Community Supported Agriculture: Building Community or Creating a Commodity?

This phenomenological study explored the idea of “community” in the context of community supported agriculture (CSA). Data from interviews with shareholders and farmers as well as multiple participant observations over two CSA seasons, revealed community as an important part of the CSA experience for both shareholders and farmers. The most important aspect of this arguably countercultural community was the relationship between the farmer and shareholder. Shareholders mentioned the idea of supporting a local farmer more frequently as the reason for their CSA participation than the vegetables themselves. The CSA replaced the anonymity of grocery store commodities with food raised by my farmer. Farmers displayed an unusual degree of satisfaction and enjoyment in their chosen profession. While most CSA farmers made less money than their shareholders, the farmers still devoted more time and attention (e.g. craftsmanship) to producing and delivering the weekly box of vegetables than one might have expected. The element of craftsmanship was a shared attributebetween farmers and many of the shareholders. Farmers developed their craftsmanship growing the vegetables and shareholders developed their craftsmanship preparing and experimenting with the vegetables. The vegetables in the CSA box represented the tangible part of the farmer/shareholder relationship, but the magic of “community” was in the relationship itself

Factors relating to the uptake of interventions for smoking cessation amongst pregnant women: a systematic review and qualitative synthesis

Introduction The review had the aim of investigating factors enabling or discouraging the uptake of smoking cessation services by pregnant women smokers. Methods The literature was searched for papers relating to the delivery of services to pregnant or recently pregnant women who smoke. No restrictions were placed on study design. A qualitative synthesis strategy was adopted to analyse the included papers. Results Analysis and synthesis of the 23 included papers suggested ten aspects of service delivery that may have an influence on the uptake of interventions. These were: whether or not the subject of smoking is broached by a health professional; the content of advice and information provided; the manner of communication; having service protocols; follow-up discussion; staff confidence in their skills; the impact of time and resource constraints; staff perceptions of ineffectiveness; differences between professionals; and obstacles to accessing interventions. Discussion The findings suggest variation in practice between services and different professional groups, in particular regarding the recommendation of quitting smoking versus cutting down, but also in regard to procedural aspects such as recording status and repeat advice giving. These differences offer the potential for a pregnant woman to receive contradicting advice. The review suggests a need for greater training in this area and the greater use of protocols, with evidence of a perception of ineffectiveness/pessimism towards intervention amongst some service providers

Exploring the relationship between baseline physical activity levels and mortality reduction associated with increases in physical activity : a modelling study

Background Increasing physical activity (PA) levels among the general adult population of developed nations is important for reducing premature mortality and the burdens of preventable illness. Assessing how effective PA interventions are as health interventions often involves categorising participants as either ‘active’ or ‘sedentary’ after the interventions. A model was developed showing that doing this could significantly misestimate the health effect of PA interventions. Methods A life table model was constructed combining evidence on baseline PA levels with evidence indicating the non-linear relationship between PA levels and all-cause mortality risks. PA intervention scenarios were modelled which had the same mean increase in PA but different levels of take-up by people who were more active or more sedentary to begin with. Results The model simulations indicated that, compared with a scenario where already-active people did most of the additional PA, a scenario where the least active did the most additional PA was around a third more effective in preventing deaths between the ages of 50 and 60 years. The relationship between distribution of PA take-up and health effect was explored systematically and appeared non-linear. Conclusions As the health gains of a given PA increase are greatest among people who are most sedentary, smaller increases in PA in the least active may have the same health benefits as much larger PA increases in the most active. To help such health effects to be assessed, PA studies should report changes in the distribution of PA level between the start and end of the study

Magnetic Field Induced Charge Instabilities in Weakly Coupled Superlattices

Using a time dependent selfconsistent model for vertical sequential tunneling,we study the appearance of charge instabilities that lead to the formation of electric field domains in a weakly coupled doped superlattice in the presence of high magnetic fields parallel to the transport direction. The interplay between the high non linearity of the system --coming from the Coulomb interaction-- and the inter-Landau-level scattering at the domain walls (regions of charge accumulation inside the superlattice) gives rise to new unstable negative differential conductance regions and extra stable branches in the sawtooth-like I-V curves.Comment: 5 pages, 4 postscript figure

Composite Poisson Models For Goal Scoring

Goal scoring in sports such as hockey and soccer is often modeled as a Poisson process. We work with a Poisson model where the mean goals scored by the home team is the sum of parameters for the home team\u27s offense, the road team\u27s defense, and a home advantage. The mean goals for the road team is the sum of parameters for the road team\u27s offense and for the home team\u27s defense. The best teams have a large offensive parameter value and a small defensive parameter value. A level-2 model connects the offensive and defensive parameters for the k teams. Parameter inference is made by imagining that goals can be classified as being strictly due to offense, to (lack of) defense, or to home-field advantage. Though not a realistic description, such a breakdown is consistent with our model assumptions and the literature, and we can work out the conditional distributions and generate random partitions to facilitate inference about the team parameters. We use the conditional Binomial distribution, given the Poisson totals and the current parameter values, to partition each observed goal total at each iteration in an MCMC algorithm

