967 resultados para driver verification


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The Everglades is a sub-tropical coastal wetland characterized among others by its hydrological features and deposits of peat. Formation and preservation of organic matter in soils and sediments in this wetland ecosystem is critical for its sustainability and hydrological processes are important divers in the origin, transport and fate of organic matter. With this in mind, organic matter dynamics in the greater Florida Everglades was studied though various organic geochemistry techniques, especially biomarkers, bulk and compound specific δ13C and δD isotope analysis. The main objectives were focused on how different hydrological regimes in this ecosystem control organic matter dynamics, such as the mobilization of particulate organic matter (POM) in freshwater marshes and estuaries, and how organic geochemistry techniques can be applied to reconstruct Everglades paleo-hydrology. For this purpose organic matter in typical vegetation, floc, surface soils, soil cores, and estuarine suspended particulates were characterized in samples selected along hydrological gradients in the Water Conservation Area 3, Shark River Slough and Taylor Slough. ^ This research focused on three general themes: (1) Assessment of the environmental dynamics and source-specific particulate organic carbon export in a mangrove-dominated estuary. (2) Assessment of the origin, transport and fate of organic matter in freshwater marsh. (3) Assessment of historical changes in hydrological conditions in the Everglades (paleo-hydrology) though biomarkes and compound specific isotope analyses. This study reports the first estimate of particulate organic carbon loss from mangrove ecosystems in the Everglades, provides evidence for particulate organic matter transport with regards to the formation of ridge and slough landscapes in the Everglades, and demonstrates the applicability of the combined biomarker and compound-specific stable isotope approach as a means to generate paleohydrological data in wetlands. The data suggests that: (1) Carbon loss from mangrove estuaries is roughly split 50/50 between dissolved and particulate carbon; (2) hydrological remobilization of particulate organic matter from slough to ridge environments may play an important role in the maintenance of the Everglades freshwater landscape; and (3) Historical changes in hydrology have resulted in significant vegetation shifts from historical slough type vegetation to present ridge type vegetation. ^

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Ensuring the correctness of software has been the major motivation in software research, constituting a Grand Challenge. Due to its impact in the final implementation, one critical aspect of software is its architectural design. By guaranteeing a correct architectural design, major and costly flaws can be caught early on in the development cycle. Software architecture design has received a lot of attention in the past years, with several methods, techniques and tools developed. However, there is still more to be done, such as providing adequate formal analysis of software architectures. On these regards, a framework to ensure system dependability from design to implementation has been developed at FIU (Florida International University). This framework is based on SAM (Software Architecture Model), an ADL (Architecture Description Language), that allows hierarchical compositions of components and connectors, defines an architectural modeling language for the behavior of components and connectors, and provides a specification language for the behavioral properties. The behavioral model of a SAM model is expressed in the form of Petri nets and the properties in first order linear temporal logic. This dissertation presents a formal verification and testing approach to guarantee the correctness of Software Architectures. The Software Architectures studied are expressed in SAM. For the formal verification approach, the technique applied was model checking and the model checker of choice was Spin. As part of the approach, a SAM model is formally translated to a model in the input language of Spin and verified for its correctness with respect to temporal properties. In terms of testing, a testing approach for SAM architectures was defined which includes the evaluation of test cases based on Petri net testing theory to be used in the testing process at the design level. Additionally, the information at the design level is used to derive test cases for the implementation level. Finally, a modeling and analysis tool (SAM tool) was implemented to help support the design and analysis of SAM models. The results show the applicability of the approach to testing and verification of SAM models with the aid of the SAM tool.

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In questo studio, un multi-model ensemble è stato implementato e verificato, seguendo una delle priorità di ricerca del Subseasonal to Seasonal Prediction Project (S2S). Una regressione lineare è stata applicata ad un insieme di previsioni di ensemble su date passate, prodotte dai centri di previsione mensile del CNR-ISAC e ECMWF-IFS. Ognuna di queste contiene un membro di controllo e quattro elementi perturbati. Le variabili scelte per l'analisi sono l'altezza geopotenziale a 500 hPa, la temperatura a 850 hPa e la temperatura a 2 metri, la griglia spaziale ha risoluzione 1 ◦ × 1 ◦ lat-lon e sono stati utilizzati gli inverni dal 1990 al 2010. Le rianalisi di ERA-Interim sono utilizzate sia per realizzare la regressione, sia nella validazione dei risultati, mediante stimatori nonprobabilistici come lo scarto quadratico medio (RMSE) e la correlazione delle anomalie. Successivamente, tecniche di Model Output Statistics (MOS) e Direct Model Output (DMO) sono applicate al multi-model ensemble per ottenere previsioni probabilistiche per la media settimanale delle anomalie di temperatura a 2 metri. I metodi MOS utilizzati sono la regressione logistica e la regressione Gaussiana non-omogenea, mentre quelli DMO sono il democratic voting e il Tukey plotting position. Queste tecniche sono applicate anche ai singoli modelli in modo da effettuare confronti basati su stimatori probabilistici, come il ranked probability skill score, il discrete ranked probability skill score e il reliability diagram. Entrambe le tipologie di stimatori mostrano come il multi-model abbia migliori performance rispetto ai singoli modelli. Inoltre, i valori più alti di stimatori probabilistici sono ottenuti usando una regressione logistica sulla sola media di ensemble. Applicando la regressione a dataset di dimensione ridotta, abbiamo realizzato una curva di apprendimento che mostra come un aumento del numero di date nella fase di addestramento non produrrebbe ulteriori miglioramenti.

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Date of Acceptance: 03/09/15 Acknowledgments Dr. Yang Liu would like to acknowledge the financial support for the Small Research Grant (31841) by the Carnegie Trust for the Universities of Scotland.

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Date of Acceptance: 03/09/15 Acknowledgments Dr. Yang Liu would like to acknowledge the financial support for the Small Research Grant (31841) by the Carnegie Trust for the Universities of Scotland.

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General note: Title and date provided by Bettye Lane.

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General note: Title and date provided by Bettye Lane.

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General note: Title and date provided by Bettye Lane.

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General note: Title and date provided by Bettye Lane.

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General note: Title and date provided by Bettye Lane.

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General note: Title and date provided by Bettye Lane.

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General note: Title and date provided by Bettye Lane.

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General note: Title and date provided by Bettye Lane.

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General note: Title and date provided by Bettye Lane.

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Inscriptions: Verso: [stamped] Credit must be given to Leinwand from Monkmeyer Press Photo Service]