Relevant bibliographies by topics / Chemistry – Statistical methods

Academic literature on the topic 'Chemistry – Statistical methods'

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Published: 4 June 2021
Last updated: 30 July 2024

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Journal articles on the topic "Chemistry – Statistical methods"

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Grieve, A. P., P. C. Meier, and R. E. Zund. "Statistical Methods in Analytical Chemistry." Journal of the Royal Statistical Society. Series A (Statistics in Society) 157, no. 2 (1994): 311. http://dx.doi.org/10.2307/2983374.

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Meier, P. C., and R. E. Zund. "Statistical Methods in Analytical Chemistry." Biometrics 50, no. 3 (September 1994): 896. http://dx.doi.org/10.2307/2532821.

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Morton, Michael J. "Statistical Methods in Applied Chemistry." Technometrics 35, no. 2 (May 1993): 228–29. http://dx.doi.org/10.1080/00401706.1993.10485055.

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Hillyer, Martin. "Statistical Methods in Analytical Chemistry." Technometrics 37, no. 1 (February 1995): 113–14. http://dx.doi.org/10.1080/00401706.1995.10485895.

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Czermiński, J., A. Iwasiewicz, Z. Paszek, A. Sikorski, and Richard G. Brereton. "Statistical methods in applied chemistry." Analytica Chimica Acta 244 (1991): 296. http://dx.doi.org/10.1016/s0003-2670(00)82518-0.

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Ziegel, Eric R., and J. Einax. "Chemometrics in Environmental Chemistry: Statistical Methods." Technometrics 38, no. 4 (November 1996): 412. http://dx.doi.org/10.2307/1271332.

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Whitbeck, Michael. "Chemometrics in environmental chemistry, statistical methods." Chemometrics and Intelligent Laboratory Systems 34, no. 1 (August 1996): 131–32. http://dx.doi.org/10.1016/0169-7439(96)00008-1.

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Muranaka, Ken. "Teaching Statistical Methods." Journal of Chemical Education 76, no. 4 (April 1999): 469. http://dx.doi.org/10.1021/ed076p469.1.

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Hart, Brian, Mark Biesinger, and Roger St C. Smart. "Improved statistical methods applied to surface chemistry in minerals flotation." Minerals Engineering 19, no. 6-8 (May 2006): 790–98. http://dx.doi.org/10.1016/j.mineng.2005.09.039.

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Smit, H. C. "Statistical methods in analytical chemistry (Chemical Analysis Series, Vol. 123)." Journal of Chromatography A 670, no. 1-2 (June 1994): 245–46. http://dx.doi.org/10.1016/0021-9673(94)80303-x.

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Dissertations / Theses on the topic "Chemistry – Statistical methods"

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FREITAS, SONIA MARIA DE. "STATISTICAL METHODOLOGY FOR ANALYTICAL METHODS VALIDATION APPLICABLE CHEMISTRY METROLOGY." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2003. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=4058@1.

