Mapping NGO Competency to Reduce Human Vulnerability in Post-disaster Communities: Comparing Strategies in Sri Lanka and Bangladesh

Natural hazards trigger disasters, the scale of which is largely determined by vulnerability. Developing countries suffer the most from disasters due to various conditions of vulnerability which exist and there is an opportunity after disasters to take mitigative action. NGOs implementing post-disaster rehabilitation projects must be able to address the issues causing communities to live at risk of disaster and therefore must build dynamic capacity, capabilities and competencies, enabling them to operate in unstable environments. This research is built upon a theoretical framework of dynamic competency established by combining elements of disaster management, strategic management and project management theory. A number of NGOs which have implemented reconstruction and rehabilitation projects both in Sri Lanka following the Asian Tsunami and Bangladesh following Cyclone Sidr are being investigated in great depth using a causal mapping procedure. ‘Event’ specific maps have been developed for each organization in each disaster. This data will be analysed with a view to discovering the strategies which lead to vulnerability reduction in post-disaster communities and the competencies that NGOs must possess in order to achieve favourable outcomes. It is hypothesized that by building organizational capacity, capabilities and competencies to be dynamic in nature, while focusing on a more emergent strategic approach, with emphasis on adaptive capability and innovation, NGOs will be better equipped to contribute to sustainable community development through reconstruction. We believe that through this study it will be possible to glean a new understanding of social processes that emerge within community rehabilitation projects.

Discretization of highly persistent correlated AR(1) shocks

The finite state Markov-chain approximation methods developed by Tauchen (1986) and Tauchen and Hussey (1991) are widely used in economics, finance and econometrics to solve functional equations in which state variables follow autoregressive processes. For highly persistent processes, the methods require a large number of discrete values for the state variables to produce close approximations which leads to an undesirable reduction in computational speed, especially in a multivariate case. This paper proposes an alternative method of discretizing multivariate autoregressive processes. This method can be treated as an extension of Rouwenhorst's (1995) method which, according to our finding, outperforms the existing methods in the scalar case for highly persistent processes. The new method works well as an approximation that is much more robust to the number of discrete values for a wide range of the parameter space.

Density-potential mapping in time-dependent density-functional theory

The key questions of uniqueness and existence in time-dependent density-functional theory are usually formulated only for potentials and densities that are analytic in time. Simple examples, standard in quantum mechanics, lead, however, to nonanalyticities. We reformulate these questions in terms of a nonlinear Schroedinger equation with a potential that depends nonlocally on the wave function.

Identification of candidate small-molecule therapeutics to cancer by gene-signature perturbation in connectivity mapping.

Connectivity mapping is a recently developed technique for discovering the underlying connections between different biological states based on gene-expression similarities. The sscMap method has been shown to provide enhanced sensitivity in mapping meaningful connections leading to testable biological hypotheses and in identifying drug candidates with particular pharmacological and/or toxicological properties. Challenges remain, however, as to how to prioritise the large number of discovered connections in an unbiased manner such that the success rate of any following-up investigation can be maximised. We introduce a new concept, gene-signature perturbation, which aims to test whether an identified connection is stable enough against systematic minor changes (perturbation) to the gene-signature. We applied the perturbation method to three independent datasets obtained from the GEO database: acute myeloid leukemia (AML), cervical cancer, and breast cancer treated with letrozole. We demonstrate that the perturbation approach helps to identify meaningful biological connections which suggest the most relevant candidate drugs. In the case of AML, we found that the prevalent compounds were retinoic acids and PPAR activators. For cervical cancer, our results suggested that potential drugs are likely to involve the EGFR pathway; and with the breast cancer dataset, we identified candidates that are involved in prostaglandin inhibition. Thus the gene-signature perturbation approach added real values to the whole connectivity mapping process, allowing for increased specificity in the identification of possible therapeutic candidates.