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Journal articles on the topic 'Validation accuracy'

Author: Grafiati

Published: 4 June 2025

Last updated: 1 August 2025

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1

Maciel, Juliana Rolim Vieira, Eduardo Yoshio Nakano, Kênia Mara Baiocchi de Carvalho, and Eliane Said Dutra. "STRONGkids validation: tool accuracy." Jornal de Pediatria 96, no. 3 (2020): 371–78. http://dx.doi.org/10.1016/j.jped.2018.12.012.

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Maciel, Juliana Rolim Vieira, Eduardo Yoshio Nakano, Kênia Mara Baiocchi de Carvalho, and Eliane Said Dutra. "STRONGkids validation: tool accuracy." Jornal de Pediatria (Versão em Português) 96, no. 3 (2020): 371–78. http://dx.doi.org/10.1016/j.jpedp.2019.05.006.

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3

Akpolat, Tekin, Melda Dilek, Turkan Aydogdu, Zelal Adibelli, Dilek Gurgenyatagi Erdem, and Emre Erdem. "Home sphygmomanometers: validation versus accuracy." Blood Pressure Monitoring 14, no. 1 (2009): 26–31. http://dx.doi.org/10.1097/mbp.0b013e3283262f31.

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4

Halperin, Gideon. "Accuracy validation in ELISA tests." Biologicals 31, no. 3 (2003): 231. http://dx.doi.org/10.1016/s1045-1056(03)00039-3.

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Sushila, Dagadu Chavan, and Mahendra Desai Deepa. "Analytical method validation: A brief review." World Journal of Advanced Research and Reviews 16, no. 2 (2022): 389–402. https://doi.org/10.5281/zenodo.7785494.

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Validation is an applied approach to verify that a method is suitable to function as a quality control tool. The objective of any analytical measurement is to obtain consistent, reliable and accurate data. Validated analytical methods play a major role in achieving this goal. An analytical method consists of the techniques, method, procedure and protocol. Analytical method validation includes the determination of accuracy, precision, LOD, LOQ, linearity and range. The results from method validation can be used to moderator the quality, reliability and consistency of analytical results, which i

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Marzolf, Bruz, and Michael H. Johnson. "Validation of microarray image analysis accuracy." BioTechniques 36, no. 2 (2004): 304–8. http://dx.doi.org/10.2144/04362mt01.

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7

Refaat, A., A. Badawy, M. Ashry, and Adel Omar. "HIGH ACCURACY SPACECRAFT ORBIT PROPAGATOR VALIDATION." International Conference on Applied Mechanics and Mechanical Engineering 18, no. 18 (2018): 1–9. http://dx.doi.org/10.21608/amme.2018.34732.

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Leininger, Lisa J., Brian J. Cook, Veronica Jones, Maria Bellumori, and Kent J. Adams. "Validation And Accuracy Of Fitbit Charge." Medicine & Science in Sports & Exercise 48 (May 2016): 96. http://dx.doi.org/10.1249/01.mss.0000485293.86436.f1.

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Darling, Allan J., Jeri Anne Boose, and John Spaltro. "Virus Assay Methods: Accuracy and Validation." Biologicals 26, no. 2 (1998): 105–10. http://dx.doi.org/10.1006/biol.1998.0134.

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Sutarno, R. "Validation of accuracy by interlaboratory programme." Talanta 32, no. 11 (1985): 1088–91. http://dx.doi.org/10.1016/0039-9140(85)80132-6.

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11

G., Sirisha* M. Gayathri Devi M. Gowri Manoja G. Sudhakar. "ANALYTICAL METHOD DEVELOPMENT AND VALIDATION FOR THE ESTIMATION OF TRABECTEDIN IN BULK AND PARENTERAL DOSAGE FORM BY RP-HPLC." INDO AMERICAN JOURNAL OF PHARMACEUTICAL SCIENCES 05, no. 03 (2018): 1642–48. https://doi.org/10.5281/zenodo.1208619.

