Originalscientificpaper

Croat. j. for. eng. 37(2016)1 131

Timber Truck Payload Management with

Different In-Forest Weighing Strategies in Australia

Mark Brown, Mohammad Reza Ghaffariyan

Abstract

A project was carried out to investigate the impact of four different weighing methods on over/under loading of forestry trucks operating in Forestry Corporation of New South Wales under two types of roads; gazetted (approved for higher legal gross vehicle weight limits) and non-gazetted (standard public road gross vehicle weight limits). For all the technologies tested, it was found that there was a substantial under-loading issue ranging from 5.3 to 6.4 tonnes per load on gazetted roads, while the same technology achieved a much better outcome on non-gazetted roads with a range of 1.4 tonnes under-loaded to 0.1 tonnes over-loaded on average. There was clearly a large under-loading issue on the gazetted routes. As the same operators with the same technology achieved a much more reasonable outcome on the standard access routes, these results suggest that the GVML available was technically not achievable on the gazetted routes (i.e. not enough volume available to add the weight) or the operators were not aware of or not inclined to load the extra GVML available (i.e. not certain what routes were gazetted or not). As the under load was so consistently close to the extra GVML allowed, the lack of awareness or inclination seems the most likely reasons of under load. The results point also to a more significant role for policy and methods than the technology used for in-forest weighing in achieving effective payload management in forestry haulage.

Keywords: forest transport, payload management, on-board scales, loading/transport effi-ciency

loadingduringeachtransportationcycle.Over-load-ingmaycauseconsiderablesafetyissuesandstruc-turalimpactsontheroads,whichcanresultinheavyfines.Under-loadingwillreducetransportationeffi-ciency,which can lead to increased transportationcosts.Alowcostandsimpletechniquetoreduceloadvariabilityisforthetruckdrivertofrequentlycom-municatewithloaderoperatortoeffectivelyestimateloadweight,ideallyusinganon-boardweighingde-viceontheloaderortruck.Thethreebasictypesofon-boardweighingdevicesareon-boardtruckscales,portableplatformscales,andgrapplescales.Bothon-boardandplatformscalescanprovidesingleaxleandtandemweightsaswellasnetpayloadweight,whilegrapplescalesrecordtheweightofthewoodinthegrappleandaccumulategrappleloadstocalculatenetpayloadweight.Thesescalescanhelptoincreaseaver-

1. IntroductionTruckingisoftenthemostexpensivephaseofa

timber-harvestingoperation,accountingforasmuchas40percentto60percentofthetotalharvestingcost(ShafferandStuart1998).Asaresult,allpossibilitiesforreducingthecostoftruckingforestproductsorimprovingtheefficiencyoftheirtransportshouldbeexamined(Boldingetal.2009).Severalfactorssuchaspayload,triptimeandfuelefficiencycanimpacttrans-portefficiency(Acunaetal.2012,Ghaffariyanetal.2013).Trucksshouldbeloadedtotheirmaximumlegalweight every time as higher payloadswill reducetransportationcostsperunitwhichcan lead to in-creasedwooddemand(Lukasonetal.2011).Loadvariationcanbeanalysedbymeasuringload

weight to determine any over-loading andunder-

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132 Croat. j. for. eng. 37(2016)1

