Miracle-dz/UniFAR-FTUs huggingface dataset:
Modelling the microwave transmission of metal arrays using modal matching

This work explores the interaction of electromagnetic radiation with periodic metal-dielectric composite materials. In particular, the majority of the studies explore the role of evanescent diffraction in the regime where the wavelength of the incident radiation is of the order of the period of the array just below the onset of diffraction. The underlying aim of the thesis is to build on the current knowledge and gain deeper understanding into the causal mechanism of the electromagnetic response of these periodic materials. Developments in metamaterial research have led to a resurgance of interest in the use of periodic metallic surface to control the transmission of electromagnetic radiation. The response of these surfaces can be ‘tuned’ to provide the required response simply by altering the geometric parameters of the material. Numerical modelling techniques are often used to predict the response of such structures. However, the aim of this work is to gain a deeper understanding of the reasons for the response and therefore an analytical modal matching method has been used. The modal matching method provides the opportunity to extract greater understanding of the resonant phenomena by linking them to specific mathematical terms in the analytical formulation. The modal matching technique is initially used to study the response from a single layer bigrating comprising a square array of square holes in a PEC sheet and its complementary system of a square array of square PEC patches. The importance of evanescent diffraction in both resonant phenomena and tunneling responses is discussed and it is shown that complete transmission (reflection) is supported by these structures even for very high (low) metal occupancy. This technique is extended and adapted to describe a variety of structures in chapters 5 and 6, exploring how resonant excitation of surface waves via evanescent diffraction leads to highly interesting electromagnetic responses. In chapter 7, alternating multilayer stacks of two different subwavelength meshes provide an observable one-dimensional topological mode in a physical system for particular mesh configurations.
Integration and disintegration of human visual awareness

The neuronal underpinnings of visual awareness has recently become the primary question of interest for many researchers, with many theories suggesting distinct mechanisms. The aim of this thesis was to test predictions of the low-level modular theory of visual awareness. This modular view is encapsulated in the 'microconsciousness' framework (Zeki & Bartels, 1999) in which each visual processing system, such as that for colour, is capable of generating a conscious correlate autonomously in parallel across space, within each of the different functionally specialised areas of the visual brain, and across time, with different attributes perceived at different times. Given the scope of this topic, we approached it from three diverse angles: (1) Two psychophysical experiments investigated temporal aspects of visual perception - in particular these addressed the issue of whether the timing of awareness is an 'online* phenomenon rather than integrated into a temporal buffer zone prior to awareness. We measured the relative perceptual times of different magnitudes of direction changes and investigated the 'flash-lag' effect (Nijhawan, 1994) and related illusions of positional localisation. (2) The first two fMRI experiments examined the necessity of frontal and parietal areas for visual awareness in the context of bistable figures, combined with dynamic causal modelling (Friston et al., 2003), and perception outside the focus of attention. (3) We looked to extend the concept of modularity of awareness to that of 'access consciousness', that is the ability to give a report of a conscious experience (Block, 1996), in addition to the previous studies on phenomenal consciousness. To this end, we combined psychophysics with fMRI to investigate the interaction between report modality and visual perception. We conclude that the low-level modular theory of stands up to direct tests of its predictions and remains a viable theory of visual awareness.
The halo+cluster system of the galactic globular cluster NGC 1851

NGC 1851 is surrounded by a stellar component that extends more than ten times beyond the tidal radius. Although the nature of this stellar structure is not known, it has been suggested to be a sparse halo of stars or associated with a stellar strea m. We analyse the nature of this intriguing stellar component surrounding NGC 1851 by investigating its radial velocities and chemical composition, in particular in comparison with those of the central cluster analysed in a homogeneous manner. In total we observed 23 stars in the halo with radial velocities consistent with NGC 1851, and for 15 of them we infer [Fe/H] abundances. Our results show that: (i) stars dynamically linked to NGC 1851 are present at least up to∼2.5 tidal radii, supporting the presence of a halo of stars surrounding the cluster; (ii) apart from the NGC 1851 radial velocity-like stars, our observed velocity distrib ution agrees with that expected from Galactic models, suggesting that no other sub-structure (such as a stream) at different radial velocities is present in our field; (iii) the chemical abundances for the s-process elements Sr and Ba are consistent with the s-normal stars observed in NGC 1851; (iv) all halo stars have metallicities, and abundances for the other studied elements Ca, Mg and Cr, consistent with those exhibited by the cluster. The complexity of the whole NGC 1851 cluster+halo system may agree with the scenario of a tidally-disrupted dwarf galaxy in which NGC 1851 was originally embedded.
The Creation of Narrative in Tabletop Role-Playing Games

