A Critical Assessment Of The Role Of The Imagination In Kant’s Exposition Of The Mathematical Sublime

Kant argues in the Critique of Judgment (CJ) that there are two distinct modes of the sublime. This essay will concentrate on the mathematical mode. It is helpful to begin an examination of the mathematical sublime by elucidating the difference between logical estimation and aesthetic estimation; it is aesthetic estimation under strain, so Kant argues, that instigates the moment of the sublime. Logical estimation forms the cognitive basis of scientific calculations. He argues that scientific enquiry only requires an understanding of the logical relationship of numbers and so does not require an aesthetic experience of those numbers

Reason, Morality, and Skill

Some economists argue that modern industrial societies must respond to ecological challenges by learning to live with diminishing economic growth. Yet it also seems that low growth societies are doomed to struggle with problems of social instability caused by economic recession, unemployment and a decline in social entitlements. In “Reason, Morality and Skill” John Stopford draws on Ancient Greek economic thought, including Aristotle’s views on the natural limitation of wealth, to discuss the problem of human flourishing in ecologically challenged societies. Economic capability theorists, influenced by the work of Sen and Nussbaum, have recently argued that the transition from a growth driven economy focused on consumption to a stable low growth economy requires us to redefine prosperity as capability development “within limits”. Stopford argues that to understand prosperity in this way we need to reexamine the role of skill in the development of capabilities. The marginalization of skill has become a systematic feature of modern industrial and consumer societies. Yet certain kinds of skill, exemplified in the work of the autonomously productive craftsman, are necessary to the development of the bounded capabilities that low growth societies need to foster

Ice Crystal Classification Using Two Dimensional Light Scattering Patterns

An investigation is presented into methods of characterising cirrus ice crystals from in-situ light scattering data. A database of scattering patterns from modelled crystals was created using the Ray Tracing with Diffraction on Facets (RTDF) model from the University of Hertfordshire, to which experimental and modelled data was fitted. Experimental data was gathered in the form of scattering patterns from ice analogue crystals with similar optical properties and hexagonal symmetry to ice, yet stable at room temperature. A laboratory rig is described which images scattering patterns from single particles while allowing precise control over the orientation of the particle with respect to the incident beam. Images of scattering patterns were captured and compared to patterns from modelled crystals with similar geometry. Methods for introducing particles en-masse and individually to the Small Ice Detector (SID) instruments are discussed, with particular emphasis on the calibration of the gain of the SID-2 instrument. The variation in gain between detector elements is found to be significant, variable over the life of the detector, and different for different detectors. Fitting was performed by comparison of test scattering patterns (either modelled or experimental) to the reference database. Representation of the two dimensional scattering patterns by asymmetry factor, moment invariants, azimuthal intensity patterns (AIP) and the Fourier transform of the AIP are compared for fitting accuracy. Direct comparison of the AIP is found to be the most accurate method. Increased resolution of the AIP is shown to improve the fitting substantially. Case studies are presented for the fitting of two ice analogue crystals to the modelled database. Fitting accuracy is found to be negatively influenced by small amounts of surface roughness and detail not currently considered by the RTDF model. Fitting of in-situ data gathered by the SID-3 instrument during the HALO 02 campaign at the AIDA cloud chamber in Germany is presented and discussed. Saturation of detector pixels is shown to affect pattern fitting. In-flight operation of the instrument involves the variation of gain of the whole detector (as opposed to individual elements) in order to obtain unsaturated images of both large and small particles

Adorno's Critique of Judgement: the recovery of negativity from the philosophies of Kant and Hegel

This thesis has four primary aims. Firstly, I develop an account of Adorno’s critique of Kant and Hegel’s philosophy. I argue that the role and structure of judgement is key to his critical analysis. Adorno's discussion of their metaphysics, epistemology revolves around an immanent critique of judgement. This critique reveals, in the dialectical sense, the irreducibility of the 'negative moment' within judgement. This critical exposition grounds the second aim of the thesis. Analysis of Kant and Hegel's philosophies enables us to discern a number of key concepts in Adorno's own thought, concepts which will help us to understand his notion of negativity. In particular, his dialectical critique produces a constellation of critical - or negative - dialectical concepts: conceptless [begriffslose], non-identity [Nichtidentität], mediation [Vermittlung]. The generation of these concepts and their elucidation provides the basis for the third aim: to give a textually viable and philosophically fruitful explanation of key commitments in Adorno’s negative dialectics. I argue that negative dialectics does not amount to a system, a standpoint, or even a set of principles. Rather, it is a critical activity. The commitments, which revolve around the constellation of concepts outlined above, indicate a critical sensitivity to the limits of epistemology and metaphysics and the problem that these limits pose for judgement. Finally, I develop the resources to answer Michael Rosen’s claim that Adorno’s rejection of Hegelian determinate negation leaves his dialectics without any dynamic force. Drawing upon aesthetics, we can better understand the dynamics of negative dialectics. Aesthetic engagement with artworks not only demonstrates an appropriate orientation of philosophy to material, it is also an appropriate medium through which we can gain a clearer understanding of the philosophical commitments elucidated above

