01373nam--2200433---450-99000048856020331620060209095501.088-85345-48-40048856USA010048856(ALEPH)000048856USA01004885620010601d1996----km-y0itay0103----baitaIT||||||||001yyBrunivo Buttarellinel luogo delle opposizionitesti critici di Giorgio Di Genova, Franca Calzavacca, Valter Rosatestimonianza di Ornella Anversatraduzioni di Sabrina BelliniBolognaBora199677 p.ill.28 cm2001730.92BUTTARELLI,Brunivo545599DI GENOVA,Giorgio<1933- >CALZAVACCA,FrancaROSA,ValterBELLINI,SabrinaITsalbcISBD990000488560203316XII.2.C. 1107(VII S 83)132085 LMVII SBKUMAPATTY9020010601USA011027PATTY9020010601USA01112920020403USA011657PATRY9020040406USA011634COPAT59020060209USA010955ANNAMARIA9020100722USA011055Brunivo Buttarelli888181UNISA04944nam 22008175 450 991068647870332120240130165511.03-030-92495-510.1007/978-3-030-92495-9(MiAaPQ)EBC7235419(Au-PeEL)EBL7235419(DE-He213)978-3-030-92495-9(OCoLC)1380467013(PPN)269658696(CKB)26428010600041(EXLCZ)992642801060004120230407d2023 u| 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierMathematical Geosciences Hybrid Symbolic-Numeric Methods /by Joseph L. Awange, Béla Paláncz, Robert H. Lewis, Lajos Völgyesi2nd ed. 2023.Cham :Springer International Publishing :Imprint: Springer,2023.1 online resource (733 pages)Print version: Awange, Joseph L. Mathematical Geosciences Cham : Springer International Publishing AG,c2023 9783030924942 Introduction -- Solution of nonlinear systems -- Solution of algebraic polynomial systems -- Homotopy solution of nonlinear systems -- Over and underdeterminated systems -- Nonlinear geodetic equations with uncertainties -- Optimization of systems -- Simulated annealing.This second edition of Mathematical Geosciences book adds five new topics: Solution equations with uncertainty, which proposes two novel methods for solving nonlinear geodetic equations as stochastic variables when the parameters of these equations have uncertainty characterized by probability distribution. The first method, an algebraic technique, partly employs symbolic computations and is applicable to polynomial systems having different uncertainty distributions of the parameters. The second method, a numerical technique, uses stochastic differential equation in Ito form; Nature Inspired Global Optimization where Meta-heuristic algorithms are based on natural phenomenon such as Particle Swarm Optimization. This approach simulates, e.g., schools of fish or flocks of birds, and is extended through discussion of geodetic applications. Black Hole Algorithm, which is based on the black hole phenomena is added and a new variant of the algorithm code is introduced and illustrated based on examples; The application of the Gröbner Basis to integer programming based on numeric symbolic computation is introduced and illustrated by solving some standard problems; An extension of the applications of integer programming solving phase ambiguity in Global Navigation Satellite Systems (GNSSs) is considered as a global quadratic mixed integer programming task, which can be transformed into a pure integer problem with a given digit of accuracy. Three alternative algorithms are suggested, two of which are based on local and global linearization via McCormic Envelopes; and Machine learning techniques (MLT) that offer effective tools for stochastic process modelling. The Stochastic Modelling section is extended by the stochastic modelling via MLT and their effectiveness is compared with that of the modelling via stochastic differential equations (SDE). Mixing MLT with SDE also known as frequently Neural Differential Equations is also introduced and illustrated by an image classification via a regression problem.Earth sciencesEnvironmental sciences—MathematicsGeography—MathematicsMathematical physicsPhysical geographyGeophysicsEarth SciencesMathematical Applications in Environmental ScienceMathematics of Planet EarthMathematical Methods in PhysicsEarth System SciencesGeophysicsGeologia aplicadathubMatemàticathubLlibres electrònicsthubEarth