Chemometrics and Intelligent Laboratory Systems最新文献

{"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}

{"title":"Surface-enhanced Raman spectroscopy combined with chemometrics for quantitative analysis and carcinogenic risk estimation of polycyclic aromatic hydrocarbons in water with complex matrix","authors":"Rongling Zhang ,&nbsp;Mengjun Guo ,&nbsp;Maogang Li ,&nbsp;Hongsheng Tang ,&nbsp;Tianlong Zhang ,&nbsp;Hua Li","doi":"10.1016/j.chemolab.2024.105293","DOIUrl":"10.1016/j.chemolab.2024.105293","url":null,"abstract":"<div><div>Polycyclic aromatic hydrocarbons (PAHs) as a kind of persistent organic pollutants have high teratogenic, carcinogenic, mutagenic properties, as well as high octanol/water partition coefficient and sediment/water partition coefficient, causing serious threat to human health and water environment. In this study, the feasibility of Surface-enhanced Raman spectroscopy (SERS) technology combined with chemometrics for quantitative analysis and carcinogenic risk estimation of PAHs in water with complex matrix was explored. Firstly, 36 water samples from lake, tap, and distilled water were prepared, and then nano-silver particles (Ag NPs) were mixed with samples. The integrated strategy of spectral preprocessing was adopted to remove spectral interference, and variable selection algorithm was used to extract the information effectively, thus improving the prediction performance of the random forest (RF) calibration model for PAHs quantitative analysis and carcinogenic risk. The final results indicated that RF combined with spectral preprocessing integration strategy and variable selection had better predictive performance compared with the Raw-RF model. For phenanthrene (Phe) and benzo[<em>a</em>]anthracene (BaA) analysis, the optimal calibration model was WT-SG-SiPLS-VIM-RF (Phe: mean relative error of prediction (MRE_p) = 0.0646, coefficient of determination of prediction (R²_p) = 0.9658; BaA: MRE_p = 0.0949, R²_p = 0.9537). SG-WT-SiPLS-VIM-RF model (MRE_p = 0.0992, R²_p = 0.9551) showed a better predictive performance for fluoranthene (Flu). WT-SG-VIM-RF model (MRE_p = 0.0902, R²_p = 0.9409) showed excellent performance for assessing the carcinogenic risk of PAHs. Therefore, the combination of SERS technology and chemometrics provides a new approach for analyzing PAHs.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"257 ","pages":"Article 105293"},"PeriodicalIF":3.7,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143156198","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":"Exploring NIR spectroscopy data: A practical chemometric tutorial for analyzing freeze-dried pharmaceutical formulations","authors":"Ambra Massei ,&nbsp;Nicola Cavallini ,&nbsp;Francesco Savorani ,&nbsp;Nunzia Falco ,&nbsp;Davide Fissore","doi":"10.1016/j.chemolab.2024.105291","DOIUrl":"10.1016/j.chemolab.2024.105291","url":null,"abstract":"<div><div>Chemometrics tools are of fundamental importance for data analysis in the pharmaceutical field, especially with the increasingly strong assertion of the Process Analytical Technologies (PAT). In fact, analytical technologies such as Near-Infrared or Raman spectroscopies generate a lot of data, the spectra, that must be analyzed in a proper way. Typically, it is quite difficult to deeply understand the information hidden within the raw data. Therefore, careful, and efficient data exploration is needed to highlight the chemical and physical features of the analyzed samples.</div><div>Here, a tutorial on all the fundamental steps and concepts needed to perform a proper data analysis based on a case-study of different freeze-dried formulations in the pharmaceutical field is proposed. The data analysis pipeline begins with the dataset explanation, to better point out the main known differences and similarities among the investigated formulations. After the first step of data preprocessing, Principal Component Analysis (PCA), Partial Least Squares (PLS) for regression, and Partial Least Squares-Discriminant Analysis (PLS-DA) for classification are presented and applied to show how to obtain deep comprehension of the real-case NIR dataset at hand. The experimental results demonstrate that trends related to increasing levels of sucrose and/or arginine, as well as distinct clusters related to the sample type and to the operator who conducted the analysis can be found and modelled in the example data.</div><div>The tutorial aims at providing clear practical steps to conduct a robust data analysis, starting from the extraction and organization of the raw data, up to building more advanced predictive models (regression and classification). At each step some key questions are asked and answered to stimulate critical thinking in the reader. Also, commented MATLAB scripts are provided together with the real-case example NIR data, so that anyone could reproduce the whole data analysis in the tutorial, and try first hand to work with the data.