If it ain't broke, break it : how corporate journalism killed the Arkansas gazette / / Donna Lampkin Stephens

• Record Nr.: UNINA9910972072903321
• Autore: Stephens Donna Lampkin
• Pubbl/distr/stampa: Fayetteville, Arkansas : , : University of Arkansas Press, , 2015; ©2015
• ISBN: 9781610755610; 1610755618
• Edizione: [1st ed.]
• Descrizione fisica: 1 online resource (293 p.)
• Disciplina: 070.172
• Soggetti: Newspapers - Ownership
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Based on the author's dissertation (Ph.D.--University of Southern Mississippi, 2012).
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: Introduction -- 1902-1946: early Heiskell family ownership -- 1947-1959: a new triumvirate takes control: J.N. Heiskell, Hugh B. Patterson, and Harry S. Ashmore face the crisis at Central High -- 1960-1970: the aftermath of Central High -- 1970-1974: Mr. Heiskell's death and the transition of ownership to the Patterson family -- 1974-1986: a change atop the Arkansas Democrat, the ensuing newspaper war, antitrust lawsuit, and sale to Gannett -- 1986-1990: Gannett ownership -- 1991: the death of the newspaper -- Lessons learned.
• Sommario/riassunto: The Arkansas Gazette, under the independent local ownership of the Heiskell/Patterson family, was one of the most honored newspapers of twentieth-century American journalism, winning two Pulitzer Prizes for its coverage of the Little Rock Central Crisis. But wounds from a fierce newspaper war against another local owner-Walter Hussman and his Arkansas Democrat -combined with changing economic realities, led to the family's decision to sell to the Gannett Corporation in 1986.Whereas the Heiskell/Patterson family had been committed to quality journalism, Gannett was focused on the bottom line. The corporation shifted the Gazette's editorial focus from giving readers what they needed to be engaged citizens to informing them about what they should do in their leisure time. While in many ways the chain trivialized the Gazette's mission, the paper managed to retain its superior quality. ; But financial concerns made the difference in Arkansas's ongoing newspaper war. As the head of a privately held company, Hussman had only himself to answer to, and he never flinched while spending $42 million in his battle with the Pattersons and millions more against Gannett. Gannett ultimately lost $108 million during its five years in Little Rock; Hussman said his losses were far less but still in the tens of millions.Gannett had to answer to nervous stockholders, most of whom had no tie to, or knowledge of, Arkansas or the Gazette. For Hussman, the Arkansan, the battle had been personal since at least 1978. It is no surprise that the corporation blinked first, and the Arkansas Gazette died on October 18, 1991, the victim of corporate journalism.

Reduced Order Methods for Modeling and Computational Reduction / / edited by Alfio Quarteroni, Gianluigi Rozza

• Record Nr.: UNINA9910299987603321
• Pubbl/distr/stampa: Cham : , : Springer International Publishing : , : Imprint : Springer, , 2014
• ISBN: 3-319-02090-0
• Edizione: [1st ed. 2014.]
• Descrizione fisica: 1 online resource (338 p.)
• Collana: MS&A, Modeling, Simulation and Applications, , 2037-5263 ; ; 9
• Disciplina: 004.0151
• Soggetti: Mathematics - Data processing; Numerical analysis; Engineering mathematics; Engineering - Data processing; Mathematical models; Mechanics, Applied; Solids; Mathematical physics; Computational Mathematics and Numerical Analysis; Numerical Analysis; Mathematical and Computational Engineering Applications; Mathematical Modeling and Industrial Mathematics; Solid Mechanics; Theoretical, Mathematical and Computational Physics
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Description based upon print version of record.
• Nota di bibliografia: Includes bibliographical references.
• Nota di contenuto: 1 W. H. A. Schilders, A. Lutowska: A novel approach to model order reduction for coupled multiphysics problems -- 2 A. C. Ionita, A. C. Antoulas: Case study. Parametrized Reduction using Reduced-Basis and the Loewner Framework -- 3 M. Bebendorf, Y. Maday, B. Stamm: Comparison of some reduced representation approximations -- 4 H. Antil, M. Heinkenschloss, D. C. Sorensen: Application of the Discrete Empirical Interpolation Method to Reduced Order Modeling of Nonlinear and Parametric System -- 5 K. Urban, S. Volkwein, O. Zeeb: Greedy Sampling using Nonlinear Optimization -- 6 P. Benner, L. Feng: A Robust Algorithm for Parametric Model Order Reduction based on Implicit Moment Matching -- 7 F. Chen, J. S. Hesthaven, X. Zhu: On the use of reduced basis methods to accelerate and stabilize the Parareal method -- 8 C. Farhat, D. Amsallem: On the stability of reduced-order linearized computational fluid dynamics models based on POD and Galerkin projection: descriptor vs non-descriptor forms -- 9 T. Lassila, A. Manzoni, A. Quarteroni, G. Rozza: Model Order Reduction in Fluid Dynamics: Challenges and Perspectives -- 10 L. Grinberg, M. Deng, A. Yakhot, G. Karniadakis: Window Proper Orthogonal Decomposition. Application to Continuum and Atomistic Data -- 11 M. Bergmann, T. Colin, A. Iollo, D. Lombardi, O. Saut, H. Telib: Reduced order models at work in Aeronautics and Medicine.
• Sommario/riassunto: This monograph addresses the state of the art of reduced order methods for modeling and computational reduction of complex parametrized systems, governed by ordinary and/or partial differential equations, with a special emphasis on real time computing techniques and applications in computational mechanics, bioengineering and computer graphics. Several topics are covered, including: design, optimization, and control theory in real-time with applications in engineering; data assimilation, geometry registration, and parameter estimation with special attention to real-time computing in biomedical engineering and computational physics; real-time visualization of physics-based simulations in computer science; the treatment of high-dimensional problems in state space, physical space, or parameter space; the interactions between different model reduction and dimensionality reduction approaches; the development of general error estimation frameworks which take into account both model and discretization effects. This book is primarily addressed to computational scientists interested in computational reduction techniques for large scale differential problems.