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This article traces the career of risk across prominent theoretical approaches by highlighting their key assumptions and premises, specifically the technical approach found in the physical sciences, and economics, psychology, and sociology in the social sciences. In each discipline, the strengths and limitations of each theoretical approach are pointed out. The discussion focuses on sociology in particular because other approaches-in treating risks as dominantly technical, psychological, or economic phenomena-tend to downplay the broader historical and socio-political context that impinges on risk construction and production, and its differential impact across society. This exploration points out that institutions play an important role in creating, managing, and distributing risks in society. After highlighting the integrated risk governance framework as a nascent practice-oriented framework, the framework is examined theoretically using sociological neoinstitutionalism and Foucault's concept of governmentality. The conclusion elaborates the challenges of using these two bodies of knowledge to study risk governance of extreme events. Although Foucault's concept of governmentality corrects neoinstitutional theory's ambivalence toward power, more work needs to be done in order to reconcile their divergent intellectual commitments.

In this paper, we focus on the base and tool calibration of a self-calibrated parallel robot. After the self-calibration of a parellel robot by using the built-in sensors in the passive joints, its kinematic transformation from the robot base to the mobile platform frame can be computed with sufficient accuracy. The base and tool calibration, hence, is to identify the kinematic errors in the fixed transformations from the world frame to the robot base frame and from the mobile platform frame to the tool (end-effector) frame in order to improve the absolute positioning accuracy of the robot. Using the mathematical tools from group theory and differential geometry, a simultaneous base and tool calibration model is formulated. Since the kinematic errors in a kinematic transformation can be represented by a twist, i.e. an element of se(3), the resultant calibration model is simple, explicit and geometrically meaningful. A least-square algorithm is employed to iteratively identify the error parameters. The simulation example shows that all the preset kinematic errors can be fully recovered within three to four iterations.

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