An effective method for the rapid detection of microplastics in soil

Application of least square support vector machine and multivariate adaptive regression spline models in long term prediction of river water pollution

The present investigation introduces a robust soft computing model by comparing twelve least square support vector machine (LSSVM), six long short-term memory (LSTM), and thirty-six artificial neural network (ANN) models to predict the unconfined compressive strength (UCS) of fine-grained soil. For that purpose, a database of fine content, dry unit weight, porosity, void ratio, degree of saturation, and specific gravity results of 85 soil specimens has been compiled from the literature. 75 and 10 soil specimens were trained and tested for each model. Six training databases have been prepared to analyze the effect of quality and quantity of training database by selecting 50%, 60%, 70%, 80%, 90%, and 100% of 75 soil specimens. The performance comparison demonstrated that the LSTM model (MD 113) requires fewer training datasets (50% of 75) than the LSSVM (MD 102 and MD 108) and ANN (MD 120, MD 127, MD 136, MD139, MD 148, and MD 150) models. Also, it was observed that the nonlinear LSSVM model (MD 108) is unaffected by multicollinearity in training datasets and predicted UCS better than the linear LSSVM model (MD 102). Furthermore, the Levenberg-Marquardt neural network model (MD 120) has outperformed the other ANN models with the root mean square error (RMSE) of 5.1214 N/cm2, the mean absolute error (MAE) of 4.1379 N/cm2, and correlation (R) of 0.9836. The overall performance comparison revealed that the LSTM model is more potent than the LSSVM and ANN models. The LSTM model predicted the UCS of fine-grained soil with the RMSE of 4.7539 N/cm2, the MAE of 4.2461 N/cm2, and R of 0.9880. Conversely, cosine amplitude sensitivity analysis demonstrated that the fine content and dry unit weight influence the prediction of virgin UCS of fine-grained soils.

In this paper, based on the Discrete Wavelet Transform (DWT) and Least Square Support Vector Machine (LSSVM), the torque observer for hybrid electric vehicles (HEV) is established. Data from the benchmarking test is employed as the training sample, and the DWT-LSSVM based multi-layer observer was trained to observe the torque distribution results of hybrid electric vehicles. The DWT -LSSVM based multi-layer observer can accurately offer the control results of the original vehicle and solve the problem that it is difficult to reproduce the control results of the original vehicle completely. The main work is as follows: first, a multi-layer observation model based on DWT-LSSVM is established. Multiple LSSVM modules are used to observe the input signals with different frequency that are decomposed by wavelet method, and wavelet reconstruction method is used to integrate the observation results of different input signals to obtain the final observation result of vehicle torque distribution. Secondly, the cross-validation method is used to optimize the parameters in LSSVM model to ensure the accuracy of model prediction. Thirdly, taking the test data of a four-wheel drive hybrid electric vehicle (4WD-HEV)as an example, the observation model proposed in this paper is compared with the simply LSSVM observation model. The results show that the observation results of the torque observation model based on DWT-LSSVM are better than those by the simply LSSVM observation model. The compare results justify the proposed method can improve the accuracy of torque observation.

Two chemometric methods were performed for the determination of acetic acid of fruit vinegars using near infrared (NIR) spectroscopy. Three varieties of fruit vinegars were prepared and 135 samples (45 samples for each variety) were selected for the calibration set, whereas 45 samples (15 samples for each variety) for the validation set. Partial least squares (PLS) analysis was the calibration method as well as extraction method for latent variables (LVs). The first eight LVs were employed as the inputs of least squares-support vector machine (LS-SVM) model. Then LS-SVM model with radial basis function (RBF) kernel was applied to build the regression model compared with PLS model. The correlation coefficient (r), root mean square error of prediction (RMSEP) and bias for validation set were 0.994, 0.814 and -0.091 by PLS, whereas 0.997, 0.651 and 0.011 by LS-SVM, respectively. LS-SVM model outperformed PLS model, but both models achieved an excellent prediction precision. The results indicated that NIR spectroscopy combined with chemometrics could be utilized as a high precision and fast way for the determination of acetic acid of fruit vinegars.