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A new RP-HPLC method for the quantitative determination of Trabectedin was developed and validated as per ICH guidelines. The drugs were injected into Zorbax SB, C18,(150x4.6mm); 3.5µm column maintained at ambient temperature and effluent monitored at 215nm. The mobile phase consisted of phosphate buffer (pH 3.0) and Acetonitrile in the ratio of 70:30 V/V. The flow rate was maintained at 0.8 ml/min. The calibration curve for Trabectedin was linear from 50-175µg/ml (r 2 for Trabectedin = 1 ). The proposed method was adequate, sensitive, reproducible, accurate and precise for the det

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Schadauer, Tobias, Susanne Karel, Markus Loew, et al. "Evaluating Tree Species Mapping: Probability Sampling Validation of Pure and Mixed Species Classes Using Convolutional Neural Networks and Sentinel-2 Time Series." Remote Sensing 16, no. 16 (2024): 2887. http://dx.doi.org/10.3390/rs16162887.

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The accurate large-scale classification of tree species is crucial for the monitoring, protection, and management of the Earth’s invaluable forest ecosystems. Numerous previous studies have recognized the suitability of satellite imagery, particularly Sentinel-2 imagery, for this task. In this study, we utilized a dense phenology Sentinel-2 time series, which offered consistent data across multiple granules, to map tree species across the entire forested area in Austria. Aiming for the classification scheme to more accurately represent actual forest conditions, we included mixed tree species a

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13

Mabuni, D., and S. Aquter Babu. "High Accurate and a Variant of k-fold Cross Validation Technique for Predicting the Decision Tree Classifier Accuracy." International Journal of Innovative Technology and Exploring Engineering 10, no. 2 (2021): 105–10. http://dx.doi.org/10.35940/ijitee.c8403.0110321.

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In machine learning data usage is the most important criterion than the logic of the program. With very big and moderate sized datasets it is possible to obtain robust and high classification accuracies but not with small and very small sized datasets. In particular only large training datasets are potential datasets for producing robust decision tree classification results. The classification results obtained by using only one training and one testing dataset pair are not reliable. Cross validation technique uses many random folds of the same dataset for training and validation. In order to o

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D., Mabuni, and Aquter Babu S. "High Accurate and a Variant of k-fold Cross Validation Technique for Predicting the Decision Tree Classifier Accuracy." International Journal of Innovative Technology and Exploring Engineering (IJITEE) 10, no. 3 (2021): 105–10. https://doi.org/10.35940/ijitee.C8403.0110321.

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In machine learning data usage is the most important criterion than the logic of the program. With very big and moderate sized datasets it is possible to obtain robust and high classification accuracies but not with small and very small sized datasets. In particular only large training datasets are potential datasets for producing robust decision tree classification results. The classification results obtained by using only one training and one testing dataset pair are not reliable. Cross validation technique uses many random folds of the same dataset for training and validation. In order to o

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Dunn, Cheryl L., Gregory J. Gerard, Severin V. Grabski, and Scott R. Boss. "Asymmetry in Identification of Multiplicity Errors in Conceptual Models of Business Processes." Journal of Information Systems 31, no. 1 (2016): 21–39. http://dx.doi.org/10.2308/isys-51581.

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ABSTRACT Business rules can be represented by multiplicities in a Unified Modeling Language (UML) class diagram. Diagrams containing erroneous multiplicities may be implemented as an inefficient/ineffective database. System validators must be able to validate such diagrams, including multiplicities, to prevent the implementation of design errors. Prior research reveals conflicting evidence regarding the expected accuracy in validating minimum multiplicities, indicating a need for additional research to further our understanding. Ontology research claims that multiplicities that depict optional

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16

Karamani, Brunela. "Automation of Data Validation Processes in Banking: A Case Study on Validating Albanian Identity Numbers." SEEU Review 19, no. 1 (2024): 51–64. http://dx.doi.org/10.2478/seeur-2024-0022.

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Abstract In the banking industry, data accuracy and integrity are paramount for ensuring regulatory compliance, operational efficiency, and risk management. However, manual data validation processes often lead to delays, errors, and inefficiencies, posing challenges for financial institutions. To address these issues, many banks are turning to automation to streamline data validation processes and improve accuracy. Using data automation, banks optimize time-consuming manual tasks such as data entry, validation, and retrieval by creating automated software processes that execute these tasks qui

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Dolloff, John, Henry Theiss, and Brian Bollin. "Assessment, Specification, and Validation of a Geolocation System's Accuracy and Predicted Accuracy." Photogrammetric Engineering & Remote Sensing 90, no. 3 (2024): 157–68. http://dx.doi.org/10.14358/pers.23-00071r2.

