Dimensional Analysis And Implicit Function: An Application .
WSEAS TRANSACTIONS on BUSINESS and ECONOMICSDOI: 10.37394/23207.2020.17.11Fernando JuárezDimensional Analysis and Implicit Function:An Application to Textual DataFERNANDO JUÁREZSchool of AdministrationUniversidad del RosarioAutopista Norte, Calle 200, ores/J/Juarez-Acosta-Fernando/Abstract: - The purpose of the research is to test the viability and usefulness of the dimensional analysisbased on the Vaschy-Buckingham Π theorem and implicit function applied to textual data. The method useshermeneuticalanalysis that allows identifying most frequent words, phrases, and the colocations of words,defining words as categories, and then, as fundamental and derived variables; collocation textual analysis alsoprovides the word links that create a conceptual structure to building the dimensional matrixes and equations bythe Vaschy-Buckingham Π theorem. The Gauss-Jordan method gives a solution to the matrixes. Besides, theimplicit function theorem allows creating relationships among the Π numbers and solving them by partialderivatives, gaining insights about the relevance of variables and their relationships. As an example, the modelapplies to the financial summary report about Isodiol International Inc. Reports Profitable Q4 FinancialStatements, delivered by EMIS. Results showed the following relevant categories:1)Company )Traction,14).Additional. They were classified as fundamental andderived variables. All of them were considered derived variables, while the fundamental cipate,9)Traction,10).Theapplication of the Vaschy-Buckingham Π theoremresulted in four Πnumbers, rearranged into an implicitfunction where the dependent variable was Company Financial Performance. The solution by partial derivativesresulted in identifying the category “Company Financial Performance” as well as “Traction” and “Additional”as core categories in the financial report; however, the other categories are also relevant. Conclusions point outthe relevance of the analysis to textual data as an interphase between qualitative and quantitative data and alsoin helping to find relevant variables.Key-Words: Dimensional Analysis; Vaschy-Buckingham Theorem; Textual Data; Implicit Function;Qualitative, Quantitative.Received: February 2, 2020. Revised: March 20, 2020. Accepted: March 24, 2020. Published: March 31, 2020.very isolated ways of interpreting texts and verydifferent results, depending on the preferences andviewpoint of the researcher.While this perspective is worthy under somequalitative approaches, it might lead to unnecessarycomplexity and lack of agreements on many issues;i.e., in analyzing data arising from focus groups, indepth interviews, or company reports, it might bedesirable some consolidated conclusions and notonly a different opinion depending on theresearcher s subjectivity.Thoseare the reasons why this research tries topresent another more standardized approach butbased on the previous well-develop tools. To that,1 IntroductionThe analysis of qualitative data has a rich set of newmethodologies originated under a quantitativeframework but promptly to enter the new field oftextual data analysis, as a complex and promisinginteraction among methods [see 1 for an outstandingexample]. Several software packs, such as ATLASTi, InVivo, MAXQDA, andVoyant Tools, offer verysophisticated methods for analyzing textual data byintroducing the mentioned research mixture.On the other hand, the profusion of methods,techniques, along with the emphasis on subjectivity,as the core of many qualitative analyses, can lead toE-ISSN: 2224-289993Volume 17, 2020
