition, and impurities content detection based on machine vision technology are proposed. The multi-scale retinex with colour restore (MSRCR) algorithm is utilized to enhance the original image for eliminating the influence of noise. HSV (Hue, saturation, value) colour space parameter threshold is set for image segmentation, and the classification and recognition results are obtained combined with the morphological operation. The comprehensive evaluation index is adopted to quantitatively evaluate the test results. Online detection results show that the comprehensive evaluation index of broken corncobs, broken bracts, and crushed stones are 83.05%, 83.87%, and 87.43%, respectively. The proposed algorithm can quickly and effectively identify the impurities in corn images, providing technical support and a theoretical basis for monitoring impurities content in the corn deep-bed drying process.

Hyperspectral dimension reduction and navel orange surface disease defect classification using independent component analysis-genetic algorithm.

Canker is a common disease of navel oranges that is visible before harvest, and penicilliosis is a common disease occurring after harvest and storage. In this research, the typical fruit surface, canker spots, penicillium spore, and hypha of navel oranges were, respectively, identified by hyperspectral imaging. First, the light intensity on the edge of samples in hyperspectral images was improved by spherical correction. Then, independent component images and weight coefficients were obtained using independent component analysis. This approach, combined with use of a genetic algorithm, was used to select six characteristic wavelengths. The method achieved dimension reduction of hyperspectral data, and the testing time was reduced from 46.21 to 1.26 s for a self-developed online detection system. Finally, a deep learning neural network model was established, and the four kinds of surface pixels were identified accurately.

Field Traction Performance Test Analysis of Bionic Paddy Wheel and Vaned Wheel.

In order to improve the traction performance of a wheel of a micro-tiller on the soil surface of a paddy field, we extracted the surface curve of a cow's hoof and used the cow's hoof as a bionic prototype to design a bionic paddy wheel. In order to verify the passability of the bionic paddy wheel in paddy soil, a wheel-soil test bench was built in the experimental field with a moisture content of 36%. The test results show that under the condition of the same load on the wheel, the changing laws of torque, drawbar pull, and slip ratio of the bionic paddy wheel and the conventional vaned wheel are similar. The torque and drawbar pull of the bionic paddy wheel are higher than those of the conventional vaned wheel at the same slip ratio. The maximum torque and hook traction provided by the bionic paddy wheel and the conventional vaned wheel both increase with the increase of the load on the wheel. Under the same load on the wheel, the bionic paddy wheel is at least 22% higher than the conventional vaned wheel. Compared with the conventional vaned wheel, the bionic paddy wheel can provide a higher driving force and hook traction, which can improve the working efficiency of the vehicle in the paddy field.

Detection model of the plasma spectrum based on the polarization recognition rate induced by a low energy density laser.

Laser-induced breakdown spectroscopy (LIBS) is a fast recognition method for heavy-metal detection. The recognition rate of the characteristic spectrum is related to the laser-induced energy. In order to analyze the polarization characteristics of plasma and the improvement mechanism of recognition rate under the condition of low energy density, a detection model of polarization recognition rate was established by exploring the intensity formula of discrete spectral data. At the Brewster angle, the LIBS and discrete spectral data of Cd and Cu elements induced by five energies were measured in combination with the polarization spectrometric path. According to the model of polarization recognition rate, the optimization effect of the polarization separation approach on the plasma spectra of heavy-metal elements has been clarified, and the recognition rate of discrete spectra induced by low energy density has been improved. This study shows that the increase of laser energy is helpful to the recognition of characteristic spectral lines. Under the same energy induction, the polarization recognition rate of heavy-metal elements is larger, and this characteristic is more obvious under low energy density. This model not only improves the recognition rate of the plasma spectrum but also greatly reduces the requirement of laser energy and the damage of the medium surface, which is a more effective nondestructive testing technology.

Land Use Evolution and Land Ecological Security Evaluation Based on AHP-FCE Model: Evidence from China.

