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附录：中英文翻译15SpeechSignalProcessing15.3AnalysisandSynthesisJ esseW. FussellA fte r an acousti c spee ch s i gnal i s conve rte d to an ele ctri cal si gnal by a mi crophone, i t m ay be desi rable toanalyzetheelectricalsignaltoestimatesometime-varyingparameterswhichprovideinformationaboutamodel of the speech producti on me chanism. S peech a na ly sis i s the process of e stim ati ng such paramete rs. Simil arl y , g ive n some parametri c model of spee ch production and a se que nce of param eters for that m odel,speechsynthesis istheprocessofcreatinganelectricalsignalwhichapproximatesspeech.Whileanalysisandsynthesistechniques maybedoneeitheronthecontinuoussignaloronasampledversionofthesignal,mostmode rn anal y sis and sy nthesis methods are base d on di gital si gnal processing.Atypicalspeechproductionmodelisshownin Fig.15.6.Inthismodeltheoutputoftheexcitationfunctionisscaledbythegainparam eterandthenfilteredtoproducespeech.Allofthesefunctionsaretime-varying.F IGUR E 15 .6 A ge ne ra l spee ch productionmodel.F IGUR E 1 5 .7 W ave form of a spoken phone me /i/ as i nbeet.Formanymodels,theparametersarevariedataperiodicrate,typically50to100timespersecond.Mostspee ch inform ati on is containe d i n the porti on of the si gnal bel ow about 4 kHz.Theexcitationisusually modeledaseitheramixtureorachoiceofrandomnoiseandperiodicwaveform.For hum an spee ch, v oi ced e x citati on occurs w hen the vocal fol ds in the lary nx vibrate; unvoi ce d e x citati onoccurs at constri cti ons i n the vocal tract w hi ch cre ate turbulent a i r fl ow [Fl anagan, 1965] . The rel ati ve mi x ofthesetw o type s ofexcitationisterme d ‚v oicing.‛In addition,theperiodi c e xcitation i s characterizedby afundamentalfrequency,termed pitch orF0.Theexcitationisscaledbyafactordesignedtoproducetheproperampli tude or level of the spee ch si gnal . The scaled ex citati on function i s then fi ltere d to produce the properspe ctral characte risti cs. W hile the filter m ay be nonli near, i t i s usuall y m odele d as a li nearfunction.AnalysisofExcitationInasimplifiedform,theexcitationfunctionmaybeconsideredtobepurelyperiodic,forvoicedspeech,orpurel y random, for unvoi ce d. T hese tw o states correspond to voi ce d phoneti c cl asse s such as vow elsand nasalsandunvoicedsoundssuchasunvoicedfricatives.Thisbinaryvoicingmodelisanoversimplificationforsounds such as v oi ced fri cati ves, whi ch consist of a mi xture of peri odi c and random compone nts. Fi gure 15.7is an ex ample of a time w ave form of a spoke n /i/ phoneme , w hi ch is w ell m odeled by onl y pe riodi c e x citation.B oth ti me dom ai n and frequency dom ai n anal y s is te chni ques have bee n used to esti m ate the de greeofvoi ci ng for a short se gme nt or frame of spee ch. One ti me dom ain fe ature, te rme d the ze ro crossing rate,i sthenumberoftimesthesignalchangessigninashortinterval.AsshowninFig.15.7,thezerocrossingrateforvoicedsoundsisrelativ elylow.Sinceunvoicedspeechtypicallyhasalargerproportionofhigh-frequencyenergy than voi ce d spee ch, the ratio of high-fre que ncy to low -frequency e nergy is a fre que ncy dom aintechni que that provi des i nform ation on voi cing.A nothe r measure use d to estim ate the de gree of voi ci ng is the autocorrel ation functi on, w hi ch is de fine d fora sam pled speech se gment, S ,aswheres(n)isthevalueofthenthsamplewithinthesegmentoflengthN.Sincetheautocorrelationfunctionofa periodi c functi on is i tsel f pe ri odi c, voi ci ng can be e sti mated from the de gree of pe ri odi city oftheautocorrel ati on function. Fi gure 15. 8 i s a graph of the nonne gati ve te rms of the autocorrel ation functi on for a64 -ms frame of the w aveform of Fi g . 15. 