WSEAS TRANSACTIONS on BUSINESS and ECONOMICSDOI: 10.37394/23207.2020.17.11Fernando JuárezThis research is an exploratory, rational-deductive,and pragmatic one, conductive todevelop aqualitative/quantitative data analysis methodologyuseful to analyzing textual data that arise fromqualitative research methodologies and techniques,such as focus group and in-depth interviews, butwithout disregarding any other type of data, such asreports, or interpretations about quantitative data,among others.Even though the research combines qualitativeand quantitative methods, the intention is not toencapsulate this combination under a specific type,but mixing the methods and data under exploratoryresearch logic.The methods used in this research arehermeneutics and non-experimental.As an example of the methodology, it analyzesthe 2019 4th quarter (Q4) financial report fromIsodiolInternational Inc., a global companyinnovatorin Bioactive CBD (Cannabidiol), which is a nonpsychoactive cannabinoid that occurs naturally, usedfor pharmaceutical and wellness products [20]. Thereport is located in the EMIS business database, andcomprises information about profit increase andstrategies used by the company to reach thatincrease; it contains 201 words.There is a rising in the use of non-financialmeasures in the financial performance reports [21];the assessment of financial performance admitsmany different tools and perspectives, among themthe combinations of ratios and linguistic analysis[22], sentiment analysis to predict risk [23] andlanguage analysis [24]. That shows an interest inother information than that in the common financiallanguage.The hermeneutic analysis conducted used theVoyant Tools software, which is web based-freesoftware that gives many analytical tools to makesubjective interpretations of textual data.The hermeneutical analysis consists of manydifferent tools to reveal the most importantcharacteristics in textual data. One of them is themost frequent words,Fi, in the text. Another one isthe colocation, Fij, or frequency of closely locatedwords. Several frequent words and colocations forthe Isodiol Report are in Table 1.The table alsoshows the frequency change of the wordsthroughout the text or trend.In the table, company is the most frequentlylocated word next to others, it also is the mostfrequent word in the text, and so it has particularrelevance in the Isodiol Report. The word itself hasthe meaning of self-reference related to differentparticular aspects of the company so that the reportcould be centered on that word, showing a lack ofthe method of dimensional analysis, widely used inother disciplines, such as physics [as an example see2, 3, 4] and at a lesser degree in economics,statistics, artificial neural network [see 5, 6, 7], andproduction systems [8], is used as a paradigm toproduce a standard framework; it also admitsgeneralized [9, 10, 11] and alternative [12] forms.The method, which has its origin as a form oflooking for dimensional homogeneity in theformulations and laws of those mentioned fields ofknowledge, can be easily translated to otherareas.The seminal studies of Vaschy [see 13] andBuckingham [see 14] creates the logic tointerpreting and analyzing the correspondencebetween the two sides of an equation in terms of thedimensions involved. A detailed historical accountof their validity proof is in [15].Despite its appearance of objectivity,dimensional analysis has plenty of subjectivity,while, at the same time, presenting very objectiveresults.However, this particular combination doesnot prevent the method of drawing relevant andreliable results. In this regard, it is a good candidateforcombining qualitative/quantitative data; as anexample, the theorem found an application toidentify some probabilistic states of the mind andideas [see 16, 17], and also in qualitative physics[18]. Besides, the Π-theorem is more than adimensional test for a certain field of knowledge, itis a general algebra theorem [19], and it is useful forapplication in any science.2 Problem FormulationThe problem is the application of dimensionalanalysis to textual data along with elements ofdifferential calculus to provide more analyticalpossibilities of combining qualitative data andquantitative analysis.The problem arises from the inherent existingsubjectivity in qualitative analysis when creatingcategories from textual and other qualitative data.The manner of creating those categories results insubjective structures difficult to compare with othersthat could be created by other researchers. In thisregard, there is an opportunity to build and analyzecategorical structures in a standardized reproductiveway together with a different and, hopefully,productive analysis.3 Problem SolutionE-ISSN: 2224-289994Volume 17, 2020