agepayloadwhilereducingoverweightfinesormillpenalties(Boldingetal.2009,Overboeetal.1998).InaCanadianstudy(Dayson2010),acomparisonofthedeliverypointplatformscaleandon-boardtruckscaleweightsofeachtrucktotalpayloadshowedthediffer-encevariedfrom–0.1%to0.9%.McNeel(1990)evalu-atedtheeffectoftractorandtrailerlogtruckscalesontruckloads.Onaverage,themeannetloadweightin-creasedby2.07tonswhenon-boardelectronicscaleswereused.Gallagheretal.(2004)analysedthediffer-enceingrossvehicleweight(GVW)betweentrucksthatusescales(eitherin-woodsplatformscalesorelec-tronicon-board scales) and trucks thatdonotusescaleswheretheyfoundthatweighedtrucksinwoodhadhighernetpayloadthanothertrucks.Variationinpayloadhasnegativeconsequencesforbothunder-loadingandover-loading.Under-loading increaseshaulingcost,decreasesprofit,contributestomillbot-tlenecks,andputsmoretrucksonthehighway.Over-loadingmayleadtocitations,safetyhazards,equip-ment damage and mill penalties (Bolding 2008).AccordingtoastudyintheUSA,thewoodsupplierswiththemostuniformweights(lessweightvariations)hadahaulingcostsavingsof4%to14%(Hamsleyetal.2007).Themoisturecontentcanalsoimpactthepayloadoftrucksandtransportationcost,whichcanbemanagedtoimprovetruckefficiency(Ghaffariyanetal.2013).Beardsell(1986)determinedusingscalehouse(weighbridge)dataandusingweighingdevicestobuffertheproblemofGVWvariability.Themethodinvolved themill settinga targetGVW range, andsendingreportsonasystematicbasistosuppliersin-dicatingtheirperformancerelativetotheperformanceofothermillsuppliers,whichcouldcreatesuitablebasetocomparetheperformances.Brown(2008)stud-iedthewoodtransportsystemsinAustraliaandindi-catedthecurrentfleetsexhibitawiderangeoftareweightswithineachvehicleconfigurationindicatingthereispotentialforconsiderablesavingsintransportcostsbyequipmentselectionandmanagementoftareweights.Managementoftareweightitprimarilydoneatthetimeofvehiclespecificationandpurchasewheredecisionsaboutwhatcomponents,materialsandde-signcanhavesignificantimpactsonthetareweightandhencealsoontheloadthevehiclecantransport.Maximumpayloadandallowableaxleloadcanalso

beimpactedbythequalityoftheroads.Improvingroadstandards(forestroadsandhighways)canalsoreduceroadusercostsinareaslinefuelconsumption,vehiclemaintenance,roadmaintenance,travelspeedandoverallproductivity(loadsize),whichcancontrib-utetothesustainabilityoftheforestindustryandin-creasethetotalamountoffibrethatcanbeeconomi-

callyharvested.Asanexample,theMinister’sCouncilonForestSectorCompetitivenessrecommendedsub-sidiestotheforestindustryformaintainingprimaryforestroadsinOntario,Canada(Hajeketal.2008).Ga-zettingofaroadisaprocessofassessingdesignatedroutestodetermineiftheycanphysicallyandsafelycarryahigherloadthanthestandardclassification.Ifallwatercrossings,roadgeometry,otherusers,etc.arefoundtobewithindefinedsafetyandtechnicallimits,whichvarybetweenlocationsandroadtypestosuitthesituation,therouteisidentifiedasgazetted.Forthisstudy,theexactcriteriarequiredforaroadtoqualifyasgazettedwerenotprovided,onlywhatroutesweregazettedandtheirlegalGVML.Truckconfigurationinthisstudywas7-axleb-doubletractortrailers.Theseheavyvehicles,coveredinthisstudy,areallowedanextra5500to6000kgontheirgrossvehiclemasslimit(GVML)dependingonthevehicleconfigurationandcontractor status on gazetted roads. Non-gazettedroadshavethestandardGVMLrestrictionsof50,000to51,500kgfor7-axleb-doublesdependingontheircon-figurationandcontractorstatus.Thereislittleinformationavailableontheeffectof

theweighingmethodorroadtypeontheover/underloadsof the forestry trucks inAustralia.Thus, thisprojectwascarriedouttoinvestigatetheimpactoffourdifferentweighingmethodsonover/underloadofforestrytrucksoperatingundercontracttotheFor-estryCorporationofNewSouthWales(FCNSW)ontwotypesofroads;gazetted(approvedforhigherle-galgrossvehicleweightlimits(GVML))andnon-ga-zetted(standardpublicroadGVML).Whilein-foreston-boardweightsystemsareverycommoninAustra-lianforesttransportation,notalloperationsusethembutinthecaseofthisstudyallloadswereweigheduponloadingintheforest.

