An image contrast enhancement method based on genetic algorithm.docx
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An image contrast enhancement method based on genetic algorithm.docx
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Animagecontrastenhancementmethodbasedongeneticalgorithm
Patient-specificmodelofbraindeformation:
Applicationtomedicalimageregistration OriginalResearchArticle
JournalofBiomechanics
Thiscontributionpresentsfiniteelementcomputationofthedeformationfieldwithinthebrainduringcraniotomy-inducedbrainshift.Theresultswereusedtoillustratethecapabilitiesofnon-linear(i.e.accountingforbothgeometricandmaterialnon-linearities)finiteelementanalysisinnon-rigidregistrationofpre-andintra-operativemagneticresonanceimagesofthebrain.Weusedpatient-specifichexahedron-dominantfiniteelementmesh,togetherwithrealisticmaterialpropertiesforthebraintissueandappropriatecontactconditionsatboundaries.Themodelwasloadedbytheenforcedmotionofnodes(i.e.throughprescribedmotionofaboundary)atthebrainsurfaceinthecraniotomyarea.Wesuggestusingexplicittime-integrationschemefordiscretisedequationsofmotion,asthecomputationaltimesaremuchshorterandaccuracy,forpracticalpurposes,thesameasinthecaseofimplicitintegrationschemes.Applicationofthecomputeddeformationfieldtoregister(i.e.align)thepre-operativeimageswiththeintra-operativeonesindicatedthatthemodelveryaccuratelypredictsthedisplacementsofthetumourandthelateralventriclesevenforlimitedinformationaboutthebrainsurfacedeformation.Thepredictionaccuracyimproveswheninformationaboutdeformationofnotonlyexposed(duringcraniotomy)butalsounexposedpartsofthebrainsurfaceisusedwhenprescribingloading.However,itappearsthattheaccuracyachievedusinginformationonlyaboutthedeformationoftheexposedsurface,thatcanbedeterminedwithoutintra-operativeimaging,isacceptable.Thepresentedresultsshowthatnon-linearbiomechanicalmodelscancomplementmedicalimageprocessingtechniqueswhenconductingnon-rigidregistration.Importantadvantageofsuchmodelsoverthepreviouslyusedlinearonesisthattheydonotrequireunrealisticassumptionsthatbraindeformationsareinfinitesimallysmallandbrainstress–strainrelationshipislinear.
ArticleOutline
1.Introduction
2.Methods
2.1.Constructionoffiniteelementmeshforpatient-specificbrainmodel
2.2.Computationofbraindeformation
2.2.1.Equationsofmathematicalmodel
2.2.2.Loadingandboundaryconditions
2.2.3.Integrationofequationsofequilibrium
3.Results
4.Discussionandconclusions
Acknowledgements
References
CanadianAboriginalpeople'sexperienceswithHIV/AIDSasportrayedinselectedEnglishlanguageAboriginalmedia(1996–2000) OriginalResearchArticle
SocialScience&Medicine
ThispaperdescribestheportrayalofHIV/AIDSin14massprintnewspapersdirectedtowardstheCanadianAboriginalpopulationandpublishedbetween1996and2000.Basedonqualitativecontentanalysistheresearchexaminesbothmanifestandlatentmeanings.ManifestresultsofthisstudyindicatethatwomenandyouthareunderrepresentedaspersonswithHIV/AIDS.ThelatentresultsnotethefrequentreferencestoAboriginalculture,andthepoliticalandeconomicpositionofAboriginalCanadianswhendiscussingthedisease,thepersonwiththedisease,thefearofthediseaseandthereactionofthecommunitytothepersonwiththedisease.Unlikemainstreammediawherethemedicalframeisdominant,HIV/AIDSareherecontextualizedbyculture,identity,spiritualityandpolitical-economicissues.
ArticleOutline
Introduction
Theportrayalofdiseaseinthemassmedia
Explanatorymodelsorframesfordisease
Aboriginalhealth
Researchdesignandmethods
Population
Preliminarycoding
Dataanalysisandinterpretation
Results
Manifestanalysis:
socio-demographiccharacteristicsofaboriginalpeoples
Latentanalysis
Thedeploymentofaboriginalculture,spiritualityandidentity
Politicalandeconomicsituation
Language
Fearofthedisease
Fearofdisclosure
Communityreactions
Lifestylerisks
Discussion
Limitations
Acknowledgements
References
Careerchoicesinhealthcare:
Isnursingaspecialcase?
Acontentanalysisofsurveydata OriginalResearchArticle
InternationalJournalofNursingStudies
Background
Asdemandfornursesandotherhealthprofessionalscontinuestooutstripsupplyitisimportanttounderstandwhatmotivatesindividualstojoinanon-medicalhealthprofession.
Objectives
Theobjectivesofthisstudyweretoinvestigatestudents’reasonsforchoosingaparticularnursingspecialism,midwiferyorothernon-medicalhealthprofession,andtocomparemotivationfactorsacrossprofessions,gender,age,levelofaward,priorqualifications,priorexperienceandovertime.
Design
Aprospectivefollow-upstudycollectedsurveyresponsesatthebeginningandendofpre-qualifyingprofessionalprogrammes.
Setting
ThestudytookplaceinonelargeUnitedKingdomfaculty.