WSEAS TRANSACTIONS on BUSINESS and ECONOMICSDOI: 10.37394/23207.2020.17.11Fernando Juárezinterest in other financial aspects not directly relatedto the company.Table 1. Several of the most frequent words andcolocations in the Isodiol Company Q4 esultsRepresentsTraction222222222Fig. 1 The structure of colocatedwords in Isodiolfinancial report. The lines represent the link betweenwords.As shown in the figure, blue-colored squares arethose words with more links to other words.Although based on link number, the figureorganization is subjective and had the intention ofspecifically creating that structure. The viewpoint isto subjectively resemble the organization existing inother types of analysis, such as structural equationmodeling or factor analysis.The analytical procedures applied to Isodiolreport were:Another interesting hermeneutical analysis is toobtain the most frequent phrases Fk in the text.Those for the Isodiol Report are in Table 2.Among the frequent phrases in Table 2, one canfind the word company again, confirming that ithas, without no doubt, a relevant meaning within thetext. Due to the small number of words in the text(201), the identification of a set of words as aphrase, different to another one but repeated allalong the text, also requires a small number ofwords; that is why the phrase length in Table two isso small (only 2-3).1. Identify categories by the hermeneutics oftextual data.2. Determining fundamentals and derivedvariables (categories) by analysis of the text.3. Create and solving the dimensional matrixbased on the Vaschy-Buckingham Πtheorem.4. Apply derivation rules to the resultingequations, to identify the most relevantvariables.The interpretation of the figure is also subjective,and one can define variables as fundamentals orderived in the dimensional analysis style; suchclassification is subjective too, as no fundamentalsor derived variables exist in textual data, except forthe classification the researcher wants to createbased on the textual report data. Accordingly, thefollowing rules could apply:Table 2. Several of the most frequent phrases in theIsodiol Company Q4 Report.TérminoThecompany hasWhollyownedsubsidiariesContinue toTraction inCountLength2222Trend33222222The analysis of the relationships among variablescan use the colocation tool to identify a network ofrelationships. For this research, the colocationhermeneutical analysis consisted of identifying themost significant structure of words linked byproximity in the text; this structural analysisdetermined the proximity by creating a window of 5pre and post words centered in every particularword, denoted as keyword, and running the windowall along the text, identifying the word links.The obtained structure of linked words was thatof Figure 1.The figure answers the question ¿Whatis the financial performance of Isodiol CompanyinQ4?That was the purpose of the report.The report contains several layers and tries tocreate a sense of hierarchy among the concepts. Asthe words and the concept associated with them arethe most prominent in the report, that reflects thecompany's understanding of what the financialperformance is, at least in this report.E-ISSN: 2224-28991. Words are categories with meaning.2. Categories are opinions/perceptions theyare subjective.3. As they are subjective, they have differentintensity, i.e.,they have quantitative/qualitative properties.4. Categories acquire variable characteristics.5. Their positionin the figure could determinethe type of variable, so derived variablesarise from fundamental variables;the higher95Volume 17, 2020