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This article presents recommendations and corresponding detailed procedures for the assessment of a geolocation system's accuracy, as well as the specification of accuracy requirements and their subsequent validation when they are available. Applicable metrics and related processing are based on samples of corresponding geolocation errors. This article also presents similar recommendations for the predicted accuracy of a geolocation system, based on samples of geolocation error, as well as corresponding predicted error covariance matrices associated with the geolocations. Reliable error covari

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Swati, Jagtap*1 P. B. Jadhav1 Vinod Bairagi2. "Pharmaceutical Validation: A Review." International Journal in Pharmaceutical Sciences 2, no. 1 (2024): 399–409. https://doi.org/10.5281/zenodo.10531821.

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The practice of validating documentation that demonstrates a process will consistently result in a product that meets expectations is known as validation. Validation studies, according to GMP, are an essential component of GMP; they must be carried out in accordance with predetermined protocols. Process, testing, and cleaning are the bare minimum that need to be validated in order to establish control procedures that monitor output and validate manufacturing processes that might be causing variability in drug products.  One of the key components in obtaining and preserving the final produ

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19

de Jesus, Kelly, Karla de Jesus, Pedro Figueiredo, João Paulo Vilas-Boas, Ricardo Jorge Fernandes, and Leandro José Machado. "Reconstruction Accuracy Assessment of Surface and Underwater 3D Motion Analysis: A New Approach." Computational and Mathematical Methods in Medicine 2015 (2015): 1–8. http://dx.doi.org/10.1155/2015/269264.

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This study assessed accuracy of surface and underwater 3D reconstruction of a calibration volume with and without homography. A calibration volume (6000 × 2000 × 2500 mm) with 236 markers (64 above and 88 underwater control points—with 8 common points at water surface—and 92 validation points) was positioned on a 25 m swimming pool and recorded with two surface and four underwater cameras. Planar homography estimation for each calibration plane was computed to perform image rectification. Direct linear transformation algorithm for 3D reconstruction was applied, using 1600000 different combinat

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Ferretti, Alessandro, Giuliano Savio, Riccardo Barzaghi, et al. "Submillimeter Accuracy of InSAR Time Series: Experimental Validation." IEEE Transactions on Geoscience and Remote Sensing 45, no. 5 (2007): 1142–53. http://dx.doi.org/10.1109/tgrs.2007.894440.

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Wang, Taoyang, Guo Zhang, Deren Li, et al. "Geometric Accuracy Validation for ZY-3 Satellite Imagery." IEEE Geoscience and Remote Sensing Letters 11, no. 6 (2014): 1168–71. http://dx.doi.org/10.1109/lgrs.2013.2288918.

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22

Puelz, Amy V., and Marion G. Sobol. "The Accuracy of Cross-Validation Results in Forecasting." Decision Sciences 26, no. 6 (1995): 803–18. http://dx.doi.org/10.1111/j.1540-5915.1995.tb01576.x.

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23

D’Arena, Giovanni, Candida Vitale, Marta Coscia, et al. "External validation of the accuracy of ‘CLLflow score’." Journal of Investigative Medicine 66, no. 7 (2018): e6-e6. http://dx.doi.org/10.1136/jim-2018-000832.

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24

Wong, Tzu-Tsung, and Po-Yang Yeh. "Reliable Accuracy Estimates from k-Fold Cross Validation." IEEE Transactions on Knowledge and Data Engineering 32, no. 8 (2020): 1586–94. http://dx.doi.org/10.1109/tkde.2019.2912815.

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25

Diamantidis, N. A., D. Karlis, and E. A. Giakoumakis. "Unsupervised stratification of cross-validation for accuracy estimation." Artificial Intelligence 116, no. 1-2 (2000): 1–16. http://dx.doi.org/10.1016/s0004-3702(99)00094-6.

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26

Feng, Shaojun, and Altti Jokinen. "Integer ambiguity validation in high accuracy GNSS positioning." GPS Solutions 21, no. 1 (2015): 79–87. http://dx.doi.org/10.1007/s10291-015-0506-9.

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27

Feinberg, Max. "Validation of analytical methods based on accuracy profiles." Journal of Chromatography A 1158, no. 1-2 (2007): 174–83. http://dx.doi.org/10.1016/j.chroma.2007.02.021.