7. Ex cept for the de cre ase i n amplitude w ith i ncre asi ng lag, whi chresultsfromtherectangularwindowfunctionwhichdelimitsthesegment,theautocorrelationfunctionisseento be quite pe riodi c for thi s voi ce dutterance.F IGUR E 1 5 .8 A utocorrel ati on functi on of one frame of /i/. Ifananalysisofthevoicingofthespeechsignalindicatesavoicedorperiodiccomponentispresent,another ste p i n the anal y si s process m ay be to estim ate the freque ncy ( or pe ri od) of the voi ce d component.Thereareanumberofwaysinwhichthismaybedone.Oneistomeasurethetimelapsebetweenpeaksinthetime dom ai n si gnal. For ex am ple i n Fi g . 15.7 the m aj or peaks are separate d by about 0. 00 71 s, for afundamentalfrequencyofabout141Hz.Note,itwouldbequitepossibletoerrintheestimateoffundamentalfre quency by mistaki ng the sm aller pe aks that occur betwee n the m a jor pe aks for the m aj or pe aks. Thesesmallerpeaksareproducedbyresonanceinthevocaltractwhich,inthisexample,happentobeatabouttwicethe ex citation fre quency . T his ty pe of e rror w ould re sult in an e sti m ate of pitch approxi m atel y tw i ce the corre ct fre quency.The di stance betw ee n m ajor pe ak s of the autocorrel ation functi on is a closel y rel ate d fe ature thatisfre quentl y use d to esti m ate the pitch pe ri od. In Fi g . 15. 8, the di stance between the m aj or peaks in the autocorrelationfunctionisabout0.0071s.Estimatesofpitchfromtheautocorrelationfunctionarealsosusce pti ble to mistaking the fi rst vocal track resonance for the g l ottal e x citati on frequency.The absol ute m agnitude di ffere nce functi on ( AM DF), de fi nedas,is another functi on w hi ch is often use d i n estim ating the pitch of voi ce d spee ch. A n ex ample of the AM DF isshownin Fig.15.9forthesame64-msframeofthe/i/phoneme.However,theminimaoftheAMDFisusedasanindicatorofthepitchperiod.TheAMDFhasbeenshownt obeagoodpitchperiodindicator[Rossetal.,19 74 ] and does not requi re multi pli cations.FourierAnalysisOne of the m ore comm on processe s for e stim ating the spe ctrum of a se gme nt of spee ch is the Fourie rtransform [ Oppenheim and S chafer, 1 97 5 ]. T he Fourie r transform of a seque nce is m athem ati call y de fine daswheres(n)representsthetermsofthesequence.Theshort-timeFouriertransformofasequenceisatimedependentfunction,definedasF IGUR E 1 5 .9 A bsolute m agnitude diffe rence functi on of one frame of /i/.wherethewindowfunctionw(n)isusuallyzeroexceptforsomefiniterange,andthevariablemisusedtoselectthesectionofthesequ enceforanalysis.ThediscreteFouriertransform(DFT)isobtainedbyuniformlysam pling the short-ti me Fourie r transform i n the fre quency dime nsi on. Thus an N-point DFT is computedusingEq.(15.14),wherethe setofNsamples,s(n),may have firstbeenmultiplied by a window function.Anexampleofthemagnitudeofa512-pointDFTofthewaveformofthe/i/from Fig.15.10isshowninFig.15.10.Noteforthisfi gure, the 512 poi nts in the se que nce have been m ulti plied by a Ham ming w i ndow de fi nedbyF IGUR E 1 5 .1 0 M agnitude of 51 2-point FFT of Ham mi ng window e d/i/.S ince the spe ctral characteristi cs of spee ch m ay change dram a ti call y in a fe w milli se conds, the le ngth, type,and l ocation of the wi ndow function are im portant consi derati ons. If the w indow is too long, changi ng spe ctralcharacteristicsmaycauseablurredresult;ifthewindowistooshort,spectralinaccuraciesresult.AHammingwi ndow of 16 to 32 m s durati on is com m onl y use d for spee ch analysis.S everal characte risti cs of a speech utte rance m ay be dete rmine d by ex amination of the DFT m agnitude. InFig.15.10,theDFTofavoicedutterancecontainsaseriesofsharppeaksinthefrequencydomain.Thesepeaks, caused by the peri odi c sampl ing acti on of the g lottal ex ci tation, are separated by the fundame ntalfrequencywhichisabout141Hz,inthisexample.Inaddition,broaderpeakscanbeseen,forexampleatabout300 Hz and at about 2300 Hz. T hese broad peaks, calle