Generating complex connectivity structures for large-scale neural models

Martin Huelse: Generating complex connectivity structures for large-scale neural models. In: V. Kurkova, R. Neruda, and J. Koutnik (Eds.): ICANN 2008, Part II, LNCS 5164, pp. 849?858, 2008. Sponsorship: EPSRC

The rapid assignment of ruminal fungi to presumptive genera using ITS1 and ITS2 RNA secondary structures to produce group-specific fingerprints

Danny S. Tuckwell, Matthew J. Nicholson, Christopher S. McSweeney, Michael K. Theodorou and Jayne L. Brookman (2005). The rapid assignment of ruminal fungi to presumptive genera using ITS1 and ITS2 RNA secondary structures to produce group-specific fingerprints. Microbiology, 151 (5) pp.1557-1567 Sponsorship: BBSRC / Stapledon Memorial Trust RAE2008

The performance of models relating species geographical distributions to climate is independent of trophic level

Brian Huntley, Rhys E. Green, Yvonne C. Collingham, Jane K. Hill, Stephen G. Willis , Patrick J. Bartlein, Wolfgang Cramer, Ward J. M. Hagemeijer and Christopher J. Thomas (2004). The performance of models relating species geographical distributions to climate is independent of trophic level. Ecology Letters, 7(5), 417-426. Sponsorship: NERC (awards: GR9/3016, GR9/04270, GR3/12542, NER/F/S/2000/00166) / RSPB RAE2008

The impact of saving and credit cooperatives on food security in the West Amhara Region of Ethiopia

In rural Ethiopia, among other things, lack of adequate financial service is considered as the basic problem to alleviate rural poverty and to solve the problem of food insecurity. Commercial banks are restricted to urban centres. Providing rural financial service through RUSACCO to the poor has been proposed as a tool for economic development and for achieving food security. Evidence from research in this regard has been so far scanty, especially in rural Ethiopia. The aims of this study are to analyze the determinants of membership, to identify socioeconomic and demographic factors that influence members’ participation in RUSACCOs and to quantify the impact of RUSACCOs on member households’ food security. The study was conducted in two purposely selected woredas in the Amhara region one from food insecure (Lay Gayint woreda) and the other from food secure (Dejen woreda). Six RUSACCOs were selected randomly from these two woredas. Both qualitative and quantitative data were collected. Key informant interviews, focus group discussions and survey techniques were used to collect primary data. Collected data was then analyzed using mixed methods depending on the nature of data. For quantitative data analysis appropriate statistical models were used. The study result reveals that the number of members in each RUSACCO is very small. However, the majority of non-member respondents are willing to join RUSACCO. Lack of information about the benefits of RUSACCO membership is the main problem why many rural poor do not join RUSACCOs. Members participate in different aspects of the cooperatives, starting from attending general assembly up to board membership. They also participate actively in saving and borrowing activities of RUSACCO. The majority of the respondents believe the RUSACCO is a vital instrument in combating food insecurity. The empirical findings indicate that gender, marital status, occupation, educational level, participation in local leadership and participation in other income generation means determine the decision of rural poor to join a RUSACCO or not. The amount of saving is determined by household head occupation, farming experience and income level. While age of household head, primary occupation, farming experience, date of membership, annual total consumption expenditure, amount of saving and participation in other income generation activities influence members’ amount of borrowing by RUSACCO members. Finally, the study confirms that RUSACCO participation improves household food security. RUSACCO membership has made positive impact on household total consumption expenditure and food expenditure.

A strategy to advance the evidence base in palliative medicine: formation of a palliative care research cooperative group.

BACKGROUND: Palliative medicine has made rapid progress in establishing its scientific and clinical legitimacy, yet the evidence base to support clinical practice remains deficient in both the quantity and quality of published studies. Historically, the conduct of research in palliative care populations has been impeded by multiple barriers including health care system fragmentation, small number and size of potential sites for recruitment, vulnerability of the population, perceptions of inappropriateness, ethical concerns, and gate-keeping. METHODS: A group of experienced investigators with backgrounds in palliative care research convened to consider developing a research cooperative group as a mechanism for generating high-quality evidence on prioritized, clinically relevant topics in palliative care. RESULTS: The resulting Palliative Care Research Cooperative (PCRC) agreed on a set of core principles: active, interdisciplinary membership; commitment to shared research purposes; heterogeneity of participating sites; development of research capacity in participating sites; standardization of methodologies, such as consenting and data collection/management; agile response to research requests from government, industry, and investigators; focus on translation; education and training of future palliative care researchers; actionable results that can inform clinical practice and policy. Consensus was achieved on a first collaborative study, a randomized clinical trial of statin discontinuation versus continuation in patients with a prognosis of less than 6 months who are taking statins for primary or secondary prevention. This article describes the formation of the PCRC, highlighting processes and decisions taken to optimize the cooperative group's success.

