Researching defended subjects with the free association narrative interviewing method

In this paper, we illustrate several key differences between our approach to interpreting accounts of research subjects and those of other qualitative researchers. In particular, we work with a theoretical premise of a defended, rather than unitary, rational subject. The methodological implications that we discuss here are two-fold: this subject can best be interpreted holistically; and central to this interpretative process are the free associations that interviewees make

The free association narrative interview method

This encyclopaedia entry describes the characteristics of the method given in the title

Impacts of rising sea temperatures on krill increase risks for predators in the Scotia Sea

Climate change is a threat to marine ecosystems and the services they provide, and reducing fishing pressure is one option for mitigating the overall consequences for marine biota. We used a minimally realistic ecosystem model to examine how projected effects of ocean warming on the growth of Antarctic krill, Euphausia superba, might affect populations of krill and dependent predators (whales, penguins, seals, and fish) in the Scotia Sea. We also investigated the potential to mitigate depletion risk for predators by curtailing krill fishing at different points in the 21st century. The projected effects of ocean warming on krill biomass were strongest in the northern Scotia Sea, with a ≥40% decline in the mass of individual krill. Projections also suggest a 25% chance that krill biomass will fall below an established depletion threshold (75% of its unimpacted level), with consequent risks for some predator populations, especially penguins. Average penguin abundance declined by up to 30% of its unimpacted level, with up to a 50% chance of falling below the depletion threshold. Simulated krill fishing at currently permitted harvest rates further increased risks for depletion, and stopping fishing offset the increased risks associated with ocean warming in our model to some extent. These results varied by location and species group. Risk reductions at smaller spatial scales also differed from those at the regional level, which suggests that some predator populations may be more vulnerable than others to future changes in krill biomass. However, impacts on predators did not always map directly to those for krill. Our findings indicate the importance of identifying vulnerable marine populations and targeting protection measures at appropriate spatial scales, and the potential for spatially-structured management to avoid aggravating risks associated with rising ocean temperatures. This may help balance tradeoffs among marine ecosystem services in an uncertain future

Micromechanics-driven variational method for diffuse-to-localised fracture in quasi-brittle solids

A novel multiscale variational method for modelling fracture propagation is proposed. The method employs strong discontinuity kinematics enhancement, enabling macroscopic cracks to be modelled explicitly, while minimum remeshing is ensured. In addition, the response of quadrature points in the bulk is up-scaled using a Micromechanical continua, which enables the evolution of directional micro-defects (e.g. microcracks) without venturing into prohibitive computational burden. Noticeably, the method allows the Micromechanical continua to interact with macroscopic cracks. The framework is conveniently formulated as a variational setting to provide a minimum energy solution. The new computational framework has been found to diagnose realistic failure mechanisms in quasi-brittle materials

Mechanical response and predictive modelling of vascular self-healing cementitious materials using novel healing agents

Self-healing systems represent an effective means of increasing the resilience of cementitious structures, extending service life and reducing cement production. This is achieved through the mitigation of cracking related durability problems. The success of a self-healing system is critically dependent on the selection of an appropriate healing agent, which depends upon the specific application, as well as a number of criteria including crack filling ability and the degree of mechanical healing required. In the present study, we develop modified formulations of a cyanoacrylate-based adhesive, suitable for use in a vascular self-healing cementitious material. The aim is to develop an ‘ideal’ healing agent for the self-healing system that has an extended shelf life and maximises load recovery. To this end, modified cyanoacrylates are tailored using a combination of predictive modelling and physical testing. The physical tests investigate both the mechanical, flow and chemical properties of the different healing agent formulations, including tensile strength, viscosity and curing. The predictive modelling employs a coupled chemo-mechanical model that is used to guide the physical testing programme through the prediction of the performance of different formulations. The results of the investigation show that a tailored formulation of a cyanoacrylate based healing agent increases the load recovery by 48% relative to the best performing original formulation. In addition, it is shown that the numerical model is able to predict the load response of new formulations with good accuracy

Near-boundary error reduction with an optimised weighted Dirichlet-Neumann boundary condition for stochastic PDE-based Gaussian random field generators

Random field generation through the solution of stochastic partial differential equations is a computationally inexpensive method of introducing spatial variability into numerical analyses. This is particularly important in systems where material heterogeneity has influence over the response to certain stimuli. Whilst it is a convenient method, spurious values arise in the near boundary of the domain due to the non-exact nature of the specific boundary condition applied. This change in the correlation structure can amplify or dampen the system response in the near-boundary region depending on the chosen boundary condition, and can lead to inconsistencies in the overall behaviour of the system. In this study, a weighted Dirichlet–Neumann boundary condition is proposed as a way of controlling the resulting structure in the near-boundary region. The condition relies on a weighting parameter which scales the application to have a more dominant Dirichlet or Neumann component, giving a closer approximation to the true correlation structure of the Matérn autocorrelation function on which the formulation is based on. Two weighting coefficients are proposed and optimal values of the weighting parameter are provided. Through parametric investigation, the weighted Dirichlet–Neumann approach is shown to yield more consistent correlation structures than the common boundary conditions applied in the current literature. We also propose a relationship between the weighting parameter and the desired length-scale parameter of the field such that the optimal value can be chosen for a given problem