Application of Landsat-8 and Sentinel-2 for retrieval of chlorophyll-a in a shallow freshwater lake

Application of artificial intelligence to forecast hydrocarbon production from shales

The basic oxygen furnace (BOF) steelmaking process in the steel industry is highly complicated, and subject to variations in raw material composition. During the BOF steelmaking process, it is essential to maintain the carbon content and the endpoint temperature at their set points in the liquid steel. This paper presents intelligent models used to estimate the endpoint temperature in the basic oxygen furnace (BOF) steelmaking process. An artificial neural network (ANN) model and a least-squares support vector machine (LSSVM) model are proposed and their estimation performance compared. The classical partial least-squares (PLS) method was also compared with the others. Results of the estimations using the ANN, LSSVM and PLS models were compared with the operation data, and the root-mean square error (RMSE) for each model was calculated to evaluate estimation performance. The RMSE of the LSSVM model 15.91, which turned out to be the best estimation. RMSE values for the ANN and PLS models were 17.24 and 21.31, respectively, indicating their relative estimation performance. The essential input parameters used in the models can be selected by sensitivity analysis. The RMSE for each model was calculated again after a sequential input selection process was used to remove insignificant input parameters. The RMSE of the LSSVM was then 13.21, which is better than the previous RMSE with all 16 parameters. The results show that LSSVM model using 13 input parameters can be utilized to calculate the required values for oxygen volume and coolant needed to optimally adjust the steel target temperature. (Received July 30, 2018; Accepted September 28, 2018)

Kalman filter based on a fractional discrete-time stochastic augmented CoVid-19 model

Online analysis of watercore apples by considering different speeds and orientations based on Vis/NIR full-transmittance spectroscopy

The widespread occurrence of microplastics (MPs) in agricultural soils raises global concern, yet a systematic understanding of their national‐scale distribution and drivers remains elusive. Here, we present the first comprehensive, nationwide analysis of MPs in Chinese farmland soils, integrating abundance, morphology, and polymer data from 169 sites across 31 provinces with regional agricultural practices and soil properties. The results showed that the abundance of MPs in Chinese farmland soils ranged from 240 to 12,720 items kg −1 . Among the identified MPs, films were the predominant morphological category, accounting for 58.4%, while small‐sized particles dominated across dimensional distributions, accounting for 65.5%. Polyethylene (PE) was consistently the most prevalent polymer in all soil samples analyzed, accounting for 36.5%. The analysis results further clarified the key driving factors of MP distribution. The accumulation of MPs in soil environments resulting from local agricultural and human activities, or atmospheric deposition, may affect the transport and accumulation of small‐sized MPs. Soil properties also affected the distribution characteristics of MPs. The abundance of MPs and pH were significantly and negatively correlated ( p < 0.01). Soil electrical conductivity (EC) had a significant positive correlation with the abundance of fiber MPs ( p < 0.05), and coarse sand content was positively correlated with pellet MP counts ( p < 0.05). This study establishes that MP pollution in Chinese farmland is a spatially structured mosaic, governed by both human input and soil‐specific retention. Our findings provide an empirical basis for targeted source control (e.g., plastic mulching film management) and advance risk assessment by incorporating soil‐mediated fate processes.

A rapid indicator of mercury in soil using a plant (Artemisia lavandulaefolia DC., ALDC) commonly distributed in mercury mining area was established by fusion of Fourier-transform near-infrared (FT-NIR) spectroscopy coupled with least squares support vector machine (LS-SVM). The representative samples of ALDC (stem and leaf) were gathered from the surrounding and distant areas of the mercury mines. As a reference method, the total mercury contents in soil and ALDC samples were determined by a direct mercury analyzer incorporating high-temperature decomposition, catalytic adsorption for impurity removal, amalgamation capture, and atomic absorption spectrometry (AAS). Based on the FT-NIR data of ALDC samples, LS-SVM models were established to distinguish mercury-contaminated and ordinary soil. The results of reference analysis showed that the mercury level of the areas surrounding mercury mines (0–3 kilometers, 7.52–88.59 mg/kg) was significantly higher than that of the areas distant from mercury mines (>5 kilometers, 0–0.75 mg/kg). The LS-SVM classification model of ALDC samples was established based on the original spectra, smoothed spectra, second-derivative (D2) spectra, and standard normal transformation (SNV) spectra, respectively. The prediction accuracy of D2-LS-SVM was the highest (0.950). FT-NIR combined with LS-SVM modeling can quickly and accurately identify the contaminated ALDC. Compared with traditional methods which rely on naked eye observation of plants, this method is objective and more sensitive and applicable.

