Chemometrics and Intelligent Laboratory Systems最新文献

{"title":"Estimation of quality variables in a continuous train of reactors using recurrent neural networks-based soft sensors","authors":"Mariano M. Perdomo ,&nbsp;Luis A. Clementi ,&nbsp;Jorge R. Vega","doi":"10.1016/j.chemolab.2024.105204","DOIUrl":"10.1016/j.chemolab.2024.105204","url":null,"abstract":"<div><p>The first stage in the industrial production of Styrene-Butadiene Rubber (SBR) typically consists in obtaining a latex from a train of continuous stirred tank reactors. Accurate real-time estimation of some key process variables is of paramount importance to ensure the production of high-quality rubber. Monitoring the mass conversion of monomers in the last reactor of the train is particularly important. To this effect, various soft sensors (SS) have been proposed, however they have not addressed the underlying complex dynamic relationships existing among the process variables. In this work, a SS based on recurrent neural networks (RNN) is developed to estimate the mass conversion in the last reactor of the train. The main challenge is to obtain an adequate estimate of the conversion both in its usual steady-state operation and during its frequent transient operating phases. Three architectures of RNN: Elman, GRU (Gated Recurrent Unit), and LSTM (Long Short-Term Memory) are compared to critically evaluate their performances. Moreover, a comprehensive analysis is conducted to assess the ability of these models to represent different operational modes of the train. The results reveal that the GRU network exhibits the best performance for estimating the mass conversion of monomers. Then, the performance of the proposed model is compared with a previously-developed SS, which was based on a linear estimation model with a Bayesian bias adaptation mechanism and the use of Control Charts for decision-making. The model proposed here proved to be more efficient for estimating the mass conversion of monomers, particularly during transient operating phases. Finally, to evaluate the methodology utilized for designing the SS, the same RNN architectures were trained to online estimate another quality variable: the mass fraction of Styrene bound to the copolymer. The obtained results were also acceptable.</p></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"253 ","pages":"Article 105204"},"PeriodicalIF":3.7,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142039656","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A 1D-CNN model for the early detection of citrus Huanglongbing disease in the sieve plate of phloem tissue using micro-FTIR","authors":"Biyun Yang ,&nbsp;Zhiling Yang ,&nbsp;Yong Xu ,&nbsp;Wei Cheng ,&nbsp;Fenglin Zhong ,&nbsp;Dapeng Ye ,&nbsp;Haiyong Weng","doi":"10.1016/j.chemolab.2024.105202","DOIUrl":"10.1016/j.chemolab.2024.105202","url":null,"abstract":"<div><p>Among the most frequently diagnosed diseases in citrus, citrus Huanglongbing disease has caused severe economic losses to the citrus industry worldwide since there is no curable method and it spreads quickly. As callose accumulation in phloem is one of the early response events to Asian species <em>Candidatus</em> Liberibacter asiaticus (<em>C</em>Las) infection, the dynamic perception of the sieve plate region can be used as an indicator for the early diagnosis of citrus HLB disease. In this study, one-dimensional convolutional neural network (1D-CNN) models were established to achieve early detection of HLB disease based on spectral information in the sieve plate region using Fourier transform infrared microscopy (micro-FTIR) spectrometer. Partial least squares regression (PLSR) and the least squares support vector machine regression (LS-SVR) models are used for the prediction of callose based on the micro-FTIR information in the sieve plate region of the citrus midrib. Furthermore, an improved data augmentation method by superimposing Gaussian noise was proposed to expand the spectral amplitude. The proposed method has achieved 98.65 % classification accuracy, which was higher than that of other traditional algorithms such as the logistic model tree (LMT), linear discriminant analysis (LDA), Bayes (BS), support vector machine (SVM) and k-nearest neighbors (kNN), and also than that of the molecular detection qPCR (Quantitative real-time polymerase chain reaction) method. Finally, based on the established early detection model with laboratory samples, it can also be used to detect the citrus HLB in complex field samples by using model updating methods, and the overall detection accuracy of the model reached 91.21 %. Our approach has potential for the early diagnosis of citrus HLB disease from the microscopic scale, which would provide useful and precise guidelines to prevent and control citrus HLB disease.