China experienced rapid urbanization and socioeconomic development at an unusual rate during the past four decades. Against such background, land use evolution and land ecological security have both been affected in a volatile way. Therefore, it is necessary to investigate the land use and the land ecological security in China. However, the traditional assessment approaches have paid more attention to the environmental and economic factors than the sustainable development of ecology, which cannot comprehensively assess the land ecological security. From the perspective of ecological sustainable development, this study identifies 3 main factors and 17 sub-factors. We also construct a model to integrate the FCE approach with the AHP. The results show that from 2004 to 2017, China's land use structure was unbalanced. The construction land, mining land, and cultivated land increased rapidly, leading to the shrinkage of ecological land. Moreover, the weight of the sustainable development of resources and the environment, economic sustainable development, social sustainable development are 0.3341, 0.3780, and 0.2879, respectively, demonstrating that economic sustainable development is the most important factor affecting land ecological security. Finally, although the value of comprehensive land ecological security in China has been on the rise from 2004 to 2017, it remains at an unsecured level. Moreover, the value of the sustainable development of resources and the environment has been declining since 2011 and is lower than the values of economic sustainable development and social sustainable development. This study demonstrates that more attention should be paid to enhancing land ecological security, especially promoting the sustainable development of resources and the environment.

Rapid detection of sulfamethazine and ofloxacin residues in duck meat using synchronous fluorescence spectroscopy coupled with chemometric methods.

Rapid detection of antibiotic residues in duck meat is of great significance for strengthening food safety and quality supervision of duck meat and fighting against inferior products in the duck meat market. The objective of the current paper was to evaluate the potential of synchronous fluorescence spectroscopy (SFS) coupled with chemometric methods for the rapid detection of sulfamethazine (SM2) and ofloxacin (OFL) residues in duck meat.The SFS spectral data from duck meat containing different concentrations of SM2 and OFL were preprocessed by baseline offset. The detection conditions, including the adding amounts of ß-mercaptoethanol solution and o-phthalaldehyde solution, as well as the reaction time, were optimized by a single factor experiment for obtaining a better detection effect, and their optimal values were 400 µL , 25 µL , and 40 min, respectively. By comparing 2 chemometric models based on peak-height algorithm and peak-area algorithm, the prediction model based on peak-height algorithm was a better quantitative model with correlation coefficient for the prediction set (Rp) of 0.9031 and 0.9981, the root mean error for the prediction set (RMSEP) of 7.9509 and 0.5267 mg/kg, recovery of 81.7 to 155.1% and 96.4 to 111.2%, and relative standard deviation (RSD) of 4.1 to 6.7% and 2.9 to 6.8% to predict SM2 and OFL residues in duck meat, respectively. Overall, the results of this investigation showed that SFS technique was an effective and rapid tool for the detection of SM2 and OFL residues in duck meat.

In situ diagnosis of mature HLB-asymptomatic citrus fruits by laser-induced breakdown spectroscopy.

Laser-induced breakdown spectroscopy (LIBS) is a promising alternative to conventional methods in classifying citrus huanglongbing (HLB). Mature citrus fruits with similar features were picked and divided into healthy and HLB-asymptomatic groups. LIBS spectra and images were collected by focusing a laser on fresh fruit surfaces without sample preparation. The pH value and soluble solids content of juice as the indicators of acidity and sugar were detected, and the content of Ca, Zn, and K in peel and pulp was analyzed. The characteristic lines from LIBS spectra were extracted by continuous wavelet transform and principal component analysis (PCA). The t-test of these indicators displayed significant difference between the two groups. Fisher discriminant analysis and multilayer perception neural network (MLP) were applied to identify the disease. The classification accuracy reached 100% by PCA-MLP. The results show that LIBS can realize in situ detection of citrus HLB fruits.

Surface-enhanced Raman spectroscopy method for classification of doxycycline hydrochloride and tylosin in duck meat using gold nanoparticles.