Ink transfer rate (ITR) is a reference index to measure the quality of 3D additive printing. In this study, an ink transfer rate prediction model is proposed by applying the least squares support vector machine (LSSVM). In addition, enhanced garden balsam optimization (EGBO) is used for selection and optimization of hyperparameters that are embedded in the LSSVM model. 102 sets of experimental sample data have been collected from the production line to train and test the hybrid prediction model. Experimental results show that the coefficient of determination (R2) for the introduced model is equal to 0.8476, the root-mean-square error (RMSE) is 6.6 × 10 (−3), and the mean absolute percentage error (MAPE) is 1.6502 × 10 (−3) for the ink transfer rate of 3D additive printing.

Background/Purpose: The acquisition of three-dimensional ground reaction forces (3D-GRF) during running is crucial for monitoring and assessing running performance. However, limitations often arise in collecting 3D-GRF due to equipment and environmental constraints. Inertial sensors are gaining popularity in running applications because of their convenience, broad applicability, and cost-effectiveness. This study aimed to utilize an inertial sensor for gathering kinematic data during running, establish a high-precision neural network model for continuous 3D-GRF estimation, and investigate estimation variances across different body segments. Method: A total of thirty-three college students (17 male, 16 female) participated in this study. Kinematic and kinetic data were concurrently recorded using inertial sensors placed on the lower back, lateral thigh, anterior lower leg, and dorsum of the foot, while three-dimensional force plates were utilized during running at speeds of 8/10/12 km/h. The dataset was partitioned into training, validation, and test sets at a ratio of 24:3:6. A Long Short-Term Memory Neural Network (LSTM) was developed for training to predict 3D-GRF. Evaluation metrics included coefficients of determination, Root Mean Square Error (RMSE), and Normalized Root Mean Square Error (NRMSE). Models customized for specific body segments underwent a nine-fold cross-validation procedure, employing one-way ANOVA to evaluate estimation variances across different anatomical sites. Results: Estimation outcomes at different sites indicated optimal estimation accuracy at the right thigh. The coefficients of determination for Anteroposterior (AP)-GRF, Mediolateral (ML)-GRF, and Vertical (V)-GRF were 0.81±0.00, 0.55±0.14, and 0.97±0.02, respectively. RMSE values were 0.06±0.01BW, 0.06±0.01BW, and 0.14±0.03BW. Significant variances were observed between the right thigh and other sites in the horizontal plane (p < 0.05). The results from the test set showed correlation coefficients, RMSE, and NRMSE for AP-GRF, ML-GRF, and V-GRF: AP-GRF, 0.94±0.01, 0.06±0.01BW, 4.78%±0.50%; ML-GRF, 0.83±0.08, 0.10±0.01BW, 9.70%±2.41%; V-GRF, 0.99±0.00, 0.14±0.02BW, 2.79%±0.24%. Conclusion/Discussion: 1. The neural network model developed in this study demonstrated exceptional accuracy across three dimensions, facilitating continuous estimation of 3D ground reaction forces during running. Correlation coefficients surpassed 0.8 in every direction, with RMSE values below 0.2BW and NRMSE below 5%. 2. Estimation results from inertial sensors positioned at different sites exhibited diverse levels of disparity. The lateral thigh emerged as the most proficient location for estimation, with the lower back excelling in vertical ground reaction force estimation but displaying comparatively weaker performance in horizontal ground reaction force estimation.

A robust model for diagnosing water stress of winter wheat by combining UAV multispectral and thermal remote sensing

A method to rapidly construct 3D canopy scenes for maize and their spectral response evaluation