Single-File Diffusion of Confined Water Inside SWNTs: An NMR Study

We report a nuclear magnetic resonance (NMR) study of confined water inside similar to 1.4 nm diameter single-walled carbon nanotubes (SWNTs). We show that the confined water does not freeze even up to 223 K. A pulse field gradient (PFG) NMR method is used to determine the mean squared displacement (MSD) of the water molecules inside the nanotubes at temperatures below 273 K, where the bulk water outside the nanotubes freezes and hence does not contribute to the proton NMR signal. We show that the mean squared displacement varies as the square root of time, predicted for single-file diffusion in a one-dimensional channel. We propose a qualitative understanding of our results based on available molecular dynamics simulations.

Investigations Of Iron Adducts Of C-60 - Novel Fec60 In The Solid-State With Fe Inside The C-60 Cage

By carrying out contact-arc vaporization of graphite in a partial atmosphere of Fe(CO)5, an iron-adduct with C60 has been obtained. The adduct has been characterized by various techniques including mass spectrometry, Fe-57 Mossbauer spectroscopy and Fe K-EXAFS. Properties of this adduct are compared with those of an adduct prepared by solution method where Fe is clearly outside the cage. Results suggest that FeC60 obtained from the gas phase reaction has the Fe atom in the cage.

Fast algorithm for data exchange in reconfigurable tree structures

This paper presents a fast algorithm for data exchange in a network of processors organized as a reconfigurable tree structure. For a given data exchange table, the algorithm generates a sequence of tree configurations in which the data exchanges are to be executed. A significant feature of the algorithm is that each exchange is executed in a tree configuration in which the source and destination nodes are adjacent to each other. It has been proved in a theorem that for every pair of nodes in the reconfigurable tree structure, there always exists two and only two configurations in which these two nodes are adjacent to each other. The algorithm utilizes this fact and determines the solution so as to optimize both the number of configurations required and the time to perform the data exchanges. Analysis of the algorithm shows that it has linear time complexity, and provides a large reduction in run-time as compared to a previously proposed algorithm. This is well-confirmed from the experimental results obtained by executing a large number of randomly-generated data exchange tables. Another significant feature of the algorithm is that the bit-size of the routing information code is always two bits, irrespective of the number of nodes in the tree. This not only increases the speed of the algorithm but also results in simpler hardware inside each node.

An Efficient Robust Algorithm for the Surface-Potential Calculation of Independent DG MOSFET

Although the recently proposed single-implicit-equation-based input voltage equations (IVEs) for the independent double-gate (IDG) MOSFET promise faster computation time than the earlier proposed coupled-equations-based IVEs, it is not clear how those equations could be solved inside a circuit simulator as the conventional Newton-Raphson (NR)-based root finding method will not always converge due to the presence of discontinuity at the G-zero point (GZP) and nonremovable singularities in the trigonometric IVE. In this paper, we propose a unique algorithm to solve those IVEs, which combines the Ridders algorithm with the NR-based technique in order to provide assured convergence for any bias conditions. Studying the IDG MOSFET operation carefully, we apply an optimized initial guess to the NR component and a minimized solution space to the Ridders component in order to achieve rapid convergence, which is very important for circuit simulation. To reduce the computation budget further, we propose a new closed-form solution of the IVEs in the near vicinity of the GZP. The proposed algorithm is tested with different device parameters in the extended range of bias conditions and successfully implemented in a commercial circuit simulator through its Verilog-A interface.

Temporal trends in reef fish assemblages inside Virgin Islands National Park and around St. John, U.S. Virgin Islands, 1988-2006

