The dynamics of bistable liquid crystal wells

A planar bistable liquid crystal device, reported in Tsakonas et al. [27], is modelled within the Landau-de Gennes theory for nematic liquid crystals. This planar device consists of an array of square micron-sized wells. We obtain six different classes of equilibrium profiles and these profiles are classified as diagonal or rotated solutions. In the strong anchoring case, we propose a Dirichlet boundary condition that mimics the experimentally imposed tangent boundary conditions. In the weak anchoring case, we present a suitable surface energy and study the multiplicity of solutions as a function of the anchoring strength. We find that diagonal solutions exist for all values of the anchoring strength W ≥ 0 while rotated solutions only exist for W ≥ Wc > 0, where Wc is a critical anchoring strength that has been computed numerically. We propose a dynamic model for the switching mechanisms based on only dielectric effects. For sufficiently strong external electric fields, we numerically demonstrate diagonal to rotated and rotated to diagonal switching by allowing for variable anchoring strength across the domain boundary

The millimeter-wave properties of superconducting microstrip lines

We have developed a novel technique for making high quality measurements of the millimeter-wave properties of superconducting thin-film microstrip transmission lines. Our experimental technique currently covers the 75-100 GHz band. The method is based on standing wave resonances in an open ended transmission line. We obtain information on the phase velocity and loss of the microstrip. Our data for Nb/SiO/Nb lines, taken at 4.2 K and 1.6 K, can be explained by a single set of physical parameters. Our preliminary conclusion is that the loss is dominated by the SiO dielectric, with a temperature-independent loss tangent of 5.3 ± 0.5 x 10^(-3) for our samples

A Tri-band-notched UWB Antenna with Low Mutual Coupling between the Band-notched Structures

A compact printed U-shape ultra-wideband (UWB) antenna with triple band-notched characteristics is presented. The proposed antenna, with compact size of 24×33 mm2, yields an impedance bandwidth of 2.8-12GHz for VSWR<2, except the notched bands. The notched bands are realized by introducing two different types of slots. Two C-shape half-wavelength slots are etched on the radiating patch to obtain two notched bands in 3.3-3.7GHz for WiMAX and 7.25-7.75GHz for downlink of X-band satellite communication systems. In order to minimize the mutual coupling between the band-notched structures, the middle notched band in 5-6GHz for WLAN is achieved by using a U-slot defected ground structure. The parametric study is carried out to understand the mutual coupling. Surface current distributions and equivalent circuit are used to illustrate the notched mechanism. The performance of this antenna both by simulation and by experiment indicates that the proposed antenna is suitable and a good candidate for UWB applications

Outlier detection in large high-dimensional data and its application in stock market surveillance

University of Technology, Sydney. Faculty of Engineering and Information Technology.Outlier detection techniques play an important role in stock market surveillance that involves analysis of large volume of high-dimensional trading data. However, outlier detection in large high-dimensional data is very challenging and is not well addressed by existing techniques. Firstly, it is difficult to select useful and relevant features from high-dimensional data. Secondly, large high-dimensional data need more efficient algorithms. To attack the above issues brought by large high-dimensional data, this thesis presents two outlier detection models and one subspace clustering model. Firstly, an outlier mining model is proposed to detect the outliers from multiple complex stock market data. In order to improve the efficiency of outlier detection, a financial model is used to select the features to construct multiple datasets. This model is able to improve the precision of outlier mining on individual measurements. The experiments on real-world stock market data show that the proposed model is effective and outperforms traditional technologies. Secondly, in order to find relevant features automatically, an agent-based algorithm is proposed to discover subspace clusters in high dimensional data. Each data object is represented by an agent, and the agents move from one local environment to another to find optimal clusters in subspaces. Heuristic rules and objective functions are defined to guide the movements of agents, so that similar agents (data objects) go to one group. The experimental results show that our proposed agent-based subspace clustering algorithm performs better than existing subspace clustering methods on both F1 measure and Entropy. The running time of our algorithm is scalable with the size and dimensionality of data. Furthermore, an application of our technique to stock market surveillance demonstrates its effectiveness in real world applications. Finally, we propose a reference-based outlier detection model by agent-based subspace clustering. At first, agent-based subspace clustering is utilized to generate clusters in subspaces. After that, the centers of clusters, together with the corresponding subspaces, are used as references, and a reference-based model is employed to find outliers in relevant subspaces. The experimental results on real-world datasets prove that the proposed model is able to effectively and efficiently identify outliers in subspaces. In summary, this thesis research on outlier detection techniques on high-dimensional data and its application in stock market surveillance. The proposed models are novel and effective. They have shown their potentials in real business

A global satellite view of aerosol cloud interactions

International audienceLong-term and large-scale correlations between Advanced Very High-Resolution Radiometer (AVHRR) aerosol optical depth and International Satellite Cloud Climatology Project (ISCCP) monthly cloud amount data show significant regional scale relationships between cloud amount and aerosols, consistent with aerosol-cloud interactions. Positive correlations between aerosols and cloud amount are associated with North American and Asian aerosols in the North Atlantic and Pacific storm tracks, and mineral aerosols in the tropical North Atlantic. Negative correlations are seen near biomass burning regions of North Africa and Indonesia, as well as south of the main mineral aerosol source of North Africa. These results suggest that there are relationships between aerosols and clouds in the observations that can be used by general circulation models to verify the correct forcing mechanisms for both direct and indirect radiative forcing by clouds

