LEADER 03880nam 2201189z- 450 001 9910557389003321 005 20210501 035 $a(CKB)5400000000042008 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/69108 035 $a(oapen)doab69108 035 $a(EXLCZ)995400000000042008 100 $a20202105d2020 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aBioactive Phytochemicals in Health and Disease 210 $aBasel, Switzerland$cMDPI - Multidisciplinary Digital Publishing Institute$d2020 215 $a1 online resource (264 p.) 311 08$a3-03943-138-2 311 08$a3-03943-139-0 330 $aNutritional deficiencies, and different nutritional and dietary lifestyles, whether poor or absent of essential nutrients, aside from excess intake, can lead to inflammatory complications and loss of function. Bioactive compounds are non-nutritional components derived from plants, foods, and beverages with a multitude of biological effects. The improvement of analytical techniques has allowed scientific community to state that the regular consumption of bioactive phytochemicals is related to the prevention of numerous pathologies, through mechanisms that involve oxidative stress reduction, gene expression modulation, and even enzymatic activation inhibition. 606 $aMedicine and Nursing$2bicssc 610 $aAcorus calamus 610 $aautophagy 610 $aB16F10 cells 610 $acancer 610 $acardiovascular disease 610 $achlorogenic acid 610 $achloroquine 610 $acitric acid 610 $aclinical trial 610 $acognitive impairment. 610 $acoronavirus 610 $aCOVID-19 610 $acurcumin 610 $adiet 610 $aethnomedicinal 610 $aflavonoids 610 $afood supplements 610 $aHCoV-OC43 610 $ahormesis 610 $ainfection 610 $akaempferol 610 $akurarinone 610 $aLC3 610 $aliposome 610 $aliposomes 610 $amalic acid 610 $amatrix metalloproteinase 610 $amelanoma 610 $ametabolic application 610 $amicronuclei 610 $amolecular signals 610 $aMRC-5 cell 610 $an/a 610 $ananocarriers 610 $ananocurcumin 610 $ananoformulation 610 $ananomedicine 610 $anaringin 610 $anervous system 610 $aneurological 610 $aneurological disorders 610 $aneuroprotective 610 $aorientin 610 $ap62/SQSTM1 protein 610 $apharmacological action 610 $apharmacological potential 610 $aphytochemistry 610 $aphytoestrogens 610 $aPNT2 610 $aquercetin 610 $aquinic acid 610 $aradiation effects 610 $aradioprotectors 610 $aremdesivir 610 $aresveratrol 610 $aRNA-dependent RNA polymerase 610 $arosmarinic acid 610 $arutin 610 $aSARS-CoV-2 610 $askin rejuvenation 610 $aspike glycoproteins 610 $asulforaphane 610 $atoxicity 610 $atransforming growth factor 610 $aUlmus parvifolia 610 $avitexin 610 $awound healing 615 7$aMedicine and Nursing 700 $aIriti$b Marcello$4edt$01311344 702 $aMartins$b Nata?lia$4edt 702 $aRodrigues$b Ce?lia F$4edt 702 $aIriti$b Marcello$4oth 702 $aMartins$b Nata?lia$4oth 702 $aRodrigues$b Ce?lia F$4oth 906 $aBOOK 912 $a9910557389003321 996 $aBioactive Phytochemicals in Health and Disease$93030557 997 $aUNINA LEADER 05496nam 22006855 450 001 9910438073503321 005 20260810115958.0 010 $a1-4614-6312-2 024 7 $a10.1007/978-1-4614-6312-2 035 $a(CKB)2550000001043719 035 $a(EBL)1205306 035 $a(SSID)ssj0000879262 035 $a(PQKBManifestationID)11486626 035 $a(PQKBTitleCode)TC0000879262 035 $a(PQKBWorkID)10850965 035 $a(PQKB)10809142 035 $a(DE-He213)978-1-4614-6312-2 035 $a(MiAaPQ)EBC1205306 035 $a(PPN)169136078 035 $a(MiAaPQ)EBC4071929 035 $a(EXLCZ)992550000001043719 100 $a20130327d2013 u| 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aMarkov Chains $eModels, Algorithms and Applications /$fby Wai-Ki Ching, Ximin Huang, Michael K. Ng, Tak-Kuen Siu 205 $a2nd ed. 2013. 210 1$aNew York, NY :$cSpringer US :$cImprint: Springer,$d2013. 215 $a1 online resource (243 p.) 225 1 $aInternational Series in Operations Research & Management Science,$x2214-7934 ;$v189 300 $aDescription based upon print version of record. 311 08$a1-4899-9752-0 311 08$a1-4614-6311-4 320 $aIncludes bibliographical references and index. 