Participants
Thestudyparticipantswere775first-yearstudentsundertakingnon-medicalhealthprofessionalprogrammesand393qualifyingstudents.
Methods
Anopen-endedquestionwasincludedinaself-completedquestionnaireadministeredatentryandatqualification.Contentanalysisidentifiedthemes.
Results
Altruismwasthemostfrequentlycitedreasonforwishingtojoinanon-medicalhealthprofession,followedbypersonalinterest/abilities,professionalvalues/rewards,andpriorexperienceofthearea.Studentsenteringnursingwerelesslikelytociteanaltruisticmotivationthanthoseenteringothernon-medicalhealthprofessions(χ2=21.61,df=1,p<0.001).Onentry,adultnursing,children'snursingandradiotherapystudentswereleastlikelytociteprofessionalvalues/rewards(χ2=20.38,df=8,p=0.009).Studentsondegreelevelprogrammesweremorelikelytoreportaltruismthanthoseondiplomalevelcourses(χ2=17.37,df=1,p<0.001).Differenceswerealsoidentifiedbetweenthetwodatacollectionpoints.Thenumberofstudentsidentifyingaltruism(χ2=3.97,p=0.046)andprofessionalvalues/rewards(χ2=6.67,p=0.010)decreasedovertime.
Conclusion
Findingssuggestthatalthoughaserviceorientationremainsakeyfactorinchoosingnursing,studentsalsolookforacareerwhichmatchestheirinterestsandattributes,aswellasofferingprofessionalvaluesandrewards.Nursingmaybeindangeroflosingserviceorientatedrecruitstoothernon-medicalhealthprofessions.
ArticleOutline
Whatisalreadyknownaboutthetopic?
Whatthispaperadds
1.Introduction
2.Relatedliterature
2.1.Careerchoice
2.2.Publicperceptionsofnon-medicalhealthprofessions
2.3.Healthstudents’andpractitioners’motivations
2.3.1.Demographicfactors
2.3.2.Levelofacademicaward
2.3.3.Changeovertime
3.Theresearchstudy:
methodology
4.Results
4.1.Responserates
4.2.Non-respondents
4.3.Demographiccharacteristics
4.4.Reportedmotivationforjoiningahealthprofessiononentry
4.5.Reportedmotivationforjoiningahealthprofessiononqualification
4.6.Professions
4.7.Gender
4.8.Age
4.9.Ethnicity
4.10.Levelofacademicaward
4.11.Educationalandvocationalqualifications
4.12.Priorexperienceofhealthandsocialcare
4.13.Attrition
5.Discussion
6.Conclusion
Acknowledgements
Trackingmedicalstudents’clinicalexperiencesusingnaturallanguageprocessing OriginalResearchArticle
JournalofBiomedicalInformatics
Graduatemedicalstudentsmustdemonstratecompetencyinclinicalskills.Currenttrackingmethodsrelyeitheronmanualeffortsoronsimpleelectronicentrytorecordclinicalexperience.Weevaluatedautomatedmethodstolocate10institution-definedcoreclinicalproblemsfromthreemedicalstudents’clinicalnotes(n = 290).EachnotewasprocessedwithsectionheaderidentificationalgorithmsandtheKnowledgeMapconceptidentifiertolocateUnifiedMedicalLanguageSystem(UMLS)concepts.Thebestperformingautomatedsearchstrategiesaccuratelyclassifieddocumentscontainingprimarydiscussionstothecoreclinicalproblemswithareaunderreceiveroperatorcharacteristiccurveof0.90–0.94.RecallandprecisionforUMLSconceptidentificationwas0.91and0.92,respectively.Oftheindividualnotesection,conceptsfoundwithinthechiefcomplaint,historyofpresentillness,andassessmentandplanwerethestrongestpredictorsofrelevance.Thisautomatedmethodoftrackingcanprovidedetailed,pertinentreportsofclinicalexperiencethatdoesnotrequireadditionalworkfrommedicaltrainees.Thecouplingofsectionheaderidentificationandconceptidentificationholdspromiseforothernaturallanguageprocessingtasks,suchasclinicalresearchorphenotypeidentification.
ArticleOutline
1.Introduction
2.Background
3.Methods
3.1.Identifyingcoreclinicalproblemsfromclinicalnotes
3.2.Evaluatingcoreclinicalproblemrankings
3.3.EvaluatingrecallandprecisionoftheKnowledgeMapconceptindexer
3.4.Statisticalanalysis
4.Results
4.1.Coreclinicalproblemrankingperformance
4.2.Recallandprecisionofconceptidentification
4.3.Studentcoverageofimportantmedicalconcepts
5.Discussion
6.Conclusion
Acknowledgements
References
HybridationofBayesiannetworksandevolutionaryalgorithmsformulti-objectiveoptimizationinanintegratedproductdesignandprojectmanagementcontext OriginalResearchArticle
EngineeringApplicationsofArtificialIntelligence
betterintegrationofpreliminaryproductdesignandprojectmanagementprocessesatearlystepsofsystemdesignisnowadaysakeyindustrialissue.Therefore,theaimistomakefirmsevolvefromclassicalsequentialapproach(firstproductdesigntheprojectdesignandmanagement)tonewintegratedapproaches.Inthispaper,amodelforintegratedproduct/projectoptimizationisfirstproposedwhich
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