Chemometrics and Intelligent Laboratory Systems最新文献

{"title":"Optimal designs for mixture choice experiments by simulated annealing","authors":"Yicheng Mao ,&nbsp;Roselinde Kessels","doi":"10.1016/j.chemolab.2024.105305","DOIUrl":"10.1016/j.chemolab.2024.105305","url":null,"abstract":"<div><div>Mixture choice experiments investigate people’s preferences for products composed of different ingredients. To ensure the quality of the experimental design, many researchers use Bayesian optimal design methods. Efficient search algorithms are essential for obtaining such designs. Yet, research in the field of mixture choice experiments is not extensive. Our paper pioneers the use of a simulated annealing (SA) algorithm to construct Bayesian optimal designs for mixture choice experiments. Our SA algorithm not only accepts better solutions, but also has a certain probability of accepting inferior solutions. This approach effectively prevents rapid convergence, enabling broader exploration of the solution space. Although our SA algorithm may start more slowly than the widely used mixture coordinate-exchange method, it generally produces higher-quality mixture choice designs after a reasonable runtime. We demonstrate the superior performance of our SA algorithm through extensive computational experiments and a real-life example.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"257 ","pages":"Article 105305"},"PeriodicalIF":3.7,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143155810","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":"An accurate prediction of drug–drug interactions and side effects by using integrated convolutional and BiLSTM networks","authors":"Sabir Ali ,&nbsp;Waleed Alam ,&nbsp;Hilal Tyara ,&nbsp;Kil To Chong","doi":"10.1016/j.chemolab.2024.105304","DOIUrl":"10.1016/j.chemolab.2024.105304","url":null,"abstract":"<div><div>Multiple drugs have gained attention for the treatment of complex diseases. However, while numerous drugs offer benefits, they also cause undesirable side effects. Accurate prediction of drug–drug interactions is crucial in drug discovery and safety research. Therefore, an efficient and reliable computational method is necessary for predicting drug–drug interactions and their associated side effects. In this study, we introduce a computational method based on integrating convolutional and BiLSTM networks to predict the types of drug–drug interactions. The Morgan fingerprints approach was utilized to encode the drug’s SMILES, and the Tanimoto coefficient structural similarity profile-based approach was used to determine similarities. These encoded drugs were passed through convolutional and BiLSTM layers to extract important feature maps. The ReLU activation function and the dense layer were employed for feature dimensionality reduction. The last dense layer used the softmax function to classify the 86 types of drug–drug interactions. The proposed model achieved a performance of 95.38% accuracy and 98.78% AUC, respectively. The proposed model outperformed and surpassed all the existing state-of-the-art models.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"257 ","pages":"Article 105304"},"PeriodicalIF":3.7,"publicationDate":"2024-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143155812","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":"Optimization methods for tensor decomposition: A comparison of new algorithms for fitting the CP(CANDECOMP/PARAFAC) model","authors":"Huiwen Yu ,&nbsp;Kasper Green Larsen ,&nbsp;Ove Christiansen","doi":"10.1016/j.chemolab.2024.105290","DOIUrl":"10.1016/j.chemolab.2024.105290","url":null,"abstract":"<div><div>Tensor decomposition is widely used for multi-way data analysis and computations in chemical science. CP decomposition is one of the most useful tensor decomposition models for capturing the essential information in massive multi-way chemical data and for efficiently performing computations with such tensors. However, efficiently and accurately computing the tensor decomposition itself is a nontrivial problem that sometimes limits the advantage of tensor decomposition methods. In this work we propose and test three new decomposition algorithms, that are defined from extrapolation ideas applied to the alternating least square (ALS) algorithm for CP tensor decomposition. The performance of the proposed algorithms are validated on both a variety of simulated datasets and real experimental datasets including fluorescence spectroscopy data, hyperspectral data and electroencephalogram (EEG) data. The results show that the proposed algorithms significantly accelerate the standard CP-ALS decomposition while maintaining favorable accuracy. One of the proposed methods, denoted direct inversion of the iterative subspace-like extrapolated ALS(CP-AD), is inspired from widely used extrapolation procedures used in the context of solving non-linear equations in quantum chemistry, and shows a particular attractive combination of a much reduced number of iterations needed for convergence, and modest computational cost. For example, CP-AD provided resulting tensors of similar accuracy but significantly lower computational cost than the standard CP-ALS algorithm and the widely used line-search based CP-ALS extrapolation procedure. The proposed methodology may thereby boost the application of tensor decomposition modeling in both experimental and computational chemistry.