</p></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"252 ","pages":"Article 105202"},"PeriodicalIF":3.7,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141992929","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mixture Gaussian process model with Gaussian mixture distribution for big data","authors":"Yaonan Guan ,&nbsp;Shaoying He ,&nbsp;Shuangshuang Ren ,&nbsp;Shuren Liu ,&nbsp;Dewei Li","doi":"10.1016/j.chemolab.2024.105201","DOIUrl":"10.1016/j.chemolab.2024.105201","url":null,"abstract":"<div><p>In the era of chemical big data, the high complexity and strong interdependencies present in the datasets pose considerable challenges when constructing accurate parametric models. The Gaussian process model, owing to its non-parametric nature, demonstrates better adaptability when confronted with complex and interdependent data. However, the standard Gaussian process has two significant limitations. Firstly, the time complexity of inverting its kernel matrix during the inference process is <span><math><mrow><mi>O</mi><msup><mrow><mrow><mo>(</mo><mi>n</mi><mo>)</mo></mrow></mrow><mrow><mn>3</mn></mrow></msup></mrow></math></span>. Secondly, all data share a common kernel function parameter, which mixes different data types and reduces the model accuracy in mixing-category data identification problems. In light of this, this paper proposes a mixture Gaussian process model that addresses these limitations. This model reduces time complexity and distinguishes data based on different data features. It incorporates a Gaussian mixture distribution for the inducing variables to approximate the original data distribution. Stochastic Variational Inference is utilized to reduce the computational time required for parameter inference. The inducing variables have distinct parameters for the kernel function based on the data category, leading to improved analytical accuracy and reduced time complexity of the Gaussian process model. Numerical experiments are conducted to analyze and compare the performance of the proposed model on different-sized datasets and various data category cases.</p></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"253 ","pages":"Article 105201"},"PeriodicalIF":3.7,"publicationDate":"2024-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142002255","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Online nonlinear data reconciliation to enhance nonlinear dynamic process monitoring using conditional dynamic variational autoencoder networks with particle filters","authors":"Kuanhsuan Chiu ,&nbsp;Junghui Chen ,&nbsp;Zhengjiang Zhang","doi":"10.1016/j.chemolab.2024.105198","DOIUrl":"10.1016/j.chemolab.2024.105198","url":null,"abstract":"<div><p>In the chemical plants, data-driven process monitoring serves as a vital tool to ensure product quality and maintain production line safety. However, the accuracy of monitoring hinges directly upon the quality of process data. Given the inherently slow and complex nature of chemical processes, coupled with the potential for gross errors in process data leading to inaccuracies in model predictions, this paper proposes a method called Conditional Dynamic Variational Autoencoder combined with a Particle Filter (CDVAE-PF) for data reconciliation and subsequent process monitoring. CDVAE-PF leverages the capabilities of Conditional Dynamic Variational Autoencoder (CDVAE) to effectively model chemical process data in the presence of noise. This probabilistic model serves as the foundation for the Particle Filter (PF), which is employed for data reconciliation. Moreover, CDVAE-PF incorporates mechanisms to detect and rectify gross errors in process data, further enhancing its efficacy in data reconciliation. Subsequently, monitoring indices based on CDVAE are established to facilitate process monitoring. Through numerical simulations of a two-to-one variables Continuous Stirred Tank Reactor (CSTR) example and a fifteen-to-one variables dichloroethane distillation process from an actual chemical plant, CDVAE-PF demonstrates its effectiveness by reducing mean absolute error to 7.8 % and 12.8 % respectively in gross error data reconciliation. Moreover, in terms of monitoring performance, CDVAE-PF successfully mitigates misjudgments caused by gross errors, thereby significantly enhancing the reliability of process monitoring in chemical plants.</p></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"253 ","pages":"Article 105198"},"PeriodicalIF":3.7,"publicationDate":"2024-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142039660","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"GA-XGBoost, an explainable AI technique, for analysis of thrombin inhibitory activity of diverse pool of molecules and supported by X-ray","authors":"Vijay H. Masand ,&nbsp;Sami Al-Hussain ,&nbsp;Abdullah Y. Alzahrani ,&nbsp;Aamal A. Al-Mutairi ,&nbsp;Arwa sultan Alqahtani ,&nbsp;Abdul Samad ,&nbsp;Gaurav S. Masand ,&nbsp;Magdi E.A. Zaki","doi":"10.1016/j.chemolab.2024.105197","DOIUrl":"10.1016/j.chemolab.2024.105197","url":null,"abstract":"<div><p>The present work involves extreme gradient boosting in combination with shapley values, a thriving amalgamation under the terrain of Explainable artificial intelligence, along with genetic algorithm for the analysis of thrombin inhibitory activity of diverse pool of 2803 molecules. The methodology involves genetic algorithm for feature selection, followed by extreme gradient boosting analysis. The eight parametric genetic algorithm - extreme gradient boosting analysis has high statistical acceptance with R²_tr = 0.895, R²_L10%O = 0.900, and Q2F3 = 0.873. Shapley additive explanations, which provide each variable in a model an importance value, served as the foundation for the interpretation. Then, <em>ceteris paribus</em> approach involving comparison of counterfactual examples has been used to understand the influence of a structural feature on activity profile. The analysis indicates that aromatic carbon, ring/non-ring nitrogen in combination with other structural features govern the inhibitory profile. The genetic algorithm - extreme gradient boosting model's simplicity and predictions suggest that “Explainable AI” is useful in the future for identifying and using structural features in drug discovery.