This report is a result of long-term fish monitoring studies supported by the National Park Service (NPS) at the Virgin Islands National Park since 1988 and is now a joint NPS and NOAA collaboration. Reef fish monitoring data collected from 1988 to 2006 within Virgin Islands National Park (VINP) and adjacent reefs around St. John, U.S. Virgin Islands (USVI) were analyzed to provide information on the status of reef fishes during the monitoring period. Monitoring projects were initiated by the National Park Service (NPS) in the 1980s to provide useful data for evaluation of resources and for development of a long-term monitoring program. Monthly monitoring was conducted at two reef sites (Yawzi Point and Cocoloba Cay) starting in November 1988 for 2.5 years to document the monthly/seasonal variability in reef fish assemblages. Hurricane Hugo (a powerful Category 4 storm) struck the USVI in September 1989 resulting in considerable damage to the reefs around St. John. Abundance of fishes was lower at both sites following the storm, however, a greater effect was observed at Yawzi Point, which experienced a more direct impact from the hurricane. The storm affected species differently, with some showing only small, short-term declines in abundance, and others, such as the numerically abundant blue chromis (Chromis cyanea), a planktivorous damselfish, exhibiting a larger and longer recovery period. This report provides: 1) an evaluation of sampling methods, sample size, and methods used during the sampling period, 2) an evaluation of the spatial and temporal variability in reef fish assemblages at selected reef sites inside and outside of VINP, and 3) an evaluation of trends over 17 years of monitoring at the four reference sites. Comparisons of methods were conducted to standardize assessments among years. Several methods were used to evaluate sample size requirements for reef fish monitoring and the results provided a statistically robust justification for sample allocation.

Flame imaging of gas-turbine relight

High-altitude relight inside a lean-direct-injection gas-turbine combustor is investigated experimentally by highspeed imaging. Realistic operating conditions are simulated in a ground-based test facility, with two conditions being studied: one inside and one outside the combustor ignition loop. The motion of hot gases during the early stages of relight is recorded using a high-speed camera. An algorithm is developed to track the flame movement and breakup, revealing important characteristics of the flame development process, including stabilization timescales, spatial trajectories, and typical velocities of hot gas motion. Although the observed patterns of ignition failure are in broad agreement with results from laboratory-scale studies, other aspects of relight behavior are not reproduced in laboratory experiments employing simplified flow geometries and operating conditions. For example, when the spark discharge occurs, the air velocity below the igniter in a real combustor is much less strongly correlated to ignition outcome than laboratory studies would suggest. Nevertheless, later flame development and stabilization are largely controlled by the cold flowfield, implying that the location of the igniter may, in the first instance, be selected based on the combustor cold flow. Copyright © 2010.

多机器人动态编队的强化学习算法研究

在人工智能领域中 ,强化学习理论由于其自学习性和自适应性的优点而得到了广泛关注 随着分布式人工智能中多智能体理论的不断发展 ,分布式强化学习算法逐渐成为研究的重点 首先介绍了强化学习的研究状况 ,然后以多机器人动态编队为研究模型 ,阐述应用分布式强化学习实现多机器人行为控制的方法 应用SOM神经网络对状态空间进行自主划分 ,以加快学习速度 ;应用BP神经网络实现强化学习 ,以增强系统的泛化能力 ;并且采用内、外两个强化信号兼顾机器人的个体利益及整体利益 为了明确控制任务 ,系统使用黑板通信方式进行分层控制 最后由仿真实验证明该方法的有效性

Fuzzy ARTMAP: A Neural Network Architecture for Incremental Supervised Learning of Analog Multidimensional Maps

A new neural network architecture is introduced for incremental supervised learning of recognition categories and multidimensional maps in response to arbitrary sequences of analog or binary input vectors. The architecture, called Fuzzy ARTMAP, achieves a synthesis of fuzzy logic and Adaptive Resonance Theory (ART) neural networks by exploiting a close formal similarity between the computations of fuzzy subsethood and ART category choice, resonance, and learning. Fuzzy ARTMAP also realizes a new Minimax Learning Rule that conjointly minimizes predictive error and maximizes code compression, or generalization. This is achieved by a match tracking process that increases the ART vigilance parameter by the minimum amount needed to correct a predictive error. As a result, the system automatically learns a minimal number of recognition categories, or "hidden units", to met accuracy criteria. Category proliferation is prevented by normalizing input vectors at a preprocessing stage. A normalization procedure called complement coding leads to a symmetric theory in which the MIN operator (Λ) and the MAX operator (v) of fuzzy logic play complementary roles. Complement coding uses on-cells and off-cells to represent the input pattern, and preserves individual feature amplitudes while normalizing the total on-cell/off-cell vector. Learning is stable because all adaptive weights can only decrease in time. Decreasing weights correspond to increasing sizes of category "boxes". Smaller vigilance values lead to larger category boxes. Improved prediction is achieved by training the system several times using different orderings of the input set. This voting strategy can also be used to assign probability estimates to competing predictions given small, noisy, or incomplete training sets. Four classes of simulations illustrate Fuzzy ARTMAP performance as compared to benchmark back propagation and genetic algorithm systems. These simulations include (i) finding points inside vs. outside a circle; (ii) learning to tell two spirals apart; (iii) incremental approximation of a piecewise continuous function; and (iv) a letter recognition database. The Fuzzy ARTMAP system is also compared to Salzberg's NGE system and to Simpson's FMMC system.