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"257 ","pages":"Article 105290"},"PeriodicalIF":3.7,"publicationDate":"2024-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143155809","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":"Stacking ensemble learning algorithm based rapid inverse modelling of copper grade using imaging spectral data","authors":"Jingli Wang ,&nbsp;Jingxiang Gao","doi":"10.1016/j.chemolab.2024.105308","DOIUrl":"10.1016/j.chemolab.2024.105308","url":null,"abstract":"<div><div>The determination of copper ore grade in a reasonably fast and accurate manner is of great practical significance for the purposes of ore dressing and ore allocation in mines. The most common method of determining the grade of copper ore is chemical analysis. However, this method has several disadvantages, including a lengthy determination period, the possibility of chemical pollution, and a lag in the results of ore dressing and ore allocation. Hyperspectral imaging technology is capable of both spectral resolution and image resolution. It is able to obtain the indicators of the sample to be measured while retaining its original physical and chemical properties. This makes it possible to overcome the shortcomings of traditional methods, allowing for accurate, non-destructive, environmentally friendly, rapid detection of samples. Stacking can often provide higher predictive accuracy than a single model by combining the predictions of multiple models, and has the advantages of reduced overfitting, model diversity, flexibility and adaptability. Stacking ensemble learning algorithm is rarely used for hyperspectral quantitative inversion modelling. In this study, 138 copper samples from the Mirador Copper Mine were employed as a data source. The spectral data of the copper samples and chemical analyses of the copper grades were collected utilising a Pika L with a Pika NIR-320 hyperspectral imager. Firstly, the raw spectral data were subjected to mutual information computation as a means of serial fusion of the spectral data, and the fused data were subjected to SG smoothing to remove noise from the spectral experiments. Subsequently, the pre-processed spectral data were subjected to feature band extraction utilising the CARS and CARS-SPA algorithms with the objective of eliminating uninformative variables and extracting valid spectral information. Finally, based on the Stacking algorithm, a highly reliable copper grade estimation model was constructed by combining various machine learning methods, and transfer learning was used to verify the accuracy and generalisation of the model. The findings of the study indicate that the feature bands selected by CARS-SPA encompass spectral ranges with sufficient chemical information, while uninformative variables are largely excluded, resulting in a notable increase in the speed and accuracy of modelling inversion operations. The Stacking ensemble learning model is more suitable for the prediction of copper grade in the Mirador copper mine compared to a single inversion model, and the CARS-SPA-Stacking inversion model has the highest accuracy, with R², RMSE, MAE, RPD, MAPE and CV reaching 0.936, 0.040, 0.019, 4.018, 0.059 and 0.267, respectively. This study is pertinent to the application of fused imaging spectral data in conjunction with the Stacking ensemble learning algorithm to copper grade inversion at the Mirador copper mine.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"257 ","pages":"Article 105308"},"PeriodicalIF":3.7,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143155762","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":"Improved multivariate sensor delay estimation using a hierarchical clustering-based approach","authors":"Bente M. van Son ,&nbsp;Tim Offermans ,&nbsp;Carlo G. Bertinetto ,&nbsp;Jeroen J. Jansen","doi":"10.1016/j.chemolab.2024.105306","DOIUrl":"10.1016/j.chemolab.2024.105306","url":null,"abstract":"<div><div>An often overlooked challenge in multivariate statistical modelling of industrial data is the presence of time delays caused by the residence time in the process, leading to event misalignment. To perform accurate data analysis, time delays must be estimated and corrected using a dedicated preprocessing step. Despite the multivariate nature of process data, most existing statistical Time Delay Estimation (TDE) methods only consider bivariate correlations. This study hypothesized that multivariate TDE methods would outperform bivariate methods, particularly with a large number of sensors. To test this, we selected data subsets with varying numbers of sensors using correlation-based hierarchical clustering and applied different TDE methods. Results showed that two multivariate methods, <em>PLS-CON-LOAD</em> and <em>PLS-SEQ</em>, outperformed the bivariate methods, exhibiting lower errors in the time delay estimation and less sensitivity to the number of sensors. Additionally, we proposed an enhancement to the TDE methods by embedding a clustering step to determine the order in which time delays should be estimated. This approach reduced TDE errors for all methods when number of sensors is high. We recommend the newly proposed clustering-based <em>PLS-CON-LOAD</em> method for low-error time delay estimation, which enhances the predictive value and insights obtainable from industrial data analysis.