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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO
A metodologia estatística escolhida para validação de métodos analíticos aplicável à metrologia em química é fundamental para assegurar a qualidade, comprovar a eficiência e demonstrar a exatidão dos resultados das medições nas análises químicas. Essa metodologia, desenvolvida em conformidade com o rigor metrológico, resulta num sistema de medições validado, confiável e com incertezas quantificadas. Este trabalho propõe uma metodologia geral para validação de métodos analíticos. A metodologia desenvolvida resultou de uma síntese de métodos parciais descritos na literatura, e inclui uma escolha crítica de técnicas mais adequadas dentro das alternativas existentes. A abordagem proposta combina quatro diferentes aspectos da validação: a modelagem da curva de calibração; o controle da especificidade do método; a comparação da tendência e precisão (repetitividade e precisão intermediária) do método com um método de referência; e a estimação das componentes de incerteza inerentes a todos esses aspectos. Como resultado, além de uma proposta para validação de métodos para uso em análises químicas, obtêm- se a função de calibração inversa e as incertezas expandidas, que permitem obter os resultados analíticos associados aos valores da resposta, com suas respectivas incertezas associadas. Na modelagem geral para obtenção da curva de calibração, empregam-se técnicas estatísticas para avaliação da linearidade e para o cálculo do mínimo valor detectável e do mínimo valor quantificável. A especificidade do método analítico é avaliada pela adição de padrões a um conjunto de amostras representativas e posterior recuperação dos mesmos, com ajuste por mínimos quadrados e testes de hipóteses. Para estudar a tendência e a precisão do método quando comparado a um método de referência, utiliza-se um modelo hierárquico de quatro níveis e a aproximação de Satterthwaite para determinação do número de graus de liberdade associados aos componentes de variância. As técnicas estatísticas utilizadas são ilustradas passo a passo por exemplos numéricos.
The use of statistical methodology for analytical methods validation is vital to assure that measurements have the quality level required by the goal to be attained. This thesis describes a statistical modelling approach for combining four different aspects of validation: checking the linearity of the calibration curve and compute the detection and the quantification limits; controlling the specificity of the analytical method; estimating the accuracy (trueness and precision) of the alternative method, for comparison with a reference method. The general approach is a synthesis of several partial techniques found in the literature, according to a choice of the most appropriate techniques in each case. For determination of the response function, statistical techniques are used for assessing the fitness of the regression model and for determination of the detection limit and the quantification limit. Method specificity is evaluated by adjusting a straight line between added and recovered concentrations via least squares regression and hypotheses tests on the slope and intercept. To compare a method B with a reference method A, the precision and accuracy of method B are estimated. A 4-factor nested design is employed for this purpose. The calculation of different variance estimates from the experimental data is carried out by ANOVA. The Satterthwaite approximation is used to determine the number of degrees of freedom associated with the variance components. The application of the methodology is thoroughly illustrated with step-by-step examples.
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Farhat, Hikmat. "Studies in computational methods for statistical mechanics of fluids." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape10/PQDD_0026/NQ50157.pdf.

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Agrawala, Gautam Kumar. "Regional ground water interpretation using multivariate statistical methods." To access this resource online via ProQuest Dissertations and Theses @ UTEP, 2007. http://0-proquest.umi.com.lib.utep.edu/login?COPT=REJTPTU0YmImSU5UPTAmVkVSPTI=&clientId=2515.

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Ganti, Satyakala. "DEVELOPMENT OF HPLC METHODS FOR PHARMACEUTICALLY RELEVANT MOLECULES; METHOD TRANSFER TO UPLC: COMPARING METHODS STATISTICALLY FOR EQUIVALENCE." Diss., Temple University Libraries, 2011. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/118587.

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Chemistry
Ph.D.
High Pressure Liquid Chromatography (HPLC) is a well-known and widely used analytical technique which is prevalent throughout the pharmaceutical industry as a research tool. Despite its prominence HPLC possesses some disadvantages, most notably slow analysis time and large consumption of organic solvents. Ultra Pressure Liquid Chromatography (UPLC) is a relatively new technique which offers the same separation capabilities of HPLC with the added benefits of reduced run time and lower solvent consumption. One of the key developments which facilitate the new UPLC technology is sub 2-µm particles used as column packing material. These particles allow for higher operating pressures and increased flow rates while still providing strong separation. Although UPLC technology has been available since early 2000, few laboratories have embraced the new technology as an alternative to HPLC. Besides the resistance to investing in new capital, another major roadblock is converting existing HPLC methodology to UPLC without disruption. This research provides a framework for converting existing HPLC methods to UPLC. An existing HPLC method for analysis of Galantamine hydrobromide was converted to UPLC and validated according to ICH guidelines. A series of statistical evaluations on the validation data were performed to prove the equivalency between the original HPLC and the new UPLC method. This research presents this novel statistical strategy which can be applied to any two methodologies to determine parity.
Temple University--Theses
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Goodpaster, Aaron M. "Statistical Analysis Methods Development for Nuclear Magnetic Resonance and Liquid Chromatography/Mass Spectroscopy Based Metabonomics Research." Miami University / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=miami1312317652.