This paper investigated on 478 duck meat samples for the identification of 2 kinds of antibiotics, that is, doxycycline hydrochloride and tylosin, that were classified based on surface-enhanced Raman spectroscopy (SERS) combined with multivariate techniques. The optimal detection parameters, including the effects of the adsorption time, and 2 enhancement substrates (i.e., gold nanoparticles as well as gold nanoparticles and NaCl) on Raman intensities, were analyzed using single factor analysis method. The results showed that the optimal adsorption time between gold nanoparticles and analytes was 2 min, and the colloidal gold nanoparticles without NaCl as the active substrate were more conducive to enhance the Raman spectra signal. The SERS data were pretreated by using the method of adaptive iterative penalty least square method (air-PLS) and second derivative, and from which the feature vectors were extracted with the help of principal component analysis. The first four principal components scores were selected as the input values of support vector machines model. The overall classification accuracy of the test set was 100%. The experimental results showed that the combination of SERS and multivariate analysis could identify the residues of doxycycline hydrochloride and tylosin in duck meat quickly and sensitively.

Latency-Optimal Computational Offloading Strategy for Sensitive Tasks in Smart Homes.

In smart homes, the computational offloading technology of edge cloud computing (ECC) can effectively deal with the large amount of computation generated by smart devices. In this paper, we propose a computational offloading strategy for minimizing delay based on the back-pressure algorithm (BMDCO) to get the offloading decision and the number of tasks that can be offloaded. Specifically, we first construct a system with multiple local smart device task queues and multiple edge processor task queues. Then, we formulate an offloading strategy to minimize the queue length of tasks in each time slot by minimizing the Lyapunov drift optimization problem, so as to realize the stability of queues and improve the offloading performance. In addition, we give a theoretical analysis on the stability of the BMDCO algorithm by deducing the upper bound of all queues in this system. The simulation results show the stability of the proposed algorithm, and demonstrate that the BMDCO algorithm is superior to other alternatives. Compared with other algorithms, this algorithm can effectively reduce the computation delay.

Classification and detection of testosterone propionate and nandrolone residues in duck meat using surface-enhanced Raman spectroscopy coupled with multivariate analysis.

There is a critical need for a rapid and simple method of qualitative and quantitative analysis of testosterone propionate (TP) and nandrolone (NT) residues in duck meat. In this study, we applied surface-enhanced Raman spectroscopy (SERS) coupled multivariate analysis for the classification and detection of TP and NT residues in duck meat. A total of 294 duck meat extract samples were obtained from duck breast meats based on a LC-MS/MS sample preparation method with slight modification including 102 duck meat extract samples without TP and NT, 43 duck meat samples containing TP, 47 duck meat extract samples containing NT, and 102 duck meat extract samples containing TP and NT. Raw Raman spectra were pretreated by using adaptive iteratively reweighted penalized least squares (airPLS), normalization and first derivative, and then the score values of first 10 principal components were selected as the inputs of the developed models. A particle swarm optimization-support vector classification (PSO-SVC) model was created to classify all the duck meat samples into the 4 groups (i.e., control group, TP group, NT group, and TP combined with NT group) with the classification accuracies of 99.49 and 100% for training set and test set, respectively. Furthermore, 2 least squares support vector regression (LS-SVR) models were developed to predict the TP values in samples with a determination coefficient (R2) value of 0.9316, root mean square error (RMSE) value of 2.1739, and ratio of prediction to deviation (RPD) value of 3.2189 for the test set, and NT values in samples with an R2 value of 0.9038, RMSE value of 2.2914, and RPD value of 2.9701 for the test set. Surface-enhanced Raman spectroscopy technology, in combination with multivariate analysis, has the potential to become the qualitative and quantitative analysis tool for TP and NT residues in duck meat extract.

Rapid identification of rice species by laser-induced breakdown spectroscopy combined with pattern recognition.