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"257 ","pages":"Article 105306"},"PeriodicalIF":3.7,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143155813","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":"In silico prediction of metabolic stability for ester-containing molecules: Machine learning and quantum mechanical methods","authors":"Shiwei Deng ,&nbsp;Yiyang Wu ,&nbsp;Zhuyifan Ye ,&nbsp;Defang Ouyang","doi":"10.1016/j.chemolab.2024.105292","DOIUrl":"10.1016/j.chemolab.2024.105292","url":null,"abstract":"<div><div>Carboxylic ester is an important functional group frequently used in the design of pro-drugs and soft-drugs. It is critical to understand the structure-metabolic stability relationships of these types of drugs. This work aims to predict the metabolic stability of ester-containing molecules in human plasma/blood by both machine learning and quantum mechanical methods. A dataset comprising metabolic half-lives with 656 molecules was collected for machine learning models. Three molecular representations (extended-connectivity fingerprint, Chemopy descriptor and Mordred3D descriptor) were used in combination with four machine learning algorithms (LightGBM, support vector machine, random forest, and k-nearest neighborhood). Furthermore, ensemble learning was applied to integrate the predictions of the individual models to achieve improved prediction results. The consensus model reached coefficient of determination values of 0.793 on the test set and 0.695 on the external validation set, respectively. Feature importances of machine learning models were interpreted from SHapley Additive exPlanations, which were consistent with previous esterase-catalyzed hydrolysis reaction mechanism. Moreover, a quantum mechanical model was built to calculate the energy gap of esterase-catalyzed hydrolysis reaction, deriving metabolic stability ranks. Abilities of quantum mechanical model to discriminate relative metabolic stability for molecules in external validation set was compared with machine learning model. Advantages and disadvantages of machine learning and quantum mechanical methods in metabolic stability prediction were discussed. In summary, this work can serve as an <em>in silico</em> high throughput screening tool to accelerate the early development process of pro-drugs and soft-drugs.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"257 ","pages":"Article 105292"},"PeriodicalIF":3.7,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143156201","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":"Estimating oil recovery efficiency of carbonated water injection with supervised machine learning paradigms and implications for uncertainty analysis","authors":"Joshua Nsiah Turkson ,&nbsp;Muhammad Aslam Md Yusof ,&nbsp;Ingebret Fjelde ,&nbsp;Yen Adams Sokama-Neuyam ,&nbsp;Victor Darkwah-Owusu","doi":"10.1016/j.chemolab.2024.105303","DOIUrl":"10.1016/j.chemolab.2024.105303","url":null,"abstract":"<div><div>Limited efforts have been made to develop a time-efficient and cost-effective predictive model capable of estimating the oil recovery efficiency of carbonated water injection (CWI). Therefore, in this study, we utilized supervised machine learning (ML) techniques: decision tree, support vector regression, and random forest (RF) to predict the recovery efficiency of CWI, with experimental conditions, rock properties, and fluid properties as predictors. The influence of various parameters on oil recovery efficiency was assessed using correlation technique, permutation importance, and Shapley Additive Explanations (SHAP), which sets our study apart from existing studies. Generally, the ML models yielded remarkable recovery efficiency prediction results, achieving coefficients of determination, mean absolute errors, and root mean square errors of 0.81–0.87, 4.30–4.96 %, and 4.82–5.89 %, respectively. The RF model outperformed its counterparts. Most importantly, the RF model successfully predicted the recovery efficiency on entirely new data with an error and absolute relative error of less than 15 % and 19 % respectively According to the SHAP analysis, high injection rate, porosity, permeability, and pressure improve oil recovery, and vice versa. Similarly, low temperature, oil density and viscosity, and salinity enhance oil recovery while injection rate and temperature were the most and least influential parameters, respectively. The RF model was successfully deployed to predict the oil recovery efficiency for 1000 randomly generated sets of independent variables in conjunction with Monte Carlo simulation, demonstrating the applicability of the model in uncertainty analysis. The current modeling study not only bridges the knowledge gaps in predictive modeling of the oil recovery efficiency of CWI but also holds significant promise for rapid estimation and optimization of oil recovery efficiency.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"257 ","pages":"Article 105303"},"PeriodicalIF":3.7,"publicationDate":"2024-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143155811","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":"Integration of CFD and machine learning for application in water treatment process modeling: Membrane ozonation process evaluation","authors":"Fanping Zhang","doi":"10.1016/j.chemolab.2024.105302","DOIUrl":"10.1016/j.chemolab.2024.105302","url":null,"abstract":"<div><div>In this study, several tree-based machine learning models were developed and evaluated to predict the <em>C</em> (mol/m³) in membrane-based separation. The case study is membrane separation using ozonation for water treatment. Simulations were first conducted using computational fluid dynamics (CFD) to solve mass transfer equations and obtain concentration distribution of ozone in the process (<em>C</em>). Then the results were implemented in building machine learning models, thereby hybrid model was developed for correlation of solute concentration. The dataset consisted of 10,000 samples, each with two features of <em>r</em> (m) and <em>z</em> (m) which are the coordinates in radial and axial dimensions, respectively. Four models including Extra Trees (ET), Random Forest (RF), Gradient Boosting (GB), and Adaptive Boosted Trees (ADT) were trained and optimized using Firefly Algorithm (FA). The performance of each model was assessed using several metrics, including R-squared, mean squared error, mean absolute error, and maximum error. The results showed that all models performed well, with R-squared values ranging from 0.994 to 0.999 and maximum errors ranging from 0.144 to 0.639. Overall, the ADT model achieved the best performance, with an R-squared value of 0.999 and a maximum error of 0.143. These findings suggest that tree-based ensemble models can be utilized to accurately predict the <em>C</em> parameter in the separation process based on membrane.