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Woocay-Prieto, Arturo. "Groundwater hydrochemical facies, flowpaths and recharge determined by multivariate statistical, isotopic and chloride mass-balance methods." To access this resource online via ProQuest Dissertations and Theses @ UTEP, 2008. http://0-proquest.umi.com.lib.utep.edu/login?COPT=REJTPTU0YmImSU5UPTAmVkVSPTI=&clientId=2515.

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Gabrielsson, Jon. "Multivariate methods in tablet formulation." Doctoral thesis, Umeå : Univ, 2004. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-268.

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Wang, Bo. "Novel statistical methods for evaluation of metabolic biomarkers applied to human cancer cell lines." Miami University / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=miami1399046331.

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Pham, Minh H. "Signal Detection of Adverse Drug Reaction using the Adverse Event Reporting System: Literature Review and Novel Methods." Scholar Commons, 2018. http://scholarcommons.usf.edu/etd/7218.

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One of the objectives of the U.S. Food and Drug Administration is to protect the public health through post-marketing drug safety surveillance, also known as Pharmacovigilance. An inexpensive and efficient method to inspect post-marketing drug safety is to use data mining algorithms on electronic health records to discover associations between drugs and adverse events. The purpose of this study is two-fold. First, we review the methods and algorithms proposed in the literature for identifying association drug interactions to an adverse event and discuss their advantages and drawbacks. Second, we attempt to adapt some novel methods that have been used in comparable problems such as the genome-wide association studies and the market-basket problems. Most of the common methods in the drug-adverse event problem have univariate structure and thus are vulnerable to give false positive when certain drugs are usually co-prescribed. Therefore, we will study applicability of multivariate methods in the literature such as Logistic Regression and Regression-adjusted Gamma-Poisson Shrinkage Model for the association studies. We also adopted Random Forest and Monte Carlo Logic Regression from the genome-wide association study to our problem because of their ability to detect inherent interactions. We have built a computer program for the Regression-adjusted Gamma Poisson Shrinkage model, which was proposed by DuMouchel in 2013 but has not been made available in any public software package. A comparison study between popular methods and the proposed new methods is presented in this study.
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Moller, Jurgen Johann. "The implementation of noise addition partial least squares." Thesis, Stellenbosch : University of Stellenbosch, 2009. http://hdl.handle.net/10019.1/3362.

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Thesis (MComm (Statistics and Actuarial Science))--University of Stellenbosch, 2009.
When determining the chemical composition of a specimen, traditional laboratory techniques are often both expensive and time consuming. It is therefore preferable to employ more cost effective spectroscopic techniques such as near infrared (NIR). Traditionally, the calibration problem has been solved by means of multiple linear regression to specify the model between X and Y. Traditional regression techniques, however, quickly fail when using spectroscopic data, as the number of wavelengths can easily be several hundred, often exceeding the number of chemical samples. This scenario, together with the high level of collinearity between wavelengths, will necessarily lead to singularity problems when calculating the regression coefficients. Ways of dealing with the collinearity problem include principal component regression (PCR), ridge regression (RR) and PLS regression. Both PCR and RR require a significant amount of computation when the number of variables is large. PLS overcomes the collinearity problem in a similar way as PCR, by modelling both the chemical and spectral data as functions of common latent variables. The quality of the employed reference method greatly impacts the coefficients of the regression model and therefore, the quality of its predictions. With both X and Y subject to random error, the quality the predictions of Y will be reduced with an increase in the level of noise. Previously conducted research focussed mainly on the effects of noise in X. This paper focuses on a method proposed by Dardenne and Fernández Pierna, called Noise Addition Partial Least Squares (NAPLS) that attempts to deal with the problem of poor reference values. Some aspects of the theory behind PCR, PLS and model selection is discussed. This is then followed by a discussion of the NAPLS algorithm. Both PLS and NAPLS are implemented on various datasets that arise in practice, in order to determine cases where NAPLS will be beneficial over conventional PLS. For each dataset, specific attention is given to the analysis of outliers, influential values and the linearity between X and Y, using graphical techniques. Lastly, the performance of the NAPLS algorithm is evaluated for various
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Books on the topic "Chemistry – Statistical methods"