</p></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"253 ","pages":"Article 105197"},"PeriodicalIF":3.7,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141992615","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel feature selection framework for incomplete data","authors":"Cong Guo,&nbsp;Wei Yang,&nbsp;Zheng Li,&nbsp;Chun Liu","doi":"10.1016/j.chemolab.2024.105193","DOIUrl":"10.1016/j.chemolab.2024.105193","url":null,"abstract":"<div><p>Feature selection on incomplete datasets is a challenging task. To address this challenge, existing methods first employ imputation methods to complete the dataset and then perform feature selection based on the imputed dataset. Since missing value imputation and feature selection are entirely independent, the importance of features cannot be considered during imputation. However, in real-world scenarios or datasets, different features have varying degrees of importance. To this end, we proposed a novel incomplete data feature selection framework that considers feature importance. The framework mainly consists of two alternating iterative stages: M-stage and W-stage. In the M-stage, missing values are imputed based on a given feature importance vector and multiple initial imputation results. In the W-stage, an improved reliefF algorithm is employed to learn the feature importance vector based on the imputed data. In particular, the feature importance output by the W-stage in the current iteration will be used as the input of the M-stage in the next iteration. Experimental results on artificial and real missing datasets demonstrate that the proposed method outperforms other approaches significantly.</p></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"252 ","pages":"Article 105193"},"PeriodicalIF":3.7,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141930608","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Structural attributes driving λmax towards NIR region: A QSPR approach","authors":"Payal Rani ,&nbsp;Sandhya Chahal ,&nbsp;Priyanka ,&nbsp;Parvin Kumar ,&nbsp;Devender Singh ,&nbsp;Jayant Sindhu","doi":"10.1016/j.chemolab.2024.105199","DOIUrl":"10.1016/j.chemolab.2024.105199","url":null,"abstract":"<div><p>Near-infrared materials find extensive applications in <em>bio</em>-sensing, photodynamic treatment, anti-counterfeiting and <em>opto</em>-electronics. Their progress has notably expanded possibilities in optical communication systems, non-invasive imaging and targeted therapy, benefiting fields such as material science, medicine, tele-communication and biology. In light of these advancements, developments of near-infrared region (NIR) based probes are highly desirable. Moreover, the prediction of the optical properties of a compound prior to its synthesis can diminish the need for expensive experimental testing. Considering the importance of prior prediction, we herein present QSPR models for the prediction of absorption maxima using a dataset of 384 compounds. The aim of the present study is to identify molecular features that could shift their <span><math><mrow><msub><mi>λ</mi><mi>max</mi></msub></mrow></math></span> in the near-infrared region. The Monte Carlo Optimization approach along with the index of ideality of correlation (TF₂) has been utilized using CORAL 2019 software for the development of ten splits. The predictability of the resulting ten models was assessed using various validation metrics. The model derived from the tenth split proved to be efficient, exhibiting <span><math><mrow><msubsup><mi>R</mi><mrow><mi>V</mi><mi>a</mi><mi>l</mi><mi>i</mi><mi>d</mi><mi>a</mi><mi>t</mi><mi>i</mi><mi>o</mi><mi>n</mi></mrow><mn>2</mn></msubsup><mo>=</mo><mn>0.8561</mn></mrow></math></span>, <span><math><mrow><mi>I</mi><mi>I</mi><mi>C</mi><mo>=</mo><mn>0.7849</mn><mspace></mspace><mi>a</mi><mi>n</mi><mi>d</mi><mspace></mspace><msup><mi>Q</mi><mn>2</mn></msup><mo>=</mo><mn>0.8512</mn></mrow></math></span>. Good and bad fragments were also identified that are responsible for the change in absorption maxima (<span><math><mrow><msub><mi>λ</mi><mi>max</mi></msub></mrow></math></span>). Identified fragments were utilized for designing ten new molecules to evaluate their reliability. It was observed that molecules designed using positive attributes shifted the absorption maxima towards the near-infrared region, specifically between 711 and 893 nm. This study opens up new possibilities for the advancement of NIR-based chromophores and will contribute significantly by reducing the overall cost of chromophore development.</p></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"252 ","pages":"Article 105199"},"PeriodicalIF":3.7,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141985556","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development of eco-efficient limestone calcined clay cement (LC3) mortars by a multi-step experimental design","authors":"Guilherme Ascensão ,&nbsp;Emanuele Farinini ,&nbsp;Victor M. Ferreira ,&nbsp;Riccardo Leardi","doi":"10.1016/j.chemolab.2024.105195","DOIUrl":"10.1016/j.chemolab.2024.105195","url":null,"abstract":"<div><p>Calcined clays and calcium carbonates can be used to reduce clinker factor in blended cements, offering significant economic and environmental benefits. Limestone calcined clay cements (LC3) combine them as supplementary cementitious materials (SCMs) for delivering a sustainable alternative to conventional Ordinary Portland Cement products, with envisioned applications in construction and rehabilitation of historical buildings.