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.

Multiscale time series modelling with an application to the relativistic electron intensity at the geosynchronous orbit

In this paper, a Bayesian system identification approach to multiscale time series modelling is proposed, where multiscale means that the output of the system is observed at one(coarse) resolution while the input of the system is observed at another (One) resolution. The proposed method identifies linear models at different levels of resolution where the link between the two resolutions is realised via non-overlapping averaging process. This averaged time series at the coarse level of resolution is assumed to be a set of observations from an implied process so that the implied process and the output of the system result in an errors-in-variables ARMAX model at the coarse level of resolution. By using a Bayesian inference and Markov Chain Monte Carlo (MCMC) method, such a modelling framework results in different dynamical models at different levels of resolution at the same time. The new method is also shown to have the ability to combine information across different levels of resolution. An application to the analysis of the relativistic electron intensity at the geosynchronous orbit is used to illustrate the new method

Biophysical modelling of a drosophila photoreceptor

It remains unclear how visual information is co-processed by different layers of neurons in the retina. In particular, relatively little is known how retina translates vast environmental light changes into neural responses of limited range. We began examining this question in a bottom-up way in a relatively simple °y eye. To gain understanding of how complex bio-molecular interactions govern the conversion of light input into voltage output (phototransduction), we are building a biophysical model of the Drosophila R1-R6 photoreceptor. Our model, which relates molecular dynamics of the underlying biochemical reactions to external light input, attempts to capture the molecular dynamics of phototransduction gain control in a quantitative way

Pilot-testing a Cancer 101 Education Curriculum with the Fairbanks Native Association’s Women & Children’s Center for Inner Healing

Cancer is the leading cause of death among Alaska Native people Nevertheless, due to improved detection awareness about cancer prevention, early screening and advances in treatment survival rates are rising

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

Driver behaviour with adaptive cruise control

This paper reports on the evaluation of adaptive cruise control (ACC) from a psychological perspective. It was anticipated that ACC would have an effect upon the psychology of driving, i.e. make the driver feel like they have less control, reduce the level of trust in the vehicle, make drivers less situationally aware, but workload might be reduced and driving might be less stressful. Drivers were asked to drive in a driving simulator under manual and ACC conditions. Analysis of variance techniques were used to determine the effects of workload (i.e. amount of traffic) and feedback (i.e. degree of information from the ACC system) on the psychological variables measured (i.e. locus of control, trust, workload, stress, mental models and situation awareness). The results showed that: locus of control and trust were unaffected by ACC, whereas situation awareness, workload and stress were reduced by ACC. Ways of improving situation awareness could include cues to help the driver predict vehicle trajectory and identify conflicts

On the dependent recognition of some long zinc finger proteins

The human genome contains about 800 C2H2 zinc finger proteins (ZFPs), and most of them are composed of long arrays of zinc fingers. Standard ZFP recognition model asserts longer finger arrays should recognize longer DNA-binding sites. However, recent experimental efforts to identify in vivo ZFP binding sites contradict this assumption, with many exhibiting short motifs. Here we use ZFY, CTCF, ZIM3, and ZNF343 as examples to address three closely related questions: What are the reasons that impede current motif discovery methods? What are the functions of those seemingly unused fingers and how can we improve the motif discovery algorithms based on long ZFPs\u27 biophysical properties? Using ZFY, we employed a variety of methods and find evidence for \u27dependent recognition\u27 where downstream fingers can recognize some previously undiscovered motifs only in the presence of an intact core site. For CTCF, high-throughput measurements revealed its upstream specificity profile depends on the strength of its core. Moreover, the binding strength of the upstream site modulates CTCF\u27s sensitivity to different epigenetic modifications within the core, providing new insight into how the previously identified intellectual disability-causing and cancer-related mutant R567W disrupts upstream recognition and deregulates the epigenetic control by CTCF. Our results establish that, because of irregular motif structures, variable spacing and dependent recognition between sub-motifs, the specificities of long ZFPs are significantly underestimated, so we developed an algorithm, ModeMap, to infer the motifs and recognition models of ZIM3 and ZNF343, which facilitates high-confidence identification of specific binding sites, including repeats-derived elements. With revised concept, technique, and algorithm, we can discover the overlooked specificities and functions of those \u27extra\u27 fingers, and therefore decipher their broader roles in human biology and diseases

Time-Domain Identification of Nonlinear Processes in Space Plasma Turbulence Using Multi-Point Measurements

Nonlinear process identification techniques in the time-domain are adopted in the study of space plasma turbulence using multi-satellite measurements. These techniques are applied to the analysis of two point measurements from AMPTE UKS-AMPTE IRM in the terrestrial foreshock to identify the dynamical features in the turbulence, which could not be determined using a frequency domain approach previously applied to the same data

Comparative analysis of NOAA REFM and SNB 3 GEO tools for the forecast of the fluxes of high-energy electrons at GEO

Reliable forecasts of relativistic electrons at geostationary orbit (GEO) are important for the mitigation of their hazardous effects on spacecraft at GEO. For a number of years the Space Weather Prediction Center at NOAA has provided advanced online forecasts of the fluence of electrons with energy >2 MeV at GEO using the Relativistic Electron Forecast Model (REFM). The REFM forecasts are based on real-time solar wind speed observations at L1. The high reliability of this forecasting tool serves as a benchmark for the assessment of other forecasting tools. Since 2012 the Sheffield SNB3GEO model has been operating online, providing a 24 h ahead forecast of the same fluxes. In addition to solar wind speed, the SNB3GEO forecasts use solar wind density and interplanetary magnetic field Bz observations at L1.The period of joint operation of both of these forecasts has been used to compare their accuracy. Daily averaged measurements of electron fluxes by GOES 13 have been used to estimate the prediction efficiency of both forecasting tools. To assess the reliability of both models to forecast infrequent events of very high fluxes, the Heidke skill score was employed. The results obtained indicate that SNB3GEO provides a more accurate 1 day ahead forecast when compared to REFM. It is shown that the correction methodology utilized by REFM potentially can improve the SNB3GEO forecast