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Meier, Peter C., and Richard E. Zünd. Statistical Methods in Analytical Chemistry. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2000. http://dx.doi.org/10.1002/0471728411.

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Meier, Peter C., and Richard E. Zünd. Statistical Methods in Analytical Chemistry. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2000. http://dx.doi.org/10.1002/0471728411.

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E, Zünd Richard, ed. Statistical methods in analytical chemistry. New York: Wiley, 1993.

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Jurand, Czermiński, ed. Statistical methods in applied chemistry. Amsterdam: Elsevier, 1990.

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Einax, Jürgen, ed. Chemometrics in Environmental Chemistry - Statistical Methods. Berlin, Heidelberg: Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/978-3-540-49148-4.

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Malinowski, Edmund R. Factor analysis in chemistry. 2nd ed. New York: Wiley, 1991.

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Malinowski, Edmund R. Factor analysis in chemistry. Malabar, Fla: R.E. Krieger Pub. Co., 1989.

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NATO Advanced Study on Propagation of Correlations in Constrained Systems (1990 Cargèse, France). Correlations and connectivity: Geometric aspects of physics, chemistry, and biology. Dordrecht: Kluwer Academic Publishers, 1990.

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Simkin, B. I͡A. Quantum chemical and statistical theory of solutions: A computational approach. Edited by Sheĭkhet I. I. London: Ellis Horwood, 1995.

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Deming, Stanley N. Experimental design: A chemometric approach. 2nd ed. Amsterdam: Elsevier, 1993.

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Book chapters on the topic "Chemistry – Statistical methods"

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Kissling, Grace E. "Statistical Methods." In The Clinical Chemistry of Laboratory Animals, 1105–20. Third edition. | Boca Raton : Taylor & Francis, 2017.: CRC Press, 2017. http://dx.doi.org/10.1201/9781315155807-26.

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Malhotra, Priti. "Statistical Methods of Analysis." In Analytical Chemistry, 1–15. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-26757-4_1.

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Dementiev, V. A. "Statistical Methods in Analytical Chemistry." In Advances in Geochemistry, Analytical Chemistry, and Planetary Sciences, 563–72. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-09883-3_37.

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Starzak, Michael E. "Statistical Mechanics." In Mathematical Methods in Chemistry and Physics, 359–408. Boston, MA: Springer US, 1989. http://dx.doi.org/10.1007/978-1-4899-2082-9_7.

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Kasprzyk, Robert, and Stephen Vardeman. "Applied Statistical Methods and the Chemical Industry." In Riegel’s Handbook of Industrial Chemistry, 83–117. Boston, MA: Springer US, 1992. http://dx.doi.org/10.1007/978-1-4757-6431-4_4.

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Kasprzyk, Robert, and Stephen Vardeman. "Applied Statistical Methods and the Chemical Industry." In Riegel’s Handbook of Industrial Chemistry, 83–117. Dordrecht: Springer Netherlands, 1992. http://dx.doi.org/10.1007/978-94-011-7691-0_4.

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Kent, James A. "Applied Statistical Methods and the Chemical Industry." In Riegel's Handbook of Industrial Chemistry, 50–81. Boston, MA: Springer US, 2003. http://dx.doi.org/10.1007/0-387-23816-6_4.