d formants, result from resonances in the vocaltract. LinearPredictiveAnalysisGivenasampled(discrete-time)signals(n),apowerfulandgeneralparametric modelfortimeseriesanalysisiswheres(n)istheoutputandu(n)istheinput(perhapsunknown).Themodelparametersare a(k)fork=1,p,b( l ) for l = 1, q, and G. b( 0) is assume d to be unity. Thi s m odel , describe d as an autore g ressi ve m ov ing average(ARM A)orpole-zeromodel,formsthefoundationfortheanalysismethodtermedlinearprediction.Anautoregressive(AR) orall-polemodel,forwhichallofthe‚b‛coe fficientsexceptb(0)arezero,isfrequentlyused for spee ch anal y si s [M arkel and Gray, 1976].In the standard A R formul ati on of li ne ar predi ction, the model paramete rs are sele cte d to mi ni mizethemean-squarederrorbetweenthemodelandthespeechdata.Inoneofthevariantsoflinearprediction,theautocorrelationmethod,themini mizationiscarriedoutforawindowedsegmentofdata.Intheautocorrelationmethod,minimizingthemean-squareerror of the time domain samples is equivalentto minimizing theintegratedratioofthesignalspectrumtothespectrumoftheall-polemodel.Thus,linearpredictiveanalysisisagoodmethod forspectralanalysiswheneverthesignalisproducedby an all-pole system.M ost speechsounds fi t thi s model w ell.One ke y consi deration for li near pre dicti ve anal y si s is the order of the model, p. For spee ch, if the orde ristoosmall,theformantstructureisnot well represented. If the orderis too large, pitch pulses as well asformantsbegintoberepresented.Tenth- or twelfth-order analysis is typical forspeech.Figures15.11 and15.12 provideexamplesof the spectrum produced by eighth-order and sixteenth-order linear predictiveanalysisofthe/i/waveformofFig.15.7.Figure15.11showstheretobethreeformantsatfrequenciesofabout30 0, 23 00, and 3200 Hz , whi ch are ty pi cal for an/i/.Homomorphic(Cepstral)AnalysisFor the speech m odel of Fi g. 15. 6, the e x citati on and filter i mpulse response are convol ved to produce thespeech.Oneoftheproblemsofspeechanalysisistoseparateordeconvolvethespeechintothesetw ocom ponents. Onesuch te chni que is called hom omorphi c filte ri ng [ Oppe nheim and S chafer, 1968 ]. Thecharacte risti c sy ste mfor a sy ste m for hom om orphi c deconvol ution conve rts a convolution operation to anadditi on ope ration. The output of such a characteristi c sy stem is calle d the com ple x cep str u m . The complexcepstrumisdefinedastheinverseFouriertransformofthecomplexlogarithmoftheFouriertransformoftheinput.Iftheinputseque nceisminimumphase(i.e.,thez-transformoftheinputsequencehasnopolesorzerosoutside the unit ci rcle), the se quence can be represe nted by the real portion of the transforms. Thus, the re alcepstrum can be com pute d by cal cul ati ng the inve rse Fourie r transform of the log- spe ctrum of theinput.FIGURE15.11Eighth-orderlinearpredictiveanalysisofan‚i‛.FIGURE15.12Sixteenth-orderlinearpredictiveanalysisofan‚i‛.Fi gure 1 5.1 3 show s an e x ample of the cepstrum for the voi ced /i/ utterance from Fi g. 15.7 . The cepstrum ofsuch a voi ce d utterance i s characte rized by rel ati vel y la rge v alues in the fi rst one or tw o milli se conds as w ellas。
Package‘genogeographer’October13,2022Type PackageTitle Methods for Analysing Forensic Ancestry Informative MarkersVersion0.1.19Author Torben TvedebrinkMaintainer Torben Tvedebrink<**************.dk>Depends R(>=3.1.0)Imports leaflet,shiny,shinyjs,knitr,DT,shinycssloaders,purrr,dplyr,magrittr,tidyr,ggplot2,tibble,forcats,readr,rmarkdown,rio,maps,shinyWidgets,rlangSuggests tidyverseDescription Evaluates likelihood ratio tests for alleged ancestry.Implements the methods of Tvede-brink et al(2018)<doi:10.1016/j.tpb.2017.12.004>.License GPL(>=2)Encoding UTF-8LazyData trueRoxygenNote6.1.1NeedsCompilation noRepository CRANDate/Publication2019-09-2710:20:08UTCR topics documented:app_genogeo (2)bar_colour (2)error_bar_plot (3)exponent_tilt (3)genogeo (4)genogeographer (5)kidd_loci (5)LR_table (6)main_alleles (7)12bar_colour map_plot (7)pops_to_DB (8)profile_AA_x0 (9)profile_admixture (9)random_AIMs_profile (10)seldin_loci (10)simulate_pops (11)Index12 app_genogeo Shiny application for GenoGeoGrapherDescriptionShiny application for GenoGeoGrapherUsageapp_genogeo(db_list=NULL,reporting_panel=TRUE)Argumentsdb_list A named list of databases of reference populations.Each component is expected to be returned from pops_to_DB.reporting_panelLogical.Should report generate and download be available after sample analy-sis.bar_colour bar_colourDescriptionCreates the colour scale for the accepted and rejected populations based on z-score and the log likelihood(log P).Usagebar_colour(df,alpha=1)Argumentsdf A data.frame with at least three coloums.Thefirst column is the logP,the second logical(z_score accept/reject),the third a unique naming column.alpha Should the alpha opacity be applied?And what value,1=solid,0=transparent.error_bar_plot3 error_bar_plot Plot log likelihoods of profiles with approximate confidence intervalsDescriptionPlots the estimated profile probabilities in each population.The colour depends on the profiles likelihood and rejection/acceptance(blue/red)based on z-scoreUsageerror_bar_plot(data)Argumentsdata The output from the genogeo functionValueA barplot of the log likelihoods for each population with confidence limitsAuthor(s)Torben Tvedebrink,<**************.dk>Examplesdf_<-simulate_pops(pop_n=20,aims_n=50)df_db<-pops_to_DB(df_)profile<-random_AIMs_profile(df_db,keep_pop=TRUE)profile$pop[1]#The true populationresult<-genogeo(profile[,c("locus","x0")],df=df_db)error_bar_plot(result)exponent_tilt P-values from Importing Sampling using Exponential tiltingDescriptionP-values from Importing Sampling using Exponential tiltingUsageexponent_tilt(x0,x1,n,p_limit=0.1,B=500,return_all=FALSE)4genogeoArgumentsx0Allele count of profilex1Population allele countn Sampled alleles in total in populationp_limit Upper limit to which we use the normal approximationB An integer specifying the number of importance samples.return_all Default is FALSE.If TRUE:Returns p-value,standard deviation,and method (and diagnostics).DetailsThe method of importance sampling described in Tvedebrink et al(2018),Section2.3is imple-mented.It relies on exponential tilting of the proposal distribution using in the importance sam-pling.ValueIf return_all=FALSE the p-value is returned.Otherwise list of elements(see return_all)is returned.genogeo Likelihood ratio tests for AIMsDescriptionComputes the likelihood ratio test statistics for each population in a database of reference popula-tions.Usagegenogeo(profile,df,CI=0.95,min_n=75,grouping="pop",tilt=FALSE,...)Argumentsprofile The AIMs profile encoded as returned by the profile_AA_x0function.df The database of reference populations as returned by the pops_to_DB function.CI The confidence level used to reject or accept the various hypotheses(between0 and1).min_n Minimum number of individuals in each database samplegrouping should"pop"(the default)or"meta"be used for aggregating the results.Can also be"cluster"if this variable is defined in the input database.tilt Should exponential titling be used to obtain more accurate$p$-values in the distribution’s tail(currently not implemented)...Further arguments that are passed to other functionsgenogeographer5ValueA tibble containing the$z$-scores,$p$-values etc for each population.Examplesdf_<-simulate_pops(pop_n=20,aims_n=50)df_db<-pops_to_DB(df_)profile<-random_AIMs_profile(df_db,keep_pop=TRUE)profile$pop[1]#The true populationresult<-genogeo(profile[,c("locus","x0")],df=df_db)genogeographer genogeographer:Methods for analysing forensic Ancestry Informa-tive MarkersDescriptionThe genogeographer