WSEAS TRANSACTIONS on BUSINESS and ECONOMICSDOI: 10.37394/23207.2020.17.11Fernando JuárezAgain, the classification of a variable as derivedor fundamental depends on the researcher criterion;besides, it is not unusual that a derived variable is atthe same time derived and fundamental, based on itsposition in the relationship among variables.The relationships among the variables are thefollowing:the number of arrows, the more derived thevariable is, and the lesser the number ofarrows, the more fundamental the variableis. However, derived variables can also bethose with the lowest number of arrows, asan interpretation of them as playing adeterminant role in the figure.1) x1 f(x2, x13, x14)2) x2 f(x3, x4, x13, x14)3) x3 f(x5, x6, x7, x8)4) x4 f(x9, x10, x11, x12)Followingtheserulesandsubjectivecriteriacould usually be part of the qualitativeinterpretation of categories, but in this esides, other criteria are useful;however, the ones used in this research are fine andsimple enough for clarity.The correspondence among categories andvariables is as follows:The analysis considers all the variables to have apower of 1; however, this criterion could changedepending on the researcher's interpretation. In thiscase, assuming this criterion, the correspondinggeneral dimensional matrix is in Table 1.x1: Company Financial Performance;x2: Company;x3: Continued;x4: Global;x5: Expand;x6: Footprint;x7: Diversification;x8: Costs;x9: Bioactive;x10: Acquisitions;x11: CBD;x12: Anticipate;x13: Traction;x14: Additional.Table 1.Matrix with the derived variables (columns)power for each fundamental variable (rows) in theIsodiol Company Q4 Report.x5x6x7x8x9x10x11x12x13x14x6 x70 01 00 10 00 00 00 00 00 00 0x80001000000x9 x10 x11 x12 x130 0 0 000 0 0 000 0 0 000 0 0 001 0 0 000 1 0 000 0 1 000 0 0 100 0 0 010 0 0 00x14 x10 10 10 10 10 10 10 10 10 11 1x21111111111x31111000000x40000111100The fundamental variables in the matrixrowsarethose on the lowest level of Figure 1, the ones withthe lowest number of arrows pointing to them;nevertheless, they are derived variables as well,because of the interpretation made of the diagramrelationshipsaccording to the previously establishedcriteria of arrow number and relevant in therelationship among variables. According to thedistribution of the fundamental and derived variable,there are n-k Πnumbers: 14-10 4, they are:By classifying those variables into derived andfundamentals, according to the number of arrowspointing at them and their additional relevancy inthe figure, derived n variables are all of thosepreviously defined, while fundamentals kvariablesare:x5:Expand;x6: Footprint;x7: Diversification;x8: Costs;x9: Bioactive;x10: Acquisitions;x11: CBD;x12: Anticipate;x13: Traction;x14: Additional.E-ISSN: 2224-2899x510000000001) Π1 (x2b, x13m, x14n, x1a)2) Π2 (x3c, x4d, x2b)3) Π3 (x5a, x6f, x7g, x8h, x3c)4) Π4 (x9i, x10j, x11k, x12l, x4d)Based on the Π numbers, there are four matrixesin Table 1, one for each Π number. Table 2 showsthe matrixes identified by their correspondingnumber on the first four rows in the table, the matrixnumbers are those of the previously defined Π96Volume 17, 2020
WSEAS TRANSACTIONS on BUSINESS and ECONOMICSDOI: 10.37394/23207.2020.17.11Fernando Juáreznumbers, i.e., Π1 corresponds to Matrix 1 in the firstrow of the matrix rows, and the variables are thoseunder the columns named 1 in the first matrix row.The table equals 0 for every row, as it is the basisfor Π numbers; it is so because in every rowincludes derived and fundamental variables, andthen, every matrix that contains a derived variableand the fundamental variables equals 0 and requiresfinding a vector of powers v(a, b, c, . l, m, n)foreach matrix Mx, such as Mxv 0. The column orderin the matrix is important because the coefficientscould have a different sign depending on that order.For instance, changing the location of the lastderived variable columns to the first columns wouldchange their sign in the equation solutions.1) Π1 (a x14; e -x14; f -x14; g -x14; h -x14; i -x14; j x14; k -x14; l -x14; m -x14;n -x14)2) Π2 (b x14; e -x14; f -x14; g -x14; h -x14; i -x14; j x14; k -x14; l -x14; m -x14;n -x14)3) Π3(c x8; e -x8; f -x8; g -x8; h -x8; i 0; j 0; k 0;l 0; m 0; n 0)4) Π4(d x12; e 0; f 0; g 0; h 0; i -x12; j -x12; k x12; l -x12; m 0; n 0)Eliminating thexivariables with power 0,andsubstituting the others xj with power 1:1) Π1 (a 1; e -1; f -1; g -1; h -1; i -1; j -1; k -1;l -1; m -1;n -1)2) Π2 (b 1; e -1; f -1; g -1; h -1; i -1; j -1; k -1;l -1; m -1;n -1)3) Π3 (c 1; e -1; f -1; g -1; h -1; i 0; j 0; k 0;l 0; m 0; n 0)4) Π4 (d 1; e 0; f 0; g 0; h 0; i -1; j -1; k -1; l 1; m 0; n 0)Table 2.Matrixes for each Π number. Every columngroup with the same number in the matrix rowsforms a matrix.Columns in each matrix number. Variables under the same columnnumber create a matrix.MRΠ1 1 1 1 1 1 1 1 1 11 1Π2 2 2 2 2 2 2 2 2 222Π3 3 3 3 3 3 3 3 3 333Π4 4 4 4 4 4 4 4 4 444x5 x6 x7 x8 x9 x10 x11 x12 x13 x14 x1 x2 x3 x4VRx5 1e 0 0 0 0 0 0 000 1a 1b 1c 0x6 0 1f 0 0 0 0 0 000 1a 1b 1c 0x7 0 0 1g 0 0 0 0 000 1a 1b 1c 0x8 0 0 0 1h 0 0 0 000 1ª 1b 1c 0x9 0 0 0 0 1i 0 0 000 1ª 1b 0 1dx10 0 0 0 0 0 1j 0 000 1ª 1b 0 1dx11 0 0 0 0 0 0 1k 000 1ª 1b 0 1dx12 0 0 0 0 0 0 0 1l 00 1ª 1b 0 1dx13 0 0 0 0 0 0 0 0 1m 0 1ª 1b 0 0x14 0 0 0 0 0 0 0 00 1n 1ª 1b 0 0MR: matrix rows; VR: variables rowsReformulating the relationships among the Πnumbers in terms of the results: Ƒ(0000000000Ƒ(,,,,,(1),,,,,,, ,,, Ф )(3),) 0(4) ,,(5)In the equation, the Π1 number is a function Ф ofthe others in such a way that (4) equals 0. Thesolution for x1 is) 0 (2)Solving the corresponding matrix for each Π byGauss-Jordan elimination system requires enteringin the system one derived variable power and thefundamental variable powers entered in Table 1. Acombination of the variables that is a product ofpowers [25]. Applying the procedure, it leads to thefollowing solutions:E-ISSN: 2224-2899, Now, as the relationship equals 0, then therelationship among the Π numbers can be definedassuming the implicit function:In terms of the variables,the relationship is:f(,Thatrelationship agrees with the relationshipsamong the variables depicted in Figure 1. However,according to (1), the relationship for the Π numbersis:The relationship among the Π numbers is:ḟ(Π1, Π2, Π3, Π4) 0 Ф ,,(6)However, in what follows the analysis uses thefunction (4), which has four variables, let them be1) Y 97 x1x5-1 .x14-1Volume 17, 2020
WSEAS TRANSACTIONS on BUSINESS and ECONOMICSDOI: 10.37394/23207.2020.17.112) X2 Fernando Juárez x2x5-1 . x14-13) X3 x3x5-1x6-1x7-1x8-1 x4x9-1x10-1x11-1x12-14) X4 1 5 141[5 6 7 81[9 10 11 12(7)1) Despite the assertion that x1 x2, results show(equation 13) that increments in Y are the same as X2because variations in the same variables cause them;so then, they confirm the strong relationshipbetween x1 and x2; actually,x2 can be the directanswer to the main question (x1). In this regard, theidentified core message of the report is the companyitself.(8)(9)Any variable xi could be the differentiationvariable, i.e., one of the denominator variables aswell. However, to keep the relevance of thefundamental variables derived variables were thedifferentiation variablesonly.Obtaining the dependent variable derivativesrequires a different procedure; it needs to takepartial derivatives for Y(dependent variable) andeach Xi (independent variables). The computationsare 2) The results agree with the hierarchy depicted inFigure 1, showing the relevance of each derived andfundamental variable (equations 13, 14, 15).3) The resultspoint out the relevance of variables x13and x14, as they have a direct effect on Y(equations13, 14). That shows how several elementary itemshave an important effect on the financialperformance report.(10) (11) (12)4) It consolidates the results intuitively obtained bylooking at Figure 1. In this sense, it is a doublecheck of the model and the results.Finally, the analysis is also useful to identifythose values of the company's financial status bylooking at those items that proved to be mostrelevant in the results.The analysis of the Isodiol report is an exampleof how this mathematical analysis could help inproviding a different insight into textual data.Despite the briefness of the report and the subjectiveapplication of powers to variables, the analysisshows many possibilities in their applications inmore complex texts.Results are 1 E-ISSN: 2224-2899 (15)In the equations (13), (14) and (15), theindependent variable (X2, X3,X4) derivatives arethose in (7), (8), and (9) respectively.Although results are somehow expected, due tothe subjective designation of power 1 on eachvariable, there are the following conclusions:The derivatives of the independent variables canbe obtained directly:[ (13)(14)98Volume 17, 2020
