994 resultados para path integral quantization


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This paper offers a contribution to contemporary studies of spatial planning. In particular, it problematises the relationship between neoliberal competitiveness and spatial planning. Neoliberal competitiveness is a hegemonic discourse in public policy as it (allegedly) provides the ‘path to economic nirvana’. However, commentators have critiqued its theoretical underpinnings and labelled it a ‘dangerous obsession’ for policy makers. Another set of literatures argues that spatial planning can be understood as a form of ‘neoliberal spatial governance’ and read in a ‘postpolitical’ framework that ‘privileges competitiveness’. Synthesising these debates this paper critically analyses the application and operationalisation of neoliberal competitiveness in Northern Ireland and Belfast. In focusing on this unique case study—a deeply divided society with a turbulent history—the paper takes the debate forward in arguing that rather than offering the ‘path to economic nirvana’ neoliberal competitiveness is a ‘postpolitical strategy’ and represents a ‘dangerous obsession’ for spatial planning.

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A new approach for extracting stress intensity factors (SIFs) by the element-free Galerkin (EFG) class of methods through a modified crack closure integral (MCCI) scheme is proposed. Its primary feature is that it allows accurate calculation of mode I and mode II SIFs with a relatively simple and straightforward analysis even when a coarser nodal density is employed. The details of the adoption of the MCCI technique in the EFG method are described. Its performance is demonstrated through a number of case studies including mixed-mode and thermal problems in linear elastic fracture mechanics (LEFM). The results are compared with published theoretical solutions and those based on the displacement method, stress method, crack closure integral in conjunction with local smoothing (CCI–LS) technique, as well as the M-integral method. Its advantages are discussed.

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Smart management of maintenances has become fundamental in manufacturing environments in order to decrease downtime and costs associated with failures. Predictive Maintenance (PdM) systems based on Machine Learning (ML) techniques have the possibility with low added costs of drastically decrease failures-related expenses; given the increase of availability of data and capabilities of ML tools, PdM systems are becoming really popular, especially in semiconductor manufacturing. A PdM module based on Classification methods is presented here for the prediction of integral type faults that are related to machine usage and stress of equipment parts. The module has been applied to an important class of semiconductor processes, ion-implantation, for the prediction of ion-source tungsten filament breaks. The PdM has been tested on a real production dataset. © 2013 IEEE.

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In semiconductor fabrication processes, effective management of maintenance operations is fundamental to decrease costs associated with failures and downtime. Predictive Maintenance (PdM) approaches, based on statistical methods and historical data, are becoming popular for their predictive capabilities and low (potentially zero) added costs. We present here a PdM module based on Support Vector Machines for prediction of integral type faults, that is, the kind of failures that happen due to machine usage and stress of equipment parts. The proposed module may also be employed as a health factor indicator. The module has been applied to a frequent maintenance problem in semiconductor manufacturing industry, namely the breaking of the filament in the ion-source of ion-implantation tools. The PdM has been tested on a real production dataset. © 2013 IEEE.

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The reaction mechanism of CO and Fe2O3 in a chemical-looping combustion (CLC) was studied based on density functional theory (DFT) at B3LYP level in this paper. The structures of all reactants, intermediate, transition structures and products of this reaction had been optimized and characterized. The reaction path was validated by means of the intrinsic reaction coordinate (IRC) approach. The result showed that the reaction was divided into two steps, the adsorbed CO molecule on Fe 2O3 surface formed a medium state with one broken Fe-O bond in step1, and in step2, O atom broken here oxidized a subsequent CO molecule in the fuel reactor. Thus, Fe2O3 molecule transport O from air to oxide CO continually in the CLC process. The activation energy and rate coefficients of the two steps were also obtained.

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In a recent paper (Automatica 49 (2013) 2860–2866), the Wirtinger-based inequality has been introduced to derive tractable stability conditions for time-delay or sampled-data systems. We point out that there exist two errors in Theorem 8 for the stability analysis of sampled-data systems, and the correct theorem is presented.

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This paper is concerned with the analysis of the stability of delayed recurrent neural networks. In contrast to the widely used Lyapunov–Krasovskii functional approach, a new method is developed within the integral quadratic constraints framework. To achieve this, several lemmas are first given to propose integral quadratic separators to characterize the original delayed neural network. With these, the network is then reformulated as a special form of feedback-interconnected system by choosing proper integral quadratic constraints. Finally, new stability criteria are established based on the proposed approach. Numerical examples are given to illustrate the effectiveness of the new approach.

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The singlet excited state of the 4-aminonaphthalimide fluorophore in 1a and 1b directs electron transfer from intramolecular but external amine groups along only one of two available paths.