Conductance of quantum impurity models from quantum monte carlo

The conductance of two Anderson impurity models, one with twofold and another with fourfold degeneracy, representing two types of quantum dots, is calculated using a world-line quantum Monte Carlo (QMC) method. Extrapolation of the imaginary time QMC data to zero frequency yields the linear conductance, which is then compared to numerical renormalization-group results in order to assess its accuracy. We find that the method gives excellent results at low temperature (T TK) throughout the mixed-valence and Kondo regimes but it is unreliable for higher temperature. © 2010 The American Physical Society.

A randomized clinical trial of a coping improvement group intervention for HIV-infected older adults.

This research tested if a 12-session coping improvement group intervention (n = 104) reduced depressive symptoms in HIV-infected older adults compared to an interpersonal support group intervention (n = 105) and an individual therapy upon request (ITUR) control condition (n = 86). Participants were 295 HIV-infected men and women 50-plus years of age living in New York City, Cincinnati, OH, and Columbus, OH. Using A-CASI assessment methodology, participants provided data on their depressive symptoms using the Geriatric Depression Screening Scale (GDS) at pre-intervention, post-intervention, and 4- and 8-month follow-up. Whether conducted with all participants (N = 295) or only a subset of participants diagnosed with mild, moderate, or severe depressive symptoms (N = 171), mixed models analyses of repeated measures found that both coping improvement and interpersonal support group intervention participants reported fewer depressive symptoms than ITUR controls at post-intervention, 4-month follow-up, and 8-month follow-up. The effect sizes of the differences between the two active interventions and the control group were greater when outcome analyses were limited to those participants with mild, moderate, or severe depressive symptoms. At no assessment period did coping improvement and interpersonal support group intervention participants differ in depressive symptoms.

Bayesian Analysis of Latent Threshold Dynamic Models

We discuss a general approach to dynamic sparsity modeling in multivariate time series analysis. Time-varying parameters are linked to latent processes that are thresholded to induce zero values adaptively, providing natural mechanisms for dynamic variable inclusion/selection. We discuss Bayesian model specification, analysis and prediction in dynamic regressions, time-varying vector autoregressions, and multivariate volatility models using latent thresholding. Application to a topical macroeconomic time series problem illustrates some of the benefits of the approach in terms of statistical and economic interpretations as well as improved predictions. Supplementary materials for this article are available online. © 2013 Copyright Taylor and Francis Group, LLC.

Using mathematical models to predict the development of aircraft cabin fires

Computer based mathematical models describing aircraft fire have a role to play in the design and development of safer aircraft, in the implementation of safer and more rigorous certification criteria and in post mortuum accident investigation. As the cost involved in performing large-scale fire experiments for the next generation 'Ultra High Capacity Aircraft' (UHCA) are expected to be prohibitively high, the development and use of these modelling tools may become essential if these aircraft are to prove a safe and viable reality. By describing the present capabilities and limitations of aircraft fire models, this paper will examine the future development of these models in the areas of large scale applications through parallel computing, combustion modelling and extinguishment modelling.

Enhancing the usability of numerical models with case-based reasoning

Numerical models are important tools used in engineering fields to predict the behaviour and the impact of physical elements. There may be advantages to be gained by combining Case-Based Reasoning (CBR) techniques with numerical models. This paper considers how CBR can be used as a flexible query engine to improve the usability of numerical models. Particularly they can help to solve inverse and mixed problems, and to solve constraint problems. We discuss this idea with reference to the illustrative example of a pneumatic conveyor problem. The paper describes example problems faced by design engineers in this context and the issues that need to be considered in this approach. Solution of these problems require methods to handle constraints in both the retrieval phase and the adaptation phase of a typical CBR cycle. We show approaches to the solution of these problesm via a CBR tool.

Spatio-temporal Bayesian network models with latent variables for revealing trophic dynamics and functional networks in fisheries ecology

