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 LEADER 03523nam 2200637z- 450 001 9910557548203321 005 20220111 035 $a(CKB)5400000000044124 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/76412 035 $a(oapen)doab76412 035 $a(EXLCZ)995400000000044124 100 $a20202201d2021 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aSkin-Gut-Breast Microbiota Axes 210 $aBasel, Switzerland$cMDPI - Multidisciplinary Digital Publishing Institute$d2021 215 $a1 online resource (110 p.) 311 08$a3-0365-0898-8 311 08$a3-0365-0899-6 330 $aThis book represents the latest research on microbiota axes, with a special focus on the gut-skin axis and the role of microbial breast bacteria on human health communication. This book also contains discussions of the microorganism-derived products that can directly or indirectly be signals for our organs and systems. Gut dysbiosis, representing a disruption of intestinal integrity, can create aberrant physiological conditions (including immunological disorders, intestinal stress, and anxiety-like behavior), as well as high serum levels of microbial metabolites increasing oxidative stress dysfunctions and generalized inflammation. Much research in this field has been carried out in animal models, and establishing whether those findings translate to humans will be crucial but challenging. On the other hand, several studies conducted on humans have evaluated the link between fecal microbiota composition and quality of life by recruiting thousands of participants. As well as identifying bacterial genera associated with higher quality of life, they carried out metagenomic analyses that indicated that the potential of microorganisms to synthesize certain active metabolites, and especially their interrelation, may also correlate with general wellbeing. It is clear that many axes can influence our lives; the most important include "the gut-brain axis" and the "skin-gut-breast axis". Together, the studies presented in this book have laid the foundations for a better understanding of the effects of gut microbiota on skin and on our body in general. The mechanisms that underlie them may represent the ideal focus for the initial efforts to explore the relevance of these axes for human wellbeing. 606 $aMedicine and Nursing$2bicssc 610 $aadenoid 610 $abowel disease 610 $achild 610 $acolitis 610 $aDesulfovibrio 610 $adysbiosis 610 $agut microbiota 610 $ahydrogen sulfide 610 $ajoint inflammation 610 $amaternal-fetal interface 610 $amicrobiome 610 $amicrobiota 610 $amicrobiota axes 610 $amicrobiota axis 610 $amiddle ear 610 $anewborn 610 $aolanzapine administration 610 $aoral microbiota arthritis 610 $aotitis media 610 $apediatric disease 610 $aschizophrenia 610 $asmall-large intestine axis 610 $asulfate reduction 610 $aupper respiratory tract 610 $aweight gain 615 7$aMedicine and Nursing 700 $aDrago$b Lorenzo$4edt$01293632 702 $aDrago$b Lorenzo$4oth 906 $aBOOK 912 $a9910557548203321 996 $aSkin-Gut-Breast Microbiota Axes$93022683 997 $aUNINA