This essay explores the novel’s vexed position within the emergent genteel culture of the early United States. It charts a broad transformation in the genre’s status between the 1780s and 1820s as the novel gradually becomes widely, if unevenly, accepted as respectable, even edifying, reading material. In tracing this shift, the essay explores how certain novelists of this period sought to establish the respectability of their own novels in part by distancing their works from the novel genre’s association with aristocratic frivolity. In this, these novels exemplify the early republic’s conflicted attitude toward gentility as such. The essay argues that these novels played an important role in this era’s more general recasting of gentility as bourgeois respectability rather than aristocratic fashionableness. The prevailing social and political conservatism of these novels, however, should not obscure the sustained literary experiment they undertook in reimagining the social meaning of the novel in the early United States.

Introduction

Modern industrial gas turbine combustors have to meet a wide range of technical requirements governed by the thermodynamic cycle, the demand for a low environmental impact, the compatibility with other machine components, and the safety and reliability aspects. Although the evolution of combustion chamber technology has resulted in substantially different hardware solutions, a high level of similarity exists as to how the subprocesses take place in combustion chambers of different design and how they interact with each other. What the basic designs of combustors for industrial gas turbines relevant to emissions have in common will be discussed in the first part of this chapter to create the basis for a better understanding of the second part, where several specific design solutions are presented. These are grouped into different classes of combustors according to fuel type and the pollutant emission abatement technique employed. The present state of the art is the result of the simultaneous optimization of several subprocesses relevant for the performance of combustors. Expanded discussion of many of the basic combustor technology drivers and how they interact with the electrical grid as a whole, the power plant, and the gas turbine are also included in Chapter 1.

Beyond Energy Conversion

Taking the thermodynamic cycle of gas turbines into consideration, the main function of the combustor is to provide a high-temperature fluid flow for driving the subsequent turbine. For older gas turbine designs dating from before 1980, the requirements of the gas turbine combustion system were quite moderate and so the development effort was kept low. Pollutant emissions were of only peripheral concern, as long as soot and yellow smoke from nitrogen dioxide formation were below the limits of visibility. These requirements were met using fairly simple combustors, which were easy to operate without sophisticated control and periphery. Because of the enormous increase of turbine inlet temperatures in high-efficiency gas turbines and the stricter emissions regulations, the design of combustors for gas turbines has become a key technology. Since 1980, the abatement of pollutant emissions has entirely driven combustor development for industrial gas turbines. This has led to the more complex and delicate multi-burner systems, which are the state of the art today.

The present chapter gives a comprehensive introduction into the display and quantitative characterization of scalp field data. After introducing the construction of scalp field maps, different interpolation methods, the effect of the recording reference and the computation of spatial derivatives are discussed. The arguments raised in this first part have important implications for resolving a potential ambiguity in the interpretation of differences of scalp field data.

In the second part of the chapter different approaches for comparing scalp field data are described. All of these comparisons can be interpreted in terms of differences of intracerebral sources either in strength, or in location and orientation in a nonambiguous way.

In the present chapter we only refer to scalp field potentials, but mapping also can be used to display other features, such as power or statistical values. However, the rules for comparing and interpreting scalp field potentials might not apply to such data.

Generic form of scalp field data

Electroencephalogram (EEG) and event-related potential (ERP) recordings consist of one value for each sample in time and for each electrode. The recorded EEG and ERP data thus represent a two-dimensional array, with one dimension corresponding to the variable “time” and the other dimension corresponding to the variable “space” or electrode. Table 2.1 shows ERP measurements over a brief time period. The ERP data (averaged over a group of healthy subjects) were recorded with 19 electrodes during a visual paradigm. The parietal midline Pz electrode has been used as the reference electrode.

Introduction

Integrating evidence from different imaging modalities is important to overcome specific limitations of any given imaging method, such as insensitivity of the EEG to unsynchronized neural events, or the lack of fMRI sensitivity to events of low metabolic demand. Processes that are visible in one modality may be related in a nontrivial way to other processes visible in another modality and insight may only be obtained by integrating both methods through a common analysis. For example, brain activity at rest seems to be at least partly determined by an interaction of cortical rhythms (visible to EEG but not to fMRI) with sub-cortical activity (visible to fMRI, but usually not to EEG without averaging). A combination of EEG and fMRI data during rest may thus be more informative than the sum of two separate analyses in both modalities.

Integration is also an important source of converging evidence about specific aspects and general principles of neural functions and their dysfunctions in certain pathologies. This is because not only electrical, but also energetic, biochemical, hemodynamic and metabolic processes characterize neural states and functions, and because brain structure provides crucial constraints upon neural functions. Focusing on multimodal integration of functional data should not distract from the privileged status of the electric field as the primary direct, noninvasive real-time measure of neural transmission.

The preceding chapters illustrate how electrical neuroimaging has turned scalp EEG into an imaging modality which directly captures the full temporal dynamics of neural activity in the brain.

Spatial analysis of the spontaneous EEG

Resting state and neurocognitive networks

A publication entitled “A default mode of brain function” initiated a new way of looking at functional imaging data. In this PET study the authors discussed the often-observed consistent decrease of brain activation in a variety of tasks as compared with the baseline. They suggested that this deactivation is due to a task-induced suspension of a default mode of brain function that is active during rest, i.e. that there exists intrinsic well-organized brain activity during rest in several distinct brain regions. This suggestion led to a large number of imaging studies on the resting state of the brain and to the conclusion that the study of this intrinsic activity is crucial for understanding how the brain works.

The fact that the brain is active during rest has been well known from a variety of EEG recordings for a very long time. Different states of the brain in the sleep–wake continuum are characterized by typical patterns of spontaneous oscillations in different frequency ranges and in different brain regions. Best studied are the evolving states during the different sleep stages, but characteristic EEG oscillation patterns have also been well described during awake periods (see Chapter 1 for details). A highly recommended comprehensive review on the brain's default state defined by oscillatory electrical brain activities is provided in the recent book by György Buzsaki, showing how these states can be measured by electrophysiological procedures at the global brain level as well as at the local cellular level.

Introduction and overview

Time series of EEG scalp potential differences typically appear to be composed of oscillations at various frequencies. Although the amplitude and spatial distributions of these oscillations may fluctuate in time, the quantification of these oscillations as a function of frequency and location (i.e. the multichannel spectral analysis of the EEG) is very reproducible within and across subjects and systematically varies depending on a series of physiologically interesting factors. To mention a few examples, EEG spectral analysis has been successfully employed to characterize a subject's age, state of arousal, the presence of neurological or psychiatric disorders, drugs or task demand as systematic deviations of spectral power from a norm.

In the present chapter, we will outline the possibilities of quantifying EEG oscillations with a special emphasis on those aspects that are specific for multichannel EEG. First, a methodological primer delineates the interdependencies among spectral amplitude, phase and recording montage. In addition, it also delineates what scalp signals we expect from one or several known oscillating sources in the brain. Next, we describe the currently employed analysis strategies: starting from the classic EEG spectral power mapping of measured EEG, we proceed to methods that take into account the relations of amplitude and phase between the different electrodes, which is an essential prerequisite for discussing the results in terms of sources and interactions of brain regions. Finally, we delineate possible methods for custom-tailored quantitative analyses of multichannel EEG oscillations and source localization in the frequency domain.