LEADER 02011nam--2200529---450 001 990001936740203316 005 20180919103716.0 035 $a000193674 035 $aUSA01000193674 035 $a(ALEPH)000193674USA01 035 $a000193674 100 $a20040817d1962----km-y0itay0103----ba 101 $aita 102 $aIT 105 $a||||||||001yy 200 1 $a<> Sicilia e l'Unità d'Italia$eatti del Congresso internazionale di studi storici sul Risorgimento italiano, Palermo, 15-20 aprile 1961 210 $aMilano$cFeltrinelli 215 $avolumi$d23 cm 225 $aStudi e ricerche storiche$v16 300 $aIn testa al front.: Istituto Giangiacomo Feltrinelli 327 $a : Relazioni . - 346 p.$aComunicazioni / a cura di Salvatore Massimo Ganci e Rosa Gccione Scaglione. - 1962. - 349-1040 p. 410 0$12001$aStudi e ricerche storiche$v16 454 1$12001 702 1$aGANCI,$bMassimo 702 $aGUCCIONE SCAGLIONE,$bRosa 710 12$aCongresso internazionale di Studi storici sul Risorgimento italiano <1961 ; Palermo>$0237344 801 0$bsalbc$gISBD 912 $a990001936740203316 951 $aX.3.B. 4413/1(III F 260/1)$b13909 L.M.$cIII F 951 $aX.3.B. 4413/2(III F 260/2)$b13910 L.M.$cIII F 951 $aX.3.B. 4413/1a(III F 259/1)$b11275 LM$cIII F 951 $aX.3.B. 4413/2a(III F 259/ 2)$b11276 LM$cIII F 959 $aBK 969 $aUMA 969 $aFVIG 979 $aSIAV7$b10$c20040817$lUSA01$h1339 979 $aCOPAT5$b90$c20050419$lUSA01$h1455 979 $aCOPAT5$b90$c20050419$lUSA01$h1501 979 $aCOPAT5$b90$c20050419$lUSA01$h1503 979 $aCOPAT5$b90$c20050419$lUSA01$h1504 979 $aCOPAT1$b90$c20070903$lUSA01$h1021 979 $aCOPAT1$b90$c20070903$lUSA01$h1024 979 $aCOPAT1$b90$c20070903$lUSA01$h1028 979 $aCOPAT1$b90$c20070903$lUSA01$h1032 979 $aCOPAT1$b90$c20070903$lUSA01$h1033 996 $aSicilia e l'unità d'Italia$9171182 997 $aUNISA LEADER 00858nam a2200205 a 4500 001 991001233389707536 008 050928s1966 fr 000 0 fre d 035 $ab13340682-39ule_inst 040 $aDip.to di Scienze Storiche Filosofiche e Geografiche$bita 100 1 $aZeller, Gaston$0137559 245 13$aLe vie economique de l'Europe au 16. siecle /$cpar Gaston Zeller 260 $aParis :$bCentre de documentation Universitaire,$c1966 300 $a235 p. ;28 cm 440 4$acous de Sorbonne. Histire modern et contemporaine 907 $a.b13340682$b21-09-06$c28-09-05 912 $a991001233389707536 945 $aLE009 STOR.50-164$g1$i2009000360358$lle009$o-$pE0.00$q-$rl$s- $t0$u0$v0$w0$x0$y.i14138748$z21-10-05 996 $aVie economique de l'Europe au 16. siecle$91092410 997 $aUNISALENTO 998 $ale009$b - - $cm$da $e-$fita$git $h0$i0 LEADER 04211nam 2200997z- 450 001 9910576887003321 005 20220621 035 $a(CKB)5720000000008304 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/84499 035 $a(oapen)doab84499 035 $a(EXLCZ)995720000000008304 100 $a20202206d2022 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aMachine Learning in Tribology 210 $aBasel$cMDPI - Multidisciplinary Digital Publishing Institute$d2022 215 $a1 online resource (208 p.) 311 08$a3-0365-3981-6 311 08$a3-0365-3982-4 330 $aTribology has been and continues to be one of the most relevant fields, being present in almost all aspects of our lives. The understanding of tribology provides us with solutions for future technical challenges. At the root of all advances made so far are multitudes of precise experiments and an increasing number of advanced computer simulations across different scales and multiple physical disciplines. Based upon this sound and data-rich foundation, advanced data handling, analysis and learning methods can be developed and employed to expand existing knowledge. Therefore, modern machine learning (ML) or artificial intelligence (AI) methods provide opportunities to explore the complex processes in tribological systems and to classify or quantify their behavior in an efficient or even real-time way. Thus, their potential also goes beyond purely academic aspects into actual industrial applications. To help pave the way, this article collection aimed to present the latest research on ML or AI approaches for solving tribology-related issues generating true added value beyond just buzzwords. In this sense, this Special Issue can support researchers in identifying initial selections and best practice solutions for ML in tribology. 606 $aHistory of engineering & technology$2bicssc 606 $aTechnology: general issues$2bicssc 610 $aamorphous carbon coatings 610 $aanalysis 610 $aartificial intelligence 610 $aartificial neural networks 610 $abearing fault diagnosis 610 $aBERT 610 $acage instability 610 $acondition monitoring 610 $aConvolutional Neural Network (CNN) 610 $adata mining 610 $adatabases 610 $adigital twin 610 $adynamic friction 610 $aevolutionary algorithms 610 $afault data generation 610 $afeature engineering 610 $aGaussian processes 610 $aGenerative Adversarial Network (GAN) 610 $agradient boosting 610 $alaser surface texturing 610 $amachine learning 610 $ameta-modeling 610 $amonitoring 610 $an/a 610 $anatural language processing 610 $aneural networks 610 $aoptimization 610 $aPINN 610 $aprediction 610 $arandom forest 610 $arandom forest classifier 610 $areduced order modelling 610 $aregression 610 $aremaining useful life 610 $areynolds equation 610 $arolling bearing dynamics 610 $arolling bearings 610 $arubber seal applications 610 $aself-lubricating journal bearings 610 $asemi-supervised learning 610 $astructure-borne sound 610 $atensor decomposition 610 $atexturing during moulding 610 $atotal knee replacement 610 $atribAIn 610 $atribo-informatics 610 $atribo-testing 610 $atriboinformatics 610 $atribology 610 $aUHWMPE 610 $aunbalanced datasets 615 7$aHistory of engineering & technology 615 7$aTechnology: general issues 700 $aTremmel$b Stephan$4edt$01311976 702 $aMarian$b Max$4edt 702 $aTremmel$b Stephan$4oth 702 $aMarian$b Max$4oth 906 $aBOOK 912 $a9910576887003321 996 $aMachine Learning in Tribology$93030644 997 $aUNINA