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28

Zeng, Xinchuan, and Tony R. Martinez. "Distribution-balanced stratified cross-validation for accuracy estimation." Journal of Experimental & Theoretical Artificial Intelligence 12, no. 1 (2000): 1–12. http://dx.doi.org/10.1080/095281300146272.

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29

Malek-Ahmadi, Michael, Kathryn Davis, Christine Belden, et al. "Validation and diagnostic accuracy of the Alzheimer's questionnaire." Age and Ageing 41, no. 3 (2012): 396–99. http://dx.doi.org/10.1093/ageing/afs008.

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30

Graca, Sebastian, Michael D. Han, and Michael Miloro. "Validation of Accuracy Analysis for Zygomatic Dental Implants." Journal of Oral and Maxillofacial Surgery 81, no. 9 (2023): S69—S70. http://dx.doi.org/10.1016/j.joms.2023.08.010.

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31

Boulealam, Chafik, Hajar Filali, Jamal Riffi, Adnane Mohamed Mahraz, and Hamid Tairi. "Feedback-Based Validation Learning." Computation 13, no. 7 (2025): 156. https://doi.org/10.3390/computation13070156.

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This paper presents Feedback-Based Validation Learning (FBVL), a novel approach that transforms the role of validation datasets in deep learning. Unlike conventional methods that utilize validation datasets for performance evaluation post-training, FBVL integrates these datasets into the training process. It employs real-time feedback to optimize the model’s weight adjustments, enhancing prediction accuracy and overall model performance. Importantly, FBVL preserves the integrity of the validation process by using prediction outcomes on the validation dataset to guide training adjustments, with

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32

Zhang, Yuan, Shaomin Liu, Lisheng Song, et al. "Integrated Validation of Coarse Remotely Sensed Evapotranspiration Products over Heterogeneous Land Surfaces." Remote Sensing 14, no. 14 (2022): 3467. http://dx.doi.org/10.3390/rs14143467.

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Validation of remotely sensed evapotranspiration (RS_ET) products is important because their accuracy is critical for various scientific applications. In this study, an integrated validation framework was proposed for evaluating RS_ET products with coarse spatial resolution extending from homogenous to heterogeneous land surfaces. This framework was applied at the pixel and river basin scales, using direct and indirect validation methods with multisource validation datasets, which solved the spatial mismatch between ground measurements and remotely sensed products. The accuracy, rationality of

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33

Uppari, Srinivas, and Shiv Brat Singh. "Comprehensive validation of Novel analytical Techniques: Assessing Robustness, precision, accuracy, and linearity for reliable pharmaceutical analysis." Journal of Advances in Science and Technology 21, no. 1 (2024): 281–88. https://doi.org/10.29070/x1fgpy53.

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The growing intricacy of pharmaceutical formulations requires the creation and validation of innovative analytical methods to guarantee quality, effectiveness, and safety. This research focuses on the thorough validation of novel analytical techniques designed for pharmaceutical analysis, assessing their robustness, precision, accuracy, and linearity. The robustness was evaluated by implementing intentional changes in experimental parameters, hence validating the method's dependability across various settings. Precision was assessed by repeatability and intermediate precision experiments, prod

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Nasef, Daniel, Demarcus Nasef, Michael Sher, and Milan Toma. "A Standardized Validation Framework for Clinically Actionable Healthcare Machine Learning with Knee Osteoarthritis Grading as a Case Study." Algorithms 18, no. 6 (2025): 343. https://doi.org/10.3390/a18060343.

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Background: High in-domain accuracy in healthcare machine learning (ML) models does not guarantee reliable clinical performance, especially when training and validation protocols are insufficiently robust. This paper presents a standardized framework for training and validating ML models intended for classifying medical conditions, emphasizing the need for clinically relevant evaluation metrics and external validation. Methods: We apply this framework to a case study in knee osteoarthritis grading, demonstrating how overfitting, data leakage, and inadequate validation can lead to deceptively h

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Parvandeh, Saeid, Hung-Wen Yeh, Martin P. Paulus, and Brett A. McKinney. "Consensus features nested cross-validation." Bioinformatics 36, no. 10 (2020): 3093–98. http://dx.doi.org/10.1093/bioinformatics/btaa046.