327 $aIntroduction -- Manufacturing and Re-manufacturing Systems -- A Hidden Markov Model for Customer Classification -- Markov Decision Processes for Customer Lifetime Value -- Higher-order Markov Chains -- Multivariate Markov Chains -- Hidden Markov Chains. 330 $aThis new edition of Markov Chains: Models, Algorithms and Applications has been completely reformatted as a text, complete with end-of-chapter exercises, a new focus on management science, new applications of the models, and new examples with applications in financial risk management and modeling of financial data. This book consists of eight chapters.  Chapter 1 gives a brief introduction to the classical theory on both discrete and continuous time Markov chains. The relationship between Markov chains of finite states and matrix theory will also be highlighted. Some classical iterative methods for solving linear systems will be introduced for finding the stationary distribution of a Markov chain. The chapter then covers the basic theories and algorithms for hidden Markov models (HMMs) and Markov decision processes (MDPs). Chapter 2 discusses the applications of continuous time Markov chains to model queueing systems and discrete time Markov chain for computing the PageRank, the ranking of websites on the Internet. Chapter 3 studies Markovian models for manufacturing and re-manufacturing systems and presents closed form solutions and fast numerical algorithms for solving the captured systems. In Chapter 4, the authors present a simple hidden Markov model (HMM) with fast numerical algorithms for estimating the model parameters. An application of the HMM for customer classification is also presented. Chapter 5 discusses Markov decision processes for customer lifetime values. Customer Lifetime Values (CLV) is an important concept and quantity in marketing management. The authors present an approach based on Markov decision processes for the calculation of CLV using real data. Chapter 6 considers higher-order Markov chain models, particularly a class of parsimonious higher-order Markov chain models. Efficient estimation methods for model parameters based on linear programming are presented. Contemporary research results on applications to demand predictions, inventory control and financial risk measurement are also presented. In Chapter 7, a class of parsimonious multivariate Markov models is introduced. Again, efficient estimation methods based on linear programming are presented. Applications to demand predictions, inventory control policy and modeling credit ratings data are discussed. Finally, Chapter 8 re-visits hidden Markov models, and the authors present a new class of hidden Markov models with efficient algorithms for estimating the model parameters. Applications to modeling interest rates, credit ratings and default data are discussed. This book is aimed at senior undergraduate students, postgraduate students, professionals, practitioners, and researchers in applied mathematics, computational science, operational research, management science and finance, who are interested in the formulation and computation of queueing networks, Markov chain models and related topics. Readers are expected to havesome basic knowledge of probability theory, Markov processes and matrix theory. 410 0$aInternational Series in Operations Research & Management Science,$x2214-7934 ;$v189 606 $aOperations research 606 $aManagement science 606 $aProbabilities 606 $aOperations Research and Decision Theory 606 $aOperations Research, Management Science 606 $aProbability Theory 615 0$aOperations research. 615 0$aManagement science. 615 0$aProbabilities. 615 14$aOperations Research and Decision Theory. 615 24$aOperations Research, Management Science. 615 24$aProbability Theory. 676 $a658.40301 701 $aChing$b Wai-Ki$0298394 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910438073503321 996 $aMarkov Chains$92512019 997 $aUNINA