Learning about learning as systemic practice in the context of environmental decision-making

This paper has been written as the author is beginning a new phase of researching learning, investigating what supports people in their environmental decision making. This process of inquiry has arisen partly as a result of the development and teaching of the UK Open University’s Masters ’ level course Environmental decision making – a systems approach. The implications of approaching an inquiry with a view of ‘learning as systemic practice ’ is considered, drawing on insights into practice, skilled behaviour and learning systems from Lave, Wenger, Schon, Varela, Ison and Russell, among others. The relevance of various action research approaches for learning about learning as systemic practice is discussed. The paper finishes by identifying and exploring three focuses, that seem both challenging and important to the author to take account of as the research progresses. They are the needs for (i) systemic praxis (ii) an awareness of distinctions made by those who participate in the process of inquiry and (iii) using an approach with an epistemological dimension

QCGAT mixer compound exhaust system design and static big model test report

A mixer exhaust system was designed to meet the proposed performance and exhaust jet noise goals for the AiResearch QCGAT engine. Some 0.35 scale models of the various nozzles were fabricated and aerodynamically and acoustically tested. Preliminary optimization, engine cycle matching, model test data and analysis are presented. A final mixer exhaust system is selected for optimum performance for the overall flight regime

The Role of Microglia in the Effects of Steroid Hormones on Brain Inflammation

The conditions defining whether microglial activation is detrimental or beneficial to neuronal survival are still poorly understood. Better understanding of the factors regulating microglia activation may lead to improved therapies for neurodegenerative diseases. Clinical and animal studies point to the neuroprotective and anti-inflammatory effects of steroid hormones. However, our comprehension of the cellular targets and mechanisms of action of these hormones in the CNS is still unresolved. In view of these limitations, the main question addressed in this dissertation was the role that microglia play in the anti-inflammatory effects of steroid hormones in the brain, with particular emphasis on the neuroprotective hormone 17β-estradiol (E2), and the anti-inflammatory steroid, corticosterone. To address this problem, microglia culture models were established using a microglia cell line and primary cultures from transgenic mice that facilitate the identification of microglia by EGFP expression. Collaborative studies were also done in mice in vivo. The expression of steroid hormone receptors was studied as well as their function. This dissertation shows that microglia cells are not direct targets of estrogen actions, but respond profoundly to glucocorticoids, which exert anti-inflammatory effects on the production of cytokines like TNFα, IL-6 and NO. Steroid hormones can be produced within the brain. In this dissertation, microglia cells are shown to participate in the metabolism of steroids through expression of steroidconverting enzymes. Expression of 11βHSD1 in microglia mediated an autocrine re-activation of glucocorticoids, whereas, expression of enzymes like 17βHSD1 and 5αR catalyzed the conversion of active androgens and estrogens from steroid hormone precursors AD and DHEA. These microglia-derived hormones had estrogenic effects on neuronal cells, as described in the last section of this dissertation where the characterization and responsiveness of a neural progenitor cell line are presented. In summary, microglia cells are highly susceptible to the action of glucocorticoids, but not estrogens. This specificity is dictated by the abundant expression of glucocorticoid receptors, and a minimal expression of estrogen receptors. A novel role of microglia is also presented. Microglia express steroid-metabolizing enzymes, which mediate the autocrine reactivation of glucocorticoids, or the production of active androgens and estrogens from steroid hormone precursors

Perturbed Three Vortex Dynamics

It is well known that the dynamics of three point vortices moving in an ideal fluid in the plane can be expressed in Hamiltonian form, where the resulting equations of motion are completely integrable in the sense of Liouville and Arnold. The focus of this investigation is on the persistence of regular behavior (especially periodic motion) associated to completely integrable systems for certain (admissible) kinds of Hamiltonian perturbations of the three vortex system in a plane. After a brief survey of the dynamics of the integrable planar three vortex system, it is shown that the admissible class of perturbed systems is broad enough to include three vortices in a half-plane, three coaxial slender vortex rings in three-space, and `restricted' four vortex dynamics in a plane. Included are two basic categories of results for admissible perturbations: (i) general theorems for the persistence of invariant tori and periodic orbits using Kolmogorov-Arnold-Moser and Poincare-Birkhoff type arguments; and (ii) more specific and quantitative conclusions of a classical perturbation theory nature guaranteeing the existence of periodic orbits of the perturbed system close to cycles of the unperturbed system, which occur in abundance near centers. In addition, several numerical simulations are provided to illustrate the validity of the theorems as well as indicating their limitations as manifested by transitions to chaotic dynamics.Comment: 26 pages, 9 figures, submitted to the Journal of Mathematical Physic