WSEAS TRANSACTIONS on BUSINESS and ECONOMICSDOI: 10.37394/23207.2020.17.11Fernando Juárezparameters for Al/SiCp MMC usingdimensional analysis and artificial neuralnetwork.EngineeringScienceandTechnology, an International Journal, Vol.22, No. 2, 2019, pp. 468–476.[8] Miragliotta, G. The power of dimensionalanalysis in production systems design.International Journal of ProductionEconomics, Vol. 131, No. 1, 2011,pp.175–182.[9] Ain A. Sonin, & Ronald F. Probstein. AGeneralization of the II-Theorem andDimensional Analysis. Proceedings of theNational Academy of Sciences of the UnitedStates of America, Vol. 101, No. 23, 2004,pp. 8525.[10] Poveda,G.AnálisisDimensionalGeneralizado. Revista EIA, Vol. 13, No. 25,2016, pp. 13–27.[11] Hainzl, J. On local generalizations of thepi-theorem of dimensional analysis. Journalof the Franklin Institute, Vol., 292, No. 6,(n.d.), pp. 463–470.[12] Pankhurst, R. C. Alternative formulationof the pi-theorem. Journal of the FranklinInstitute, Vol. 292, No.6, (n.d.),pp. 451–462.[13] Vaschy, A. Sur les lois de similitude enphysique. AnnalesTélégraphiques, Vol. 19,1892, pp. 25-28.[14] Buckingham, E.On physically similarsystems. Illustrations of the use ofdimensional equations. Physical Review,Vol. 4, 1914, pp. 345-376.[15] Pobedrya, B. E., &Georgievskii, D. V. Onthe proof of the Π-theorem in dimensiontheory. Russian Journal of MathematicalPhysics, Vol. 13, No. 4, (2006). pp. 431–437.[16] Martínez, A., Pando, V., del Río, J.Generalizaciones al teorema Pi deBuckingham con algunas aplicaciones.Technical Report, 2007. Retrieved pdf[17] Martínez, A., Pando, V., del Río, J. DelTeorema π al axioma de las conjeturasrazonadas.Boletín de la Sociedad PuigAdam, No. 86, 2010, pp. 55-73.[18] Bhaskar, R., Nigam, A. Qualitativephysics using dimensional analysis.Artificial Intelligence, Vol. 45, No. 1–2,1990, pp. 73-111.[19] Curtis, W., Logan,J., Parker. W.Dimensional analysis and the Π-theorem,4 ConclusionThis research shows the analysis for qualitative datathat complement those already existing byintroducing a new method applied to thereof. Itprovides new insights into the possibilities that amixture of quantitative/qualitative data has.The method constitutes a methodologicalintegration that goes further than a mix ofquantitative and qualitative research. The researchintentionally avoided that type of methodologicalclassification regarding the method; however, dataare textual or numerical, and, in that sense,qualitative/quantitative denominations are perfectlyright.The method introduced here also allowscomparing data within a standard analysisframework, and the application to simultaneousanalysis of different texts is easy.The text, type of variables, and theirrelationships used in this example have the intentionof showing the possibilities of the analysis;however,it admits many levels of complexity, depending onthe research objectives and the researcher'ssubjective consideration about data.Some useful studies relevant tothis research canbe found in[26], [27], and [28].References:[1] aturalLanguageProcessing, Cambridge, Massachusetts: TheMIT Press, 1999.[2] Sonin, A. A., The Physical Basis ofDimensional Analysis (2nd ed.), Cambridge,Massachusetts: The MIT Press, 2001.[3] Lira, I., Dimensional analysis made simple,European Journal of Physics, Vol. 34,2013, pp. 1391-1401.[4] Albrecht, M., Nachtsheim, C., Albrecht, T.,& Cook, R. D. Experimental Design forEngineeringDimensionalAnalysis.Technometrics, Vol. 55, No. 3, 2013, pp.257-270.[5] Poveda, G., El análisis dimensional eneconomía, Revista Soluciones de PostgradoEIA, Vol. 12, 2014, pp. 67-94.[6] Weijie, D. T., Lin, D. K. J., &Nachtsheim,C. J. Dimensional Analysis and ItsApplications in Statistics. Journal ofQuality Technology, Vol. 46, No. 3, 2014,pp.185–198.[7] Phate, M. R., & Toney, S. B. Modeling andpredictionofWEDMperformanceE-ISSN: 2224-289999Volume 17, 2020