Despite the rise of computer gaming, millions of adults still play face-to-face role-playing games, which rely in part on social interaction to create stories. This work explores tabletop role-playing game (TRPG) as a genre separate from computer role-playing games. The relationship of TRPGs to other games is examined, as well as the influence of Dungeons & Dragons as a tabletop module, a computer game, and a novel. Given particular attention are the narrative and linguistic structures of the gaming session, and the ways that players and gamemasters work together to construct narratives. The text also explores wider cultural influences that surround tabletop gamers.
Verification of Costless Merge Pairing Heaps

Most algorithms’ performance is limited by the data structures they use. Internal algorithms then decide the performance of the data structure. This cycle continues until fundamental results, verified by analysis and experiment, prevent further improvement. In this paper I examine one specific example of this. The focus of this work is primarily on a new variant of the pairing heap. I will review the new implementation, compare its theoretical performance, and discuss my original contribution: the first preliminary data on its experimental performance. It is instructive to provide some background information, followed by a formal definition of heaps in 1.1. I also provide a brief overview of existing literature on the design of these data structures in 1.2 and discuss the methods for evaluating these types of structures in 1.3. Full details about the implementation of a pairing heap can be found in 2.2. Ongoing research has produced a variety of different types of heaps, which will be briefly discussed. 1 Vander Hook: Verification of Costless Merge Pairing Heaps Published by Cornerstone: A Collection of Scholarly and Creative Works for Minnesota State University, Mankato, 2010
Multiproduct Monopoly, Commodity Bundling, and Correlation of Values

Through what selling strategy can a multiproduct monopolist maximize his profits when his knowledge about individual consumers' preferences is limited? One possibility, extensively studied in the context of a single-good monopoly, is to use quantity-dependent pricing as a means of discriminating among customers with differing tastes (see, for example, Oi [1971] and Maskin and Riley [1984]). An alternative technique for price discrimination, first suggested by Stigler [1968] and analyzed further by Adams and Yellen [1976], is for the monopolist to package two or more products in bundles rather than selling them separately. ' Through a series of examples Adams and Yellen illustrate that bundling can serve as a useful price discrimination device, even when all consumers' willingnesses to pay for each of the goods individually are unaffected by whether they are also consuming the other product. A typical example is illustrated in Figure I (adapted from Figure IV in Adams and Yellen), where there are two goods, three consumers (AB,C) who consume at most one unit of each good (with reservation values for each good that are independent of whether the other good is consumed), and zero costs of production. There, a bundle offered at a price of 100 fully extracts all potential surplus, which would be impossible pricing the goods independently. Unfortunately, though, these authors do not provide any general characterization of the circumstances in which bundling is actually a multiproduct monopolist's optimal strategy. Their examples, however (such as Figure I), create the impression that the profitable use of bundling
Crystal-Field Interactions in RPd_2Al_3 Intermetallics (R = Pr, Nd, Sm, and U)

Magnetic and electronic properties of NdPd2Al3 have been nicely described with the Nd 3+ ions within the crystaleld theory revealing that all Nd atoms equally contribute to the temperature dependence of the paramagnetic susceptibility and of the heat capacity with the λ-peak. We consistently described paramagnetic and magnetically-ordered state. The ne electronic structure of the Nd ion nicely correlates with the electronic structure of the U ion in UPd2Al3 giving strong evidence for the trivalent uranium state in the isostructural heavy-fermion superconductor UPd2Al3.
Modifications That Preserve Language and Content.

