LEADER 01135nam--2200373---450- 001 990001553770203316 005 20070711101737.0 035 $a000155377 035 $aUSA01000155377 035 $a(ALEPH)000155377USA01 035 $a000155377 100 $a20040402d1976----km-y0itay0103----ba 101 0 $aita 102 $aIT 105 $a||||||||001yy 200 1 $a<> commedie di Dari Fo$eIsabella tre caravelle e un cacciaballe$eSettimo ruba un po meno la colpa del diavolo$fDario Fo 210 $aTorino$cEinaudi$d1976 215 $a322 p.$d18 cm 225 2 $aStruzzi$v54 410 0$12001$aStruzzi$v54 454 1$12001 461 1$1001-------$12001 700 1$aFO,$bDario$0158734 801 0$aIT$bsalbc$gISBD 912 $a990001553770203316 951 $aXIII.1.A. 451/2(Varie Coll. 203/55)$b75473 L.M.$cVarie Coll. 959 $aBK 969 $aUMA 979 $aSIAV2$b10$c20040402$lUSA01$h1054 979 $aSIAV1$b10$c20040406$lUSA01$h1751 979 $aCOPAT3$b90$c20050119$lUSA01$h1039 979 $aCOPAT1$b90$c20070711$lUSA01$h1017 996 $aCommedie di Dari Fo$9940877 997 $aUNISA LEADER 03314nam 2200433z- 450 001 9910227350403321 005 20210211 035 $a(CKB)4100000000883829 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/56326 035 $a(oapen)doab56326 035 $a(EXLCZ)994100000000883829 100 $a20202102d2017 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aPlant Competition in a Changing World 210 $cFrontiers Media SA$d2017 215 $a1 online resource (154 p.) 225 1 $aFrontiers Research Topics 311 08$a2-88945-205-0 330 $aCompetitiveness describes a key ability important for plants to grow and survive abiotic and biotic stresses. Under optimal, but particularly under non-optimal conditions, plants compete for resources including nutrients, light, water, space, pollinators and other. Competition occurs above- and belowground. In resource-poor habitats, competition is generally considered to be more pronounced than in resource-rich habitats. Although competition occurs between different players within an ecosystem such as between plants and soil microorganisms, our topic focusses on plant-plant interactions and includes inter-specific competition between different species of similar and different life forms and intra-specific competition. Strategies for securing resources via spatial or temporal separation and different resource needs generally reduce competition. Increasingly important is the effect of invasive plants and subsequent decline in biodiversity and ecosystem function. Current knowledge and future climate predictions suggest that in some situations competition will be intensified with occurrence of increased abiotic (e.g. water and nutrient limitations) and biotic stresses (e.g. mass outbreak of insects), but competition might also decrease in situations where plant productivity and survival declines (e.g. habitats with degraded soils). Changing interactions, climate change and biological invasions place new challenges on ecosystems. Understanding processes and mechanisms that underlie the interactions between plants and environmental factors will aid predictions and intervention. There is much need to develop strategies to secure ecosystem services via primary productivity and to prevent the continued loss of biodiversity. This Research Topic provides an up-to-date account of knowledge on plant-plant interactions with a focus on identifying the mechanisms underpinning competitive ability. The Research Topic aims to showcase knowledge that links ecological relevance with physiological processes to better understanding plant and ecosystem function. 606 $aBotany & plant sciences$2bicssc 610 $aAllelochemicals 610 $aClimate Change 610 $acompetition 610 $aconservation 610 $afacilitation 610 $aGlobal Warming 610 $ainvasion 610 $aplant-plant interactions 615 7$aBotany & plant sciences 700 $aJudy Simon$4auth$01311883 702 $aSusanne Schmidt$4auth 906 $aBOOK 912 $a9910227350403321 996 $aPlant Competition in a Changing World$93030511 997 $aUNINA LEADER 04983nam 22007095 450 001 9910254843703321 005 20200703010555.0 010 $a981-10-6683-3 024 7 $a10.1007/978-981-10-6683-2 035 $a(CKB)4100000001381908 035 $a(DE-He213)978-981-10-6683-2 035 $a(MiAaPQ)EBC5178310 035 $a(PPN)222226544 035 $a(EXLCZ)994100000001381908 100 $a20171201d2017 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aBankruptcy Prediction through Soft Computing based Deep Learning Technique /$fby Arindam Chaudhuri, Soumya K Ghosh 205 $a1st ed. 2017. 