The Bayesian Decision Tree Technique with a Sweeping Strategy

The uncertainty of classification outcomes is of crucial importance for many safety critical applications including, for example, medical diagnostics. In such applications the uncertainty of classification can be reliably estimated within a Bayesian model averaging technique that allows the use of prior information. Decision Tree (DT) classification models used within such a technique gives experts additional information by making this classification scheme observable. The use of the Markov Chain Monte Carlo (MCMC) methodology of stochastic sampling makes the Bayesian DT technique feasible to perform. However, in practice, the MCMC technique may become stuck in a particular DT which is far away from a region with a maximal posterior. Sampling such DTs causes bias in the posterior estimates, and as a result the evaluation of classification uncertainty may be incorrect. In a particular case, the negative effect of such sampling may be reduced by giving additional prior information on the shape of DTs. In this paper we describe a new approach based on sweeping the DTs without additional priors on the favorite shape of DTs. The performances of Bayesian DT techniques with the standard and sweeping strategies are compared on a synthetic data as well as on real datasets. Quantitatively evaluating the uncertainty in terms of entropy of class posterior probabilities, we found that the sweeping strategy is superior to the standard strategy

A Robust Classification of Galaxy Spectra: Dealing with Noisy and Incomplete Data

Over the next few years new spectroscopic surveys (from the optical surveys of the Sloan Digital Sky Survey and the 2 degree Field survey through to space-based ultraviolet satellites such as GALEX) will provide the opportunity and challenge of understanding how galaxies of different spectral type evolve with redshift. Techniques have been developed to classify galaxies based on their continuum and line spectra. Some of the most promising of these have used the Karhunen and Loeve transform (or Principal Component Analysis) to separate galaxies into distinct classes. Their limitation has been that they assume that the spectral coverage and quality of the spectra are constant for all galaxies within a given sample. In this paper we develop a general formalism that accounts for the missing data within the observed spectra (such as the removal of sky lines or the effect of sampling different intrinsic rest wavelength ranges due to the redshift of a galaxy). We demonstrate that by correcting for these gaps we can recover an almost redshift independent classification scheme. From this classification we can derive an optimal interpolation that reconstructs the underlying galaxy spectral energy distributions in the regions of missing data. This provides a simple and effective mechanism for building galaxy spectral energy distributions directly from data that may be noisy, incomplete or drawn from a number of different sources.Comment: 20 pages, 8 figures. Accepted for publication in A

Optimising decision trees using multi-objective particle swarm optimisation

Copyright © 2009 Springer-Verlag Berlin Heidelberg. The final publication is available at link.springer.comBook title: Swarm Intelligence for Multi-objective Problems in Data MiningSummary. Although conceptually quite simple, decision trees are still among the most popular classifiers applied to real-world problems. Their popularity is due to a number of factors – core among these is their ease of comprehension, robust performance and fast data processing capabilities. Additionally feature selection is implicit within the decision tree structure. This chapter introduces the basic ideas behind decision trees, focusing on decision trees which only consider a rule relating to a single feature at a node (therefore making recursive axis-parallel slices in feature space to form their classification boundaries). The use of particle swarm optimization (PSO) to train near optimal decision trees is discussed, and PSO is applied both in a single objective formulation (minimizing misclassification cost), and multi-objective formulation (trading off misclassification rates across classes). Empirical results are presented on popular classification data sets from the well-known UCI machine learning repository, and PSO is demonstrated as being fully capable of acting as an optimizer for trees on these problems. Results additionally support the argument that multi-objectification of a problem can improve uni-objective search in classification problems

SRB Environment Evaluation and Analysis. Volume 3: ASRB Plume Induced Environments

Contract NAS8-37891 was expanded in late 1989 to initiate analysis of Shuttle plume induced environments as a result of the substitution of the Advanced Solid Rocket Booster (ASRB) for the Redesigned Solid Rocket Booster (RSRB). To support this analysis, REMTECH became involved in subscale and full-scale solid rocket motor test programs which further expanded the scope of work. Later contract modifications included additional tasks to produce initial design cycle environments and to specify development flight instrumentation. Volume 3 of the final report describes these analyses and contains a summary of reports resulting from various studies

Inexact Bayesian point pattern matching for linear transformations

PublishedArticleWe introduce a novel Bayesian inexact point pattern matching model that assumes that a linear transformation relates the two sets of points. The matching problem is inexact due to the lack of one-to-one correspondence between the point sets and the presence of noise. The algorithm is itself inexact; we use variational Bayesian approximation to estimate the posterior distributions in the face of a problematic evidence term. The method turns out to be similar in structure to the iterative closest point algorithm.This work was supported by the University of Exeter’s Bridging the Gaps initiative, which was funded by EPSRC award EP/I001433/1 and the collaboration was formed through the Exeter Imaging Network