package provides:genogeo()genogeo functionsSee?genogeokidd_loci Kenn Kidd Lab markersDescriptionList of markers identified by Kenn Kidd lab.Usagekidd_lociFormatList of55markerslocus Locus/Marker namesSourceK.K.Kidd et al.Progress toward an efficient panel of SNPs for ancestry inference.Forensic Science International:Genetics10(2014)23–326LR_table LR_table Compute pairwise likelihood ratiosDescriptionFor each pair of a specified vector of profiles the likelihood ratios are computed.The list can include all populations in the data or only a subset.We may for inferral purposes restrict to ratios including at least one"accepted"population.UsageLR_table(result_df,lr_populations=NULL,only_accepted=TRUE,CI=0.95,digits=NULL,keep_logP=FALSE)Argumentsresult_df The output from genogeolr_populations A vector of population names(pop in result_df).If NULL all populations are used.only_accepted Restrict the ratios to include minimum one accepted population.CI The level of confidence interval to be computeddigits If rounding of the output should be performed.keep_logP Logical.Should the logP’s be returned in outputValueA tibble with numerator and denominator populations with their log10LR and uncertainty.Author(s)Torben Tvedebrink<**************.dk>Examplesdf_<-simulate_pops(pop_n=4,aims_n=50)df_db<-pops_to_DB(df_)profile<-random_AIMs_profile(df_db,keep_pop=TRUE)profile$pop[1]#The true populationresult<-genogeo(profile[,c("locus","x0")],df=df_db)LR_table(result)main_alleles7 main_alleles AIMs markers in Precision ID Ancestry Panel(Thermo Fisher Scien-tific)DescriptionList of markers with their main and alternative allele.The markers is the union of Seldin’s and Kidd’s markers.Usagemain_allelesFormatList of164markerslocus Locus/Marker namesmain_allele The main allele(alleles are in lexicographic order)other_allele The other variantmap_plot Plot LTR z-scores on mapDescriptionPlots the results from LRT on a map based on lat/lon info in the database.If no location is found in the data(ing simulte_pops)nothing is plotted.Usagemap_plot(data)Argumentsdata The output from the genogeo functionValueA map with population z-scores at their geographic originAuthor(s)Torben Tvedebrink,<**************.dk>8pops_to_DB Examplesdf_<-simulate_pops(pop_n=4,aims_n=50)df_db<-pops_to_DB(df_)profile<-random_AIMs_profile(df_db,keep_pop=TRUE)profile$pop[1]#The true populationresult<-genogeo(profile[,c("locus","x0")],df=df_db,min_n=0)result$lon<-runif(n=4,min=-125,max=125)result$lat<-runif(n=4,min=-50,max=80)##Not run:map_plot(result)pops_to_DB Pre-compute the scores for a given reference databaseDescriptionConvert the counts from each population over a range of AIMs SNPs q to observed likelihood ratio test,its mean and variance.Based on these pre-computed the evaluation of a specific profile is done using genogeo with the resulting dataframe as df.Usagepops_to_DB(db,...)Argumentsdb A dataframe with columns similar to those of simulate_pops().If db contains information(recommended!)about"meta"(meta population)and"lat"/"lon"(location)these are carried over into the calculations...Additional arguments passed to score_add_dfValueA tibble with population and locus specific score informationExamplesdf_<-simulate_pops(pop_n=4,aims_n=50)df_db<-pops_to_DB(df_)profile_AA_x09 profile_AA_x0Function that compute the genotype probability for each population(rows in df)DescriptionFunction that compute the genotype probability for each population(rows in df)Usageprofile_AA_x0(AA_profile,df,select=c("locus","x0"),keep_dropped=FALSE)ArgumentsAA_profile A tibble/data.frame with columns’locus’,’A1’and’A2’holding the separated version of a genotype,eg.AG->A1:A,A2:Gdf The database with main alleles per locusselect Which columns to returnkeep_dropped Logical.Keep the non-matching alleles(compared to‘db‘)and those with geno-type‘NN‘profile_admixture Compute the z-score(and more)for admixed hypothesesDescriptionCompute the z-score(and more)for admixed hypothesesUsageprofile_admixture(x0,df,hyp=NULL,grouping="meta",return_all=FALSE,calc_logP=TRUE,...)Argumentsx0A data frame/tibble with two columns:‘locus‘and‘x0‘df A tibble of reference profiles(as for‘genogeo‘)hyp If NULL all levels of‘grouping‘is crossed and looped over as pairwise hy-potheses.If a single level of‘grouping‘,this value is crossed with the remaininglevels.If vector of two levels this is the only tested hypothesis.grouping Should the calculations be for meta populations("meta")or sample populations ("pop")?return_all Should z-score be returned(FALSE)or all locus results(TRUE)?calc_logP Should log P(Geno|Hyp)be calculated(TRUE)or not(FALSE)?...additional arguments passed on to other functions10seldin_lociValueA tibble of z-scores,or a list of pairwise results if‘return_all=TRUE‘random_AIMs_profile Simulate a random AIMs profileDescriptionUse the information from pops_to_DB to simulate a profile from a random or given population.The sampling is done with respect to the null hypothesis,such that the total count is adjusted accordingly.For further details see Tvedebrink et al(2018),Section3.1(Simulations).Usagerandom_AIMs_profile(df,grouping="pop",population=NULL,n=FALSE,keep_pop=FALSE)Argumentsdf Database of reference profiles as returned by pops_to_DBgrouping Simualte from pop(default)or meta.population The population to sample from.If NULL chosen at random.n Use numbers of samples as weights to choose the population randomlykeep_pop Keep information on populationAuthor(s)Torben Tvedebrink<**************.dk>seldin_loci Seldin Lab markersDescriptionList of markers identified by Seldin lab.Usageseldin_lociFormatList of122markerslocus Locus/Marker namessimulate_pops11SourceKosoy et al.Ancestry Informative Marker Sets for Determining Continental Origin and Admixture Proportions in Common Populations in America.HUMAN MUTATION,V ol.30,No.1,69–78, 2009.simulate_pops Simulate random populationsDescriptionSimulate random populationsUsagesimulate_pops(pop_n=100,pop_names=NULL,pop_totals=NULL,aims_n=50,aims_names=NULL)Argumentspop_n Number of populations to simulatepop_names Their names.If NULL:The names are"pop_001"through"pop_pop_n"pop_totals How many observations/sampled individuals per population.If one number this is used as parameter in a Poisson distributionaims_n Number of AIMsaims_names Their names.If NULL:The names are"rs_001"through"rs_aims_n"Author(s)Torben Tvedebrink<**************.dk>Index∗datasetskidd_loci,5main_alleles,7seldin_loci,10app_genogeo,2bar_colour,2error_bar_plot,3exponent_tilt,3genogeo,4genogeographer,5genogeographer-package(genogeographer),5kidd_loci,5LR_table,6main_alleles,7map_plot,7pops_to_DB,8profile_AA_x0,9profile_admixture,9random_AIMs_profile,10seldin_loci,10simulate_pops,1112。
相关分析方法研究报告英文Research Report on Methods of Comparative Analysis Introduction:Comparative analysis is a research method that involves comparing and contrasting different variables in order to study the relationships between them. This method is widely used in various fields, including social sciences, economics, and business. This research report aims to explore and analyze different methods of comparative analysis and their applications.Methods of Comparative Analysis:1. Qualitative Comparative Analysis (QCA):QCA is a method used to analyze qualitative data. It involves comparing different cases or variables based on their qualitative attributes. QCA allows researchers to identify patterns and relationships between variables and understand the complex causalities that may exist. This method is particularly useful when dealing with small samples or when aiming to understand the underlying mechanisms of a phenomenon.2. Quantitative Comparative