The power operation structure on Morava E-theory of height 2 at the prime 3

We give explicit calculations of the algebraic theory of power operations for a specific Morava E-theory spectrum and its K(1)-localization. These power operations arise from the universal degree-3 isogeny of elliptic curves associated to the E-theory

Numerical Simulation of Vortex-Induced Vibrations of Free Span Pipelines Including Nonlinear Soil Models

This thesis introduces a fully three dimensional (3D) numerical simulation method of the VIV behaviors of free span pipelines by considering the nonlinear pipe-soil interaction effect. The pipeline is modeled as a tensioned beam of which the governing equations are numerically solved by applying a fully implicit discretization scheme. Reynolds Averaged Navier-Stokes (RANS) equations are numerically solved to compute the fluid domain. An overset grid method is utilized in discretizing the fluid field around the pipeline. Six computational blocks and nearly 1 million grid points are needed in the simulation. It is a good strategy to generate finer grid in the near body regions and relatively coarse grid in the far field. By exchanging motions and forces between the pipeline motion solver and the fluid solver, fluid-structure interaction is achieved. This research also includes a nonlinear soil model to simulate the pipe-soil interaction which is considered as a spring-pipeline system while the stiffness characteristics are expressed by using a nonlinear force-displacement (P-y) curves. The simulation results are compared with model tests or other numerical simulations for validation in two cases: (1) a free span pipeline of G/D=2.0 at different reduced velocities including linear and nonlinear soil models; (2) a free span pipeline of different G/D which ranges from 1.2 to 3.0

Microscopic Particle Manipulation via Optoelectronic Devices

The optoelectronic tweezers (or optically induced dielectrophoresis (DEP)) have showed the ability to parallelly position a large number of colloidal microparticles without any template. The microparticles can be trapped and driven by the dielectrophoretic forces induced by the optical micropatterns in OET devices. In this chapter, the design and fabrication of flat optoelectronic devices (FOD) and hybrid optoelectronic device (HOD) are described. In the typical FOD, the manipulation modes including filtering, transporting, concentrating and focusing controlling regimes are experimentally demonstrated and analyzed. The controllable rotation of self-assembled microparticle chains in FOD has also been investigated, and a method incorporating the optically induced electrorotation (OER) and AC electroosmotic (ACEO) effects is numerically and experimentally implemented for manipulating microparticle chains. Based on the above research of FOD, a hybrid DEP microdevice HOD is conceptually and experimentally proposed. The HOD integrates with metallic microelectrode layer and the underneath photoconductive layer with projected optical virtual electrodes. FOD and HOD hybrid device enables the active driving, large-scale patterning and local position adjustment of microparticles. These techniques make up the shortcoming of low flexibility of traditional metallic microelectrodes and integrate the merits of both the metal electrode-induced and microimage-induced DEP techniques

The Impact of Bowman Creek on the Schoharie River

The Schoharie Creek runs from the Catskill Mountains to the Mohawk River. The Creek was first settled by the Dutch in 1710, but at that time it was called the Schoharie River1. The Bowman Creek runs into the Schoharie Creek in Duanesburg, New York. To see how Bowman Creek affects the Schoharie Creek’s water, the pH, alkalinity and various ion concentrations were recorded. The values for upstream of Bowman Creek (UBC), downstream of Bow- man Creek (DBC) and Bowman Creek (BC) were compared. These characteristics of the water affect the wildlife that live in the Schoharie Creek. The Creek sup- ports Brown Trout, and Smallmouth Bass. It also a drinking water source for many land animals. Therefore, the water from both of these Creeks were compared to tap water from Union College, which has to meet EPA regulations.https://digitalworks.union.edu/waterprojectposters/1002/thumbnail.jp

Cue recognition and cue elaboration in learning from examples

This paper describes the processes used by students to learn from worked-out examples and by working through problems. Evidence is derived from protocols of students learning secondary school mathematics and physics. The students acquired knowledge from the examples in the form of productions (condition --&gt; action): first discovering conditions under which the actions are appropriate and then elaborating the conditions to enhance efficiency. Students devoted most of their attention to the condition side of the productions. Subsequently, they generalized the productions for broader application and acquired specialized productions for special problem classes.</p

Machine Learning Methods for Autonomous Classification and Decision Making

This thesis focuses on developing machine learning methods for autonomous classification and decision making, especially on two case studies: traffic speed prediction and cancer bone segmentation. For traffic speed prediction, the convolutional neural network (CNN) achieves state-of-the-art results in complex traffic networks. However, the pooling layers cause the loss of information within the data. This thesis proposes an efficient capsule network for traffic speed prediction. The proposed capsule network replaces the pooling layer with capsules connected by dynamic routing and encodes the features and probability of those features showing on the local region. The proposed capsule network provides outperformed results compared to state-of-the-art CNNs. However, the CNN and capsule network (CapsNet) are parametric models and the uncertainty is, thus, not analysed. Two Gaussian process (GP) frameworks are proposed for traffic speed prediction, equipping the CNN with the ability to quantify uncertainty. The first framework proposes to equate a state-of-the-art CNN with a shallow GP. The proposed approach is evaluated and the uncertainty is analysed by applying the confidence interval. In addition, the impact of the noise is investigated by adding a different level of noise. The second framework is a novel deep kernel CNN-GP framework with spatio-temporal kernels, allowing it to abstract high-level features and consider both time and space. The proposed CNN-GP framework is validated and evaluated using CO2 concentration and traffic prediction for the short-term and long-term. An efficient uniform error bound is proposed and evaluated with simulated and real data. For cancer bone segmentation, machine learning methods are proposed to segment bone lesions in cancer-induced bone disease from Micro Computed Tomography (µCT) images, which brings a new perspective of dealing with bone caner segmentation. The performances are evaluated and their effectiveness is compared. Due to the limited number of datasets and the lack of labelled lesions within the dataset, an approach to generate simulated data is proposed. With an enhanced dataset, a generative adversarial network is proposed to reconstruct the bone with a lesion to a healthy bone. Consequently, the location of the lesion can be obtained by subtracting the original image from the reconstructed image