2. Material and methods

2.1 Data collectionDatawascollectedfromexistinglog-haulopera-

tionsinNewSouthWaleswithoutanypriornoticetotheoperationstohelpensurenormaloperationswereobserved.Fig.1showsthelogtruckloadedbyagrap-pleloaderwithpinelogs.Thetreeswerefelledandprocessedmechanicallybyharvesters.Thenthelogswereextractedtotheroadsideforwarder.Thelogswerestackedbyforwarderintopilesalongtheroadsidetobeloadedlaterbygrappleloader.Usingdatacollectedandmaintainedforcommer-

cialpurposesbythemillsreceivingthelogs,a12monthdatasetwasextractedtoensureasufficientrangeof

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Croat. j. for. eng. 37(2016)1 133

datawasobtained.Thedatasetincludedrecordsforjustover17,700deliveriesbyjustover50individualtrucksincluding7-axleb-doubleconfigurations,oper-atedby4contractors.Grossweights,tareweights,loadvolumeandnetweightswererecordedateachmillusingweighbridgescertifiedaslegalfortrade.Thede-scriptivestatisticsoftheseparametersarepresentedinTable1andTable2forbothtypesofroad.Theforestmanageralsoprovided:ÞThegrossvehiclemasslimit(GVML)foreachvehicleIDforbothgazettedandnon-gazettedroutes,

ÞThein-forestweighingsystem(s)usedbyeachtruckID,

ÞStatus of thedriver for each truck ID (hired driverorowner/operator),

ÞWhichroutesbetweenwoodsourcesandmilldestinationsincludedinthedatabasewerega-zettedandnon-gazetted.Ifanyportionoftheroutebetweentheloggingcoupeandthedeliv-erydestinationwasnon-gazetted,theloadwastreated as non-gazetted.As gazetting of therouteistypicallytargetedatoperationalroutes,inmostcasestheentireroutewaseithergazett-edornot,mixedrouteswereveryuncommon.

Thein-forestweighingmethodswerethengroupedintofourcategories(scalingmethods):

ÞLoaderscale–weightmeasuredusingaloadcellsystemincorporatedinthegrappleoftheloader,

ÞTruckscale(driver)–truckbasedscaleusingeitherloadcellsorairpressuresensorsintegrat-ed into the truck and trailer suspension andfifth-wheel;operatedbyahireddriver,

ÞTruckscale(owner/operator)–truckbasedscaleusingeitherloadcellsorairpressuresensorsintegratedintothetruckandtrailersuspensionandfifth-wheel;operatedbytheownerofthetruck,

ÞLoaderandtruckscale–bothloaderandtruckscalesbeingused.

Whilethetechnologybetweenairsensors,straingaugesandloadcellsfortruckscalesystemsisverydifferent, the results achieved fromproperly usedcommercialsystemsareverysimilar.Pastoperationalobservationshadindicatedthatcareandattentiontousearecriticaltogettinggoodperformancefromtruckscalesandthatanowner-operator,beingmoredirect-lymotivatedtogetthebestloadperformance(maxi-mum loadputmoreprofit right inhispocketandoverloadfinesgodirectly tohim), tend touse thescalesdifferentlyandgetsignificantlydifferentout-comessotheowner-operatorsusingtruckscaleswereexaminesasadifferentgroup.Thedatasetswerethenexaminedandoutliersand

corruptedentries(weightrecordedwaslessthan60%oftheGVML–i.e.halfloads,vehicleshavingmadelessthan5deliveriesinthe12monthperiod,missingorunknownvehicleID,missingorunknownharvestblockIDandmissingorunknownproductID.)wereremovedleavingjustover17,700recordsforanalysis,13,050ongazettedroadsand4704onnon-gazettedroads.ThecollecteddataforeachcategoryongazettedroadsincludedLoaderandtruckscale:2861,Loader

Fig. 1 Log truck loaded by pine logs at the forest road side being prepared to travel to mill

Table 1 Descriptive statistics of recorded parameters for each truck load for gazetted roads

N Mean Standard deviation

Gross weight, t 13 050 50.37 2.23

Tare, t 13 050 18.60 1.40

Volume, m3 13 050 31.88 2.52

Nett weight, t 13 050 31.78 2.57

Table 2 Descriptive statistics of recorded parameters for each truck load for non-gazetted roads

N Mean Standard deviation

Gross weight, t 40 704 49.53 1.45

Tare, t 40 704 18.67 1.45

Volume, m3 40 704 30.95 2.02

Nett weight, t 40 704 30.86 2.04

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134 Croat. j. for. eng. 37(2016)1

scale:9289,Truckscale(driver):475andTruckscale(owner/operator):425.Thenumberofobservationspereachcategory fornon-gazettedroads includedLoaderandtruckscale:1103,Loaderscale:2593,Truckscale(driver):467andTruckscale(owner/operator):541.Thedatasetswerethenstatisticallyanalysedtoexploretherelationshipbetweenthein-forestweigh-ingmethodandoverorunderloading.Theloglengthandproducttypewereplottedversusloadvariationbutthesevariablesdidnothaveanysignificantcor-relationwiththeloadvariation.