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Burdick, Richard K., David J. LeBlond, Lori B. Pfahler, Jorge Quiroz, Leslie Sidor, Kimberly Vukovinsky, and Lanju Zhang. "Statistical Methods for CMC Applications." In Statistical Applications for Chemistry, Manufacturing and Controls (CMC) in the Pharmaceutical Industry, 11–113. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-50186-4_2.

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Vardeman, Stephen, and Robert Kasprzyk. "Applied Statistical Methods and the Chemical Industry." In Handbook of Industrial Chemistry and Biotechnology, 1889–919. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-52287-6_35.

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Vardeman, Stephen, and Robert Kasprzyk. "Applied Statistical Methods and the Chemical Industry." In Handbook of Industrial Chemistry and Biotechnology, 131–54. Boston, MA: Springer US, 2012. http://dx.doi.org/10.1007/978-1-4614-4259-2_4.

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Conference papers on the topic "Chemistry – Statistical methods"

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Manoppo, Yance, Leny S. Latuny, N. J. de Kock, and J. Wattimena. "Detecting indications of cheating in school exams of chemistry subjects using several statistical methods." In 1ST INTERNATIONAL SEMINAR ON CHEMISTRY AND CHEMISTRY EDUCATION (1st ISCCE-2021). AIP Publishing, 2023. http://dx.doi.org/10.1063/5.0110485.

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Sato, Hirofumi, Chisa Kikumori, and Shigeyoshi Sakaki. "Coronene-transition metal complex: View from quantum chemistry and statistical mechanics." In INTERNATIONAL CONFERENCE OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 2009: (ICCMSE 2009). AIP, 2012. http://dx.doi.org/10.1063/1.4771837.

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Militky, Jiri, Milan Meloun, and Karel Kupka. "Chemometrical package for PC." In Proceedings of the First Scientific Meeting of the IASE. International Association for Statistical Education, 1993. http://dx.doi.org/10.52041/srap.93312.

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Chemometrics is a relatively young discipline ranging over chemistry, mathematical statistics and informatics. Its important part comprises methods for the extraction of relevant information from chemical experiments. These methods make use of selected computer algorithms of mathematical statistics, which are discussed e.g., in Melon et al. (1992) and Militky (1989). Some chem-metrical computations can be performed by Bernal statical packages, namely SYSTAT, SAS, STAT-GRAPHICS. Extensive tests and studies of these and other packages have shown, however, that in many cases they do not support a researcher with suitable statistical methods nor are the numerical algorithms reliable enough (see Militky, 1990).
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Zwagerman, Ralph. "Development of ISO18363-4 / AOCS Cd29f-2021: A new standardized method to quantify MCPDE and GE in edible oils." In 2022 AOCS Annual Meeting & Expo. American Oil Chemists' Society (AOCS), 2022. http://dx.doi.org/10.21748/dqcb6439.

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In August 2021 the International Organization for Standardization (ISO) Â has published part 4 to the ISO 18363 series of methods to quantify MCPD- and glycidyl esters in edible oils. AOCS will follow by publishing the method as AOCS Cd 29f-2021 in 2022. This new method describes a high throughput method for the quantification of these process contaminants, for which the EU has recently updated its legislation on the maximum levels allowed in edible oils. In short, its chemistry is based on fast alkaline transesterification of the fat matrix, which also cleaves the MCPDE and GE esters before the analytes are derivatization and analysis by GC-MS/MS. An in vitro correction for glycidol overestimation, which can occur in alkaline environment, is included. In the fall of 2018 both ISO and AOCS technical committees agreed to standardize the method in cooperation. An overview is given of the standardization project together with statistical data collected during the collaborative study together with a short introduction to the chemistry of the method.
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Obeyesekere, Nihal U., Jonathan J. Wylde, Thusitha Wickramarachchi, and Lucious Kemp. "Formulation of High-Performance Corrosion Inhibitors in the 21St Century: Robotic High Throughput Experimentation and Design of Experiments." In SPE International Conference on Oilfield Chemistry. SPE, 2021. http://dx.doi.org/10.2118/204353-ms.