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Abstract Summary Feature selection can improve the accuracy of machine-learning models, but appropriate steps must be taken to avoid overfitting. Nested cross-validation (nCV) is a common approach that chooses the classification model and features to represent a given outer fold based on features that give the maximum inner-fold accuracy. Differential privacy is a related technique to avoid overfitting that uses a privacy-preserving noise mechanism to identify features that are stable between training and holdout sets. We develop consensus nested cross-validation (cnCV) that combines the idea

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36

Jiang, Boyang, Xiaohuan Dong, Mingjun Deng, et al. "Geolocation Accuracy Validation of High-Resolution SAR Satellite Images Based on the Xianning Validation Field." Remote Sensing 15, no. 7 (2023): 1794. http://dx.doi.org/10.3390/rs15071794.

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The geolocation accuracy of Synthetic Aperture Radar (SAR) images is crucial for their application in various industries. Five high-resolution SAR satellites, namely ALOS, TerraSAR-X, Cosmo-SkyMed, RadarSat-2, and Chinese YG-3, provide a vast amount of image data for research purposes, although their geometric accuracies differ despite similar resolutions. To evaluate and compare the geometric accuracy of these satellites under the same ground control reference, a validation field was established in Xianning, China. The rational function model (RFM) was used to analyze the geometric performanc

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Suzuki, Kaiyu, and Tomofumi Matsuzawa. "Model Soups for Various Training and Validation Data." AI 3, no. 4 (2022): 796–808. http://dx.doi.org/10.3390/ai3040048.

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Model soups synthesize multiple models after fine-tuning them with different hyperparameters based on the accuracy of the validation data. They train different models on the same training and validation data sets. In this study, we maximized the model fine-tuning accuracy using the inference time and memory cost of a single model. We extended the model soups to create subsets of k training and validation data using a method similar to k-fold cross-validation and trained models on these subsets. First, we showed the correlation between the validation and test data when the models are synthesize

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Jiao, Yanan, Fengli Zhang, Qiqi Huang, Xiaochen Liu, and Lu Li. "Analysis of Interpolation Methods in the Validation of Backscattering Coefficient Products." Sensors 23, no. 1 (2023): 469. http://dx.doi.org/10.3390/s23010469.

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Validation is the basis of synthetic aperture radar (SAR) image quantification applications. Based on the point target of the field site, the radiation characteristics of the backscattering coefficient image can be used to optimize the SAR imaging, and the product production system can be more closely targeted, to ensure the image product accuracy in the actual quantification application. In this study, the validation of the backscattering coefficient image was examined using calibrators, and the radiometric properties of the image were evaluated by extracting the radar cross-section of each p

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Chen, Pengfei, Junjie Ye, Guangyong Chen, Jingwei Zhao, and Pheng-Ann Heng. "Robustness of Accuracy Metric and its Inspirations in Learning with Noisy Labels." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 13 (2021): 11451–61. http://dx.doi.org/10.1609/aaai.v35i13.17364.

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For multi-class classification under class-conditional label noise, we prove that the accuracy metric itself can be robust. We concretize this finding's inspiration in two essential aspects: training and validation, with which we address critical issues in learning with noisy labels. For training, we show that maximizing training accuracy on sufficiently many noisy samples yields an approximately optimal classifier. For validation, we prove that a noisy validation set is reliable, addressing the critical demand of model selection in scenarios like hyperparameter-tuning and early stopping. Prev

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Vinchurkar, Kuldeep. "Spreadsheet Validation: A Detailed Review." Nanomedicine & Nanotechnology Open Access 8, no. 4 (2023): 1–11. http://dx.doi.org/10.23880/nnoa-16000275.

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Spreadsheet validation is a critical process for ensuring data accuracy and integrity in various domains, such as finance, research, and project management. As spreadsheets continue to be widely used for data analysis and decision-making, the need to validate their contents becomes paramount. It explores various techniques and best practices for validating spreadsheets, including formula auditing, data consistency checks, range validation, and error handling mechanisms. To address these concerns, this sheet delivers components of spreadsheet validation, including establishing robust validation

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41

Sooai, Adri Gabriel, Sisilia Daeng Bakka Mau, Yovinia Carmeneja Hoar Siki, Donatus Joseph Manehat, Shine Crossifixio Sianturi, and Alicia Herlin Mondolang. "OPTIMIZING LANTANA CLASSIFICATION: HIGH-ACCURACY MODEL UTILIZING FEATURE EXTRACTION." Jurnal Ilmiah Kursor 12, no. 2 (2023): 49–58. http://dx.doi.org/10.21107/kursor.v12i2.347.