Attitude Estimation in Fractionated Spacecraft Cluster Systems

An attitude estimation was examined in fractioned free-flying spacecraft. Instead of a single, monolithic spacecraft, a fractionated free-flying spacecraft uses multiple spacecraft modules. These modules are connected only through wireless communication links and, potentially, wireless power links. The key advantage of this concept is the ability to respond to uncertainty. For example, if a single spacecraft module in the cluster fails, a new one can be launched at a lower cost and risk than would be incurred with onorbit servicing or replacement of the monolithic spacecraft. In order to create such a system, however, it is essential to know what the navigation capabilities of the fractionated system are as a function of the capabilities of the individual modules, and to have an algorithm that can perform estimation of the attitudes and relative positions of the modules with fractionated sensing capabilities. Looking specifically at fractionated attitude estimation with startrackers and optical relative attitude sensors, a set of mathematical tools has been developed that specify the set of sensors necessary to ensure that the attitude of the entire cluster ( cluster attitude ) can be observed. Also developed was a navigation filter that can estimate the cluster attitude if these conditions are satisfied. Each module in the cluster may have either a startracker, a relative attitude sensor, or both. An extended Kalman filter can be used to estimate the attitude of all modules. A range of estimation performances can be achieved depending on the sensors used and the topology of the sensing network

KLF6 and STAT3 Co-Occupy Regulatory DNA and Functionally Synergize to Promote Axon Growth in CNS Neurons

The failure of axon regeneration in the CNS limits recovery from damage and disease. Members of the KLF family of transcription factors can exert both positive and negative effects on axon regeneration, but the underlying mechanisms are unclear. Here we show that forced expression of KLF6 promotes axon regeneration by corticospinal tract neurons in the injured spinal cord. RNA sequencing identified 454 genes whose expression changed upon forced KLF6 expression in vitro, including sub-networks that were highly enriched for functions relevant to axon extension including cytoskeleton remodeling, lipid synthesis, and bioenergetics. In addition, promoter analysis predicted a functional interaction between KLF6 and a second transcription factor, STAT3, and genome-wide footprinting using ATAC-Seq data confirmed frequent co-occupancy. Co-expression of the two factors yielded a synergistic elevation of neurite growth in vitro. These data clarify the transcriptional control of axon growth and point the way toward novel interventions to promote CNS regeneration

G-DYN Multibody Dynamics Engine

G-DYN is a multi-body dynamic simulation software engine that automatically assembles and integrates equations of motion for arbitrarily connected multibody dynamic systems. The algorithm behind G-DYN is based on a primal-dual formulation of the dynamics that captures the position and velocity vectors (primal variables) of each body and the interaction forces (dual variables) between bodies, which are particularly useful for control and estimation analysis and synthesis. It also takes full advantage of the spare matrix structure resulting from the system dynamics to numerically integrate the equations of motion efficiently. Furthermore, the dynamic model for each body can easily be replaced without re-deriving the overall equations of motion, and the assembly of the equations of motion is done automatically. G-DYN proved an essential software tool in the simulation of spacecraft systems used for small celestial body surface sampling, specifically in simulating touch-and-go (TAG) maneuvers of a robotic sampling system from a comet and asteroid. It is used extensively in validating mission concepts for small body sample return, such as Comet Odyssey and Galahad New Frontiers proposals

Decision region approximation by polynomials or neural networks

We give degree of approximation results for decision regions which are defined by polynomial and neural network parametrizations. The volume of the misclassified region is used to measure the approximation error, and results for the degree of L1 approximation of functions are used. For polynomial parametrizations, we show that the degree of approximation is at least 1, whereas for neural network parametrizations we prove the slightly weaker result that the degree of approximation is at least r, where r can be any number in the open interval (0, 1)