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Abstract Critical micelle concentration (CMC) is a known indicator for surfactants such as corrosion inhibitors’ ability to partition to water from two phase systems such as oil and water. Most corrosion inhibitors are surface active. At critical micelle concentration, the chemical is partitioned to water from the interface, physisorption on metallic surfaces and forms a physical barrier between steel and corrosive water. This protective barrier thus prevents corrosion initiating on the metal surface. When the applied chemical concentration is equal or higher than the CMC, the surfactant is partitioned to aqueous phase from the oil-water interface. This would lead to higher chemical availability of the inhibitor in water, preventing corrosion. Therefore, it was suggested that CMC can be used as an indicator to optimal chemical dose for corrosion control1-5. The lower the CMC of a corrosion inhibitor product, the better is this chemical for corrosion control as the availability of the chemical in the aqueous phase increases. This can achieve corrosion control with lesser amount of corrosion inhibitor product. Thus, increasing the performance of corrosion inhibitor product. In this work, the physical property, CMC, was used as an indicator to differentiate corrosion inhibitor performance. A vast array of corrosion inhibitor formulations was achieved by combinatorial chemical methods using Design of Experiment (DoE) methodologies and these arrays of chemical formulations were screened by utilizing high throughput screening (HTE)6-8, using CMC as the selection guide. To validate the concept, a known corrosion inhibitor formulation (Inhibitor Abz) was selected to optimize its efficacy. This formula contains several active ingredients and a solvent package. Three raw materials of this formulation were selected and varied in combinatorial fashion, keeping the solvents and other raw materials constant9. These three raw materials were blended in a random but in a controled manner utizing DoE and using combinatorial techniques. Instead of rapidly blending a large amount of formulations using robotics, the design of experiment (DoE) methods were utilized to constrain the number of blends. When attempting to discover the important factors, DoE gives a powerful suite of statistical methodologies10. In this work, Design Expert software utilizes DoE methods and this prediction model was used to explore a desired design space. The more relevant (not entirely random) formulations were generated by DoE methods, using Design Expert software that can effectively explore a desired design space. The Design of Experiment software mathematically analyzes the space in which fundamental properties are being measured. The development of an equally robust prescreening analysis was also developed. After blending a vast array of formulations by using automated workstation, these products were screened for CMC by utilizing an automated surface tension workstation. Several formulations with lower CMCs than the reference product (Inhibitor Abz) were discovered and identified for further study. The selected corrosion inhibitor formulations were blended in larger scales. The efficacy of these products was tested by classical laboratory testing methods such as rotating cylinder electrode (RCE) and rotating cage autoclave (RCA) to determine their performance as anti-corrosion agents. As the focus of this project was to optimize the corrosion Inhibitor Abz, this chemical was used as the reference product throughout of this work. The testing indicated that several new corrosion inhibitor formulations discovered from this work outperformed the original blend, thus validating the proof of concept.
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Patience, Richard, Mark Bastow, Martin Fowler, Julian Moore, and Craig Barrie. "The Application of Petroleum Geochemical Methods to Production Allocation of Commingled Fluids." In SPE Europec featured at 82nd EAGE Conference and Exhibition. SPE, 2021. http://dx.doi.org/10.2118/205130-ms.