WSEAS TRANSACTIONS on BUSINESS and ECONOMICSDOI: 10.37394/23207.2020.17.11Fernando JuárezLinear Algebra and its Applications, Vol.47, 1982, pp. 117-126.[20] Isodiol. What is CBD. (n.d.) rand/bioactive/[21] Muehlbacher,J.,Siebenaler,T.,&Würflingsdobler, U. The rise of nonfinancial performance measures in annualreports. An analysis of ATX-listedcompanies.TrendyEkonomikyaManagement, Vol. 25, No. 9, 2016, pp. 9-21[22] Myskova, R., & Hajek, P. ComprehensiveAssessment of Firm Financial PerformanceUsing Financial Ratios and LinguisticsAnalysis of Annual Reports. Journal ofInternational Studies, Vol. 10, No. 4, 2017,pp. 96–108.[23] Tsai, M. F., & Wang, C.J. On the RiskPrediction and Analysis of Soft Informationin Finance Reports. European Journal ofOperational Research, Vol. 257, No. 1,2017, pp. 243–250.[24] Slattery, D. M. The Language of FinancialReports and News. Journal of TechnicalWriting & Communication, Vol. 45, No. 1,2015, pp. 77–94.[25] Hanche-Olsen, H. Buckingham’s pitheorem. TMA4195 Mathematical che/notes/buckingham/buckingham-a4.pdf[26] Jothi, V., &Nithya, L.M.Semi-supervisedTaxonomy Aware Integration of Catalogs.WSEAS Transactions on InformationScience and applications. Vol. 11, 2014, pp.169-176.[27] Ghazarian, A. A Dimension-OrientedTheory of Requirements Space inBusinessInformation Systems. WSEASTransactions on Systems. Vol. 13, 2014, pp.76-95.[28] Skala, V. Fast Interpolation andApproximationofScatteredMultidimensional and Dynamic Data UsingRadialBasisFunctions.WSEASTransactions on Mathematics. Vol. 12, No.5, 2013, pp. 501-511.E-ISSN: 2224-2899100Volume 17, 2020
Dimensional Analysis and Implicit Function: An Application to Textual Data FERNANDO JUÁREZ School of Administration Universidad del Rosario . the Isodiol Report are in Table 1.The table also shows the frequency change of the words throughout the text or trend. In the
describe earlier published methods of creating interpolating implicit surfaces. 2.1 Implicit Surfaces An implicit surface is defined by an implicit function, a continuous scalar-valued function over the domainR3. The implicit surface of such a function is the locus of points at which the function takes on the value zero.
RECENT and CLASSIC IMPLICIT BIAS LITERATURE REPORTS ON IMPLICIT BIAS 1. The Science of Equality, Volume 1: Addressing Implicit Bias, Racial Anxiety, and Stereotype Threat in Education and Health Care [Godsil, Tropp, Goff & Powell, 2014] . State of The Science ‐‐ Implicit Bias Review 2014 .File Size: 338KB
Implicit associations do not necessarily align with our declared beliefs. We generally hold implicit biases that favor our own in group. Implicit biases have real world affects on our behaviors. Implicit biases are malleable, therefore can be unlearned. Kirwan Institute
Implicit attitudes and beliefs adapt to situations: A decade of research on the malleability of implicit prejudice, stereotypes, and the self-concept. Advances in Experimental Social Psychology 47: 233-279. Kirwan Institute. State of the Science: Implicit Bias Review 2015. Kirwan Institute. State of
employees about implicit bias (Meyer, 2018), and philosophers worry that implicit bias poses epistemic threats to philosophy (e.g., Saul, 2013a, 2013b; Peters, forthcoming). Nonetheless, some are skeptical about the existence of implicit bias or the efficacy of corporate
INSIDE THE BLACK BOX: IMPLICIT LEADERSHIP THEORIES AND LEADERSHIP PROTOTYPES Both research in personality theory (Shweder, 197.5, 1982) and a book on implicit psy- chology (Wegner & Vallacher. 1977) argue that individuals develop implicit theories (implicit in the sense of not being spelled out) to give meaning to events. .
Displaying Implicit Bias Implicit bias affects more than our cognitive decision-making. Research has shown that body language is far more affected by implicit bias than verbal statements - but that most people cue in and respond more to nonverbal than to verbal cues. Particularly from people in power. (Dovidio, 2002)
accounting equation as shown above. The accounting equation is a simple expression of the fact that at any point in time the assets of the entity will be equal to its liabilities plus its equity. The transactions of a new business entity in its first five days are as follows: Required:
