Environmental and Experimental Botany Controlled relative humidity testing for the characterisation.docx

Environmental and Experimental Botany Controlled relative humidity testing for the characterisation.docx

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EnvironmentalandExperimentalBotanyControlledrelativehumiditytestingforthecharacterisation

Ageneticalgorithmforhybridflowshopswithsequencedependentsetuptimesandmachineeligibility  OriginalResearchArticle

EuropeanJournalofOperationalResearch,Volume169,Issue3,16March2006,Pages781-800

RubénRuiz,ConcepciónMaroto

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277

Microbialα-amylases:

abiotechnologicalperspective  OriginalResearchArticle

ProcessBiochemistry,Volume38,Issue11,30June2003,Pages1599-1616

RaniGupta,PareshGigras,HarapriyaMohapatra,VineetKumarGoswami,BhavnaChauhan

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Abstract

Amylasesareoneofthemostimportantandoldestindustrialenzymes.Thesecomprisehydrolases,whichhydrolysestarchmoleculestofinediverseproductsasdextrins,andprogressivelysmallerpolymerscomposedofglucoseunits.Largearraysofamylasesareinvolvedinthecompletebreakdownofstarch.However,α-amylaseswhicharethemostindemandhydrolyseα-1,4glycosidicbondintheinteriorofthemolecule.α-Amylaseholdsthemaximummarketshareofenzymesaleswithitsmajorapplicationinthestarchindustryaswellasitswell-knownusageinbakery.Withtheadventofnewfrontiersinbiotechnology,thespectrumofα-amylaseapplicationhasalsoexpandedtomedicinalandanalyticalchemistryaswellasinautomaticdishwashingdetergents,textiledesizingandthepulpandpaperindustry.Amylasesareofubiquitousoccurrence,producedbyplants,animalsandmicroorganisms.However,microbialsourcesarethemostpreferredoneforlargescaleproduction.Todayalargenumberofmicrobialα-amylasesaremarketedwithapplicationsindifferentindustrialsectors.Thisreviewfocusesonthemicrobialamylasesandtheirapplicationwithabiotechnologicalperspective.

ArticleOutline

1.Introduction

2.Distributionofα-amylaseamongmicroorganisms

3.Determinationofα-amylaseactivity

3.1.Decreaseinstarch–iodinecolourintensity

3.1.1.Determinationofdextrinisingactivity

3.1.2.SandstedtKneenandBlish（SKB）method

3.1.3.Indianpharmacopoeiamethod

3.2.Increaseinreducingsugarsordinitrosalicyclicacid（DNSA）method

3.3.Degradationofcolour-complexedsubstrate

3.4.Decreaseinviscosityofthestarchsuspension

3.4.1.Fallingnumber（FN）method

3.4.2.Amylograph/Farinographtest

4.Physiologyofα-amylaseproduction

4.1.Physiochemicalparameters

4.1.1.Substratesource:

inductionofα-amylase

4.1.2.Nitrogensources

4.1.3.Roleofphosphate

4.1.4.Roleofotherions

4.1.5.pH

4.1.6.Temperature

4.1.7.Agitation

5.Fermentationstudiesonα-amylaseproduction

6.Purificationofmicrobialα-amylases

7.Biochemicalpropertiesofα-amylases

7.1.Substratespecificity

7.2.pHoptimaandstability

7.3.Temperatureoptimaandstability

7.4.Molecularweight

7.5.Inhibitors

7.6.Calciumandstabilityofα-amylase

8.Industrialapplicationsofα-amylase

8.1.Breadandbakingindustryandasanantistalingagent

8.2.Starchliquefactionandsaccharification

8.3.Textiledesizing

8.4.Paperindustry

8.5.Detergentapplications

8.6.Analysisinmedicinalandclinicalchemistry

9.Conclusions

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Predictionofspeciesspecificforestinventoryattributesusinganonparametricsemi-individualtreecrownapproachbasedonfusedairbornelaserscanningandmultispectraldata  OriginalResearchArticle

RemoteSensingofEnvironment,Volume114,Issue4,15April2010,Pages911-924

JohannesBreidenbach,ErikNæsset,VegardLien,TerjeGobakken,SveinSolberg

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Abstract

Whileforestinventoriesbasedonairbornelaserscanningdata（ALS）usingtheareabasedapproach（ABA）havereachedoperationalstatus,methodsusingtheindividualtreecrownapproach（ITC）havebasicallyremainedaresearchissue.OneofthemainobstaclesforoperationalapplicationsofITCisbiasedresultsoftenexperiencedduetosegmentationerrors.Inthisarticle,weproposeanewmethod,called“semi-ITC”thatovercomesthemainproblemsrelatedtoITCbyimputinggroundtruthdatawithincrownsegmentsfromthenearestneighboringsegment.Thismaybenone,one,orseveraltrees.Thedistancesbetweensegmentswerederivedbasedonasetofexplanatoryvariablesusingtwononparametricmethods,i.e.,mostsimilarneighborinference（MSN）andrandomforest（RF）.RFfavoredtheimputationofcommonobservationsinthedatasetwhichresultedinsignificantbiases.MainconclusionsarethereforebasedonMSN.TheexplanatoryvariableswerecalculatedbymeansofsmallfootprintALSandmultispectraldata.Whentestingwithempiricaldatathenewmethodcomparedfavorablytothewell-knownABA.AnotheradvantageofthenewmethodovertheABAisthatitallowedforthemodelingofraretreespecies.Theresultsofpredictingtimbervolumewiththesemi-ITCmethodwereunbiasedandtherootmeansquarederror（RMSE）onplotlevelwassmallerthanthestandarddeviationoftheobservedresponsevariables.TherelativeRMSEsaftercrossvalidationusingsemi-ITCfortotalvolumeandvolumeoftheindividualspeciespine,spruce,birch,andaspenonplotlevelwere17,38,40,101,and222%,respectively.Duetotheunbiasednessoftheestimation,thisstudyisashowcaseforhowtousecrownsegmentsresultingfromITCalgorithmsinaforestinventorycontext.

ArticleOutline

1.Introduction

2.Material

2.1.Studyarea

2.2.Fielddata

2.3.Airbornelaserscannerandimagedata

3.Methods

3.1.Generaldescriptionofthemethod

3.2.Technicaldetailsofthemethodsused

3.2.1.Stepi）;datapreparation

3.2.2.Stepii）;delineationofcrownsegmentsandcomputationofexplanatoryvariables

3.2.3.Stepiii）;dataanalysisandmodeling

3.2.4.Stepiv）;imputationofnearestneighborsandupscaling

3.2.5.Areabasedapproach

4.Results

4.1.Segmentation

4.2.Selectedvariables

4.3.Resultsofthecrossvalidatedimputationforsinglesegments

4.4.Resultsoftheimputationupscaledtosampleplots

4.5.ResultsoftheABAandcomparisonwithsemi-ITC

5.Discussion

6.Conclusions

Acknowledgements

References

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Investigationsonweldingresidualstressesinpenetrationnozzlesbymeansof3DthermalelasticplasticFEMandexperiment  OriginalResearchArticle

ComputationalMaterialsScience,Volume45,Issue4,June2009,Pages1031-1042

KazuoOgawa,DeanDeng,ShoichiKiyoshima,NobuyoshiYanagida,KoichiSaito

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Abstract

Recentdiscoveriesofstresscorrosioncracking（SCC）inweldmentsincludingpenetrationnozzlesatpressurizedwaterreactors（PWRs）andboilingwaterreactors（BWRs）haveraisedconcernsaboutsafetyandintegrityofplantcomponents.ItiswellknownthatweldingresidualstressisanimportantfactorresultinginSCCinweldments.Inthepresentwork,bothexperimentalmethodandnumericalsimulationtechnologyareusedtoinvestigatethecharacteristicsofweldingresidualstressdistributioninpenetrationnozzlesweldedbymulti-passJ-groovejoint.Anexperimentalmock-upisfabricatedtomeasureweldingresidualstressatfirst.Intheexperiment,eachweldpassisperformedusingasemi-circlebalancedweldingprocedure.Then,acorrespondingfiniteelementmodelswithconsideringmovingheatsource,depositionsequence,inter-passtemperature,temperature-dependentthermalandmechanicalproperties,strainhardeningandannealingeffectisdevelopedtosimulateweldingtemperatureandresidualstressfields.Thesimulationresultspredictedbythe3Dmodelaregenerallyingoodagreementwiththemeasurements.Meanwhile,toclarifytheinfluenceofdepositionsequenceontheweldingresidualstress,theweldingresidualstressfieldinthesamegeometricalmodelinducedbyacontinuousweldingprocedureisalsocalculated.Finally,theinfluenceofajointobliqueangleonweldingresidualstressisinvestigatednumerically.Thenumericalresultssuggestthatbothdepositionsequenceandobliqueangleshaveeffectonweldingresidualstressdistribution.

ArticleOutline

1.Introduction

2.Experimentalprocedure

3.Finiteelementmodel

3.1.Thermalanalysis

3.2.Mechanicalanalysis

4.Resultsanddiscussion

4.1.SimulationresultsofModelA

4.2.Comparisonbetweensimulationresultsandmeasureddata

4.2.1.Comparisonofaxialresidualstress

4.2.2.Comparisonofhoopresidualstress

4.3.SimulationresultsofModelBanddiscussion

4.4.SimulationresultsofModelCanddiscussion

5.Conclusion

Acknowledgements

References

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Lossaversion,equityconstraintsandsellerbehaviorintherealestatemarket  OriginalResearchArticle

RegionalScienceandUrbanEconomics,Volume41,Issue1,January2011,Pages67-76

ElliotAnenberg

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Abstract

IdevelopanestimationstrategythatcanpointidentifytheeffectsoflossaversionandequityconstraintsonsellingpricesusingalongpanelofdatafromtheSanFranciscoBayArearealestatemarket.Ifindstrongevidencethatownersfacingnominallossesontheirhousinginvestmentsandownerswithh