</p><p>This study reports a chemometric approach to the development of LC3 mortars, targeting to minimize clinker content while maintaining or improving the technical characteristics. To evaluate their performance, apparent density, modulus of elasticity, open porosity, water absorption, flexural and compressive strength have been considered as responses. Multiple Linear Regression (MLR) and Principal Component Analysis (PCA) were employed in a three-step mixture-process design allowing to obtain LC3 mixtures with a 21 wt% reduction in clinker while achieving notable enhancements in the physical properties (open porosity −9% and water absorption −10 %), along with commendable increases in compressive strength (+17 %) when compared to benchmark mortars produced without SCMs. The successful integration of multivariate techniques in designing sustainable building materials is showcased, highlighting the potential of chemometric methodologies to reduce the environmental impact as well as to increase the performance of building materials.</p></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"253 ","pages":"Article 105195"},"PeriodicalIF":3.7,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0169743924001357/pdfft?md5=df2595bb491efe74bd659d9e5af5222e&pid=1-s2.0-S0169743924001357-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142039659","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Temporal graph convolutional network soft sensor for molecular weight distribution prediction","authors":"Weiwei Guo ,&nbsp;Jialiang Zhu ,&nbsp;Xinyi Yu ,&nbsp;Mingwei Jia ,&nbsp;Yi Liu","doi":"10.1016/j.chemolab.2024.105196","DOIUrl":"10.1016/j.chemolab.2024.105196","url":null,"abstract":"<div><p>In chemical processes with distributed outputs, characteristics of products are influenced by their distributions and significantly correlated with process variables. It is crucial for an accurate distribution characteristic prediction to adequately describe variable relationships and their temporal variations. For this purpose, a temporal graph convolutional network (TGCN) soft sensor is developed to describe the distribution of outputs. First, the variable relationships are represented in a topology subgraph based on prior knowledge. Then, the graph is supplemented based on variable screening results with the maximal information coefficient (MIC) as standard. Finally, the graph convolutional mechanism is used to model variable relationships, the gated recurrent unit to capture temporal dependencies, and GNNexplainer to provide a comprehensive explanation for the prediction. Results suggest that prediction accuracy and explainability is improved by the proposed TGCN soft sensor on the basis of prior knowledge, and verified in the case of molecular weight distribution (MWD) modeling.</p></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"252 ","pages":"Article 105196"},"PeriodicalIF":3.7,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141930610","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Generating spectral samples with analyte concentration values using the adversarial autoencoder","authors":"Guangzao Huang ,&nbsp;Xinyu Zhao ,&nbsp;Xiao Chen ,&nbsp;Shujat Ali ,&nbsp;Wen Shi ,&nbsp;Zhonghao Xie ,&nbsp;Xiaojing Chen","doi":"10.1016/j.chemolab.2024.105194","DOIUrl":"10.1016/j.chemolab.2024.105194","url":null,"abstract":"<div><p>The prediction of analyte concentration by spectral responses using a calibration model is a commonly used method in chemical analysis. However, insufficient modeling samples will limit the performance of the calibration model. Artificial generation of spectral samples with analyte concentration values is an effective way to address the shortage of modeling samples. However, traditional methods for generating spectral samples with concentration values still have problems in terms of diversity and accuracy. We proposed a method for generating spectral samples with analyte concentration values based on an adversarial autoencoder (AAE). The proposed method combined spectral responses and analyte concentration as the inputs and fitted the extracted latent variables into a prior distribution. By decoding the random sampling points of the prior distribution, the spectral samples with analyte concentration values were generated. Four spectral datasets were used to validate the effectiveness of the proposed method. Two traditional spectral generation methods were used to evaluate the performance of the proposed methods. It was found that the proposed method performed significantly better than traditional ones. The spectral responses generated by the proposed method had good diversity and similarity to the real ones. In addition, the generated spectral samples could also accurately simulate the actual relationship between spectral responses and analyte properties. The proposed method is an effective solution to the problem of insufficient modeling samples in the quantitative analysis of spectral technology.</p></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"252 ","pages":"Article 105194"},"PeriodicalIF":3.7,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141949460","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}