Laser-induced breakdown spectroscopy (LIBS) combined with pattern recognition was proposed to discriminate rice species. LIBS spectra in the range of 210-480 nm wavelength from 11 different rice species were collected and preprocessed. Principal component analysis was applied to extract the characteristic variables from LIBS spectral data. Three pattern recognition methods, discriminant analysis, radial basis function neural network, and multi-layer perceptron neural network (MLP) were performed to compare the precision in identifying rice species. The results showed that the performance of the MLP model was better. The average identification rate of rice species reached 100% and 97.9% in the training and test sets, respectively, with MLP. The highest and lowest percentages for correct identification were 100% for early indica rice, Huai rice 5, Yan japonica 6, Lian japonica 8, Xuhan 1, Lvhan 1, Sheng rice 16, Yang japonica 687, and Fenghan 30, and 77.8% for Wuyu japonica rice in test sets. The overall results demonstrate that LIBS combined with MLP could be utilized to rapidly discriminate rice species.

Identification of Huanglongbing-infected navel oranges based on laser-induced breakdown spectroscopy combined with different chemometric methods.

In order to realize rapid identification of Gannan navel oranges infected by Huanglongbing (HLB), a full optical diagnostic method of laser-induced breakdown spectroscopy (LIBS) was proposed. All navel oranges were collected from Ganzhou, Jiangxi, China, and samples contain healthy and HLB-infected navel oranges. The LIBS spectra of the plasma plume were collected directly from the epidermis of these navel oranges. The navel orange LIBS spectra in the wavelength range of 200-1050 nm were pretreated with smoothing and multiple scatter correction; on the basis of 10×10-fold cross validation, a random forest (RF) model based on continuous wavelet transform (CWT) and principal component analysis (PCA) were analyzed to identify the navel orange of HLB. The results showed that the PCA-RF and CWT-RF models coupled with suitable methods in preprocessing data can identify HLB-infected navel oranges. The average accuracy obtained from the CWT-RF model was 96.86% in the training set and 97.45% in the test set; the average accuracy by the PCA-RF model was 97.64% in the training set and 97.89% in the test set. The overall results demonstrate that LIBS combined with CWT-RF or PCA-RF, as a valuable analytical tool, could be used for HLB-infected navel orange identification.

Analysis of Diethylstilbestrol Residues in Chicken Using Surface-Enhanced Raman Spectroscopy (SERS) Coupled with Multivariate Analysis.

Estrogen residues, including diethylstilbestrol in chicken, are one of the main food safety concerns all over the world owing to a series of negative effects on the human body. Surface-enhanced Raman spectroscopy (SERS) coupled with multivariate analysis was applied to detect rapidly diethylstilbestrol residues in chicken. The detection conditions, including the sizes of colloidal gold nanoparticles (Au NPs) and the additional amounts of Au NPs, chicken extract containing diethylstilbestrol, and magnesium sulfate solution, as well as the adsorption time, were optimized by a single factor experiment to obtain a better detection effect of diethylstilbestrol residues in chicken. Partial least squares regression (PLSR) was the best quantitative model for the detection of diethylstilbestrol residues in chicken by comparing four chemometric models. Diethylstilbestrol residues in chicken could be predicted by PLSR with the low root mean square error (RMSE = 0.4128 mg/L), and the high determination coefficient (R2 = 0.9811) and ratio of prediction to deviation (RPD = 7.2566) for the test set. A novel approach, which has the potential for the analysis of other estrogen residues in meat, was developed to detect rapidly the diethylstilbestrol residues in chicken by using SERS coupled with multivariate analysis.

Detection of heavy metal Cd in polluted fresh leafy vegetables by laser-induced breakdown spectroscopy.