2.2 Statistical evaluationTheover/underloadwascalculatedbysubtract-

inggrossweightfromGVML.Afrequencyhistogramof the over/under load datawith a fitted normalcurvewaspreparedbySPSS21foreachroadtype.Thenormalityofthedatawasprovedbycheckingthefrequencyhistograms.Then,ananalysisofvari-ance(ANOVA)wasappliedtotestthehypothesisofequalityoftheaverageofover/underloadpereachscalingmethod.Asapost-hoctest,Duncan’smultiplerange testwasapplied toderive thehomogenoussubsets(Zar1974,YazdiSamadietal.1998)tocom-parethedifferencesbetweenthepairoftreatments.Thistestcouldidentifywhattreatment(inthiscasestudymeansscalingmethod)wassignificantlydif-ferentfromtheothers.Therearevariouspost-hocteststoapplyincludingleastsignificantdifference(LSD), Student-Newman-Keuls test (SNK), Tueky,DunnettandDuncanmultiplerangetest.SNK meth-odseemstobemorepowerfultestthanothermeth-ods suchas least significantdifference (LSD). ForLSD method, in independent comparison withinpairwisecomparisonofthetreatments,forsomeofthemtheprobabilitylevel(α)wouldbelargerthandeterminedprobability.Withlargernumberoftreatments,theerrorwillbe

higher.DuncanandTukeymethodsdonothavethisdisadvantage of the LSD but the disadvantage of

Tukeyisthatitshowslesssignificantdifferencesasitapplies largest range for themultiple range tests.However,SNKmethoddoesnothavesuchadisad-vantage(YazdiSamadietal.1998).Inthiscasestudy,DuncanresultsweredoublecheckedwithTukeyandSNKoutcomeandthestatisticalsignificancelevelof5%(α=0.05)wasappliedinthedataanalysis.Thenullhypothesiscouldbeexpressedasfollows:H0:Averageunder/overloadofloaderandtruck

scale=Averageunder/overloadofloaderscale=Aver-ageunder/overloadoftruckscale(driver)=Averageunder/overloadoftruckscale(owner/operator)

3. Results

3.1 Over/under load in transportation on gazett-ed roadsThedescriptivestatisticsoftheover/underload

ofeachscalingmethodhavebeenpresentedinTable3.Thehigheststandarderror(0.09t)occurredfortheover/underloaddataoftruckscales(driverorowner/operatortypes),whiletheloweststandarderrorbe-longedtounder/overloadofloaderscale(0.03t).ThefrequencyhistogramisshownisFig.2,whichindi-cates the data follows a normal distribution. Theskewnessandkurtosisvaluesofthisdatasetwere0.095and0.438,respectively,whilethemeanvalueforover/underloadwas–5.66twithastandardde-viationof2.46t.Thenull hypothesiswas rejectedbecause there

weresignificantdifferencesamongthemeansofover/underloadsfordifferentscalingmethods,fordatacol-lectedongazettedroads(Table4).Therewasnosig-nificantdifferencebetween themeansof theover/underloadsbetweenloaderandtruckscalevs.truckscale(driver).However,boththesegroupsweresig-nificantlydifferentfromloaderscaleandtruckscale(owner/operator)intermsofthemeansoftheover/underloads(Table5).ApplicationofTukeyandStu-

Table 3 Descriptive statistics for under/over load (t) for gazetted roads

Scaling method N Mean Std. deviation Std. error95% confidence interval for mean

Lower bound Upper bound

Loader and truck scale 2861 –5.31 2.25 0.04 –5.40 –5.23

Loader scale 9289 –5.74 2.56 0.03 –5.79 –5.69

Truck scale, driver 475 –5.48 1.97 0.09 –5.66 –5.31

Truck scale, owner/operator 425 –6.44 1.84 0.09 –6.62 –6.27

Total 13,050 –5.66 2.46 0.02 –5.70 –5.62

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Croat. j. for. eng. 37(2016)1 135

dent-Newman-Keulsapproachesofferedsimilarre-sultstotheDuncan.Loaderandtruckscalehadthelowestmeanunderload(–5.31t),whiletruckscale(owner/operator)usageresultedinthelargestmeanunderload(–6.44t)(Fig.3).