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Abstract Production allocation from petroleum geochemistry is defined here as the quantitative determination of the amount or portion of a commingled fluid to be assigned to two or more individual fluid sources (e.g., a pipeline, field, reservoir, well) at a particular moment in time, based on the fluid chemistry. It requires: i) knowledge of the original chemical compositions of each of the fluids prior to mixing (referred to here as the "end members"), and ii) that statistically valid differences in their chemistries can be identified. Petroleum geochemical-based methods for production monitoring and allocation are much lower cost than using production logging tools, as there is no additional rig time or extra personnel required at the well site. Additionally, no intervention to the production of hydrocarbons from a well is required and, hence, there is none of the risk entailed in additional operational activity. Geochemical methods are applicable to a wide range of fields, irrespective of pressure, temperature, reservoir quality and reservoir fluid type. The method has been in existence for over 30 years, during which time a number of different analytical methods, data pre-processing and treatment approaches have been applied. This paper summarises these approaches, and provides examples, but also describes a "best practice" which is not a "one size fits all" approach, as is sometimes seen in the literature. A successful production allocation study consists of the following steps: i) Selection of end member samples that contribute to the commingled production fluid; ii) Determination of the differences in chemical composition of the end members through laboratory analysis of the end members (e.g. by WO-GC), replicate analyses of samples and statistical treatment of the data (e.g. PCA); iii) If statistically significant differences exist, laboratory analysis of the end members and commingled fluids with appropriate replicate analyses of samples; iv) Data selection, pre-processing (e.g. selection of ratios or concentrations of components); v) Determination of end member contributions by solving equations (e.g. least squares best fit) and uncertainty estimation (e.g. Monte Carlo or Bootstrap methods). The differences in approach for conventional versus unconventional plays are also discussed.
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Bharathi, Arvind Krishnasamy, and Adri van Duin. "Analysis of Thermal Transport in Zinc Oxide Nanowires Using Molecular-Dynamics Simulations With the ReaxFF Reactive Force-Field." In 2010 14th International Heat Transfer Conference. ASMEDC, 2010. http://dx.doi.org/10.1115/ihtc14-22733.

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The objective of this paper is to determine the thermal conductivity of Zinc Oxide nanowire by Steady State Non-equilibrium and Transient Non-equilibrium Molecular Dynamics (SS-NEMD and T-NEMD) simulations using the ReaxFF reactive force field [5]. While SS-NEMD uses an equilibrated system and statistical averaging; T-NEMD uses cooling/heating rates in order to calculate the conductivity. The validity of the methods is first verified using Argon as a test case. The thermal conductivity of Argon thus calculated is compared with those presented by Bhowmick and Shenoy [20]. We then study the effects of system size using SS-NEMD method while effects of periodic boundary conditions — 1D, 2D and bulk variation of conductivity with temperature are analyzed using T-NEMD simulations. The results obtained compare favorably with those measured experimentally [12, 13]. Thus the SS-NEMD and T-NEMD methods are alternatives to the traditional Green-Kubo approach. In conjunction with ReaxFF, they are computationally cheaper than the Green-Kubo method and can be used to determine the thermal conductivity of materials involved in surface chemistry reactions such as catalysis and sintering.
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Aileni, Raluca maria. "HEALTHCARE PREDICTIVE MODELS BASED ON BIG DATA FUSION FROM BIOMEDICAL SENSORS." In eLSE 2016. Carol I National Defence University Publishing House, 2016. http://dx.doi.org/10.12753/2066-026x-16-046.

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The paper presents a method for analyzing data from sensors and developing the predictive models based on learning methods. There are some methods, described on scientific literature, such as statistical methods (linear regression, logistic regression, and Bayesian models), advanced methods based on machine learning and data mining (decision trees and artificial neural networks) and survival models. All of these methods are intended to discover the correlation and covariance between biomedical parameters. This paper presents the decision tree method for predictive health modeling based on machine learning and data mining. Based on this method used can be developed a decision support system for healthcare. Machine learning is used in healthcare predictive modeling for learning to recognize complex patterns within big data received from biomedical sensors. The sensors data fusion refers to the usage of the sensors wireless network and data fusion on the same level (for similar sensors - e. g. temperature sensors) and on different levels (different sensors category - pulse, breath, temperature, moisture sensors) for developing the decision systems. Big data concept is familiar for medical sciences (genomics, biomedical research) and also for physical sciences (meteorology, physics and chemistry), financial institutions (banking and capital markets) and government (defense). For predictive models in clinical analysis is important to establish the time steps discretization of occurrence of a particular event (critical state required continuous monitoring) for observe the impact of the correlated values for biomedical parameters. These aspects presented are useful for healthcare learning about correlation between diseases and biomedical parameters.
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Riva, Andrea. "On the Scatter of Creep Data: Methods to Increase Modelling Accuracy Accounting for Batch-to-Batch Dispersion." In ASME Turbo Expo 2022: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/gt2022-82499.