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"257 ","pages":"Article 105291"},"PeriodicalIF":3.7,"publicationDate":"2024-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143155126","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":"Lactose prediction in dry milk with hyperspectral imaging: A data analysis competition at the “International Workshop on Spectroscopy and Chemometrics 2024”","authors":"Maria Frizzarin ,&nbsp;Vicky Caponigro ,&nbsp;Katarina Domijan ,&nbsp;Arnaud Molle ,&nbsp;Timilehin Aderinola ,&nbsp;Thach Le Nguyen ,&nbsp;Davide Serramazza ,&nbsp;Georgiana Ifrim ,&nbsp;Agnieszka Konkolewska","doi":"10.1016/j.chemolab.2024.105279","DOIUrl":"10.1016/j.chemolab.2024.105279","url":null,"abstract":"<div><div>In April 2024, the Vistamilk SFI Research Centre organized the fourth edition of the “International Workshop on Spectroscopy and Chemometrics — Spectroscopy meets modern Statistics”. Within this event, a data challenge was organized among workshop participants, focusing on hyperspectral imaging (HSI) of milk samples.</div><div>Milk is a complex emulsion comprising of fats, water, proteins, and carbohydrates. Due to the widespread prevalence of lactose intolerance, precise lactose quantification in milk samples became necessary for the dairy industry.</div><div>The dataset provided to the participants contained spectral data extracted from HSI, without the spatial information, obtained from 72 samples with reference laboratory values for lactose concentration [mg/mL]. The winning strategy was built using ROCKET, a convolutional-based method that was originally designed for time series classification, which achieved a Pearson correlation of 0.86 and RMSE of 9.8 on the test set. The present paper describes the approaches and statistical methods adopted by all the participants to analyse the data and develop the lactose prediction models.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"258 ","pages":"Article 105279"},"PeriodicalIF":3.7,"publicationDate":"2024-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143419059","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":"Sparse attention regression network-based soil fertility prediction with UMMASO","authors":"RVRaghavendra Rao ,&nbsp;U Srinivasulu Reddy","doi":"10.1016/j.chemolab.2024.105289","DOIUrl":"10.1016/j.chemolab.2024.105289","url":null,"abstract":"<div><div>The challenge of imbalanced soil nutrient datasets significantly hampers accurate predictions of soil fertility. To tackle this, a new method is suggested in this research, combining Uniform Manifold Approximation and Projection (UMAP) with Least Absolute Shrinkage and Selection Operator (LASSO). The main aim is to counter the impact of uneven data distribution and improve soil fertility models' predictive precision. The model introduced uses Sparse Attention Regression, effectively incorporating pertinent features from the imbalanced dataset. UMAP is utilised initially to reduce data complexity, unveiling hidden structures and essential patterns. Following this, LASSO is applied to refine features and enhance the model's interpretability. The experimental outcomes highlight the effectiveness of the UMAP and LASSO hybrid approach. The proposed model achieves outstanding performance metrics, reaching a predictive accuracy of 98 %, demonstrating its capability in accurate soil fertility predictions. It also showcases a Precision of 91.25 %, indicating its adeptness in accurately identifying fertile soil instances. The Recall metric stands at 90.90 %, emphasizing the model's ability to capture true positive cases effectively.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"257 ","pages":"Article 105289"},"PeriodicalIF":3.7,"publicationDate":"2024-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143156199","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":"Multivariate image analysis for assessment of textural attributes in transglutaminase-reconstituted meat","authors":"Samuel Verdú ,&nbsp;Ignacio García ,&nbsp;Carlos Roda ,&nbsp;José M. Barat ,&nbsp;Raúl Grau ,&nbsp;Alberto Ferrer ,&nbsp;J.M. Prats-Montalbán","doi":"10.1016/j.chemolab.2024.105280","DOIUrl":"10.1016/j.chemolab.2024.105280","url":null,"abstract":"<div><div>The control of sensorial textural attributes has high interest to the meat industry focused on the recovery of the value of meat by-products by developing reconstituted meat pieces with added sensory and nutritional values. Sensorial analysis of foods is still a quite subjective methodology, highly dependent of a well-trained team of inspectors, which is simulated by textural analysis in order to measure objective physical properties. This work presents a non-destructive and contactless experimental methodology to predict the physical properties of a reconstituted meat product, based on integrating multispectral imaging and multivariate image analysis (MIA). The experiment was based on reconstituting grounded meat with different concentrations of transglutaminase (0.1, 1, 3, 6 and 10 %), from which textural properties and multispectral imaging data were measured. Multispectral images (UV, VIS and NIR wavelengths) were processed with chemometric procedures to obtain the distribution maps and score images, from which different blocks of features were extracted to generate feature vectors (basic statistics and co-occurrence matrix) for each image. The obtained regression models built with these features predicted all physical properties of the meat with Q² &gt; 0.90, after feature selection using VIPs. These results evidenced the capacity of multispectral imaging, combined with chemometric procedures, to capture the variability of physical properties induced by transglutaminase in a derivate meat product. It could represent the base of a potential contactless application for a meat industrial inspection, where work environments have strong hygienic requirements.