3.2 Over/under load in transportation on non-gazetted roadsTable6includesdescriptivestatisticsofover/under

loadforeachscalingmethodintransportationonnon-gazettedroads.Over/underloadsfromusingloaderscaleandtruckscale(owner/operator)hadtheloweststandarderror(0.03t),whilethelargeststandarderror

Fig. 2 Frequency histogram for data on gazetted roads Fig. 3 Means of under-loads for four types of scaling methods on gazetted roads

Table 4 Analysis of variance for gazetted roads

Sum of squares

dfMean square

F Sig.

Between groups 676.85 3 225.62 37.43 0.00

Within groups 78636.48 13046 6.03 – –

Total 79313.34 13049 – – –

Table 5 Homogeneous Subsets obtained by Duncan method for gazetted roads

NSubset for alpha = 0.05

1 2 3

Truck scale, owner/operator 425 –6.44 – –

Loader scale 9289 – –5.74 –

Truck scale 475 – – –5.48

Loader and truck scale 2861 – – –5.31

Sig. – 1.00 1.00 0.16

Fig. 4 Frequency histogram for data on non-gazetted roads

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136 Croat. j. for. eng. 37(2016)1

occurredinthecaseoftruckscale(driver)application,whichwasabout0.06t.ThefrequencyhistogramofthedataisshowninFig.4.Theskewnessandkurtosisvaluesofthisdatasetwere–1.64and20.58,whilethemeanvalueforover/under loadwas–0.67twithastandarddeviationof1.53t.Foroperationsonnon-gazettedroads,therewasa

significantdifferencebetweenthemeansofover/un-derloadsfordifferentscalingmethods(Table7).Therewasnosignificantdifferencebetweenmeansofover/underloadforusingloaderscaleandtruckscale(driv-er).However,bothgroupsweredifferentfromloaderandtruckscale(driver)andtruckscale(owner/opera-

tor)intermsofloadvariation(Table8)(similarresultswereachievedbyTukeyandStudent-Newman-Keulsmethods).Theloaderandtruckscalehadthelargestmeanunderload(–1.43t),whilethetruckscale(owner/operator)resultedinasmallmeanoverload(+0.09t)(Fig.5).

4. Discussion and conclusionsTheresultsofthisstudyindicatedthatthemean

underloadingvariedfrom0.5tto6.4tforbothtypesofroadsandthemeanoverloadingoccurredonlyinonecase(0.1t).TheseresultscontrastwiththoseofanAmerican case study (McNeel 1990),where an in-creaseof2.1tmeanloadweightwasachievedthroughusingon-boardelectronicscales.Althoughinourcase

Table 6 Descriptive statistics for over/under load (t) for non-gazetted roads

Scaling method N Mean Std. deviation Std. error95% confidence interval for mean

Lower bound Upper bound

Loader and truck scale 1103 –1.43 1.68 0.05 –1.53 –1.33

Loader scale 2593 –0.53 1.50 0.03 –0.59 –0.47

Truck scale, driver 467 –0.54 1.39 0.06 –0.66 –0.41

Truck scale, owner/operator 541 0.09 0.59 0.03 0.04 0.14

Total 4704 –0.67 1.53 0.02 –0.72 –0.63

Table 7 Analysis of variance for non-gazetted roads

Sum of squares

dfMean square

F Sig.