TRANSCENDING FACH: A SEARCH FOR IDENTITY INSIDE AND OUT OF MEZZO-SOPRANO REPERTOIRE

The German Fach system is a tool to classify voices in classical singing. This dissertation comprises three different programs that reflect my search for identity as a mezzo-soprano and my desire to transcend the limitations of Fach. The three programs, all presented at The Clarice Performing Arts Center, contain repertoire written for male and female voices thus allowing me to explore areas outside of the mezzo-soprano Fach, gain a better understanding of the Fach system and guide me as I strive to become a more mature performer. In my first program, I sang the role of Sesto, a role that was composed originally for a castrate, in the opera La Clemenza di Tito by W.A. Mozart. The Maryland Opera Studio production took place April 30, May 2,4&6,2003. Performing this gender-bending role provided an experience of physical behavior from the male view point along with the demands of coloratura singing. Program two (November 30,2004) contained the song cycle Dichterliebe by Robert Schumann and songs by Ludwig van Beethoven, Franz Schubert and Felix Mendelssohn, which are usually sung by male voices. This program experimented with extended range, tessitura and a gender-bending performance in the art song arena. 8 In program three (April 21 &23,2005), I sang the contralto role of Cornelia from Giulio Cesare in Egitto by George Frederic Handel. The role of Cornelia is psychologically complex, expressing emotions such as love, melancholy, rage, malice, joy and fear. To convey these emotions a voice needs warmth and darkness of quality. Although the range is close to that of the mezzo-soprano, Handel wrote Cornelia for contralto voice because he wanted a dark timbre and this role allowed me to develop my lower register and manage suitable ornamentations. The programs are documented in a digital format available on compact disc and are accompanied by the oral presentation at the defense of this dissertation.

An algorithm for total variation inpainting based on nonlinear multi-grid methods

Image inpainting refers to restoring a damaged image with missing information. The total variation (TV) inpainting model is one such method that simultaneously fills in the regions with available information from their surroundings and eliminates noises. The method works well with small narrow inpainting domains. However there remains an urgent need to develop fast iterative solvers, as the underlying problem sizes are large. In addition one needs to tackle the imbalance of results between inpainting and denoising. When the inpainting regions are thick and large, the procedure of inpainting works quite slowly and usually requires a significant number of iterations and leads inevitably to oversmoothing in the outside of the inpainting domain. To overcome these difficulties, we propose a solution for TV inpainting method based on the nonlinear multi-grid algorithm.

Advanced Modelling and Optimization of Infared Oven in Injection Stretch Blow-moulding for Energy Saving

In the production process of polyethylene terephthalate (PET) bottles, the initial temperature of preforms plays a central role on the final thickness, intensity and other structural properties of the bottles. Also, the difference between inside and outside temperature profiles could make a significant impact on the final product quality. The preforms are preheated by infrared heating oven system which is often an open loop system and relies heavily on trial and error approach to adjust the lamp power settings. In this paper, a radial basis function (RBF) neural network model, optimized by a two-stage selection (TSS) algorithm combined with partial swarm optimization (PSO), is developed to model the nonlinear relations between the lamp power settings and the output temperature profile of PET bottles. Then an improved PSO method for lamp setting adjustment using the above model is presented. Simulation results based on experimental data confirm the effectiveness of the modelling and optimization method.

A Fast Algorithm for Sparse Support Vector Machines for Mobile Computing Applications

This research presents a fast algorithm for projected support vector machines (PSVM) by selecting a basis vector set (BVS) for the kernel-induced feature space, the training points are projected onto the subspace spanned by the selected BVS. A standard linear support vector machine (SVM) is then produced in the subspace with the projected training points. As the dimension of the subspace is determined by the size of the selected basis vector set, the size of the produced SVM expansion can be specified. A two-stage algorithm is derived which selects and refines the basis vector set achieving a locally optimal model. The model expansion coefficients and bias are updated recursively for increase and decrease in the basis set and support vector set. The condition for a point to be classed as outside the current basis vector and selected as a new basis vector is derived and embedded in the recursive procedure. This guarantees the linear independence of the produced basis set. The proposed algorithm is tested and compared with an existing sparse primal SVM (SpSVM) and a standard SVM (LibSVM) on seven public benchmark classification problems. Our new algorithm is designed for use in the application area of human activity recognition using smart devices and embedded sensors where their sometimes limited memory and processing resources must be exploited to the full and the more robust and accurate the classification the more satisfied the user. Experimental results demonstrate the effectiveness and efficiency of the proposed algorithm. This work builds upon a previously published algorithm specifically created for activity recognition within mobile applications for the EU Haptimap project [1]. The algorithms detailed in this paper are more memory and resource efficient making them suitable for use with bigger data sets and more easily trained SVMs.

