Leadership, fairness and gender: a practitioner's perspective

This Portfolio of Exploration outlines the course of learning by a practitioner on a personal leadership journey. It constructs a functional examination of ideas around leadership. It explores the belief system which underpins leadership practice and it also explores to what extent the increased participation of women can lead to a qualitatively different type of leadership. Different models of leadership are explored to find the model which can best fit with the author’s perception of leadership. The Women’s Intercultural Leadership Model is discussed. This model conceptualises leadership as the envisioning of oneself and others as potential leaders. It seeks to promote that recognition whilst at the same time encouraging personal transformation. The promotion of a values based leadership is examined. The value of fairness is examined as an element of functional leadership. The extent to which increasing the number of women in leadership can lead to a qualitatively different type of leadership is explored. Drucker’s assertion that every practice rests on theory is used to unearth the underlying assumptions of this reflective practitioner doctorate. Kegan’s Theory of Adult Mental Development and Subject-Object separation is used to explicitly examine knowledge construction. Myers Briggs Type Indicator (MBTI) and Assumption/Commitment/Behaviour/Outcome (ACBO) template are used to promote practice change.

Statistical methods for polyhedral shape classification with incomplete data - application to cryo-electron tomographic images

A certain type of bacterial inclusion, known as a bacterial microcompartment, was recently identified and imaged through cryo-electron tomography. A reconstructed 3D object from single-axis limited angle tilt-series cryo-electron tomography contains missing regions and this problem is known as the missing wedge problem. Due to missing regions on the reconstructed images, analyzing their 3D structures is a challenging problem. The existing methods overcome this problem by aligning and averaging several similar shaped objects. These schemes work well if the objects are symmetric and several objects with almost similar shapes and sizes are available. Since the bacterial inclusions studied here are not symmetric, are deformed, and show a wide range of shapes and sizes, the existing approaches are not appropriate. This research develops new statistical methods for analyzing geometric properties, such as volume, symmetry, aspect ratio, polyhedral structures etc., of these bacterial inclusions in presence of missing data. These methods work with deformed and non-symmetric varied shaped objects and do not necessitate multiple objects for handling the missing wedge problem. The developed methods and contributions include: (a) an improved method for manual image segmentation, (b) a new approach to 'complete' the segmented and reconstructed incomplete 3D images, (c) a polyhedral structural distance model to predict the polyhedral shapes of these microstructures, (d) a new shape descriptor for polyhedral shapes, named as polyhedron profile statistic, and (e) the Bayes classifier, linear discriminant analysis and support vector machine based classifiers for supervised incomplete polyhedral shape classification. Finally, the predicted 3D shapes for these bacterial microstructures belong to the Johnson solids family, and these shapes along with their other geometric properties are important for better understanding of their chemical and biological characteristics.