Between groups 1008.73 3 336.24 156.69 0.00

Within groups 10,085.46 4700 2.15 – –

Total 11,094.19 4703 – – –

Table 8 Homogeneous subsets obtained by Duncan method for non-gazetted roads

NSubset for alpha = 0.05

1 2 3

Loader and truck scale 1103 –1.43 – –

Truck scale, driver 467 – –0.54 –

Loader scale 2593 – –0.53 –

Truck scale, owner/operator 541 – – 0.09

Sig. – 1.00 0.96 1.00

Fig. 5 Means of under/over loads for four types of scaling methods on non-gazetted roads

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Croat. j. for. eng. 37(2016)1 137

studyitisnotclearwhetherintroductionofon-boardscales increasedmean loadweights, it is clear thatmaximumpayloadshavenotbeenreachedinmostcases.Inourcasestudy,iftheunderloadingofthetruckscouldbeeliminated,thenthepotentialsavingfor the company couldbebetween$3millionand$7million($0.60/m3–$1.20/m3).Significantsavingscanbeachievedthrougheliminatingloadvariation.Beard-sell(1986)foundgrossannualsavingswere$153,000and$431,000fortwodifferentmills,andDeckardetal.2011predictedthepotentialimpactonthesouthernUnitedStateswoodsupplychainatbetween$44.1millionand$87.1million.Incomparingthestudyresultsforthetwotypesof

roads,thereisclearlyasubstantialunderloadingissueonthegazettedroadsascomparedtothenon-gazettedroads.Asthesameoperatorswiththesametechnol-ogyachievedamuchbetteroutcomeonthenon-ga-zetted roads, these results suggest that theGVML availablewas technicallynotachievableonthega-zettedroads(i.e.notenoughtruckvolumecapacityavailable toadd theextraweight)or theoperatorswerenotawareofornot inclinedtoloadtheextraGVMLavailable(i.e.notcertainwhatrouteswerega-zettedornot).Theloglengthandproducttypedidnothaveanysignificantcorrelationwiththeloadvaria-tion,whichcanexplainthesefactorsdidnotinfluencetheunderandoverloadsinthiscasestudy.Asthedatawerecollectedpost-operationswithoutanydirectob-servationsoftheoperations,notmuchinsightcanbegivenonthereason,butsincetheunder loadissoconsistentlyclosetotheextraGVMLallowedonga-zetted roads, the lack of awareness or inclinationseemsthemostlikely.Poststudydiscussionswiththeoperationsmanagersrevealedthattheratesystematthetimeofthestudyhadnoincentivefortheoperatorstoloadthehigherweightsongazettedroads(same4/t-kmrateforbothroutetypessorevenuetargetsaremetwithnon-gazetted loadsonboth route types),whichisanissuethathasbeenaddressedwithnewcontract arrangements.A follow-upstudy isbeingconsideredtoexploretheinfluenceofthisnewcon-tractarrangement.Toremoveanyloadvariation,thetransportmanagementshouldconsiderapplyinganaccurateweighingmethodattheloadingareasbeforedepartingthetruckstomills.Distributingtherespon-sibility ofweighmeasurement over twooperators(suchasloaderandtruckoperatorinthiscasestudy)shouldbeavoided,sincethestudyshowedthesharedresponsibilitygavepoorerresults(oneassumingtheotherislookingafterit).Whererouteshavebeeniden-tifiedtoallowhigherloads,operationsneedtoensureinformationisreadilyavailabletodrivers,vehiclesas-

signedtotherouteareabletotakeadvantageofthepotentialproductivitygainofthehigherloadandcon-tractarrangementsareinplacesoallstakeholdersareappropriatelymotivatedtotakeadvantageofthepo-tentialproductivitygainthroughincreasedpayloads.Lookingatthethreeresultstogether,itappearsthe

benefitofusing the loaderscaleandtruckscale incombinationisnotnecessarilyrealisedinpracticewiththenon-gazettedroads,showingaconsiderablypoor-eroutcomethanwhenthetechnologiesareusedsepa-rately.Thissuggestsworkmethodsandtechniquescanplayasgreatarole,ifnotgreater,thanthetechnol-ogyitself,andshouldbeexploredinfutureresearch.Furtherresearchontheperformanceofthediffer-

ent weighing technologies, under different policyframeworksandmethodsofusage,need tobeex-ploredtobetterunderstandhowbesttoachieveeffi-cientpayloadmanagementinAustralianforesthaul-ageoperations.

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Received:November12,2014Accepted:February5,2015

Authors’addresses:

Prof.MarkBrown,PhD.e-mail:mbrown2@usc.edu.auMohammadRezaGhaffariyan,PhD.*e-mail:ghafari901@yahoo.comUniversityoftheSunshineCoastLockedBag44558Maroochydore,QueenslandAUSTRALIA

*Correspondingauthor