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"256 ","pages":"Article 105280"},"PeriodicalIF":3.7,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142745558","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":"Robust adaptive control for nonlinear discrete-time systems based on DE-GMAW","authors":"Jing Wang ,&nbsp;Engang Tian ,&nbsp;Huaicheng Yan ,&nbsp;Fanrong Qu","doi":"10.1016/j.chemolab.2024.105274","DOIUrl":"10.1016/j.chemolab.2024.105274","url":null,"abstract":"<div><div>Gas Metal Arc Welding (GMAW) is a critical process in manufacturing, known for its efficiency and versatility. The double-electrode GMAW (DE-GMAW) technique further enhances these attributes, offering superior welding speed and improved melting effects. However, controlling the DE-GMAW process effectively remains a complex challenge due to the nonlinear and dynamic nature of the system. The process involves intricate interactions between electrical, thermal, and mechanical phenomena, resulting in highly nonlinear behavior. Variations in material properties, environmental conditions, and external disturbances can adversely affect the welding process. Moreover, traditional control methods often fail to account for unmodeled dynamics and modeling errors, leading to performance degradation and potential instability. To address these challenges, this paper introduces a robust adaptive control scheme tailored for DE-GMAW systems, which combines online projection estimation identification and pole placement strategy at the same time to compensate for parameter uncertainties, external disturbances, and unmodeled dynamics. Simulation examples in welding process are carried out to demonstrate the effectiveness of the proposed robust adaptive control scheme.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"256 ","pages":"Article 105274"},"PeriodicalIF":3.7,"publicationDate":"2024-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142719741","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":"Enhanced satellite image resolution with a residual network and correlation filter","authors":"Ajay Sharma ,&nbsp;Bhavana P. Shrivastava ,&nbsp;Praveen Kumar Tyagi ,&nbsp;Ebtasam Ahmad Siddiqui ,&nbsp;Rahul Prasad ,&nbsp;Swati Gautam ,&nbsp;Pranshu Pranjal","doi":"10.1016/j.chemolab.2024.105277","DOIUrl":"10.1016/j.chemolab.2024.105277","url":null,"abstract":"<div><div>This study addresses the predominant challenge of very low-resolution satellite images in remote sensing applications, a common issue in satellite image-based surveillance. Existing satellite image recognition algorithms struggle with such low-resolution images, and traditional Super-Resolution (SR) techniques fall short for very low-resolution cases. We propose the Progressive Satellite Image Super-Resolution (PSISR) model to bridge this gap. Unlike current learning-based SR methods, the PSISR model specifically targets very low-resolution satellite images. In satellite image super-resolution, problems with feature fusion that result in image noise, blind spots, poor perceptual quality, and checkboard artifacts are encountered during the reconstruction process. Current models try to improve perceptual quality, but they frequently show challenges in attaining acceptable outcomes because of losses during reconstruction. Using a combined loss function, correlation filters, and a loss-aware upscaling network layer, the PSISR model presents a revolutionary methodology. The model adopts a cascading structure with dense skip connections, sequentially upscaling images by factors of <span><math><mrow><mn>2</mn><mo>×</mo></mrow></math></span>, <span><math><mrow><mn>4</mn><mo>×</mo></mrow></math></span>, and <span><math><mrow><mn>8</mn><mo>×</mo></mrow></math></span> through three modules. To validate the model's superiority, a study is conducted, confirming its effectiveness compared to baseline models and also training the other models using the available dataset to prove the effectiveness of the model. The PSISR model effectively addresses the challenge of extracting more features with minimal losses, resulting in high magnification during reconstruction. Our method outperforms state-of-the-art techniques, including Swin2-MoSE, MambaFormer, SRFBN and RCAN, with a PSNR improvement of up to 0.4 dB and a 0.003 SSIM enhancement across various datasets. This demonstrates the effectiveness of our approach in producing high-quality outputs, achieving a 99.25 % correlation efficiency between the generated and original images.</div></div>","PeriodicalId":9774,"journal":{"name":"Chemometrics and Intelligent Laboratory Systems","volume":"256 ","pages":"Article 105277"},"PeriodicalIF":3.7,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142745557","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}