sciences.Environmental sciences—Mathematics.Geography—Mathematics.Mathematical physics.Physical geography.Geophysics.Earth Sciences.Mathematical Applications in Environmental Science.Mathematics of Planet Earth.Mathematical Methods in Physics.Earth System Sciences.Geophysics.Geologia aplicadaMatemàtica550.151550.151Awange Joseph L719102Paláncz Béla1075535Lewis Robert H153032Völgyesi Lajos1075536MiAaPQMiAaPQMiAaPQBOOK9910686478703321Mathematical Geosciences3089354UNINA04508nam 22006855 450 991088698710332120250807135922.03-031-65986-410.1007/978-3-031-65986-7(CKB)34985226900041(MiAaPQ)EBC31657827(Au-PeEL)EBL31657827(DE-He213)978-3-031-65986-7(EXLCZ)993498522690004120240911d2024 u| 0engur|||||||||||txtrdacontentcrdamediacrrdacarrierAntimicrobial Resistance: Factors to Findings Omics and Systems Biology Approaches /edited by Vijay Soni, Ajay Suresh Akhade1st ed. 2024.Cham :Springer International Publishing :Imprint: Springer,2024.1 online resource (459 pages)3-031-65985-6 Antimicrobial resistance and factors -- Genomics as a tool to track global AMR -- Meta-transcriptomics to reveal mechanisms of drug action and resistance -- Use of proteomics to study bacterial virulence and AMR -- Metabolomics to understand bacterial and drug metabolism -- Microbiome and AMR: A One Health perspective -- Environmental reservoirs, genomic epidemiology, and mobile genetic elements -- Multiomics approach to control AMR -- Systems biology and AMR -- Host-directed omics approaches to control AMR -- Role of AI and Machine Learning in omics analysis of AMR evolution and surveillance -- Drug discovery and AMR treatments using an omics-based approach -- Future perspectives of omics-systems biology to control AMR: Recommendations and future directions.Antimicrobial resistance (AMR) is increasing globally at an incredible rate, and many infectious diseases have already reached an alarming stage of resistance to existing treatments. WHO reports that nearly1.27 million people currently die each year due to resistant infections, and AMR is projected to account for 10 million annual deaths globally by 2050. There is an urgent need for novel approaches to address this issue. Omics technologies are powerful research tools used extensively to study pathogen biology and the activity of microbial agents. These tools, paired with systems biology approaches, can provide novel insights into antimicrobial susceptibility and resistance, and aid in the development of new, more effective measures to combat resistant pathogens. This book provides a comprehensive overview of omics technologies to study pathogen biology, including proteomics, genomics, transcriptomics, metabolomics, and microbiome analysis, and the role of systems biology in developing strategies to combat resistant pathogens. It addresses environmental reservoirs and mobile genetic agents in AMR, host-pathogen interactions and physiology in the development of resistance, drug repurposing and development, and cutting-edge tools such as machine learning, AI for big data analysis, and genomic surveillance. The final section discusses future perspectives on omics-systems biology in AMR, and identifies opportunities for scientific collaboration in the global fight against antimicrobial resistance. This book serves as a comprehensive and accessible resource for researchers in academia and industry focused on immunology, drug development, biotechnology, and systems biology.ImmunologyImmune responsePathogenic microorganismsBioinformaticsDiseasesCauses and theories of causationGenomicsImmunologyAntimicrobial ResponsesComputational and Systems BiologyPathogenesisGenomicsImmunology.Immune response.Pathogenic microorganisms.Bioinformatics.DiseasesCauses and theories of causation.Genomics.Immunology.Antimicrobial Responses.Computational and Systems Biology.Pathogenesis.Genomics.571.96616.079Soni Vijay1768452Akhade Ajay Suresh1768453MiAaPQMiAaPQMiAaPQBOOK9910886987103321Antimicrobial Resistance: Factors to Findings4229442UNINA