Alternative approaches to slum upgrading in Kibera, Nairobi

This paper discusses the sustainability of two different approaches to upgrade water and sanitation infrastructure in Kenya's largest informal settlement, Kibera. A background to the urbanisation of poverty is outlined along with approaches to urban slums. Two case studies of completed interventions of infrastructure upgrading have been investigated. In one case study, the upgrading method driven by a non-government organisation uses an integrated livelihoods and partnership technique at community level to create an individual project. In the other case study, the method is a collaboration between the government and a multi-lateral agency to deliver upgraded services as part of a country-wide programme. The 'bottom-up' (project) and 'top-down' (programme) approaches both seek sustainability and aim to achieve this in the same context using different techniques. This paper investigates the sustainability of each approach. The merits and challenges of the approaches are discussed with the projected future of Kibera. The paper highlights the valuable opportunity for the role of appropriate engineering infrastructure for sustainable urban development, as well as the alleviation of poverty in a developing context.

Guided self-organization in a dynamic embodied system based on attractor selection mechanism

Guided self-organization can be regarded as a paradigm proposed to understand how to guide a self-organizing system towards desirable behaviors, while maintaining its non-deterministic dynamics with emergent features. It is, however, not a trivial problem to guide the self-organizing behavior of physically embodied systems like robots, as the behavioral dynamics are results of interactions among their controller, mechanical dynamics of the body, and the environment. This paper presents a guided self-organization approach for dynamic robots based on a coupling between the system mechanical dynamics with an internal control structure known as the attractor selection mechanism. The mechanism enables the robot to gracefully shift between random and deterministic behaviors, represented by a number of attractors, depending on internally generated stochastic perturbation and sensory input. The robot used in this paper is a simulated curved beam hopping robot: a system with a variety of mechanical dynamics which depends on its actuation frequencies. Despite the simplicity of the approach, it will be shown how the approach regulates the probability of the robot to reach a goal through the interplay among the sensory input, the level of inherent stochastic perturbation, i.e., noise, and the mechanical dynamics. © 2014 by the authors; licensee MDPI, Basel, Switzerland.