Agriculture and poverty in the Kentucky mountains: Beech Creek and Clay County, 1850-1910

The poverty of Appalachia is not the product of modernization. Nor is it a unique phenomenon. An examination of the history of farming in Beech Creek, Kentucky, reveals that this community, which was prosperous in 1860, owed its fall into poverty to a number of factors that had impoverished other regions: the high rate of population growth among the families living in the area, the division and re-division of the limited land to accommodate the new generations of families, the need to use woodland for agriculture before reforestation succeeded in restoring the old soil to its original productivity, and slow economic growth resulting from the emphasis on subsistence rather than commercial agriculture. The same pattern had occurred in New England in the eighteenth century. What was unique in Appalachia was that subsistence farming lasted so long, owing to growing isolation from the rest of the country as the area was bypassed in the construction of modern means of transportation.

A novel nonlinear approach to suppress resonant vibrations

A novel approach to suppress resonant vibration is presented by employing a single degree of freedom transmissibility system which utilizes a nonlinear damping element. Studies have shown that the nonlinear damping element can reduce the output energy at the driving frequency and at the same time spread the output signal energy over a wider range of harmonics. It will also be shown that the reduction becomes larger as the nonlinear damping characteristic gets stronger and in most cases, the power at the harmonics in the output spectrum will be much less if the nonlinear damping characteristic is an odd function. Hence, an odd polynomial nonlinear damping element can be introduced between the incoming signal and the structure of interest to suppress resonant vibration. An expression is derived to express the transmitted force spectrum in terms of the nonlinear generalized frequency response functions, to clearly show how the energy, at the excitation frequency, is modified by the nonlinearity

Suppressing resonant vibrations using nonlinear springs and dampers

The energy entering the resonant region of a system can be significantly reduced by introducing designed nonlinearities into the system. The basic choice of the nonlinearity can be either a nonlinear spring element or a nonlinear damping element. A numerical algorithm to compute and compare the energy reduction produced by these two types of designed elements is proposed in this study. Analytical results are used to demonstrate the procedure. The numerical results indicate that the designed nonlinear damping element produces low levels of energy at the higher order harmonics and no bifurcations in the system output response. In contrast the nonlinear spring based designs induce significant energy at the harmonics and can produce bifurcation behaviour. The conclusions provide an important basis for the design of nonlinear materials and nonlinear engineering systems

Model structure detection and system identification of metal rubber devices

Metal rubber (MR) devices, a new wire mesh material, have been extensively used in recent years due to several unique properties especially in adverse environments. Although many practical studies have been completed, the related theoretical research on metal rubber is still in its infancy. In this paper, a semi-constitutive dynamic model that involves nonlinear elastic stiffness, nonlinear viscous damping and bilinear hysteresis Coulomb damping is adopted to model MR devices. After approximating the bilinear hysteresis damping using Chebyshev polynomials of the first kind, a very efficient procedure based on the orthogonal least squares (OLS) algorithm and the adjustable prediction error sum of squares (APRESS) criterion is proposed for model structure detection and parameter estimation of an MR device for the first time. The OLS algorithm provides a powerful tool to effectively select the significant model terms step by step, one at a time, by orthogonalizing the associated terms and maximizing the error reduction ratio, in a forward stepwise procedure. The APRESS statistic regularizes the OLS algorithm to facilitate the determination of the optimal number of model terms that should be included into the dynamic model. Because of the orthogonal property of the OLS algorithm, the approach leads to a parsimonious model. Numerical ill-conditioning problems confronted by the conventional least squares algorithm can also be avoided by the new approach. Finally by utilising the transient response of a MR specimen, it is shown how the model structure can be detected in a practical application. The identified model agrees with the experimental measurements very well

Dynamical epidemic suppression using stochastic prediction and control

We consider the effects of noise on a model of epidemic outbreaks, where the outbreaks appear. randomly. Using a constructive transition approach that predicts large outbreaks, prior to their occurrence, we derive an adaptive control. scheme that prevents large outbreaks from occurring. The theory inapplicable to a wide range of stochastic processes with underlying deterministic structure.Comment: 14 pages, 6 figure

Volterra Series Truncation and Kernel Estimation of Nonlinear Systems in the Frequency Domain

The Volterra series model is a direct generalisation of the linear convolution integral and is capable of displaying the intrinsic features of a nonlinear system in a simple and easy to apply way. Nonlinear system analysis using Volterra series is normally based on the analysis of its frequency-domain kernels and a truncated description. But the estimation of Volterra kernels and the truncation of Volterra series are coupled with each other. In this paper, a novel complex-valued orthogonal least squares algorithm is developed. The new algorithm provides a powerful tool to determine which terms should be included in the Volterra series expansion and to estimate the kernels and thus solves the two problems all together. The estimated results are compared with those determined using the analytical expressions of the kernels to validate the method. To further evaluate the effectiveness of the method, the physical parameters of the system are also extracted from the measured kernels. Simulation studies demonstrates that the new approach not only can truncate the Volterra series expansion and estimate the kernels of a weakly nonlinear system, but also can indicate the applicability of the Volterra series analysis in a severely nonlinear system case

A nonparametric test for industrial specialization

We introduce a nonparametric microdata based test for industrial specialization and apply it to a single urban area. Our test employs establishment densities for specific industries, a population counterfactual, and a new correction for múltiple hypothesis testing to determine the statistical significance of specialization across both places and industries. Results highlight patterns of specialization which are extremely varied, with downtown places specializing in a number of service sector industries, while more suburban places specialize in both manufacturing and service industries. Business service industries are subject to more specialization than non-business service industries while the manufacturing sector contains the lowest representation of industries with specialized places. Finally, we compare the results of our test for specialization with recent tests of localization and show how these two classes of measures highlight the presence of both industry as well as place specific agglomerative forces