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Abstract Gas turbine components and many industrial high temperature components suffer from creep, a viscous effect of the material that induce irreversible deformation, microstructural damage, and eventually failure. Creep strain (where the creep strain εcr is a function of time, stress, temperature) and creep rupture models (where the rupture time texp is a function of stress and temperature) are fitted to the results of expensive and time-consuming experimental tests, which can last for several years (e.g. test duration up to 100kh – 200kh). At longer times, in the range of components expected life target, when it is more likely to observe creep damage, the accuracy of creep models is required to be as high as possible. It is therefore crucial to optimize the model fitting process in order to minimize the error and reduce the number of tests required. To achieve such results the experimental result dispersion needs to be properly addressed. In particular, the differences between the different heats of the same material are known to be a dominant source of uncertainty in the experimental results. The differences are mainly linked to small variations in the fabrication process or chemical composition (even within the allowed variations of the purchase specification or standard recommendations), which can generate different microstructures and mechanical behavior. This is known as batch-to-batch dispersion and this phenomenon is responsible for significant creep strength differences between heats. It is essential for the model reliability to gain the best possible insight of how the model itself can be influenced by the peculiarities and homogeneity of the available data. In order to achieve such goal, many analyses can be performed: quantitative identification strong/weak batches, analysis of the dataset inhomogeneity (i.e. a predominance of weak batches at a certain temperature or times), identification of correlations (e.g. tensile strength and chemistry, etc.), identification of creep mechanisms transitions that affect the applicability range of the model. A statistical analysis of the test results is conducted in order to enable a non-deterministic modelling of creep rupture and strength, separately accounting for in-batch and batch-to-batch sources of dispersion. The soundness of the proposed probabilistic framework is validated via Monte Carlo simulation. The paper is intended to provide an overview of the most recent proposals and progresses of the existing methods to deal with the problem and propose additional original methods to improve the analysis and the fitting procedure.
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Odunmbaku, Adiat, and Moriliat Jumoke Afolabi. "Pedagogic Transformation: Blending of Reinforcement and Inquiry Learning in Innovative Science as Resilience Technique." In Tenth Pan-Commonwealth Forum on Open Learning. Commonwealth of Learning, 2022. http://dx.doi.org/10.56059/pcf10.1509.

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In this paper, blending reinforcement and inquiry learning in innovative science (technological approaches) as resilience technique was presented. The study was designed to determine the effectiveness of these innovative approaches in improving students’ performance. The views about reinforcement and inquiry methods were used as data collection instrument. To this end two research questions were raised and two research hypotheses were formulated to answer the research questions. The study adopted the quasi-experimental design of pre-test post-test non-equivalent control groups. Sixty 100Level undergraduate students of National Open University of Nigeria (NOUN) were used. After four weeks of online facilitation of quantitative and qualitative techniques, acid-base reaction and determination of radicals, a-20 item questions which was well validated and had reliability coefficient of 0.80 were administered to the students before and after application of the teaching strategies on the students. After that the students were reinforced and tested with the same questions. Mean, Standard Deviation and t.test were the statistical tools used to analysis the data. The result of the study shows that students with the blend of guided inquiry and reinforcement learning performed significantly well than students with only guided inquiry learning. In addition, the types of degree (BSc/BSc. (Ed.)) has significant influence on students’ performance in favour of BSc. Students. Based on the findings of this study, it was recommended among others that university lecturers should be encouraged to blend guided inquiry with reinforcement as a teaching method in delivery of their lessons especially in the teaching of some concepts in chemistry.
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