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"257 ","pages":"Article 105302"},"PeriodicalIF":3.7,"publicationDate":"2024-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143156203","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":"Multi-objective feature selection algorithm using Beluga Whale Optimization","authors":"Kiana Kouhpah Esfahani,&nbsp;Behnam Mohammad Hasani Zade,&nbsp;Najme Mansouri","doi":"10.1016/j.chemolab.2024.105295","DOIUrl":"10.1016/j.chemolab.2024.105295","url":null,"abstract":"<div><div>The advancement of science and technology has resulted in large datasets with noisy or redundant features that hamper classification. In feature selection, relevant attributes are selected to reduce dimensionality, thereby improving classification accuracy. Multi-objective optimization is crucial in feature selection because it allows simultaneous evaluation of multiple, often conflicting objectives, such as maximizing model accuracy and minimizing the number of features. Traditional single-objective methods might focus solely on accuracy, often leading to models that are complex and computationally expensive. Multi-objective optimization, on the other hand, considers trade-offs between different criteria, identifying a set of optimal solutions (a Pareto front) where no one solution is clearly superior. It is especially useful when analyzing high-dimensional datasets, as it reduces overfitting and enhances model performance by selecting the most informative subset of features. This article introduces and evaluates the performance of the Binary version of Beluga Whale Optimization and the Multi-Objective Beluga Whale Optimization (MOBWO) algorithm in the context of feature selection. Features are encoded as binary matrices to denote their presence or absence, making it easier to stratify datasets. MOBWO emulates the exploration and exploitation patterns of Beluga Whale Optimization (BWO) through continuous search space. Optimal classification accuracy and minimum feature subset size are two conflicting objectives. The MOBWO was compared using 12 datasets from the University of California Irvine (UCI) repository with eleven well-known optimization algorithms, such as Genetic Algorithm (GA), Sine Cosine Algorithm (SCA), Bat Optimization Algorithm (BOA), Differential Evolution (DE), Whale Optimization Algorithm (WOA), Non-dominated Sorting Genetic Algorithm II (NSGA-II), Multi-Objective Particle Swarm Optimization (MOPSO), Multi-Objective Grey Wolf Optimizer (MOGWO), Multi-Objective Grasshopper Optimization Algorithm (MOGOA), Multi-Objective Non-dominated advanced Butterfly Optimization Algorithm (MONSBOA), and Multi-Objective Slime Mould Algorithm (MOSMA). In experiments using Random Forest (RF) as the classifier, different performance metrics were evaluated. The computational results show that the proposed BBWO algorithm achieves an average accuracy rate of 99.06 % across 12 datasets. Additionally, the proposed MOBWO algorithm outperforms existing multi-objective feature selection methods on all 12 datasets based on three metrics: Success Counting (SCC), Inverted Generational Distance (IGD), and Hypervolume indicators (HV). For instance, MOBWO achieves an average HV that is at least 3.54 % higher than all other methods.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"257 ","pages":"Article 105295"},"PeriodicalIF":3.7,"publicationDate":"2024-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143156202","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":"BGA-YOLOX-s: Real-time fine-grained detection of silkworm cocoon defects with a ghost convolution module and a joint multiscale fusion attention mechanism","authors":"Qingping Mei ,&nbsp;Wujin Jiang ,&nbsp;Kunpeng Mao ,&nbsp;Yunchao Ding ,&nbsp;Yuanli Hu","doi":"10.1016/j.chemolab.2024.105294","DOIUrl":"10.1016/j.chemolab.2024.105294","url":null,"abstract":"<div><div>The study addresses deficiencies in silkworm cocoon defect detection, enhancing the YOLOX-s network with the BGA-YOLOX-s model. By incorporating BiFPN-m, it reduces feature information loss, improving model reasoning speed. Ghost convolution reduces complexity and parameters, decreasing computational expenses. An attention module (CA) enhances fine-grained feature extraction. Experimental results on a cocoon dataset reveal a 4.1 % accuracy boost to 94.89 % compared to YOLOX-s. Furthermore, BGA-YOLOX-s outperforms SSD, YOLOv3, YOLOv4, and YOLOv5 in defect detection. The model proves effective in online cocoon defect detection, offering guidance for future applications in the production process.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"257 ","pages":"Article 105294"},"PeriodicalIF":3.7,"publicationDate":"2024-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143156200","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}