This article reviews the research on the simplification of reading materials for second language (SL) learners and reports on an experiment of the effects of text simplification and elaboration on the reading comprehension of SL learners. Elaboration can improve the comprehensibility of texts without removing new linguistic forms that students need to learn or diluting the semantic content of the original. A review of the research suggests that linguistic simplification of texts fails on both counts, producing unnatural target language models. A study involving 483 Japanese college students studying English as a Foreign Language (EFL) for at least 8 years was conducted to determine their reading comprehension of unmodified, simplified, and elaborated texts. The study found that students who read the simplified passages scored higher on a multiple-choice comprehension test than students who read the unmodified version. Students who read the elaborated versions of the passages scored higher than those who read the unmodified versions, but not statistically significantly so. It also found that there was no statistically significant difference between the reading scores of students who read the simplified and elaborated versions of the passages. (MDM) *********************************************************************** * Reproductions supplied by EDRS are the best that can be made * * from the original document. * ***********************************************************************
Comparative Study of Three Indigenous Chicken Breeds of South Africa: Body Weight and Linear Body Measurements

The aim of this study was to undertake a comparative evaluation of the body weight and linear body measurements of three indigenous chicken breeds of South Africa, namely: Naked neck, Venda and Potchefstroom koekoek. A total of 222 records obtained from the three intensively reared breeds were used in the study. Data were collected at the experimental farm of the University of Limpopo, South Africa. Treatment means were separated using t-test and analysis of variance with the level of significance set at 5%. Body weight was correlated with the linear body measurements. The parameters evaluated were: Body Weight: BW, Body Length: BL, Body Girth: BG, Wing Length: WL, Shank Thickness: ST and Shank Length: SL. The measurements were taken at maturity (22 weeks of age). Results showed that males of the three breeds were statistically (p 0.05) differences were observed between the three breeds. No difference s (p>0.05) were observed between the Naked neck and Venda chickens for body weight, body girth and shank thickness. BW was best correlated with SL (Naked neck; r = 0.92), ST (Venda; r = 0.80) and ST (Potchefstroom koekoek; r = 0.80). BW was best predicted using SL (Naked neck; -0.5129+0.0825SL), ST (Venda; -0.1381+ 0.4515ST) and ST (Potchefstroom koekoek; -0.2004+0.4621ST). It was concluded that breed differences do exist between the three indigenous South African chicken breeds studied and these differences are in favour of the males and the Potchefstroom koekoek chickens.
Evolution of Robotic Behaviour Using Gene Expression Programming

The main objective in automatic robot controller development is to devise mechanisms whereby robot controllers can be developed with less reliance on human developers. One such mechanism is the use of evolutionary algorithms (EAs) to automatically develop robot controllers and occasionally, robot morphology. This area of research is referred to as evolutionary robotics (ER). Through the use of evolutionary techniques such as genetic algorithms (GAs) and genetic programming (GP), ER has shown to be a promising approach through which robust robot controllers can be developed The standard ER techniques use monolithic evolution to evolve robot behaviour: monolithic evolution involves the use of one chromosome to code for an entire target behaviour. In complex problems, monolithic evolution has been shown to suffer from bootstrap problems; that is, a lack of improvement in fitness due to randomness in the solution set [103, 105, 100, 90]. Thus, approaches to dividing the tasks, such that the main behaviours emerge from the interaction of these simple tasks with the robot environment have been devised. These techniques include the subsumption architecture in behaviour based robotics, incremental learning and more recently the layered learning approach [55, 103, 56, 105, 136, 95]. These new techniques enable ER to develop complex controllers for autonomous robots. Work presented in this thesis extends the field of evolutionary robotics by introducing Gene Expression Programming (GEP) to the ER field. GEP is a newly developed evolutionary algorithm akin to GA and GP, which has shown great promise in optimisation problems. The presented research shows through experimentation that the unique formulation of GEP genes is sufficient for robot controller representation and development. The obtained results show that GEP is a plausible technique for ER problems. Additionally, it is shown that controllers evolved using GEP algorithm are able to adapt when introduced to new environments. Further, the capabilities of GEP chromosomes to code for more than one gene have been utilised to show that GEP can be used to evolve manually sub-divided robot behaviours. Additionally, this thesis extends the GEP algorithm by proposing two new evolutionary techniques named multigenic GEP with Linker Evolution (mgGEP-LE) and multigenic GEP with a Regulator Gene (mgGEP-RG). The results obtained from the proposed algorithms show that the new techniques can be used to automatically evolve modularity in robot behaviour. This ability to automate the process of behaviour sub-division and optimisation in a modular chromosome is unique to the GEP formulations discussed, and is an important advance in the development of machines that are able to evolve stratified behavioural architectures with little human intervention.
Learning in Real Time: Synchronous Teaching and Learning Online