Out on the Inside: The Rights, Experiences and Needs of LGBT People in Prison

Lesbian, Gay, Bisexual and Transgender (LGBT) people have long been a ‘hidden’ or overlooked population in prisons. In recent years, however, international research and policy has begun to focus on the experiences and needs of this group. This research has revealed a range of issues that affect LGBT individuals in prison. This includes heteronormativity, homophobia and transphobia both within and outside prison, the threat of physical and sexual violence within prison, institutional discrimination and neglect, health needs, and social isolation. Based on a review of international literature and primary research with representatives from the LGBT and criminal justice sectors, prisoners and former prisoners, this report represents the first study of the needs and experiences of LGBT prisoners within the Irish context.

Quantum criptography in optical fibers

As comunicações quânticas aplicam as leis fundamentais da física quântica para codificar, transmitir, guardar e processar informação. A mais importante e bem-sucedida aplicação é a distribuição de chaves quânticas (QKD). Os sistemas de QKD são suportados por tecnologias capazes de processar fotões únicos. Nesta tese analisamos a geração, transmissão e deteção de fotões únicos e entrelaçados em fibras óticas. É proposta uma fonte de fotões única baseada no processo clássico de mistura de quatro ondas (FWM) em fibras óticas num regime de baixas potências. Implementamos essa fonte no laboratório, e desenvolvemos um modelo teórico capaz de descrever corretamente o processo de geração de fotões únicos. O modelo teórico considera o papel das nãolinearidades da fibra e os efeitos da polarização na geração de fotões através do processo de FWM. Analisamos a estatística da fonte de fotões baseada no processo clássico de FWM em fibras óticas. Derivamos um modelo teórico capaz de descrever a estatística dessa fonte de fotões. Mostramos que a estatística da fonte de fotões evolui de térmica num regime de baixas potências óticas, para Poissoniana num regime de potências óticas moderadas. Validamos experimentalmente o modelo teórico, através do uso de fotodetetores de avalanche, do método estimativo da máxima verossimilhança e do algoritmo de maximização de expectativa. Estudamos o processo espontâneo de FWM como uma fonte condicional de fotões únicos. Analisamos a estatística dessa fonte em termos da função condicional de coerência de segunda ordem, considerando o espalhamento de Raman na geração de pares de fotões, e a perda durante a propagação de fotões numa fibra ótica padrão. Identificamos regimes apropriados onde a fonte é quase ideal. Fontes de pares de fotões implementadas em fibras óticas fornecem uma solução prática ao problema de acoplamento que surge quando os pares de fotões são gerados fora da fibra. Exploramos a geração de pares de fotões através do processo espontâneo de FWM no interior de guias de onda com suceptibilidade elétrica de terceira ordem. Descrevemos a geração de pares de fotões em meios com elevado coeficiente de absorção, e identificamos regimes ótimos para o rácio contagens coincidentes/acidentais (CAR) e para a desigualdade de Clauser, Horne, Shimony, and Holt (CHSH), para o qual o compromisso entre perda do guia de onda e não-linearidades maximiza esses parâmetros.

Model comparison for temperature estimation inside buildings

This paper presents a comparison between a physical model and an artificial neural network model (NN) for temperature estimation inside a building room. Despite the obvious advantages of the physical model for structure optimisation purposes, this paper will test the performance of neural models for inside temperature estimation. The great advantage of the NN model is a big reduction of human effort time, because it is not needed to develop the structural geometry and structural thermal capacities and to simulate, which consumes a great human effort and great computation time. The NN model deals with this problem as a “black box” problem. We describe the use of the Radial Basis Function (RBF), the training method and a multi-objective genetic algorithm for optimisation/selection of the RBF neural network inputs and number of neurons.