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As an invasive and poisonous plant, Lantana has become a pest in the agricultural world. Still, on the other hand, it becomes an ornamental plant with different positive potentials. Lantana flower datasets are not yet widely available for open image classification research, given that the research needs are still broad in remote sensing. This study aims to provide a model with classifier accuracy that outperforms similar studies and Lantana datasets for classification needs using several algorithms that can be run on small source computers. This study used five types of lantana colors, red, wh

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42

Lumumba, Victor, Dennis Kiprotich, Mary Mpaine, Njoka Makena, and Musyimi Kavita. "Comparative Analysis of Cross-Validation Techniques: LOOCV, K-folds Cross-Validation, and Repeated K-folds Cross-Validation in Machine Learning Models." American Journal of Theoretical and Applied Statistics 13, no. 5 (2024): 127–37. http://dx.doi.org/10.11648/j.ajtas.20241305.13.

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Effective model evaluation is crucial for robust machine learning, and cross-validation techniques play a significant role. This study compares Repeated k-folds Cross Validation, k-folds Cross Validation, and Leave-One-Out Cross Validation (LOOCV) on imbalanced and balanced datasets across four models: Support Vector Machine (SVM), K-Nearest Neighbors (K-NN), Random Forest (RF), and Bagging, both with and without parameter tuning. On imbalanced data without parameter tuning, Repeated k-folds cross-validation demonstrated strong performance for SVM with a sensitivity of 0.541 and balanced accur

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Nedel’ko, V. M. "On the Accuracy of Cross-Validation in the Classification Problem." Bulletin of Irkutsk State University. Series Mathematics 38 (2021): 84–95. http://dx.doi.org/10.26516/1997-7670.2021.38.84.

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In this work we will study the accuracy of the cross-validation estimates for decision functions. The main idea of the research consists in the scheme of statistical modeling that allows using real data to obtain statistical estimates, which are usually obtained only by using model (synthetic) distributions. The studies confirm the well-known empirical recommendation to choose the number of folds equal to 5 or more. The choice of more than 10 folds does not yield a significant increase in accuracy. The use of repeated cross-validation also does not provide fundamental gain in precision. The re

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Dael, Nele, Katja Schlegel, Adele E. Weaver, Mollie A. Ruben, and Marianne Schmid Mast. "Validation of a performance measure of broad interpersonal accuracy." Journal of Research in Personality 97 (April 2022): 104182. http://dx.doi.org/10.1016/j.jrp.2021.104182.

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Laas-Bourez, Myrtille, Clement Courde, Etienne Samain, et al. "Accuracy validation of T2L2 time transfer in co-location." IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control 62, no. 2 (2015): 255–65. http://dx.doi.org/10.1109/tuffc.2014.006662.

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Methe, Scott A., John M. Hintze, and Randy G. Floyd. "Validation and Decision Accuracy of Early Numeracy Skill Indicators." School Psychology Review 37, no. 3 (2008): 359–73. http://dx.doi.org/10.1080/02796015.2008.12087883.

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Shim, Eiji, and Akio Ochi. "Validation of CFD Drag Prediction Accuracy for Aircraft design." Proceedings of The Computational Mechanics Conference 2003.16 (2003): 747–48. http://dx.doi.org/10.1299/jsmecmd.2003.16.747.

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Liu, Zhe, Xiang Deng, and Guang-zhi Wang. "Accuracy Validation for Medical Image Registration Algorithms: a Review." Chinese Medical Sciences Journal 27, no. 3 (2012): 176–81. http://dx.doi.org/10.1016/s1001-9294(14)60052-3.

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Seah, M. P., and M. T. Brown. "Validation and accuracy of peak synthesis software for XPS." Applied Surface Science 144-145 (April 1999): 183–87. http://dx.doi.org/10.1016/s0169-4332(98)00787-9.

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Ruas, Alexandre, Warda Ben Messaoud, Cédric Rivier, Isabelle Solinhac, and Danièle Roudil. "Validation of gravimetry for high-accuracy analysis of uranium." Journal of Radioanalytical and Nuclear Chemistry 311, no. 3 (2016): 1831–38. http://dx.doi.org/10.1007/s10967-016-5116-7.

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