Preface. About the Author. 1 Learning, Live Online. Synchronous Interaction Across the Learning Continuum. Needs Served by Synchronous Interaction. The Synchronous Compact. Indicators for Real-Time Online Learning. Learner Skills Developed and Assessed Live Online. 2 Real-Time Learning as Good Practice. Contact Between Students and Faculty. Cooperation Among Students. Active Learning. Prompt Feedback. Time on Task. High Expectations. Diverse Talents and Ways of Learning. 3 Tools for Learning in Real Time. Text-Based Tools. Live Audio and Video Tools. Content, Display, and Other Interactive Tools. 4 Real-Time Learning Venues. A Moving Target. Chat Rooms. Instant Messenger. Multi-user Virtual Environments. Virtual Reference, Tutoring, and Help Desks. Virtual Offices and Virtual Meeting Rooms. Virtual Classrooms. Interactive Webcasting. Webcasting or Broadcasting. In-class Online Aids. 5 Facilitating Learning in Real Time. Be a Good Host. Inflate a Bubble of Concentration. Be a Ringmaster. Use Virtual Body Language. Rely on Others: You Are Not Alone. Use the Synchronous Dashboard. Be Prepared and Manage Crises. 6 Real-Time Online Learning Activities. Magnetic Brainstorms. Stone Soup. Solo Fishbowls. Paired Partners or Dyads. Cracker Barrels. Guest Appearances and Co-hosts. Live Blogging and Cclogging. Expeditions and Virtual Training Labs. Multiple Venue Productions or Presentations. Additional Resources. Further Reading and Related Web Sites. Ten Common Misconceptions About Synchronous Instruction. Scheduling Synchronous Interaction. Buyer's Guide: Questions to Ask When Considering a Synchronous Tool. References. Index.
Nonlinear neural network for conditional mean and variance forecasts

Over-fitting usually appears in financial time series forecasting by neural networks, where the target series are usually contaminated with noise. To avoid this problem, regularization and committee machine methods are considered. In the context of financial applications, especially in the stock market predictions, the variance of asset returns is of particular interest in addition to the mean, in order to provide information to balance risk and return in investment at each point of time. To be able to fit two-dimensional mean and variance output to one-dimensional asset return data, an objective function based on the negative loglikelihood of the conditional distribution of the target variable is considered as an alternative to conventional least squares function. The likelihood approach also enables flexibility in the choice of distribution to model the underlying error process. In this thesis, regularization and ensemble techniques are adapted for networks with likelihood-based objective function. As well as a conditional Gaussian error distribution typically assumed, a long-tailed t-distribution is another choice for extreme leptokurtic situations. The regularization terms are formed by likelihood contributions corresponding to simple autoregressive models for the conditional mean and log variance parameters. In the ensemble technique, the mean and variance predictions are averaged rather than the probability density function. In addition, learning algorithms are developed for an Output-to-Output recurrent structure applied to a network with two-dimensional mean and variance output. These differ with respect to the terms and direction of unfolding the dynamic structure. The proposed methods are investigated by application to simulation data and real data. These following conclusions can be drawn. Regularization is found to improve generalization ability. Although the individual networks differ only with respect to the initial weights used for training, they appear to be in sufficient disagreement for the ensemble averaging approach. The error of the ensemble network is found to be smaller than the weighted average error over all ensemble members, and the resulting forecasts vary more smoothly over time. In order to reduce the memory and time requirements, truncated versions with a bounded history approximation ii are applied to recurrent networks, because of their efficiency without a trade-off in effectiveness. The recurrent networks yield more accurate forecasts and less fluctuation than non-recurrent networks. Ensemble techniques with proposed neural networks are successfully applied to real financial time series data. With a Gaussian error distribution assumed, the network with regularization outperforms and produces smoother outputs than the networks without regularization. The non-recurrent network with regularization has simpler structure but is reasonably effective, compared to the recurrent network without regularization. In case of extreme leptokurtic data, the network with a t-error distribution is superior to the network with a Gaussian error distribution.
Advances In Genome-Scale Modeling Applied To Expression-Based Flux Estimation And Epistasis Prediction

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