210 1$aSingapore :$cSpringer Singapore :$cImprint: Springer,$d2017. 215 $a1 online resource (XVII, 102 p. 59 illus.) 311 $a981-10-6682-5 320 $aIncludes bibliographical references. 327 $aIntroduction -- Need of this Research -- Literature Review -- Bankruptcy Prediction Methodology -- Need for Risk Classification -- Experimental Framework: Bankruptcy Prediction using Soft Computing based Deep Learning Technique.- Datasets Used -- Experimental Results -- Conclusion . 330 $aThis book proposes complex hierarchical deep architectures (HDA) for predicting bankruptcy, a topical issue for business and corporate institutions that in the past has been tackled using statistical, market-based and machine-intelligence prediction models. The HDA are formed through fuzzy rough tensor deep staking networks (FRTDSN) with structured, hierarchical rough Bayesian (HRB) models. FRTDSN is formalized through TDSN and fuzzy rough sets, and HRB is formed by incorporating probabilistic rough sets in structured hierarchical Bayesian model. Then FRTDSN is integrated with HRB to form the compound FRTDSN-HRB model. HRB enhances the prediction accuracy of FRTDSN-HRB model. The experimental datasets are adopted from Korean construction companies and American and European non-financial companies, and the research presented focuses on the impact of choice of cut-off points, sampling procedures and business cycle on the accuracy of bankruptcy prediction models. The book also highlights the fact that misclassification can result in erroneous predictions leading to prohibitive costs to investors and the economy, and shows that choice of cut-off point and sampling procedures affect rankings of various models. It also suggests that empirical cut-off points estimated from training samples result in the lowest misclassification costs for all the models. The book confirms that FRTDSN-HRB achieves superior performance compared to other statistical and soft-computing models. The experimental results are given in terms of several important statistical parameters revolving different business cycles and sub-cycles for the datasets considered and are of immense benefit to researchers working in this area. 606 $aUser interfaces (Computer systems) 606 $aArtificial intelligence 606 $aComputer simulation 606 $aManagement information systems 606 $aComputer science 606 $aBanks and banking 606 $aStatistics 606 $aUser Interfaces and Human Computer Interaction$3https://scigraph.springernature.com/ontologies/product-market-codes/I18067 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aSimulation and Modeling$3https://scigraph.springernature.com/ontologies/product-market-codes/I19000 606 $aManagement of Computing and Information Systems$3https://scigraph.springernature.com/ontologies/product-market-codes/I24067 606 $aBanking$3https://scigraph.springernature.com/ontologies/product-market-codes/626010 606 $aStatistics for Business, Management, Economics, Finance, Insurance$3https://scigraph.springernature.com/ontologies/product-market-codes/S17010 615 0$aUser interfaces (Computer systems) 615 0$aArtificial intelligence. 615 0$aComputer simulation. 615 0$aManagement information systems. 615 0$aComputer science. 615 0$aBanks and banking. 615 0$aStatistics. 615 14$aUser Interfaces and Human Computer Interaction. 615 24$aArtificial Intelligence. 615 24$aSimulation and Modeling. 615 24$aManagement of Computing and Information Systems. 615 24$aBanking. 615 24$aStatistics for Business, Management, Economics, Finance, Insurance. 676 $a005.437 676 $a4.019 700 $aChaudhuri$b Arindam$4aut$4http://id.loc.gov/vocabulary/relators/aut$0763017 702 $aGhosh$b Soumya K$4aut$4http://id.loc.gov/vocabulary/relators/aut 906 $aBOOK 912 $a9910254843703321 996 $aBankruptcy Prediction through Soft Computing based Deep Learning Technique$92500581 997 $aUNINA