LEADER 01021cam0-22003971i-450- 001 990000043000403321 005 20160421095439.0 035 $a000004300 035 $aFED01000004300 035 $a(Aleph)000004300FED01 035 $a000004300 100 $a20020821d1915----km-y0itay50------ba 101 0 $aita 105 $ay-------001yy 200 1 $aDel compromesso e del giudizio arbitrale$fEgidio Codovilla 205 $a2 ed. rifatta 210 $aTorino$cUTET$d1915 215 $a683 p.$d22 cm 610 0 $aCompromesso (Arbitrato) 610 0 $aArbitrato (Diritto privato) 676 $a347.09 676 $a346 676 $a347.01 700 1$aCodovilla,$bEgidio 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aBK 912 $a990000043000403321 952 $aDPR 32-110$b02688$fDEC 952 $a16-L-II-5$b573$fDDCP 952 $aVIII F 327$b3919$fFGBC 952 $a13 F 53 03$b2737$fFINBC 959 $aDEC 959 $aFINBC 959 $aDDCP 959 $aFGBC 997 $aUNINA LEADER 00837nam0 22002651i 450 001 990005012700403321 005 20240523171848.0 010 $a0-520-03265-9 035 $a000501270 100 $a19990604g19789999km-y0itay50------ba 101 0 $aeng 102 $aUS 105 $af-------00--- 200 1 $a<>Mediterranean society$ethe Jewish communities of the Arab world as portrayed in the documents of the Cairo Geniza$fS. D. Goitein 210 $aBerkeley [etc.]$cUniversity of California Press$d1978 215 $a4 v., tav.$cill.$d24 cm 327 1 $a3.: The family 700 1$aGoitein,$bShlomo Dov$0761711 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aBK 912 $a990005012700403321 952 $aALPHA 0963$bFil.Mod. 32078$fFLFBC 959 $aFLFBC 996 $aMediterranean society$91542486 997 $aUNINA LEADER 05346nam 2201441z- 450 001 9910639985003321 005 20251116142536.0 010 $a3-0365-6171-4 035 $a(CKB)5470000001633504 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/95837 035 $a(EXLCZ)995470000001633504 100 $a20202301d2022 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aElectronics, Close-Range Sensors and Artificial Intelligence in Forestry 210 $aBasel$cMDPI - Multidisciplinary Digital Publishing Institute$d2022 215 $a1 electronic resource (248 p.) 311 08$a3-0365-6172-2 330 $aThe use of electronics, close-range sensing, and artificial intelligence has changed the management paradigm in many contemporary industries in which Big Data analytics by automated processes has become the backbone of decision making and improvement. Acknowledging the integration of electronics, devices, sensors, and intelligent algorithms in much of the equipment used in forest operations, as well as their use in various forestry-related applications, it is apparent that many disciplines within forestry and forest science still rely on data collected traditionally, which is resource-intensive. In turn, this brings limitations in characterizing the specific behaviors of forest product systems and wood supply chains, and often prevents the development of solutions for improvement or inferring the laws behind the operation and management of such systems. Undoubtedly, many solutions still need to be developed in the future to provide the technology required for the effective management of forests. In this regard, the Special Issue entitled ?Electronics, Close-Range Sensors and Artificial Intelligence in Forestry? highlights many examples of how technological improvements can be brought to forestry and to other related fields of science and practice. 606 $aResearch & information: general$2bicssc 606 $aBiology, life sciences$2bicssc 606 $aForestry & related industries$2bicssc 610 $aforest fire detection 610 $adeep learning 610 $aensemble learning 610 $aYolov5 610 $aEfficientDet 610 $aEfficientNet 610 $abig data 610 $aautomation 610 $aartificial intelligence 610 $amulti-modality 610 $aacceleration 610 $aclassification 610 $aevents 610 $aperformance 610 $amotor-manual felling 610 $awillow 610 $aRomania 610 $aregion detection of forest fire 610 $agrading of forest fire 610 $aweakly supervised loss 610 $afine segmentation 610 $aregion-refining segmentation 610 $alightweight Faster R-CNN 610 $aultrasound sensors 610 $aroad scanner 610 $aterrestrial laser scanning 610 $aTLS 610 $aforest road maintenance 610 $aforest road monitoring 610 $acrowned road surface 610 $adigital twinning 610 $aclimate smart 610 $aLiDAR 610 $adigitalization 610 $aforest loss 610 $aland-cover change 610 $amachine learning 610 $aspatial heterogeneity 610 $arandom forest model 610 $ageographically weighted regression 610 $aaboveground biomass 610 $aestimation 610 $aremote sensing 610 $aSentinel-2 610 $aIran 610 $amultiple regression 610 $aartificial neural network 610 $ak-nearest neighbor 610 $arandom forest 610 $acanopy 610 $adrone 610 $aleaf 610 $aleaves 610 $afoliar 610 $asamples 610 $asampling 610 $aAerial robotics 610 $aUAS 610 $aUAV 610 $aIoT 610 $aforest ecology 610 $aaccessibility 610 $awood 610 $adiameter 610 $alength 610 $aclose-range sensing 610 $aAugmented Reality 610 $acomparison 610 $aaccuracy 610 $aeffectiveness 610 $apotential 610 $aforestry 4.0 610 $awood technology 610 $asawmilling 610 $aproductivity 610 $aprediction 610 $along-term 610 $atree ring 610 $aforestry detection 610 $aresistance sensor 610 $amicro-drilling resistance method 610 $asignal processing 610 $aSignal-to-Noise Ratio (SNR) 615 7$aResearch & information: general 615 7$aBiology, life sciences 615 7$aForestry & related industries 700 $aBorz$b Stelian Alexandru$4edt$01279878 702 $aProto$b Andrea R$4edt 702 $aKeefe$b Robert$4edt 702 $aNita$b Mihai$4edt 702 $aBorz$b Stelian Alexandru$4oth 702 $aProto$b Andrea R$4oth 702 $aKeefe$b Robert$4oth 702 $aNita$b Mihai$4oth 906 $aBOOK 912 $a9910639985003321 996 $aElectronics, Close-Range Sensors and Artificial Intelligence in Forestry$93016103 997 $aUNINA