In seeking a novel method with the ability of green analysis in monitoring toxic heavy metals residue in fresh leafy vegetables, laser-induced breakdown spectroscopy (LIBS) was applied to prove its capability in performing this work. The spectra of fresh vegetable samples polluted in the lab were collected by optimized LIBS experimental setup, and the reference concentrations of cadmium (Cd) from samples were obtained by conventional atomic absorption spectroscopy after wet digestion. The direct calibration employing intensity of single Cd line and Cd concentration exposed the weakness of this calibration method. Furthermore, the accuracy of linear calibration can be improved a little by triple Cd lines as characteristic variables, especially after the spectra were pretreated. However, it is not enough in predicting Cd in samples. Therefore, partial least-squares regression (PLSR) was utilized to enhance the robustness of quantitative analysis. The results of the PLSR model showed that the prediction accuracy of the Cd target can meet the requirement of determination in food safety. This investigation presented that LIBS is a promising and emerging method in analyzing toxic compositions in agricultural products, especially combined with suitable chemometrics.

Simultaneous analysis of Cr and Pb in contaminated pork by laser-induced breakdown spectroscopy.

Laser-induced breakdown spectroscopy (LIBS) as a rapid and green method was used to detect heavy metals Cr and Pb in pork contaminated in the lab. The laser-induced plasma was generated by a Q-switched Nd:YAG laser, and the LIBS signal was collected by a spectrometer with a charge-coupled device detector. The traditional calibration curves (CC) and multivariate partial least squares (PLS) algorithm were applied and compared to validate the accuracy in predicting the content of heavy metals in samples. The results demonstrated that the correlation coefficient of CC is poor by the classical univariate calibration method, so the univariate calibration analysis cannot effectively serve the quantitative purpose in analyzing heavy metals' residue in pork with a complex matrix. The analysis accuracy was improved effectively by the PLS method, and the correlation coefficient is 0.9894 for Cr and 0.9908 for Pb. The concentration of Cr and Pb in samples from a prediction set was obtained using the PLS calibration method, and the average relative errors for the 21 samples in the prediction set are lower than 6.53% and 7.82% for Cr and Pb, respectively. The investigated results display that the matrix effect would be reduced effectively during the quantitative analysis of pork by a LIBS-combined PLS model, and the predictive accuracy would be improved greatly compared to traditional univariate analysis.

Highly Efficient Synthesis of Functionalizable Polymers from a CO2/1,3-Butadiene-Derived Lactone.

Making polymers from CO2 and olefins has been long sought and is of particular significance for chemical utilizations of CO2. Herein, high molecular-weight polymers with 29 wt % CO2 were obtained by polymerizing a δ-lactone (L) synthesized from a C-C coupling reaction between CO2 and 1,3-butadiene, an economical large-volume chemical that can also be derived from top biomass platform chemicals. Although L has been known for many years, little was investigated in its polymerization. We found that L's polymerizability can be vitalized upon simply heating in the presence of O2. The polymerization is additive/solvent-free with abundant preserved olefins and up to full monomer conversion, providing a convenient, economical, and scalable avenue to obtain CO2-derived polymers with potentially tailorable properties via the readily modifiable olefins.

Density Functional Theory Calculation and Raman Spectroscopy Studies of Carbamate Pesticides.

In order to obtain the molecular structure vibration information of carbamate pesticide, three carbamate pesticides (carbaryl, carbofuran and aldicarb) were optimized and calculated with B3LYP hybrid functional and 6-31G(d,p) basis set, and their experimental spectra were collected with the Raman spectrometer. The theoretically calculated spectra were compared with the experimental spectra carefully. The results indicated that the theoretically calculated spectra have a very good match with the experimental spectra. The vibrational peaks of three carbamate pesticides were assigned between the range of 400～3 200 cm-1, and the characteristic peaks of carbamate pesticide were found at 874, 1 014, 1 162 and 1 716 cm-1. The characteristic peaks of three carbamate pesticides were found by the contrast of the experimental spectra. The results can provide a theoretical basis for the detection of carbamate pesticide, and will be applied to the identification of carbamate pesticide residues in agricultural products.