Ecosystems consist of complex dynamic interactions among species and the environment, the understanding of which has implications for predicting the environmental response to changes in climate and biodiversity. However, with the recent adoption of more explorative tools, like Bayesian networks, in predictive ecology, few assumptions can be made about the data and complex, spatially varying interactions can be recovered from collected field data. In this study, we compare Bayesian network modelling approaches accounting for latent effects to reveal species dynamics for 7 geographically and temporally varied areas within the North Sea. We also apply structure learning techniques to identify functional relationships such as prey–predator between trophic groups of species that vary across space and time. We examine if the use of a general hidden variable can reflect overall changes in the trophic dynamics of each spatial system and whether the inclusion of a specific hidden variable can model unmeasured group of species. The general hidden variable appears to capture changes in the variance of different groups of species biomass. Models that include both general and specific hidden variables resulted in identifying similarity with the underlying food web dynamics and modelling spatial unmeasured effect. We predict the biomass of the trophic groups and find that predictive accuracy varies with the models' features and across the different spatial areas thus proposing a model that allows for spatial autocorrelation and two hidden variables. Our proposed model was able to produce novel insights on this ecosystem's dynamics and ecological interactions mainly because we account for the heterogeneous nature of the driving factors within each area and their changes over time. Our findings demonstrate that accounting for additional sources of variation, by combining structure learning from data and experts' knowledge in the model architecture, has the potential for gaining deeper insights into the structure and stability of ecosystems. Finally, we were able to discover meaningful functional networks that were spatially and temporally differentiated with the particular mechanisms varying from trophic associations through interactions with climate and commercial fisheries.

De la cooperativa agroalimentaria a la "learning netchain". Hacia un planteamiento teorico interorganizativo e interpersonal

Se propone un planteamiento teórico/conceptual para determinar si las relaciones interorganizativas e interpersonales de la netchain de las cooperativas agroalimentarias evolucionan hacia una learning netchain. Las propuestas del trabajo muestran que el mayor grado de asociacionismo y la mayor cooperación/colaboración vertical a lo largo de la cadena están positivamente relacionados con la posición horizontal de la empresa focal más cercana del consumidor final. Esto requiere una planificación y una resolución de problemas de manera conjunta, lo que está positivamente relacionado con el mayor flujo y diversidad de la información/conocimiento obtenido y diseminado a lo largo de la netchain. Al mismo tiempo se necesita desarrollar un contexto social en el que fluya la información/conocimiento y las nuevas ideas de manera informal y esto se logra con redes personales y, principalmente, profesionales y con redes internas y, principalmente, externas. Todo esto permitirá una mayor satisfacción de los socios de la cooperativa agroalimentaria y de sus distribuidores y una mayor intensidad en I+D, convirtiéndose la netchain de la cooperativa agroalimentaria, así, en una learning netchain.

Widening our knowledge concerning young looked after children: the case for research using sociological models of childhood

The Assessment and Action framework for looked after children, designed to improve outcomes for all children in public care and those at home on care orders, is now well established in the UK. This paper offers a critical evaluation of the framework by examining the model of childhood upon which it is premised and by exploring its relationship to children's rights as conceptualized in the United Nations Convention on the Rights of the Child (1989). It will be argued that the particular child development model which underpins the framework addresses the rights of looked after children to protection and provision but does not allow for their participation rights to be sufficiently addressed. A critical review of the research concerning the education and health of looked after children is used to illustrate these points. It will be argued that what are missing are the detailed accounts of looked after children themselves. It is concluded that there is a need for the development of additional research approaches premised upon sociological models of childhood. These would allow for a greater engagement with the participation rights of this group of children and complement the pre-existing research agenda

Three-dimensional delayed-detonation models with nucleosynthesis for type ia supernovae

We present results for a suite of 14 three-dimensional, high-resolution hydrodynamical simulations of delayed-detonation models of Type Ia supernova (SN Ia) explosions. This model suite comprises the first set of three-dimensional SN Ia simulations with detailed isotopic yield information. As such, it may serve as a data base for Chandrasekhar-mass delayed-detonation model nucleosynthetic yields and for deriving synthetic observables such as spectra and light curves. We employ aphysically motivated, stochastic model based on turbulent velocity fluctuations and fuel density to calculate in situ the deflagration-to-detonation transition probabilities. To obtain different strengths of the deflagration phase and thereby different degrees of pre-expansion, we have chosen a sequence of initial models with 1, 3, 5, 10, 20, 40, 100, 150, 200, 300 and 1600 (two different realizations) ignition kernels in a hydrostatic white dwarf with a central density of 2.9 × 10 g cm, as well as one high central density (5.5 × 10 g cm) and one low central density (1.0 × 10 g cm) rendition of the 100 ignition kernel configuration. For each simulation, we determined detailed nucleosynthetic yields by postprocessing10 tracer particles with a 384 nuclide reaction network. All delayed-detonation models result in explosions unbinding thewhite dwarf, producing a range of 56Ni masses from 0.32 to 1.11M. As a general trend, the models predict that the stableneutron-rich iron-group isotopes are not found at the lowest velocities, but rather at intermediate velocities (~3000×10 000 km s) in a shell surrounding a Ni-rich core. The models further predict relatively low-velocity oxygen and carbon, with typical minimum velocities around 4000 and 10 000 km s, respectively. © 2012 The Authors. Published by Oxford University Press on behalf of the Royal Astronomical Society.