Analysis (QCA):QCA is a method used to analyze quantitative data. It involves comparing different variables or groups based on their numerical values. QCA employs statistical techniques such as regression analysis, correlation analysis, and factor analysis to determine the relationships between variables. This method is commonly used in economic research and allows researchers to make generalizations about large populations based on the analyzed data.3. Cross-national Comparative Analysis:Cross-national comparative analysis involves comparing different countries or regions to understand the differences and similarities in various aspects such as social, economic, or political indicators. This method allows researchers to gain insights into the factors that contribute to the variations between countries and to identify best practices that can be adopted in different contexts.4. Case Study Analysis:Case study analysis involves in-depth examination and comparison of one or more specific cases. This method allows researchers to gain a comprehensive understanding of the variables of interest and their interdependencies. Case study analysis often involves collecting both qualitative and quantitative data and relies heavily on techniques such as interviews, observations, and document analysis. Case study analysis is widely used in social sciences and business research.Applications of Comparative Analysis:1. Policy Evaluation:Comparative analysis is often used to evaluate the effectiveness of policies implemented in different regions or countries. By comparing the outcomes and impacts of different policies, researchers can identify the best practices and areas for improvement. This information can be used to inform policy-making decisions and improve the efficiency and effectiveness of future policies.2. Market Research:Comparative analysis is frequently employed in market research tounderstand consumers' preferences, behaviors, and attitudes towards different products or brands. By comparing the attributes and performances of competing products, companies can identify their competitive advantages and develop targeted marketing strategies.Conclusion:Comparative analysis is a valuable research method that allows the examination of relationships between different variables and the comparison of various cases. The methods discussed in this research report, including qualitative and quantitative comparative analysis, cross-national analysis, and case study analysis, have diverse applications in various fields. Understanding and utilizing these methods can significantly contribute to evidence-based decision-making and the advancement of knowledge in different domains.。
Biologists?and?chemists?divide?compounds?into?two?principal?classes,?inorgan ic?and?organic.?Inorganic?compounds?are?defined?as?molecules,?usually?small, ?that?typically?lack?carbon?and?in?which?ionic?bonds?may?play?an?important?r ole.?Inorganic?compounds?include?water,?oxygen,?carbon?dioxide,?and?many?sal ts,?acids,?and?bases.??生物学家和化学家分裂成两个主要种类化合物、无机和有机。
无机化合物被定义为分子,通常小,通常缺乏的碳离子束缚,那么它就能起到重要的作用。
无机化合物包括水、氧气、二氧化碳和许多盐、酸、和根据地。
???All?living?organisms?require?a?wide?variety?of?inorganic?compounds?for?grow th,?repair,?maintenance,?and?reproduction.?Water?is?one?of?the?most?importan t,?as?well?as?one?of?the?rmost?abundant,?of?these?compounds,?and?it?is?parti cularly?vital?to?microorganisms.?Outside?the?cell,?nutrients?are?dissolved?i n?water,?which?facilitates?their?passage?through?cell?membranes.?And?inside? the?cell?,?water?is?the?medium?for?most?chemical?reactions.?In?fact,?water?i s?by?far?the?most?abundant?component?of?almost?all?living?cells.?Water?makes ?up?5%?to?95%?or?more?of?each?cell,?the?average?being?between?65%?and?75%.?S imply?stated,?no?organism?can?survive?without?water?所有生物体需要多种无机化合物的增长、维修、维护、和繁衍。
