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1. |
Record Nr. |
UNINA9910792337403321 |
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Titolo |
Genetic diversity [[electronic resource] /] / Conner L. Mahoney and Douglas A. Springer, editors |
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Pubbl/distr/stampa |
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New York, : Nova Science Publishers, c2009 |
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ISBN |
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Descrizione fisica |
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xiii, 304 p. : ill. (some col.), maps |
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Collana |
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Genetics--research and issues series |
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Altri autori (Persone) |
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MahoneyConner L |
SpringerDouglas A |
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Disciplina |
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Soggetti |
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Variation (Biology) |
Genetics - Research |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Bibliographic Level Mode of Issuance: Monograph |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Sommario/riassunto |
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"Genetic diversity is a level of biodiversity that refers to the total number of genetic characteristics in the genetic makeup of a species. It is distinguished from genetic variability, which describes the tendency of genetic characteristics to vary. Research has found that genetic diversity and biodiversity are dependent upon each other, that diversity within a species is necessary to maintain diversity among species, and vice versa. If any one type is removed from the system, the cycle can break down, and the community may become dominated by a single species. Thus, genetic diversity plays a huge role in survival and adaptability of a species. This book provides research on genetic diversity in plant, animal and human species. Relationships to environment changes and global warming are also studied."--Publisher's description. |
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2. |
Record Nr. |
UNINA9910485046003321 |
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Titolo |
Mathematical Descriptions of Traffic Flow: Micro, Macro and Kinetic Models / / edited by Gabriella Puppo, Andrea Tosin |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021 |
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ISBN |
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Edizione |
[1st ed. 2021.] |
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Descrizione fisica |
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1 online resource (102 pages) : illustrations |
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Collana |
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ICIAM 2019 SEMA SIMAI Springer Series, , 2662-7191 ; ; 12 |
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Disciplina |
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Soggetti |
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Mathematical models |
Mathematics - Data processing |
Mathematical Modeling and Industrial Mathematics |
Computational Mathematics and Numerical Analysis |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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M. Herty et al., Reconstruction of traffic speed distributions from kinetic models with uncertainties -- M. Herty et al., From kinetic to macroscopic models and back -- R. Ramadan et al., Structural Properties of the Stability of Jamitons -- C. Balzotti and E. Iacomini, Stop-and-go waves: A Microscopic and a Macroscopic Description -- F. A. Chiarello, An overview of non-local traffic flow models. |
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Sommario/riassunto |
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The book originates from the mini-symposium "Mathematical descriptions of traffic flow: micro, macro and kinetic models" organised by the editors within the ICIAM 2019 Congress held in Valencia, Spain, in July 2019. The book is composed of five chapters, which address new research lines in the mathematical modelling of vehicular traffic, at the cutting edge of contemporary research, including traffic automation by means of autonomous vehicles. The contributions span the three most representative scales of mathematical modelling: the microscopic scale of particles, the mesoscopic scale of statistical kinetic description and the macroscopic scale of partial differential equations. The work is addressed to researchers in the field. |
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3. |
Record Nr. |
UNINA9910484698803321 |
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Titolo |
Applied Advanced Analytics : 6th IIMA International Conference on Advanced Data Analysis, Business Analytics and Intelligence / / edited by Arnab Kumar Laha |
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Pubbl/distr/stampa |
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Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2021 |
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ISBN |
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Edizione |
[1st ed. 2021.] |
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Descrizione fisica |
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1 online resource (236 pages) |
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Collana |
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Springer Proceedings in Business and Economics, , 2198-7254 |
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Disciplina |
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Soggetti |
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Operations research |
Business - Data processing |
Business information services |
Business enterprises - Finance |
Management |
Statistics |
Operations Research and Decision Theory |
Business Analytics |
IT in Business |
Corporate Finance |
Applied Statistics |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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Data Streams -- Robust Statistics -- Explainable Artificial Intelligence Model: Analysis of Neural Network Parameters -- Mitigating Agricultural Lending Risk: An Advanced Analytical Approach. |
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Sommario/riassunto |
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This book covers several new areas in the growing field of analytics with some innovative applications in different business contexts. The book is conceptually divided in seven parts. The first part gives expository briefs on some topics of current academic and practitioner interests, such as data streams, binary prediction and reliability shock models. In the second part, the contributions look at artificial intelligence applications with chapters related to explainable AI, personalized search and recommendation, and customer retention |
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management. The third part deals with credit risk analytics, with chapters on optimization of credit limits and mitigation of agricultural lending risks. In its fourth part, the book explores analytics and data mining in the retail context. In the fifth part, the book presents some applications of analytics to operations management. This part has chapters related to improvement of furnace operations, forecasting food indices and analytics for improving student learning outcomes. The sixth part has contributions related to adaptive designs in clinical trials, stochastic comparisons of systems with heterogeneous components and stacking of models. The seventh and final part contains chapters related to finance and economics topics, such as role of infrastructure and taxation on economic growth of countries and connectedness of markets with heterogenous agents, The different themes ensure that the book would be of great value to practitioners, post-graduate students, research scholars and faculty teaching advanced business analytics courses. |
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