Data assimilation in a system with two scales-combining two initialization techniques

11 pages, 11 figures, 1 tableFull-text version available Open Access at: http://clivar.iim.csic.es/?q=es/node/319An ensemble Kalman filter (EnKF) is used to assimilate data onto a non-linear chaotic model, coupling two kinds of variables. The first kind of variables of the system is characterized as large amplitude, slow, large scale, distributed in eight equally spaced locations around a circle. The second kind of variables are small amplitude, fast, and short scale, distributed in 256 equally spaced locations. Synthetic observations are obtained from the model and the observational error is proportional to their respective amplitudes. The performance of the EnKF is affected by differences in the spatial correlation scales of the variables being assimilated. This method allows the simultaneous assimilation of all the variables. The ensemble filter also allows assimilating only the large-scale variables, letting the small-scale variables to freely evolve. Assimilation of the large-scale variables together with a few small-scale variables significantly degrades the filter. These results are explained by the spurious correlations that arise from the sampled ensemble covariances. An alternative approach is to combine two different initialization techniques for the slow and fast variables. Here, the fast variables are initialized by restraining the evolution of the ensemble members, using a Newtonian relaxation toward the observed fast variables. Then, the usual ensemble analysis is used to assimilate the large-scale observationsThis study is supported by the Spanish National Science Program under contracts ESP2005–06823-C05 and ESP2007–65667-C04Peer reviewe

Search for first-generation scalar and vector leptoquarks

We describe a search for the pair production of first-generation scalar and vector leptoquarks in the eejj and enujj channels by the D0 Collaboration. The data are from the 1992--1996 ppbar run at sqrt{s} = 1.8 TeV at the Fermilab Tevatron collider. We find no evidence for leptoquark production; in addition, no kinematically interesting events are observed using relaxed selection criteria. The results from the eejj and enujj channels are combined with those from a previous D0 analysis of the nunujj channel to obtain 95% confidence level (C.L.) upper limits on the leptoquark pair-production cross section as a function of mass and of beta, the branching fraction to a charged lepton. These limits are compared to next-to-leading-order theory to set 95% C.L. lower limits on the mass of a first-generation scalar leptoquark of 225, 204, and 79 GeV/c^2 for beta=1, 1/2, and 0, respectively. For vector leptoquarks with gauge (Yang-Mills) couplings, 95% C.L. lower limits of 345, 337, and 206 GeV/c^2 are set on the mass for beta=1, 1/2, and 0, respectively. Mass limits for vector leptoquarks are also set for anomalous vector couplings

Gaussian-Gamma collaborative filtering: a hierarchical Bayesian model for recommender systems

The traditional collaborative filtering (CF) suffers from two key challenges, namely, the normal assumption that it is not robust, and it is difficult to set in advance the penalty terms of the latent features. We therefore propose a hierarchical Bayesian model-based CF and the related inference algorithm. Specifically, we impose a Gaussian-Gamma prior on the ratings, and the latent features. We show the model is more robust, and the penalty terms can be adapted automatically in the inference. We use Gibbs sampler for the inference and provide a statistical explanation. We verify the performance using both synthetic and real dataset

Modelling future patterns of urbanization, residential energy use and greenhouse gas emissions in Dar es Salaam with the Shared Socio-Economic Pathways

This paper presents three scenarios of urban growth, energy use and greenhouse gas (GHG) emissions in Dar es Salaam using narratives that are consistent with the Shared Socio-Economic Pathways (SSPs). We estimate residential energy demand and GHG emissions from 2015 to 2050 for household activities (including upstream electricity generation) and passenger (road) transport (Scopes 1 and 2). We project that by 2050, Dar es Salaam's total residential emissions would increase from 1,400 ktCO2e (in 2015) up to 25,000–33,000 ktCO2e (SSP1); 11,000–19,000 ktCO2e (SSP2); and 5,700–11,000 ktCO2e (SSP3), with ranges corresponding to different assumptions about household size. This correlates with an increase in per capita emissions from 0.2 tCO2e in 2015 to 1.5–2 tCO2e (SSP1); 0.7–1.3 tCO2e (SSP2); and 0.5–0.9 tCO2e (SSP3). Higher emissions in SSP1 (the sustainability scenario) are driven by a higher urban population in 2050 and increased energy access and electricity consumption. Through aggressive GHG mitigation policies focused on decarbonization of the electricity sector and road transport, total emissions under SSP1 can be reduced by ∼66% in 2050. Study insights aim to inform policies that identify and capture synergies between low-GHG investments and broader socio-economic development goals in Sub-Saharan African cities

Plasticity in eye movement control

The cerebellum plays an important role in the recalibration and adaptive adjustment of movements, as well as learning new motor skills and motor related associations. In this thesis, we investigated the mechanisms underlying cerebellar motor learning. To obtain a better understanding, in how the cerebellum processes and stores information, we used specific perturbations that affected the information processing of the cerebellum. Signal transduction pathways were affected that were considered important for cerebellar motor learning by using genetic tools (transgenic mice) and the application of antibodies. Alterations in cerebellar motor learning were studied by monitoring the oculomotor system of these transgenic and treated mice