Applying machine learning methods for characterization of hexagonal prisms from their 2D scattering patterns – an investigation using modelled scattering data

This document is the Accepted Manuscript version of the following article: Emmanuel Oluwatobi Salawu, Evelyn Hesse, Chris Stopford, Neil Davey, and Yi Sun, 'Applying machine learning methods for characterization of hexagonal prisms from their 2D scattering patterns – an investigation using modelled scattering data', Journal of Quantitative Spectroscopy and Radiative Transfer, Vol. 201, pp. 115-127, first published online 5 July 2017. Under embargo. Embargo end date: 5 July 2019. The Version of Record is available online at doi: https://doi.org/10.1016/j.jqsrt.2017.07.001. © 2017 Elsevier Ltd. All rights reserved.Better understanding and characterization of cloud particles, whose properties and distributions affect climate and weather, are essential for the understanding of present climate and climate change. Since imaging cloud probes have limitations of optical resolution, especially for small particles (with diameter < 25 μm), instruments like the Small Ice Detector (SID) probes, which capture high-resolution spatial light scattering patterns from individual particles down to 1 μm in size, have been developed. In this work, we have proposed a method using Machine Learning techniques to estimate simulated particles’ orientation-averaged projected sizes (PAD) and aspect ratio from their 2D scattering patterns. The two-dimensional light scattering patterns (2DLSP) of hexagonal prisms are computed using the Ray Tracing with Diffraction on Facets (RTDF) model. The 2DLSP cover the same angular range as the SID probes. We generated 2DLSP for 162 hexagonal prisms at 133 orientations for each. In a first step, the 2DLSP were transformed into rotation-invariant Zernike moments (ZMs), which are particularly suitable for analyses of pattern symmetry. Then we used ZMs, summed intensities, and root mean square contrast as inputs to the advanced Machine Learning methods. We created one random forests classifier for predicting prism orientation, 133 orientation-specific (OS) support vector classification models for predicting the prism aspect-ratios, 133 OS support vector regression models for estimating prism sizes, and another 133 OS Support Vector Regression (SVR) models for estimating the size PADs. We have achieved a high accuracy of 0.99 in predicting prism aspect ratios, and a low value of normalized mean square error of 0.004 for estimating the particle’s size and size PADs.Peer reviewe

Medieval Floor Tiles of Northern England

This study of the design, manufacture and use of medieval floor tiles shows the long-lasting influence achieved in the north of England, especially by the Cistercian monasteries. It serves to demonstrate how these monastic houses made use of the resources and contacts available to them. The study focuses on one of the richest medieval floor tile assemblages in the world, with material from 118 sites. Over 500 different designs and 60 mosaic arrangements have been identified. Jennie Stopford examines the monastic influence on northern England's manufacture and use of floor tiles. Split into three sections - Chronological Survey, The Tile Groups, and The Sites and Collections - this in-depth study covers an immense body of work

A High Speed Particle Phase Discriminator (PPD-HS) for the classification of airborne particles, as tested in a continuous flow diffusion chamber

© Author(s) 2019. This work is distributed under the Creative Commons Attribution 4.0 License.A new instrument, the High-speed Particle Phase Discriminator (PPD-HS), developed at the University of Hertfordshire, for sizing individual cloud hydrometeors and determining their phase is described herein. PPD-HS performs an in situ analysis of the spatial intensity distribution of near-forward scattered light for individual hydrometeors yielding shape properties. Discrimination of spherical and aspherical particles is based on an analysis of the symmetry of the recorded scattering patterns. Scattering patterns are collected onto two linear detector arrays, reducing the complete 2-D scattering pattern to scattered light intensities captured onto two linear, one-dimensional strips of light sensitive pixels. Using this reduced scattering information, we calculate symmetry indicators that are used for particle shape and ultimately phase analysis. This reduction of information allows for detection rates of a few hundred particles per second. Here, we present a comprehensive analysis of instrument performance using both spherical and aspherical particles generated in a well-controlled laboratory setting using a vibrating orifice aerosol generator (VOAG) and covering a size range of approximately 3-32&thinsp;μm. We use supervised machine learning to train a random forest model on the VOAG data sets that can be used to classify any particles detected by PPD-HS. Classification results show that the PPD-HS can successfully discriminate between spherical and aspherical particles, with misclassification below 5% for diameters &gt;3μm. This phase discrimination method is subsequently applied to classify simulated cloud particles produced in a continuous flow diffusion chamber setup. We report observations of small, near-spherical ice crystals at early stages of the ice nucleation experiments, where shape analysis fails to correctly determine the particle phase. Nevertheless, in the case of simultaneous presence of cloud droplets and ice crystals, the introduced particle shape indicators allow for a clear distinction between these two classes, independent of optical particle size. From our laboratory experiments we conclude that PPD-HS constitutes a powerful new instrument to size and discriminate the phase of cloud hydrometeors. The working principle of PPD-HS forms a basis for future instruments to study microphysical properties of atmospheric mixed-phase clouds that represent a major source of uncertainty in aerosol-indirect effect for future climate projections..Peer reviewe